From 44c25d89f7a3364b2a86db9a57eeb846d3f64c55 Mon Sep 17 00:00:00 2001 From: kidwellj Date: Sun, 8 Mar 2020 12:18:21 +0000 Subject: [PATCH 1/3] Updating scripts, added license, contributor guidelines, shifting TODO lines to github issues --- CODE_OF_CONDUCT.md | 25 + LICENSE.txt | 661 +++++++++++++++++++++ Makefile | 8 + README.md | 49 +- TODO.md | 32 + TODO_postpub.md | 8 + data/poi_2015_12_scot06340459.csv | 0 mapping_draft-hpc_optimised.Rmd | 81 ++- mapping_draft-hpc_optimised_wilderness.Rmd | 4 +- 9 files changed, 834 insertions(+), 34 deletions(-) create mode 100644 CODE_OF_CONDUCT.md create mode 100644 LICENSE.txt create mode 100644 Makefile create mode 100644 TODO.md create mode 100644 TODO_postpub.md mode change 100755 => 100644 data/poi_2015_12_scot06340459.csv diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..0343527 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,25 @@ +# Contributor Code of Conduct + +As contributors and maintainers of this project, we pledge to respect all people who +contribute through reporting issues, posting feature requests, updating documentation, +submitting pull requests or patches, and other activities. + +We are committed to making participation in this project a harassment-free experience for +everyone, regardless of level of experience, gender, gender identity and expression, +sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion. + +Examples of unacceptable behavior by participants include the use of sexual language or +imagery, derogatory comments or personal attacks, trolling, public or private harassment, +insults, or other unprofessional conduct. + +Project maintainers have the right and responsibility to remove, edit, or reject comments, +commits, code, wiki edits, issues, and other contributions that are not aligned to this +Code of Conduct. Project maintainers who do not follow the Code of Conduct may be removed +from the project team. + +Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by +opening an issue or contacting one or more of the project maintainers. + +This Code of Conduct is adapted from the Contributor Covenant +(https://www.contributor-covenant.org), version 1.0.0, available at +https://contributor-covenant.org/version/1/0/0/. diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..be3f7b2 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +our General Public Licenses are intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. + + When we speak of free software, we are referring to freedom, not +price. Our General Public Licenses are designed to make sure that you +have the freedom to distribute copies of free software (and charge for +them if you wish), that you receive source code or can get it if you +want it, that you can change the software or use pieces of it in new +free programs, and that you know you can do these things. + + Developers that use our General Public Licenses protect your rights +with two steps: (1) assert copyright on the software, and (2) offer +you this License which gives you legal permission to copy, distribute +and/or modify the software. + + A secondary benefit of defending all users' freedom is that +improvements made in alternate versions of the program, if they +receive widespread use, become available for other developers to +incorporate. Many developers of free software are heartened and +encouraged by the resulting cooperation. However, in the case of +software used on network servers, this result may fail to come about. +The GNU General Public License permits making a modified version and +letting the public access it on a server without ever releasing its +source code to the public. + + The GNU Affero General Public License is designed specifically to +ensure that, in such cases, the modified source code becomes available +to the community. It requires the operator of a network server to +provide the source code of the modified version running there to the +users of that server. Therefore, public use of a modified version, on +a publicly accessible server, gives the public access to the source +code of the modified version. + + An older license, called the Affero General Public License and +published by Affero, was designed to accomplish similar goals. This is +a different license, not a version of the Affero GPL, but Affero has +released a new version of the Affero GPL which permits relicensing under +this license. + + The precise terms and conditions for copying, distribution and +modification follow. + + TERMS AND CONDITIONS + + 0. Definitions. + + "This License" refers to version 3 of the GNU Affero General Public License. + + "Copyright" also means copyright-like laws that apply to other kinds of +works, such as semiconductor masks. + + "The Program" refers to any copyrightable work licensed under this +License. Each licensee is addressed as "you". "Licensees" and +"recipients" may be individuals or organizations. + + To "modify" a work means to copy from or adapt all or part of the work +in a fashion requiring copyright permission, other than the making of an +exact copy. The resulting work is called a "modified version" of the +earlier work or a work "based on" the earlier work. + + A "covered work" means either the unmodified Program or a work based +on the Program. + + To "propagate" a work means to do anything with it that, without +permission, would make you directly or secondarily liable for +infringement under applicable copyright law, except executing it on a +computer or modifying a private copy. Propagation includes copying, +distribution (with or without modification), making available to the +public, and in some countries other activities as well. + + To "convey" a work means any kind of propagation that enables other +parties to make or receive copies. Mere interaction with a user through +a computer network, with no transfer of a copy, is not conveying. + + An interactive user interface displays "Appropriate Legal Notices" +to the extent that it includes a convenient and prominently visible +feature that (1) displays an appropriate copyright notice, and (2) +tells the user that there is no warranty for the work (except to the +extent that warranties are provided), that licensees may convey the +work under this License, and how to view a copy of this License. If +the interface presents a list of user commands or options, such as a +menu, a prominent item in the list meets this criterion. + + 1. Source Code. + + The "source code" for a work means the preferred form of the work +for making modifications to it. "Object code" means any non-source +form of a work. + + A "Standard Interface" means an interface that either is an official +standard defined by a recognized standards body, or, in the case of +interfaces specified for a particular programming language, one that +is widely used among developers working in that language. + + The "System Libraries" of an executable work include anything, other +than the work as a whole, that (a) is included in the normal form of +packaging a Major Component, but which is not part of that Major +Component, and (b) serves only to enable use of the work with that +Major Component, or to implement a Standard Interface for which an +implementation is available to the public in source code form. A +"Major Component", in this context, means a major essential component +(kernel, window system, and so on) of the specific operating system +(if any) on which the executable work runs, or a compiler used to +produce the work, or an object code interpreter used to run it. + + The "Corresponding Source" for a work in object code form means all +the source code needed to generate, install, and (for an executable +work) run the object code and to modify the work, including scripts to +control those activities. However, it does not include the work's +System Libraries, or general-purpose tools or generally available free +programs which are used unmodified in performing those activities but +which are not part of the work. For example, Corresponding Source +includes interface definition files associated with source files for +the work, and the source code for shared libraries and dynamically +linked subprograms that the work is specifically designed to require, +such as by intimate data communication or control flow between those +subprograms and other parts of the work. + + The Corresponding Source need not include anything that users +can regenerate automatically from other parts of the Corresponding +Source. + + The Corresponding Source for a work in source code form is that +same work. + + 2. Basic Permissions. + + All rights granted under this License are granted for the term of +copyright on the Program, and are irrevocable provided the stated +conditions are met. This License explicitly affirms your unlimited +permission to run the unmodified Program. The output from running a +covered work is covered by this License only if the output, given its +content, constitutes a covered work. This License acknowledges your +rights of fair use or other equivalent, as provided by copyright law. + + You may make, run and propagate covered works that you do not +convey, without conditions so long as your license otherwise remains +in force. You may convey covered works to others for the sole purpose +of having them make modifications exclusively for you, or provide you +with facilities for running those works, provided that you comply with +the terms of this License in conveying all material for which you do +not control copyright. Those thus making or running the covered works +for you must do so exclusively on your behalf, under your direction +and control, on terms that prohibit them from making any copies of +your copyrighted material outside their relationship with you. + + Conveying under any other circumstances is permitted solely under +the conditions stated below. Sublicensing is not allowed; section 10 +makes it unnecessary. + + 3. Protecting Users' Legal Rights From Anti-Circumvention Law. + + No covered work shall be deemed part of an effective technological +measure under any applicable law fulfilling obligations under article +11 of the WIPO copyright treaty adopted on 20 December 1996, or +similar laws prohibiting or restricting circumvention of such +measures. + + When you convey a covered work, you waive any legal power to forbid +circumvention of technological measures to the extent such circumvention +is effected by exercising rights under this License with respect to +the covered work, and you disclaim any intention to limit operation or +modification of the work as a means of enforcing, against the work's +users, your or third parties' legal rights to forbid circumvention of +technological measures. + + 4. Conveying Verbatim Copies. + + You may convey verbatim copies of the Program's source code as you +receive it, in any medium, provided that you conspicuously and +appropriately publish on each copy an appropriate copyright notice; +keep intact all notices stating that this License and any +non-permissive terms added in accord with section 7 apply to the code; +keep intact all notices of the absence of any warranty; and give all +recipients a copy of this License along with the Program. + + You may charge any price or no price for each copy that you convey, +and you may offer support or warranty protection for a fee. + + 5. Conveying Modified Source Versions. + + You may convey a work based on the Program, or the modifications to +produce it from the Program, in the form of source code under the +terms of section 4, provided that you also meet all of these conditions: + + a) The work must carry prominent notices stating that you modified + it, and giving a relevant date. + + b) The work must carry prominent notices stating that it is + released under this License and any conditions added under section + 7. This requirement modifies the requirement in section 4 to + "keep intact all notices". + + c) You must license the entire work, as a whole, under this + License to anyone who comes into possession of a copy. This + License will therefore apply, along with any applicable section 7 + additional terms, to the whole of the work, and all its parts, + regardless of how they are packaged. This License gives no + permission to license the work in any other way, but it does not + invalidate such permission if you have separately received it. + + d) If the work has interactive user interfaces, each must display + Appropriate Legal Notices; however, if the Program has interactive + interfaces that do not display Appropriate Legal Notices, your + work need not make them do so. + + A compilation of a covered work with other separate and independent +works, which are not by their nature extensions of the covered work, +and which are not combined with it such as to form a larger program, +in or on a volume of a storage or distribution medium, is called an +"aggregate" if the compilation and its resulting copyright are not +used to limit the access or legal rights of the compilation's users +beyond what the individual works permit. Inclusion of a covered work +in an aggregate does not cause this License to apply to the other +parts of the aggregate. + + 6. Conveying Non-Source Forms. + + You may convey a covered work in object code form under the terms +of sections 4 and 5, provided that you also convey the +machine-readable Corresponding Source under the terms of this License, +in one of these ways: + + a) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by the + Corresponding Source fixed on a durable physical medium + customarily used for software interchange. + + b) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by a + written offer, valid for at least three years and valid for as + long as you offer spare parts or customer support for that product + model, to give anyone who possesses the object code either (1) a + copy of the Corresponding Source for all the software in the + product that is covered by this License, on a durable physical + medium customarily used for software interchange, for a price no + more than your reasonable cost of physically performing this + conveying of source, or (2) access to copy the + Corresponding Source from a network server at no charge. + + c) Convey individual copies of the object code with a copy of the + written offer to provide the Corresponding Source. This + alternative is allowed only occasionally and noncommercially, and + only if you received the object code with such an offer, in accord + with subsection 6b. + + d) Convey the object code by offering access from a designated + place (gratis or for a charge), and offer equivalent access to the + Corresponding Source in the same way through the same place at no + further charge. You need not require recipients to copy the + Corresponding Source along with the object code. If the place to + copy the object code is a network server, the Corresponding Source + may be on a different server (operated by you or a third party) + that supports equivalent copying facilities, provided you maintain + clear directions next to the object code saying where to find the + Corresponding Source. Regardless of what server hosts the + Corresponding Source, you remain obligated to ensure that it is + available for as long as needed to satisfy these requirements. + + e) Convey the object code using peer-to-peer transmission, provided + you inform other peers where the object code and Corresponding + Source of the work are being offered to the general public at no + charge under subsection 6d. + + A separable portion of the object code, whose source code is excluded +from the Corresponding Source as a System Library, need not be +included in conveying the object code work. + + A "User Product" is either (1) a "consumer product", which means any +tangible personal property which is normally used for personal, family, +or household purposes, or (2) anything designed or sold for incorporation +into a dwelling. In determining whether a product is a consumer product, +doubtful cases shall be resolved in favor of coverage. For a particular +product received by a particular user, "normally used" refers to a +typical or common use of that class of product, regardless of the status +of the particular user or of the way in which the particular user +actually uses, or expects or is expected to use, the product. A product +is a consumer product regardless of whether the product has substantial +commercial, industrial or non-consumer uses, unless such uses represent +the only significant mode of use of the product. + + "Installation Information" for a User Product means any methods, +procedures, authorization keys, or other information required to install +and execute modified versions of a covered work in that User Product from +a modified version of its Corresponding Source. The information must +suffice to ensure that the continued functioning of the modified object +code is in no case prevented or interfered with solely because +modification has been made. + + If you convey an object code work under this section in, or with, or +specifically for use in, a User Product, and the conveying occurs as +part of a transaction in which the right of possession and use of the +User Product is transferred to the recipient in perpetuity or for a +fixed term (regardless of how the transaction is characterized), the +Corresponding Source conveyed under this section must be accompanied +by the Installation Information. But this requirement does not apply +if neither you nor any third party retains the ability to install +modified object code on the User Product (for example, the work has +been installed in ROM). + + The requirement to provide Installation Information does not include a +requirement to continue to provide support service, warranty, or updates +for a work that has been modified or installed by the recipient, or for +the User Product in which it has been modified or installed. Access to a +network may be denied when the modification itself materially and +adversely affects the operation of the network or violates the rules and +protocols for communication across the network. + + Corresponding Source conveyed, and Installation Information provided, +in accord with this section must be in a format that is publicly +documented (and with an implementation available to the public in +source code form), and must require no special password or key for +unpacking, reading or copying. + + 7. Additional Terms. + + "Additional permissions" are terms that supplement the terms of this +License by making exceptions from one or more of its conditions. +Additional permissions that are applicable to the entire Program shall +be treated as though they were included in this License, to the extent +that they are valid under applicable law. If additional permissions +apply only to part of the Program, that part may be used separately +under those permissions, but the entire Program remains governed by +this License without regard to the additional permissions. + + When you convey a copy of a covered work, you may at your option +remove any additional permissions from that copy, or from any part of +it. (Additional permissions may be written to require their own +removal in certain cases when you modify the work.) You may place +additional permissions on material, added by you to a covered work, +for which you have or can give appropriate copyright permission. + + Notwithstanding any other provision of this License, for material you +add to a covered work, you may (if authorized by the copyright holders of +that material) supplement the terms of this License with terms: + + a) Disclaiming warranty or limiting liability differently from the + terms of sections 15 and 16 of this License; or + + b) Requiring preservation of specified reasonable legal notices or + author attributions in that material or in the Appropriate Legal + Notices displayed by works containing it; or + + c) Prohibiting misrepresentation of the origin of that material, or + requiring that modified versions of such material be marked in + reasonable ways as different from the original version; or + + d) Limiting the use for publicity purposes of names of licensors or + authors of the material; or + + e) Declining to grant rights under trademark law for use of some + trade names, trademarks, or service marks; or + + f) Requiring indemnification of licensors and authors of that + material by anyone who conveys the material (or modified versions of + it) with contractual assumptions of liability to the recipient, for + any liability that these contractual assumptions directly impose on + those licensors and authors. + + All other non-permissive additional terms are considered "further +restrictions" within the meaning of section 10. If the Program as you +received it, or any part of it, contains a notice stating that it is +governed by this License along with a term that is a further +restriction, you may remove that term. If a license document contains +a further restriction but permits relicensing or conveying under this +License, you may add to a covered work material governed by the terms +of that license document, provided that the further restriction does +not survive such relicensing or conveying. + + If you add terms to a covered work in accord with this section, you +must place, in the relevant source files, a statement of the +additional terms that apply to those files, or a notice indicating +where to find the applicable terms. + + Additional terms, permissive or non-permissive, may be stated in the +form of a separately written license, or stated as exceptions; +the above requirements apply either way. + + 8. Termination. + + You may not propagate or modify a covered work except as expressly +provided under this License. Any attempt otherwise to propagate or +modify it is void, and will automatically terminate your rights under +this License (including any patent licenses granted under the third +paragraph of section 11). + + However, if you cease all violation of this License, then your +license from a particular copyright holder is reinstated (a) +provisionally, unless and until the copyright holder explicitly and +finally terminates your license, and (b) permanently, if the copyright +holder fails to notify you of the violation by some reasonable means +prior to 60 days after the cessation. + + Moreover, your license from a particular copyright holder is +reinstated permanently if the copyright holder notifies you of the +violation by some reasonable means, this is the first time you have +received notice of violation of this License (for any work) from that +copyright holder, and you cure the violation prior to 30 days after +your receipt of the notice. + + Termination of your rights under this section does not terminate the +licenses of parties who have received copies or rights from you under +this License. If your rights have been terminated and not permanently +reinstated, you do not qualify to receive new licenses for the same +material under section 10. + + 9. Acceptance Not Required for Having Copies. + + You are not required to accept this License in order to receive or +run a copy of the Program. Ancillary propagation of a covered work +occurring solely as a consequence of using peer-to-peer transmission +to receive a copy likewise does not require acceptance. However, +nothing other than this License grants you permission to propagate or +modify any covered work. These actions infringe copyright if you do +not accept this License. Therefore, by modifying or propagating a +covered work, you indicate your acceptance of this License to do so. + + 10. Automatic Licensing of Downstream Recipients. + + Each time you convey a covered work, the recipient automatically +receives a license from the original licensors, to run, modify and +propagate that work, subject to this License. You are not responsible +for enforcing compliance by third parties with this License. + + An "entity transaction" is a transaction transferring control of an +organization, or substantially all assets of one, or subdividing an +organization, or merging organizations. If propagation of a covered +work results from an entity transaction, each party to that +transaction who receives a copy of the work also receives whatever +licenses to the work the party's predecessor in interest had or could +give under the previous paragraph, plus a right to possession of the +Corresponding Source of the work from the predecessor in interest, if +the predecessor has it or can get it with reasonable efforts. + + You may not impose any further restrictions on the exercise of the +rights granted or affirmed under this License. For example, you may +not impose a license fee, royalty, or other charge for exercise of +rights granted under this License, and you may not initiate litigation +(including a cross-claim or counterclaim in a lawsuit) alleging that +any patent claim is infringed by making, using, selling, offering for +sale, or importing the Program or any portion of it. + + 11. Patents. + + A "contributor" is a copyright holder who authorizes use under this +License of the Program or a work on which the Program is based. The +work thus licensed is called the contributor's "contributor version". + + A contributor's "essential patent claims" are all patent claims +owned or controlled by the contributor, whether already acquired or +hereafter acquired, that would be infringed by some manner, permitted +by this License, of making, using, or selling its contributor version, +but do not include claims that would be infringed only as a +consequence of further modification of the contributor version. For +purposes of this definition, "control" includes the right to grant +patent sublicenses in a manner consistent with the requirements of +this License. + + Each contributor grants you a non-exclusive, worldwide, royalty-free +patent license under the contributor's essential patent claims, to +make, use, sell, offer for sale, import and otherwise run, modify and +propagate the contents of its contributor version. + + In the following three paragraphs, a "patent license" is any express +agreement or commitment, however denominated, not to enforce a patent +(such as an express permission to practice a patent or covenant not to +sue for patent infringement). To "grant" such a patent license to a +party means to make such an agreement or commitment not to enforce a +patent against the party. + + If you convey a covered work, knowingly relying on a patent license, +and the Corresponding Source of the work is not available for anyone +to copy, free of charge and under the terms of this License, through a +publicly available network server or other readily accessible means, +then you must either (1) cause the Corresponding Source to be so +available, or (2) arrange to deprive yourself of the benefit of the +patent license for this particular work, or (3) arrange, in a manner +consistent with the requirements of this License, to extend the patent +license to downstream recipients. "Knowingly relying" means you have +actual knowledge that, but for the patent license, your conveying the +covered work in a country, or your recipient's use of the covered work +in a country, would infringe one or more identifiable patents in that +country that you have reason to believe are valid. + + If, pursuant to or in connection with a single transaction or +arrangement, you convey, or propagate by procuring conveyance of, a +covered work, and grant a patent license to some of the parties +receiving the covered work authorizing them to use, propagate, modify +or convey a specific copy of the covered work, then the patent license +you grant is automatically extended to all recipients of the covered +work and works based on it. + + A patent license is "discriminatory" if it does not include within +the scope of its coverage, prohibits the exercise of, or is +conditioned on the non-exercise of one or more of the rights that are +specifically granted under this License. You may not convey a covered +work if you are a party to an arrangement with a third party that is +in the business of distributing software, under which you make payment +to the third party based on the extent of your activity of conveying +the work, and under which the third party grants, to any of the +parties who would receive the covered work from you, a discriminatory +patent license (a) in connection with copies of the covered work +conveyed by you (or copies made from those copies), or (b) primarily +for and in connection with specific products or compilations that +contain the covered work, unless you entered into that arrangement, +or that patent license was granted, prior to 28 March 2007. + + Nothing in this License shall be construed as excluding or limiting +any implied license or other defenses to infringement that may +otherwise be available to you under applicable patent law. + + 12. No Surrender of Others' Freedom. + + If conditions are imposed on you (whether by court order, agreement or +otherwise) that contradict the conditions of this License, they do not +excuse you from the conditions of this License. If you cannot convey a +covered work so as to satisfy simultaneously your obligations under this +License and any other pertinent obligations, then as a consequence you may +not convey it at all. For example, if you agree to terms that obligate you +to collect a royalty for further conveying from those to whom you convey +the Program, the only way you could satisfy both those terms and this +License would be to refrain entirely from conveying the Program. + + 13. Remote Network Interaction; Use with the GNU General Public License. + + Notwithstanding any other provision of this License, if you modify the +Program, your modified version must prominently offer all users +interacting with it remotely through a computer network (if your version +supports such interaction) an opportunity to receive the Corresponding +Source of your version by providing access to the Corresponding Source +from a network server at no charge, through some standard or customary +means of facilitating copying of software. This Corresponding Source +shall include the Corresponding Source for any work covered by version 3 +of the GNU General Public License that is incorporated pursuant to the +following paragraph. + + Notwithstanding any other provision of this License, you have +permission to link or combine any covered work with a work licensed +under version 3 of the GNU General Public License into a single +combined work, and to convey the resulting work. The terms of this +License will continue to apply to the part which is the covered work, +but the work with which it is combined will remain governed by version +3 of the GNU General Public License. + + 14. Revised Versions of this License. + + The Free Software Foundation may publish revised and/or new versions of +the GNU Affero General Public License from time to time. Such new versions +will be similar in spirit to the present version, but may differ in detail to +address new problems or concerns. + + Each version is given a distinguishing version number. If the +Program specifies that a certain numbered version of the GNU Affero General +Public License "or any later version" applies to it, you have the +option of following the terms and conditions either of that numbered +version or of any later version published by the Free Software +Foundation. If the Program does not specify a version number of the +GNU Affero General Public License, you may choose any version ever published +by the Free Software Foundation. + + If the Program specifies that a proxy can decide which future +versions of the GNU Affero General Public License can be used, that proxy's +public statement of acceptance of a version permanently authorizes you +to choose that version for the Program. + + Later license versions may give you additional or different +permissions. However, no additional obligations are imposed on any +author or copyright holder as a result of your choosing to follow a +later version. + + 15. Disclaimer of Warranty. + + THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY +APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT +HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY +OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM +IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF +ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + + 16. Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Affero General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Affero General Public License for more details. + + You should have received a copy of the GNU Affero General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..59ef741 --- /dev/null +++ b/Makefile @@ -0,0 +1,8 @@ +html: + Rscript -e 'library(rmarkdown); rmarkdown::render("mapping_draft.Rmd", "html_document")' + +hpc: + Rscript -e 'library(rmarkdown); rmarkdown::render("mapping_draft-hpc_optimised.Rmd", "html_document")' + +pdf: + Rscript -e 'library(rmarkdown); rmarkdown::render("./mapping_draft.Rmd", "pdf_document")' \ No newline at end of file diff --git a/README.md b/README.md index 41e529d..e5a493f 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,12 @@ # Mapping Environmental Action +This code is associated with a scholarly paper "Mapping Environmental Action" (currently unpublished). In the paper, I draw on original data gathered from my research with Eco-groups in Scotland (2013-2017) in order to do comparative geospatial analysis of the coincidence of these groups with a number of standard demographics. You can read (an unpublished version of) the paper at (http://mapenvcom.jeremykidwell.info/mapping_draft.html). + ## Why Reproducible Research? ## -If you're new to github and reproducible research, welcome! It's nice to have you here. Github is ordinarily a place where software developers working on open source software projects deposit their code as they write software collaboratively. However, in recent years a number of scholarly researchers, especially people working on research which involves a digital component (including me!) have begun to deposit their papers in these same software repositories. The idea here is that you can download all of the source-code and data used in this paper alongside the actual text, run it yourself and ["reproduce" the results](http://kbroman.org/steps2rr/). This can serve as a useful safeguard, a layer of research transparency, and a cool teaching tool for other persons interested in doing similar work. +If you're new to github and reproducible research, welcome! It's nice to have you here. Github is ordinarily a place where software developers working on open source software projects deposit their code as they write software collaboratively. However, in recent years a number of scholarly researchers, especially people working on research that involves a digital component (including me!) have begun to deposit their papers in these same software repositories. The idea is that you can download all of the source-code and data used in this paper alongside the actual text, run it yourself and ["reproduce" the results](http://kbroman.org/steps2rr/). This can serve as a useful safeguard, a layer of research transparency, and a cool teaching tool for other persons interested in doing similar work. Particularly when, as is the case in subject areas that are only just starting to get involved in the digital humanities, like religious studies, there is a dearth of work of this nature, it can be helpful to have examples of practice which can be reused, or at least used as an example. -Eschewing proprietary, expensive and unreliable software like Microsoft Word, I write in a combination of two languages: (1) [Markdown](https://en.wikipedia.org/wiki/Markdown) which is intended to be as close as possible to plain text while still allowing for things like boldfaced type, headings and footnotes; and (2) a programming language called [R](https://en.wikipedia.org/wiki/R_(programming_language)) to do all the data analysis. R is an object oriented language which was specifically designed for statistical analysis. It's also great fun to tinker with. As you look through this paper, you'll see that R code is integrated into the text of the document. This is indicated by a series of three backticks (```). There is a formal specification now at a mature stage of development, which is RMarkdown. You can read semi-official specification [for this here](https://bookdown.org/yihui/rmarkdown/pdf-document.html). +Eschewing proprietary, expensive and unreliable software like Microsoft Word, I write in a combination of two languages: (1) [Markdown](https://en.wikipedia.org/wiki/Markdown) which is intended to be as close as possible to plain text while still allowing for things like boldfaced type, headings and footnotes; and (2) a programming language called [R](https://en.wikipedia.org/wiki/R_(programming_language)) to do all the data analysis. R is an object oriented language that was specifically designed for statistical analysis. It's also great fun to tinker with. As you look through this paper, you'll see that R code is integrated into the text of the document. This is indicated by a series of three backticks (```). There is a formal specification now at a mature stage of development, which is RMarkdown. You can read semi-official specification [for this here](https://bookdown.org/yihui/rmarkdown/pdf-document.html). To read a bit more on these things and start on your own path towards plain text reproducible research, I highly recommend: - Karl Broman's guide, "[Initial Steps Toward Reproducible Research](http://kbroman.org/steps2rr/)" @@ -12,25 +14,54 @@ To read a bit more on these things and start on your own path towards plain text The other advantage of putting this paper here is that readers and reviewers can suggest changes and point out errors in the document. To do this, I recommend that you create a github issue by clicking on the green "New issue" button [here](https://github.com/kidwellj/mapping_environmental_action/issues). If you must, you can also send me emails. More stuff about me [can be found here](http://jeremykidwell.info). -To skip ahead and start reading the actual paper, click on [`mapping_draft.rmd`](https://github.com/kidwellj/mapping_environmental_action/blob/master/mapping_draft.Rmd) above. +To skip ahead and start reading the actual paper in raw format, click on [`mapping_draft.rmd`](https://github.com/kidwellj/mapping_environmental_action/blob/master/mapping_draft.Rmd) above. If you were looking for the article (without code) you can also find a working draft here: (http://mapenvcom.jeremykidwell.info/mapping_draft.html). Now for... ## The quick technical version ## -This repository contains the code and writing towards a (working draft of a) scholarly paper which presents my analysis of the geospatial footprint of eco-groups in the UK. This is based on research I have been conducting since 2013 and which is ongoing. The paper is written in R Markdown and for the most part, I'm using the conventions outlined by Kieran Healy [here](https://kieranhealy.org/blog/archives/2014/01/23/plain-text/) and is best viewed (I think) in [R Studio](https://www.rstudio.com) though it will be reasonably comprehensible to anyone using a Markdown editor. If I'm not working in RStudio, I'm probably in Sublime text, FYI. Co-authors and collaborators take note, generally, I use [Hadley Wickham's venerable R Style Guide](http://adv-r.had.co.nz/Style.html). +This repository contains the code and writing towards a (working draft of a) scholarly paper that presents my analysis of the geospatial footprint of eco-groups in the UK. This is based on research I have been conducting since 2013 and that is ongoing. The paper is written in R Markdown and for the most part, I'm using the conventions outlined by Kieran Healy [here](https://kieranhealy.org/blog/archives/2014/01/23/plain-text/) and is best viewed (I think) in [R Studio](https://www.rstudio.com) though it will be reasonably comprehensible to anyone using a Markdown editor. If I'm not working in RStudio, I'm probably in Sublime text, FYI. Co-authors and collaborators take note, generally, I use [Hadley Wickham's venerable R Style Guide](http://adv-r.had.co.nz/Style.html). - I'd be extremely happy if someone found errors, or imagined a more efficient means of analysis and either reported them as an issue on this github repository or sent me an email. +I'd be extremely happy if someone found errors, or imagined a more efficient means of analysis and either reported them as an issue on this github repository or sent me an email. -The actual article is in `mapping_draft.Rmd` and can be compiled using knitr (assuming you have R installed as well as required packages) using the script provided `knit_it_html.sh` +The actual article is in `mapping_draft.Rmd` and can be compiled using knitr (assuming you have R installed as well as required packages) using the `Makefile` provided. -Note: actual execution may take over an hour, as calls to `st_buffer` and `st_within` under `wilderness_data_prep` are computationally intensive. To compile more briskly, I recommend you comment out this final section and knit the markdown/html files. +Note: actual execution may take over an hour, as calls to `st_buffer` and `st_within` under `wilderness_data_prep` are computationally intensive. To compile more briskly, I recommend you comment out this final section and knit the markdown/html files. I have been relying on the University of Birmingham supercomputing cluster for execution, which has resulted in a parallel version of this script `mapping_draft-hpc_optimised.Rmd`. The latter will only run on the BlueBEAR cluster at UOB, though other scholars may want to consult this script to get a sense of how geospatial operations can be parallelised for more efficient execution. -Paths in this folder are used mostly for R processing. Towards this end folders have the following significance: +Paths in this folder are used mostly for R processing. I'm using a "project" oriented workflow, on which you can read more [in a blog by Jenny Bryan here](https://www.tidyverse.org/blog/2017/12/workflow-vs-script/). This uses the R package [here](https://cran.r-project.org/web/packages/here/index.html). +Towards this end folders have the following significance: - `data` contains datasets used for analysis. - `derived_data` contains files which represent modified forms of files in the above path. - `figures` contains images and visualisations (graphic files) which are generated by R for the final form of the document. - `cache` isn't included in github but is usually used for working files -Note: none of the contents of the above are included in the github repository unless they are unavailable from an external repository. \ No newline at end of file +Note: none of the contents of the above are included in the github repository unless they are unavailable from an external repository. + +## And, a few notes for the data scientists + +Over the course of this research project (since 2013, really), the state of geospatial tools for datascience in R (and python) has shifted and the increased attention and resources that have been brought to bear on geospatial has resulted in a dramatic improvement in the quality and precision of tools available, particularly the development of SimpleFeatures and the [sf](https://github.com/r-spatial/sf) and [tmap](https://github.com/mtennekes/tmap) packages for R. Ggplot2 is awesome, but starts to creak quickly when you push it in more creative geospatial directions. There are also inefficiencies with data handling in some of the older packages (such as sp) that aren't apparent until you start working with large datasets. Underlying data formats have been shifting quite a lot as well, from csvt and [very problematic and proprietary ESRI shapefiles](http://switchfromshapefile.org/) to geojson/topojson and [Geopackage](http://switchfromshapefile.org/#geopackage) formats. The result of this has a need to completely rewrite this script mid-way through the research process. I've left some of the messy bits in with as comprehensive comments as possible to give a sense of things, but there remain some bits which are accidentally messy. + +There are a few aspects of this code which are novel or were difficult that I'm proud of, which I hope may be useful and on ehich I'd especially value + +- The use of sf() and tmap() +- The creation of vignettes for visualisations +- The level of reproducibility +- Optimisation of intensive geospatial operations for htpc and parallel computing + +# Prerequisites + +I've tried to follow best practices in setting up this script for reproducibility, but given some of the choices I've had to make computationally (e.g. running some operations in PostGIS) some setup is required before execution will be successful. + +These steps are: + +1. Acquire a working installation of R. I have produced a Docker container which replicates the environment I have used to execute this script which is probably the easiest way to complete this task. +2. Set up a working Postgres database with PostGIS extensions installed. The script will download necessary data and load it into your database if it is not already in place. + +# Contributing + +Please note that this project is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms. + +# License + +The content of this research paper are licensed under the [Creative Commons Attribution-ShareAlike 4.0 International Public License](https://creativecommons.org/licenses/by-sa/4.0/legalcode), and the underlying source code used to generate the paper is licensed under the [GNU AGPLv3](https://www.gnu.org/licenses/agpl-3.0.en.html) license. Underlying datasets designed as part of this research have their own licenses that are specified in their respective repositories. diff --git a/TODO.md b/TODO.md new file mode 100644 index 0000000..7a5cb38 --- /dev/null +++ b/TODO.md @@ -0,0 +1,32 @@ +This script is a work in progress! In a spirit of open-ended collaboration and a continuous development cycle, I've collected aspirations towards improvement here in this file. There is a list of urgent tasks which need to be completed before the research is complete, and a batch of subsequent work which, while not necessary, can improve and extend the work here. + +# Pre-pub changes todo: + +Optimising for command-line use of knitr: +- [ ] fix issues preventing simultaneous output towards PDF/md, see [here](https://stackoverflow.com/questions/23621012/display-and-save-the-plot-simultaneously-in-r-rstudio) Related to problems with colliding knitr preferences for each document type + +Complete transition away from sp() to sf() +- [ ] remove use of sp(), rgdal(), GISTools(), rgeos() and commands: readOGR, spTransform, poly.counts, prop.table +- [ ] remove use of ggplot2 (in favour of tmap) + +Code changes: +- [ ] Shift intensive geospatial analysis using sf() within R to operations within PostGIS per https://www.r-bloggers.com/interact-with-postgis-from-r/ and https://rviews.rstudio.com/2019/03/21/how-to-avoid-publishing-credentials-in-your-code/ +- [ ] Shift to "projects" as per (https://www.tidyverse.org/blog/2017/12/workflow-vs-script/) +- [ ] Set up conventions (per https://annakrystalli.me/talks/r-in-repro-research-dc.html#58) and +- [ ] Shift appendices to compendium + +Streamline code: +- [ ] Merge htpc and wilderness versions back into main draft streamline drafts + note: htpc version created 25 Mar 2019, commit 9a8934935a57c4e9790b7c420eef7454d3fb7326; wilderness mods include pub data line 373 and lines 480ff +- [ ] remove "cuts" versions + + +Process oriented tasks: +- [ ] Install and use [ReDoc](https://github.com/noamross/redoc/blob/master/README.md) for reversible conversion to docx +- [ ] Consider implementing [knitcitations](https://github.com/cboettig/knitcitations) +- [ ] Install and use [here](https://here.r-lib.org/) +- [ ] Install and use [renv](https://rstudio.github.io/renv/) + + +Underlying data work: +- [ ] Convert shapefiles and csv to geopackages or geojson \ No newline at end of file diff --git a/TODO_postpub.md b/TODO_postpub.md new file mode 100644 index 0000000..7a107bd --- /dev/null +++ b/TODO_postpub.md @@ -0,0 +1,8 @@ +# Future changes (post-publication) to consider: + +Spin off replicable operations into functions or packages: +- [ ] Ingest OrdnanceSurvey open data +- [ ] ProcessPubs, ProcessPlacesofWorship +- [ ] Ingest geolytics grocery store data + + \ No newline at end of file diff --git a/data/poi_2015_12_scot06340459.csv b/data/poi_2015_12_scot06340459.csv old mode 100755 new mode 100644 diff --git a/mapping_draft-hpc_optimised.Rmd b/mapping_draft-hpc_optimised.Rmd index a913b01..03c35b3 100644 --- a/mapping_draft-hpc_optimised.Rmd +++ b/mapping_draft-hpc_optimised.Rmd @@ -36,48 +36,67 @@ output: --- ```{r setup, include=FALSE} -require(knitr) -require(kableExtra) +# Note, this script has been written largely with RStudio on MacOS, but is compiled on +# an hpc cluster which runs Linux, so some tweaks below ensure smooth execution +# in both environments. Also, as above, the script is meant to output to both PDF and +# html_document knitr formats. +require(knitr) # used to knit RMarkdown format script into working documents in various formats +require(kableExtra) # used for markdown table formatting compatible with knitr +# note: some features of the below line are specific to html/pdf format and will need to be adapted pre-compile until dual outputs are working (see https://github.com/kidwellj/mapping_environmental_action/issues/2) knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FALSE, dpi=300, fig.width=7) -# TODO: consider implementing knitcitations - https://github.com/cboettig/knitcitations -# TODO: fix simultaneous output towards PDF, see here: https://stackoverflow.com/questions/23621012/display-and-save-the-plot-simultaneously-in-r-rstudio ``` ```{r load_packages, message=FALSE, warning=FALSE, include=FALSE} -## Default repo -# setwd("/Users/jeremy/gits/mapping_environmental_action") -# setwd("/Users/kidwellj/OneDrive\ -\ bham.ac.uk/writing/201708_mapping_environmental_action") +# Set up working machine independent working directory and environment +require(here) # used to keep working directory organised and portable +require(renv) # used to set up environment +require(usethis) # integrates git support for rStudio # Set repository to be new standard, e.g. cloud server. -# This will avoid a dialogue box if packages are to be installed for below on first run. +# For smooth execution on command-line knitr - this will avoid a dialogue box +# if packages are to be installed for below on first run local({r <- getOption("repos") r["CRAN"] <- "https://cloud.r-project.org" options(repos=r) }) -# TODO: remove sp etc. once sf is fully implemented -# TODO: automatically test for packages below on given execution environment and run install.packages() as needed. + require(RCurl) # used for fetching reproducible datasets require(sf) # new simplefeature data class, supercedes sp in many ways -require(sp) # needed for proj4string, deprecated by sf() -require(rgdal) # deprecated by sf() -require(GISTools) # deprecated by sf() -require(rgeos) # deprecated by sf() -require(maptools) +# See issue https://github.com/kidwellj/mapping_environmental_action/issues/3 for progress re: migration from sp() +# require(sp) # needed for proj4string, deprecated by sf() +# require(rgdal) # deprecated by sf() +# require(GISTools) # deprecated by sf() +# require(rgeos) # deprecated by sf() +# require(maptools) require(ggplot2) require(tmap) # using as an alternative to base r graphics and ggplot for geospatial plots require(tmaptools) # for get_asp_ratio below require(grid) # using for inset maps on tmap require(broom) # required for tidying SPDF to data.frame for ggplot2 require(tidyr) # using for grouped bar plot -require(plyr) -require(dplyr) +# require(plyr) # already a dependency of knitr, remove? +# require(dplyr) # already a dependency of knitr, remove? require(reshape2) # using for grouped bar plot require(scales) # require(sqldf) # using sqldf to filter before loading very large data sets + +## Packages required for PostGIS database access +# Many thanks to Sébastien Rochette for documentation here: https://www.r-bloggers.com/interact-with-postgis-from-r/ +library(DBI) +library(RPostgres) +library(sqlpetr) # useful for visual DB panels in RStudio, see https://smithjd.github.io/sqlpetr/ +# library(rpostgis) +library(dbplyr) + +## Packages required for knitr output +## Packages used for features or issues relating to html_document knitr output require(plotly) # allows for export of plots to dynamic web pages require(gtable) # more powerful package for multi-plot layouts, not necessary for knitr -require(showtext) # for loading in fonts -require(extrafont) # font support + +## Packages used for features or issues relating to pdf_document knitr format +# Note: implementation of fonts (currently commented out) is specific to pdf_document output +# require(showtext) # for loading in fonts +# require(extrafont) # font support # Set up local workspace: if (dir.exists("data") == FALSE) { @@ -96,12 +115,15 @@ if (dir.exists("derivedData") == FALSE) { # data-sets and papers. # Working with EPSG codes for spatialfeature CRS given the usage of this approach with sf() +# for discussion related to this fix, see https://gis.stackexchange.com/q/313761/41474 +# TODO: remove below as part of overall migration to sf() +# See issue https://github.com/kidwellj/mapping_environmental_action/issues/3 for progress re: migration from sp() bng <- CRS("+init=epsg:27700") wgs84 <- CRS("+init=epsg:4326") -# Configure fonts for plots below - +## Configure fonts for plots below, commented out currently because of incompatibilities ## Loading Google fonts (http://www.google.com/fonts) +# Note: implementation of fonts (currently commented out) is specific to pdf_document output # font_add_google("Merriweather", "merriweather") # The following will load in system fonts (uncomment and run as needed on first execution) # font_import(pattern="[A/a]rial", prompt=FALSE) @@ -116,12 +138,25 @@ Until recently, environmentalism has been treated by governments and environment ```{r load_ecs_data, message=FALSE, warning=FALSE} # read in Eco-Congregation Scotland data and------------------- # ...turn it into a SpatialPointsDataFrame--------------------- -# TODO: upload ECS-GIS-Locations_3.0.csv to zenodo repository, i.e. +# TODO: update below to match new dataset once it has been uploaded to zenodo +# if (file.exists("data/ECS-GIS-Locations_3.0.csv") == FALSE) { +# download.file("https://____.zip", +# destfile = "data/____.zip") +# unzip("data/____.zip", exdir = "data") +# } + +# TODO: remove below as part of overall migration to sf() +# See issue https://github.com/kidwellj/mapping_environmental_action/issues/3 for progress re: migration from sp() ecs <- read.csv("data/ECS-GIS-Locations_3.0.csv", comment.char="#") # unnecessary with advent of sf (above) coordinates(ecs) <- c("X", "Y") # Modified to use EPSG code directly 27 Feb 2019 proj4string(ecs) <- bng +# Note, use of paste0 here relates to fix noted above. +# for discussion related to this approach, see https://gis.stackexchange.com/q/313761/41474 +# read in Eco-Congregation Scotland data and------------------- +# ...turn it into a SpatialPointsDataFrame--------------------- + ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=paste0("+init=epsg:",27700)) ``` @@ -357,7 +392,7 @@ Though there are too few eco-congregations and transition groups for a numerical ```{r 01_admin_ecs_choropleth, fig.width=4, fig.cap="Figure 1"} # Note: for more information on EU administrative levels, see here: https://ec.europa.eu/eurostat/web/nuts/national-structures-eu -# TODO: clip choropleth polygons to buildings shapefile (possble superceded by pverlay on lev2) +# TODO: clip choropleth polygons to buildings shapefile (possibly superceded by pverlay on lev2) # Draw initial choropleth map of ECS concentration (using tmap and sf below by default) # Revising re: CRS inset maps complete to here diff --git a/mapping_draft-hpc_optimised_wilderness.Rmd b/mapping_draft-hpc_optimised_wilderness.Rmd index 4cd32c3..baaff2c 100644 --- a/mapping_draft-hpc_optimised_wilderness.Rmd +++ b/mapping_draft-hpc_optimised_wilderness.Rmd @@ -35,7 +35,7 @@ output: --- -```{r setup, include=FALSE} +```{R setup, include=FALSE} require(knitr) require(kableExtra) knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FALSE, dpi=300, fig.width=7) @@ -43,7 +43,7 @@ knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FA # TODO: fix simultaneous output towards PDF, see here: https://stackoverflow.com/questions/23621012/display-and-save-the-plot-simultaneously-in-r-rstudio ``` -```{r load_packages, message=FALSE, warning=FALSE, include=FALSE} +```{R load_packages, message=FALSE, warning=FALSE, include=FALSE} ## Default repo # setwd("/Users/jeremy/gits/mapping_environmental_action") # setwd("/Users/kidwellj/OneDrive\ -\ bham.ac.uk/writing/201708_mapping_environmental_action") From def2fa417899aa104c1c52e93a73106abc480576 Mon Sep 17 00:00:00 2001 From: kidwellj Date: Tue, 10 Mar 2020 19:35:20 +0000 Subject: [PATCH 2/3] Resolved issue #2 migrated all operations away from sp() to sf(). Installed renv snapshot. Merged in changes in staging file `mapping_draft-hpc_optimised_wilderness.Rmd`. --- renv/.gitignore | 3 + renv/activate.R | 185 ++++++++++++++++++++++++++++++++++++++++++++++ renv/settings.dcf | 6 ++ 3 files changed, 194 insertions(+) create mode 100644 renv/.gitignore create mode 100644 renv/activate.R create mode 100644 renv/settings.dcf diff --git a/renv/.gitignore b/renv/.gitignore new file mode 100644 index 0000000..82740ba --- /dev/null +++ b/renv/.gitignore @@ -0,0 +1,3 @@ +library/ +python/ +staging/ diff --git a/renv/activate.R b/renv/activate.R new file mode 100644 index 0000000..4baa934 --- /dev/null +++ b/renv/activate.R @@ -0,0 +1,185 @@ + +local({ + + # the requested version of renv + version <- "0.9.3" + + # avoid recursion + if (!is.na(Sys.getenv("RENV_R_INITIALIZING", unset = NA))) + return(invisible(TRUE)) + + # signal that we're loading renv during R startup + Sys.setenv("RENV_R_INITIALIZING" = "true") + on.exit(Sys.unsetenv("RENV_R_INITIALIZING"), add = TRUE) + + # signal that we've consented to use renv + options(renv.consent = TRUE) + + # load the 'utils' package eagerly -- this ensures that renv shims, which + # mask 'utils' packages, will come first on the search path + library(utils, lib.loc = .Library) + + # check to see if renv has already been loaded + if ("renv" %in% loadedNamespaces()) { + + # if renv has already been loaded, and it's the requested version of renv, + # nothing to do + spec <- .getNamespaceInfo(.getNamespace("renv"), "spec") + if (identical(spec[["version"]], version)) + return(invisible(TRUE)) + + # otherwise, unload and attempt to load the correct version of renv + unloadNamespace("renv") + + } + + # construct path to renv in library + libpath <- local({ + + root <- Sys.getenv("RENV_PATHS_LIBRARY", unset = "renv/library") + prefix <- paste("R", getRversion()[1, 1:2], sep = "-") + + # include SVN revision for development versions of R + # (to avoid sharing platform-specific artefacts with released versions of R) + devel <- + identical(R.version[["status"]], "Under development (unstable)") || + identical(R.version[["nickname"]], "Unsuffered Consequences") + + if (devel) + prefix <- paste(prefix, R.version[["svn rev"]], sep = "-r") + + file.path(root, prefix, R.version$platform) + + }) + + # try to load renv from the project library + if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { + + # warn if the version of renv loaded does not match + loadedversion <- utils::packageDescription("renv", fields = "Version") + if (version != loadedversion) { + + # assume four-component versions are from GitHub; three-component + # versions are from CRAN + components <- strsplit(loadedversion, "[.-]")[[1]] + remote <- if (length(components) == 4L) + paste("rstudio/renv", loadedversion, sep = "@") + else + paste("renv", loadedversion, sep = "@") + + fmt <- paste( + "renv %1$s was loaded from project library, but renv %2$s is recorded in lockfile.", + "Use `renv::record(\"%3$s\")` to record this version in the lockfile.", + "Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library.", + sep = "\n" + ) + + msg <- sprintf(fmt, loadedversion, version, remote) + warning(msg, call. = FALSE) + + } + + # load the project + return(renv::load()) + + } + + # failed to find renv locally; we'll try to install from GitHub. + # first, set up download options as appropriate (try to use GITHUB_PAT) + install_renv <- function() { + + message("Failed to find installation of renv -- attempting to bootstrap...") + + # ensure .Rprofile doesn't get executed + rpu <- Sys.getenv("R_PROFILE_USER", unset = NA) + Sys.setenv(R_PROFILE_USER = "") + on.exit({ + if (is.na(rpu)) + Sys.unsetenv("R_PROFILE_USER") + else + Sys.setenv(R_PROFILE_USER = rpu) + }, add = TRUE) + + # prepare download options + pat <- Sys.getenv("GITHUB_PAT") + if (nzchar(Sys.which("curl")) && nzchar(pat)) { + fmt <- "--location --fail --header \"Authorization: token %s\"" + extra <- sprintf(fmt, pat) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "curl", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } else if (nzchar(Sys.which("wget")) && nzchar(pat)) { + fmt <- "--header=\"Authorization: token %s\"" + extra <- sprintf(fmt, pat) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "wget", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } + + # fix up repos + repos <- getOption("repos") + on.exit(options(repos = repos), add = TRUE) + repos[repos == "@CRAN@"] <- "https://cloud.r-project.org" + options(repos = repos) + + # check for renv on CRAN matching this version + db <- as.data.frame(available.packages(), stringsAsFactors = FALSE) + if ("renv" %in% rownames(db)) { + entry <- db["renv", ] + if (identical(entry$Version, version)) { + message("* Installing renv ", version, " ... ", appendLF = FALSE) + dir.create(libpath, showWarnings = FALSE, recursive = TRUE) + utils::install.packages("renv", lib = libpath, quiet = TRUE) + message("Done!") + return(TRUE) + } + } + + # try to download renv + message("* Downloading renv ", version, " ... ", appendLF = FALSE) + prefix <- "https://api.github.com" + url <- file.path(prefix, "repos/rstudio/renv/tarball", version) + destfile <- tempfile("renv-", fileext = ".tar.gz") + on.exit(unlink(destfile), add = TRUE) + utils::download.file(url, destfile = destfile, mode = "wb", quiet = TRUE) + message("Done!") + + # attempt to install it into project library + message("* Installing renv ", version, " ... ", appendLF = FALSE) + dir.create(libpath, showWarnings = FALSE, recursive = TRUE) + + # invoke using system2 so we can capture and report output + bin <- R.home("bin") + exe <- if (Sys.info()[["sysname"]] == "Windows") "R.exe" else "R" + r <- file.path(bin, exe) + args <- c("--vanilla", "CMD", "INSTALL", "-l", shQuote(libpath), shQuote(destfile)) + output <- system2(r, args, stdout = TRUE, stderr = TRUE) + message("Done!") + + # check for successful install + status <- attr(output, "status") + if (is.numeric(status) && !identical(status, 0L)) { + text <- c("Error installing renv", "=====================", output) + writeLines(text, con = stderr()) + } + + + } + + try(install_renv()) + + # try again to load + if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { + message("Successfully installed and loaded renv ", version, ".") + return(renv::load()) + } + + # failed to download or load renv; warn the user + msg <- c( + "Failed to find an renv installation: the project will not be loaded.", + "Use `renv::activate()` to re-initialize the project." + ) + + warning(paste(msg, collapse = "\n"), call. = FALSE) + +}) diff --git a/renv/settings.dcf b/renv/settings.dcf new file mode 100644 index 0000000..bba46f4 --- /dev/null +++ b/renv/settings.dcf @@ -0,0 +1,6 @@ +external.libraries: +ignored.packages: +package.dependency.fields: Imports, Depends, LinkingTo +snapshot.type: packrat +use.cache: TRUE +vcs.ignore.library: TRUE From 6a531782d3ee509f39eab425366df4d57bf58416 Mon Sep 17 00:00:00 2001 From: kidwellj Date: Tue, 10 Mar 2020 19:35:20 +0000 Subject: [PATCH 3/3] Resolved issue #3 migrated all operations away from sp() to sf(). Installed renv snapshot. Merged in changes in staging file `mapping_draft-hpc_optimised_wilderness.Rmd`. --- README.md | 21 +- TODO_postpub.md | 4 +- data/scotland_admin_2011pop.csv | 2 +- mapping_draft-hpc_optimised.Rmd | 419 ++++++----- mapping_draft-hpc_optimised_wilderness.Rmd | 764 --------------------- renv/.gitignore | 3 + renv/activate.R | 185 +++++ renv/settings.dcf | 6 + 8 files changed, 455 insertions(+), 949 deletions(-) delete mode 100644 mapping_draft-hpc_optimised_wilderness.Rmd create mode 100644 renv/.gitignore create mode 100644 renv/activate.R create mode 100644 renv/settings.dcf diff --git a/README.md b/README.md index e5a493f..d3e0e87 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ To skip ahead and start reading the actual paper in raw format, click on [`mappi Now for... -## The quick technical version ## +## The technical version ## This repository contains the code and writing towards a (working draft of a) scholarly paper that presents my analysis of the geospatial footprint of eco-groups in the UK. This is based on research I have been conducting since 2013 and that is ongoing. The paper is written in R Markdown and for the most part, I'm using the conventions outlined by Kieran Healy [here](https://kieranhealy.org/blog/archives/2014/01/23/plain-text/) and is best viewed (I think) in [R Studio](https://www.rstudio.com) though it will be reasonably comprehensible to anyone using a Markdown editor. If I'm not working in RStudio, I'm probably in Sublime text, FYI. Co-authors and collaborators take note, generally, I use [Hadley Wickham's venerable R Style Guide](http://adv-r.had.co.nz/Style.html). @@ -49,14 +49,29 @@ There are a few aspects of this code which are novel or were difficult that I'm - The level of reproducibility - Optimisation of intensive geospatial operations for htpc and parallel computing -# Prerequisites +# Prerequisites for reproducing this codebase I've tried to follow best practices in setting up this script for reproducibility, but given some of the choices I've had to make computationally (e.g. running some operations in PostGIS) some setup is required before execution will be successful. These steps are: -1. Acquire a working installation of R. I have produced a Docker container which replicates the environment I have used to execute this script which is probably the easiest way to complete this task. +1. Acquire a working installation of R (and RStudio). I have produced a Docker container that replicates the environment I have used to execute this script that is probably the easiest way to complete this task. 2. Set up a working Postgres database with PostGIS extensions installed. The script will download necessary data and load it into your database if it is not already in place. +3. Install platform appropriate prerequisites for the R odbc() package, see here: [https://github.com/r-dbi/odbc#installation] +4. Configure a local `config.yml` file with the following information (used to connect to your PostGIS database): +``` +default: + datawarehouse: + driver: 'Postgres' + server: 'change.to.yourserver.com' + uid: 'change-to-your-username' + pwd: 'change-to-your-password' + port: 5432 + database: 'database-name' +``` +5. Clone or download the code from this repository +6. Set up a proper R/RStudio working environment. I use the `renv` package to manage working environment, which takes snapshots and stores them to `renv.lock`. If you run `renv::restore()` in R after loading this code, it will install necessary libraries at proper versions. +7. Nearly all of the data used in this study is open, with one exception, that of the Ordnance Survey PointX data product. This is available to most UK academics via the EDINA service, so the user will need to manually download this data and place it in the `/data/` directory. # Contributing diff --git a/TODO_postpub.md b/TODO_postpub.md index 7a107bd..1ee55de 100644 --- a/TODO_postpub.md +++ b/TODO_postpub.md @@ -5,4 +5,6 @@ Spin off replicable operations into functions or packages: - [ ] ProcessPubs, ProcessPlacesofWorship - [ ] Ingest geolytics grocery store data - \ No newline at end of file +Clip shapes to buildings in admin plots, using st_difference +- [ ] 01_admin_ecs_choropleth +- [ ] 02_admin_ecs_normed_choropleth \ No newline at end of file diff --git a/data/scotland_admin_2011pop.csv b/data/scotland_admin_2011pop.csv index 8998e63..8d4d354 100644 --- a/data/scotland_admin_2011pop.csv +++ b/data/scotland_admin_2011pop.csv @@ -1,4 +1,4 @@ -Council areas,2011_pop,CODE +council areas,2011_pop,code Aberdeen City,"222,793",S12000033 Aberdeenshire,"252,973",S12000034 Angus,"115,978",S12000041 diff --git a/mapping_draft-hpc_optimised.Rmd b/mapping_draft-hpc_optimised.Rmd index 03c35b3..629982f 100644 --- a/mapping_draft-hpc_optimised.Rmd +++ b/mapping_draft-hpc_optimised.Rmd @@ -64,9 +64,6 @@ require(RCurl) # used for fetching reproducible datasets require(sf) # new simplefeature data class, supercedes sp in many ways # See issue https://github.com/kidwellj/mapping_environmental_action/issues/3 for progress re: migration from sp() # require(sp) # needed for proj4string, deprecated by sf() -# require(rgdal) # deprecated by sf() -# require(GISTools) # deprecated by sf() -# require(rgeos) # deprecated by sf() # require(maptools) require(ggplot2) require(tmap) # using as an alternative to base r graphics and ggplot for geospatial plots @@ -82,11 +79,12 @@ require(scales) ## Packages required for PostGIS database access # Many thanks to Sébastien Rochette for documentation here: https://www.r-bloggers.com/interact-with-postgis-from-r/ -library(DBI) -library(RPostgres) -library(sqlpetr) # useful for visual DB panels in RStudio, see https://smithjd.github.io/sqlpetr/ +# require(config) # used to access database connection credentials in config.yml file below +# library(DBI) +# library(odbc) +# library(RPostgres) # library(rpostgis) -library(dbplyr) +# library(dbplyr) ## Packages required for knitr output ## Packages used for features or issues relating to html_document knitr output @@ -109,6 +107,18 @@ if (dir.exists("derivedData") == FALSE) { dir.create("derivedData") } +# Setup PostGIS database connection +dw <- config::get("datawarehouse") + +con <- dbConnect(odbc::odbc(), + driver = dw$driver, + database = dw$database, + uid = dw$uid, + pwd = dw$pwd, + host = dw$server, + port = 5432 + ) + # Define Coordinate Reference Systems (CRS) for later use # Note: I've used British National Grid (27000) in this paper, but have found that # it is falling out of use in many cases, so will be defaulting to WGS84 in future @@ -139,28 +149,23 @@ Until recently, environmentalism has been treated by governments and environment # read in Eco-Congregation Scotland data and------------------- # ...turn it into a SpatialPointsDataFrame--------------------- # TODO: update below to match new dataset once it has been uploaded to zenodo -# if (file.exists("data/ECS-GIS-Locations_3.0.csv") == FALSE) { +# if (file.exists("data/ECS-GIS-Locations_3.0.geojson") == FALSE) { # download.file("https://____.zip", # destfile = "data/____.zip") # unzip("data/____.zip", exdir = "data") # } -# TODO: remove below as part of overall migration to sf() -# See issue https://github.com/kidwellj/mapping_environmental_action/issues/3 for progress re: migration from sp() -ecs <- read.csv("data/ECS-GIS-Locations_3.0.csv", comment.char="#") -# unnecessary with advent of sf (above) -coordinates(ecs) <- c("X", "Y") -# Modified to use EPSG code directly 27 Feb 2019 -proj4string(ecs) <- bng # Note, use of paste0 here relates to fix noted above. # for discussion related to this approach, see https://gis.stackexchange.com/q/313761/41474 # read in Eco-Congregation Scotland data and------------------- -# ...turn it into a SpatialPointsDataFrame--------------------- - -ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=paste0("+init=epsg:",27700)) +# ...turn it into a SimpleFeature--------------------- +ecs <- st_read("data/ECS-GIS-Locations_3.0.geojson") %>% st_transform(paste0("+init=epsg:",27700)) +# Write data to PostGIS database for later analysis, currently commented out for later integration +# st_write(ecs, dsn = con, layer = "ecs", +# overwrite = FALSE, append = FALSE) ``` -There are `r length(ecs)` eco-congregations in Scotland. By some measurements, particularly in terms of individual sites and possibly also with regards to volunteers, this makes Eco-Congregation Scotland one of the largest environmental third-sector groups in Scotland.[^159141043] +There are `r length(ecs$name)` eco-congregations in Scotland. By some measurements, particularly in terms of individual sites and possibly also with regards to volunteers, this makes Eco-Congregation Scotland one of the largest environmental third-sector groups in Scotland.[^159141043] In seeking to conduct GIS and statistical analysis of ECS, it is important to note that there some ways in which these sites are statistically opaque. Our research conducted through interviews at a sampling of sites and analysis of a variety of documents suggests that there is a high level of diversity both in terms of the number of those participating in environmental action and the types of action underway at specific sites. Work at a particular site can also ebb and flow over the course of time. Of course, as research into other forms of activism and secular environmental NGOs has shown, this is no different from any other third sector volunteer group. Variability is a regular feature of groups involved in activism and/or environmental concern. @@ -176,7 +181,7 @@ For the sake of comparison, we also measured the geographical footprint of two o # Technical Background -Analysis was conducted using QGIS 2.8 and R `r getRversion()`, and data-sets were generated in CSV format.[^15541313] To begin with, I assembled a data set consisting of x and y coordinates for each congregation in Scotland and collated this against a variety of other specific data. Coordinates were checked by matching UK postcodes of individual congregations against geo-referencing data in the Office for National Statistics postcode database. In certain instances a single "congregation" is actually a series of sites which have joined together under one administrative unit. In these cases, each site was treated as a separate data point if worship was held at that site at least once a month, but all joined sites shared a single unique identifier. As noted above, two other datasets were generated for the sake of comparative analysis.[^177171536] These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);[^15541342] and another community-based NGO, Scottish Community Development Trusts.[^158261232] As this report will detail, these three overlap in certain instances both literally and in terms of their aims, but each also has a separate identity and footprint in Scotland. Finally, in order to normalise data, we utilised the PointX POI dataset which maintains a complete database of Places of Worship in Scotland.[^15541614] +Analysis was conducted using QGIS 3.12 and R `r getRversion()`, and data-sets were generated in CSV, geopackage, and geojson format.[^15541313] To begin with, I assembled a data set consisting of x and y coordinates for each congregation in Scotland and collated this against a variety of other specific data. Coordinates were checked by matching UK postcodes of individual congregations against geo-referencing data in the Office for National Statistics postcode database. In certain instances a single "congregation" is actually a series of sites which have joined together under one administrative unit. In these cases, each site was treated as a separate data point if worship was held at that site at least once a month, but all joined sites shared a single unique identifier. As noted above, two other datasets were generated for the sake of comparative analysis.[^177171536] These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);[^15541342] and another community-based NGO, Scottish Community Development Trusts.[^158261232] As this report will detail, these three overlap in certain instances both literally and in terms of their aims, but each also has a separate identity and footprint in Scotland. Finally, in order to normalise data, we utilised the PointX POI dataset which maintains a complete database of Places of Worship in Scotland.[^15541614] # Background and History of Eco-Congregation Scotland @@ -184,17 +189,16 @@ Eco-Congregation Scotland began a year before the official launch of Eco-Congreg ```{r calculate_ecs_by_year, message=FALSE, warning=FALSE} # Tidy up date fields and convert to date data type -ecs$registration <- as.Date(ecs$registration, "%Y-%m-%d") -# TODO: Fix issues here with R complaining that "character string is not in a std... -# ecs$award1 <- as.Date(ecs$award1) -# ecs$award2 <- as.Date(ecs$award2) -# ecs$award3 <- as.Date(ecs$award3) -# ecs$award4 <- as.Date(ecs$award4) +ecs$registration <- as.Date(ecs$registration, "%d/%m/%Y") +ecs$award1 <- as.Date(ecs$award1, "%d/%m/%Y") +ecs$award2 <- as.Date(ecs$award2, "%d/%m/%Y") +ecs$award3 <- as.Date(ecs$award3, "%d/%m/%Y") +ecs$award4 <- as.Date(ecs$award4, "%d/%m/%Y") # TODO: add "R" to command in paragraph below once this is resolved, do search for all non 'r instances ecs_complete_cases <- ecs[complete.cases(ecs$year_begun),] ``` -The programme launched officially in 2001 at Dunblane Cathedral in Stirling and by 2005 the project had `r length(ecs_complete_cases[ecs_complete_cases$year_begun < 2006, ])` congregations registered to be a part of the programme and 25 which had completed the curriculum successfully and received an Eco-Congregation award. By 2011, the number of registrations had tripled to `r length(ecs_complete_cases[ecs_complete_cases$year_begun < 2012, ])` and the number of awarded congregations had quadrupled to `sum(ecs$award1 < "01/01/2012", na.rm=TRUE)`. This process of taking registrations and using a tiered award or recognition scheme is common to many voluntary organisations. The ECS curriculum was developed in part by consulting the Eco-Congregation England and Wales materials which had been released just a year earlier in 1999, though it has been subsequently revised, particularly with a major redesign in 2010. In the USA, a number of similar groups take a similar approach including Earth Ministry (earthministry.org) and Green Faith (greenfaith.org). +The programme launched officially in 2001 at Dunblane Cathedral in Stirling and by 2005 the project had `r length(which(as.numeric(as.character(ecs$year_begun)) < 2006))` congregations registered to be a part of the programme and 25 which had completed the curriculum successfully and received an Eco-Congregation award. By 2011, the number of registrations had tripled to `r length(which(as.numeric(as.character(ecs$year_begun)) < 2012))` and the number of awarded congregations had quadrupled to `r length(which((ecs$award1 < "2012/01/01")))`. This process of taking registrations and using a tiered award or recognition scheme is common to many voluntary organisations. The ECS curriculum was developed in part by consulting the Eco-Congregation England and Wales materials which had been released just a year earlier in 1999, though it has been subsequently revised, particularly with a major redesign in 2010. In the USA, a number of similar groups take a similar approach including Earth Ministry (earthministry.org) and Green Faith (greenfaith.org).[^footnote1] In the case of Eco-Congregation Scotland, congregations are invited to begin by "registering" their interest in the programme by completing a basic one-sided form. The next step requires the completion of an award application, which includes a facilitated curriculum called a "church check-up" and after an application is submitted, the site is visited and assessed by third-party volunteer assessors. Sites are invited to complete additional applications for further awards which are incremental (as is the application process). Transition communities, at least in the period reflected on their map, go through a similar process (though this does not involve the use of a supplied curriculum) by which they are marked first as "interested," become "active" and then gain "official" status.[^1554162] @@ -202,16 +206,14 @@ In the case of Eco-Congregation Scotland, congregations are invited to begin by ```{r import_admin_data, message=FALSE, warning=FALSE, include=FALSE} # read in polygon for Scottish admin boundaries -# TODO: upload bundle of admin data to new zenodo repository and alter below to use new URLs -# TODO: need to remove readOGR below once st_read is confirmed to be working as sf - if (file.exists("data/scotland_ca_2010.shp") == FALSE) { download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/Scotland_ca_2010.zip", destfile = "data/Scotland_ca_2010.zip") unzip("data/Scotland_ca_2010.zip", exdir = "data") } -admin_lev1 <- readOGR("./data", "scotland_ca_2010") -admin_lev1_sf <- st_read("data/scotland_ca_2010.shp") %>% st_transform(paste0("+init=epsg:",27700)) +admin_lev1 <- st_read("data/scotland_ca_2010.shp") %>% st_transform(paste0("+init=epsg:",27700)) +st_write(admin_lev1, dsn = con, layer = "admin_lev1", + overwrite = FALSE, append = FALSE) # read in polygon for intermediate admin boundary layers if (file.exists("data/scotland_parlcon_2011.shp") == FALSE) { @@ -219,12 +221,9 @@ download.file("http://census.edina.ac.uk/ukborders/easy_download/prebuilt/shape/ destfile = "data/Scotland_parlcon_2011.zip") unzip("data/Scotland_parlcon_2011.zip", exdir = "data") } -admin_lev2 <- readOGR("./data", "scotland_parlcon_2011") -admin_lev2_sf <- st_read("data/scotland_parlcon_2011.shp") %>% st_transform(paste0("+init=epsg:",27700)) - -# Set CRS using epsg code on spdf for symmetry with datasets below -proj4string(admin_lev1) <- bng -proj4string(admin_lev2) <- bng +admin_lev2 <- st_read("data/scotland_parlcon_2011.shp") %>% st_transform(paste0("+init=epsg:",27700)) +st_write(admin_lev2, dsn = con, layer = "admin_lev2", + overwrite = FALSE, append = FALSE) # Generate new sf shape using bounding box for central belt for map insets below # Note: coordinates use BNG as CRS (EPSG: 27700) @@ -245,99 +244,117 @@ scotland_ratio<- get_asp_ratio(scotland) ```{r import_groups_data, message=FALSE, warning=FALSE, include=FALSE} # read in Transition Towns data and turn it into a SpatialPointsDataFrame -transition_wgs <- read.csv(text=getURL("https://zenodo.org/record/165519/files/SCCAN_1.4.csv")) -coordinates(transition_wgs) <- c("X", "Y") -proj4string(transition_wgs) <- CRS("+init=epsg:4326") -transition <- spTransform(transition_wgs, bng) -transition_sf <- st_as_sf(transition, coords = c("X", "Y"), crs=27700) -# read in all_churches data (data set generated by Jeremy Kidwell to replace PointX data used from Ordnance Survey) -# TODO: need to remove all data points which are outside BNG area to resolve error -# also need to make symmetrical with ECS denominations, add Methodist -# churches, remove nazarene and salvation army - -# churches_all <- read.csv("data/all_churches_0.9.csv") -# churches_all_clean<-churches_all[complete.cases(churches_all),] -# churches_all_null<-churches_all[!complete.cases(churches_all),] -# coordinates(churches_all) <- c("X", "Y") -# proj4string(churches_all) <- proj4string(admin_lev1) +# Original approach using sp() +# transition_wgs <- read.csv(text=getURL("https://zenodo.org/record/165519/files/SCCAN_1.4.csv")) +# coordinates(transition_wgs) <- c("X", "Y") +# proj4string(transition_wgs) <- CRS("+init=epsg:4326") +# transition <- spTransform(transition_wgs, bng) +# transition_sf <- st_as_sf(transition, coords = c("X", "Y"), crs=27700) + +transition <- st_read("data/transition-scotland_2.3.geojson") %>% st_transform(paste0("+init=epsg:",27700)) # read in pointX data and turn it into a SpatialPointsDataFrame -pow_pointX <- read.csv("./data/poi_2015_12_scot06340459.csv", sep="|") -coordinates(pow_pointX) <- c("feature_easting", "feature_northing") -# TODO: need to alter to draw from wgs84 or bng defined in preamble above -proj4string(pow_pointX) <- CRS("+init=epsg:27700") -pow_pointX_sf <- st_as_sf(pow_pointX, coords = c("X", "Y"), crs=27700) +# TODO to add code here to parse out raw pointx file (or OS local?) -# read in Scottish Community Dev. trust data and turn it into a SpatialPointsDataFrame -dtas <- read.csv("data/community-dev-trusts-2.6.csv") -coordinates(dtas) <- c("X", "Y") -proj4string(dtas) <- CRS("+init=epsg:27700") -dtas_sf <- st_as_sf(dtas, coords = c("X", "Y"), crs=27700) +# Original approach using sp() +# pow_pointX <- read.csv("./data/poi_2015_12_scot06340459.csv", sep="|") +# coordinates(pow_pointX) <- c("feature_easting", "feature_northing") +# # TODO: need to alter to draw from wgs84 or bng defined in preamble above +# proj4string(pow_pointX) <- CRS("+init=epsg:27700") +# pow_pointX_sf <- st_as_sf(pow_pointX, coords = c("X", "Y"), crs=27700) -# read in permaculture data and turn it into a SpatialPointsDataFrame -permaculture <- read.csv("data/permaculture_scot-0.8.csv") -coordinates(permaculture) <- c("X", "Y") -proj4string(permaculture) <- CRS("+init=epsg:27700") -permaculture_sf <- st_as_sf(permaculture, coords = c("X", "Y"), crs=27700) +pow_pointX <- st_read("data/pointx_201512_scotland_pow.geojson") %>% st_transform(paste0("+init=epsg:",27700)) + +## read in Scottish Community Dev. trust data and turn it into a SpatialPointsDataFrame +# removing deprecated use of sp() and csv +# dtas <- read.csv("data/community-dev-trusts-2.6.csv") +# coordinates(dtas) <- c("X", "Y") +# proj4string(dtas) <- CRS("+init=epsg:27700") +# dtas_sf <- st_as_sf(dtas, coords = c("X", "Y"), crs=27700) + +dtas <- st_read("data/community-dev-trusts-2.6.geojson") %>% st_transform(paste0("+init=epsg:",27700)) + +## read in permaculture data and turn it into a SpatialPointsDataFrame +# removing deprecated use of sp() and csv +# permaculture <- read.csv("data/permaculture_scot-0.8.csv") +# coordinates(permaculture) <- c("X", "Y") +# proj4string(permaculture) <- CRS("+init=epsg:27700") +# permaculture_sf <- st_as_sf(permaculture, coords = c("X", "Y"), crs=27700) + +permaculture <- st_read("data/permaculture_scot-0.8.geojson") %>% st_transform(paste0("+init=epsg:",27700)) # subset point datasets for inset maps below -ecs_sf_centralbelt <- st_intersection(ecs_sf, centralbelt_region) -dtas_sf_centralbelt <- st_intersection(dtas_sf, centralbelt_region) -transition_sf_centralbelt <- st_intersection(transition_sf, centralbelt_region) -pow_sf_centralbelt <- st_intersection(pow_pointX_sf, centralbelt_region) -permaculture_sf_centralbelt <- st_intersection(permaculture_sf, centralbelt_region) +# commenting out deprecated naming convention +# ecs_sf_centralbelt <- st_intersection(ecs_sf, centralbelt_region) +# dtas_sf_centralbelt <- st_intersection(dtas_sf, centralbelt_region) +# transition_sf_centralbelt <- st_intersection(transition_sf, centralbelt_region) +# pow_sf_centralbelt <- st_intersection(pow_pointX_sf, centralbelt_region) +# permaculture_sf_centralbelt <- st_intersection(permaculture_sf, centralbelt_region) +ecs_centralbelt <- st_intersection(ecs, centralbelt_region) +dtas_centralbelt <- st_intersection(dtas, centralbelt_region) +transition_centralbelt <- st_intersection(transition, centralbelt_region) +pow_centralbelt <- st_intersection(pow_pointX, centralbelt_region) +permaculture_centralbelt <- st_intersection(permaculture, centralbelt_region) ``` ```{r process_admin_data} # Augment existing dataframes to run calculations and add columns with point counts per polygon, # percentages, and normalising data. -# This code will generate a table of frequencies for each spatialpointsdataframe in admin +# Generate a table of frequencies for each set of points in admin_lev1 # calculate count of ECS for fields in admin and provide percentages -# JK Note: need to convert from poly.counts, which uses sp() to st_covers() which uses sf() - cf. https://stackoverflow.com/questions/45314094/equivalent-of-poly-counts-to-count-lat-long-pairs-falling-inside-of-polygons-w#45337050 - -admin_lev1$ecs_count <- poly.counts(ecs,admin_lev1) +# Thanks to commenter for tip on sf() approach here at https://stackoverflow.com/questions/45314094/equivalent-of-poly-counts-to-count-lat-long-pairs-falling-inside-of-polygons-w#45337050 +admin_lev1$ecs_count <- lengths(st_covers(admin_lev1, ecs)) admin_lev1$ecs_percent<- prop.table(admin_lev1$ecs_count) + +# # Test new approach +# library(sp) +# library(GISTools) +# admin_lev1$ecs_count_sp <- poly.counts(ecs,admin_lev1) +# admin_lev1$ecs_count_sp - admin_lev1$ecs_count +# admin_lev1$ecs_percent <- prop.table(admin_lev1$ecs_count) + # calculate count of places of worship in PointX db for fields in admin and provide percentages -admin_lev1$pow_count <- poly.counts(pow_pointX,admin_lev1) +# TODO ingest data here from OS Open Map Local dataset (rather than non-open PointX dataset; need to filter and upload to zenodo) +admin_lev1$pow_count <- lengths(st_covers(admin_lev1, pow_pointX)) admin_lev1$pow_percent<- prop.table(admin_lev1$pow_count) # calculate count of Transition for fields in admin and provide percentages -admin_lev1$transition_count <- poly.counts(transition,admin_lev1) +admin_lev1$transition_count <- lengths(st_covers(admin_lev1, transition)) admin_lev1$transition_percent<- prop.table(admin_lev1$transition_count) # calculate count of dtas for fields in admin and provide percentages -admin_lev1$dtas_count <- poly.counts(dtas,admin_lev1) +admin_lev1$dtas_count <- lengths(st_covers(admin_lev1, dtas)) admin_lev1$dtas_percent<- prop.table(admin_lev1$dtas_count) # calculate count of permaculture for fields in admin and provide percentages -admin_lev1$permaculture_count <- poly.counts(permaculture,admin_lev1) +admin_lev1$permaculture_count <- lengths(st_covers(admin_lev1, permaculture)) admin_lev1$permaculture_percent<- prop.table(admin_lev1$permaculture_count) # run totals for intermediate boundaries level 2 # This code will generate a table of frequencies for each spatialpointsdataframe in admin_lev2 # calculate count of ECS for fields in admin_lev2 and provide percentages -admin_lev2$ecs_count <- poly.counts(ecs,admin_lev2) +admin_lev2$ecs_count <- lengths(st_covers(admin_lev2, ecs)) admin_lev2$ecs_percent<- prop.table(admin_lev2$ecs_count) # calculate count of places of worship in PointX db for fields in admin_lev2 and provide percentages -admin_lev2$pow_count <- poly.counts(pow_pointX,admin_lev2) +admin_lev2$pow_count <- lengths(st_covers(admin_lev2, pow_pointX)) admin_lev2$pow_percent<- prop.table(admin_lev2$pow_count) # calculate count of Transition for fields in admin_lev2 and provide percentages -admin_lev2$transition_count <- poly.counts(transition,admin_lev2) +admin_lev2$transition_count <- lengths(st_covers(admin_lev2, transition)) admin_lev2$transition_percent<- prop.table(admin_lev2$transition_count) # calculate count of dtas for fields in admin_lev2 and provide percentages -admin_lev2$dtas_count <- poly.counts(dtas,admin_lev2) +admin_lev2$dtas_count <- lengths(st_covers(admin_lev2, dtas)) admin_lev2$dtas_percent<- prop.table(admin_lev2$dtas_count) # calculate count of permaculture for fields in admin_lev2 and provide percentages -admin_lev2$permaculture_count <- poly.counts(permaculture,admin_lev2) +admin_lev2$permaculture_count <- lengths(st_covers(admin_lev2, permaculture)) admin_lev2$permaculture_percent<- prop.table(admin_lev2$permaculture_count) -# calculate count of ECS for fields in admin_lev2_sf +# original approach - calculate count of ECS for fields in admin_lev2_sf # TODO: for future migration to sf throughout, remove above content and swap out references. -admin_lev2_sf$ecs_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(ecs_sf, coords = c("long", "lat"), crs = st_crs(27700)))) -admin_lev2_sf$pow_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(pow_pointX_sf, coords = c("long", "lat"), crs = st_crs(27700)))) -admin_lev2_sf$transition_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(transition_sf, coords = c("long", "lat"), crs = st_crs(27700)))) -admin_lev2_sf$dtas_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(dtas_sf, coords = c("long", "lat"), crs = st_crs(27700)))) -admin_lev2_sf$permaculture_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(permaculture_sf, coords = c("long", "lat"), crs = st_crs(27700)))) +# admin_lev2_sf$ecs_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(ecs_sf, coords = c("long", "lat"), crs = st_crs(27700)))) +# admin_lev2_sf$pow_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(pow_pointX_sf, coords = c("long", "lat"), crs = st_crs(27700)))) +# admin_lev2_sf$transition_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(transition_sf, coords = c("long", "lat"), crs = st_crs(27700)))) +# admin_lev2_sf$dtas_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(dtas_sf, coords = c("long", "lat"), crs = st_crs(27700)))) +# admin_lev2_sf$permaculture_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(permaculture_sf, coords = c("long", "lat"), crs = st_crs(27700)))) # Import csv with population data for each level of administrative subdivision and join to spatialdataframe @@ -348,14 +365,18 @@ admin_lev2_sf$permaculture_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(pe # TODO - consider adapting to use ONS mid-year statistics: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland +JK revising done to here 2020-Mar-11 + # Load population statistics for normalising data by population and merge with admin_lev1 admin_lev1_pop <- read.csv("./data/scotland_admin_2011pop.csv") +# TODO: sf() merge doesn't support by.x etc. operations, need to adapt admin_lev1 <- merge(x=admin_lev1, y=admin_lev1_pop, by.x = "code", by.y = "CODE") +admin_lev1_merged <- merge(admin_lev1, admin_lev1_pop) # Convert number stored as string with a comma to an integer admin_lev1$X2011_pop <- as.numeric(gsub(",", "", admin_lev1@data$X2011_pop)) # Calculate counts as percentages of total for normalising below -admin_lev1$pop_percent<- prop.table(admin_lev1$X2011_pop) -admin_lev1$pow_percent<- prop.table(admin_lev1$pow_count) +admin_lev1$pop_percent <- prop.table(admin_lev1$X2011_pop) +admin_lev1$pow_percent <- prop.table(admin_lev1$pow_count) # Normalise ecs_count using population figures (using ArcGIS method) admin_lev1$ecs_count_popnorm <- admin_lev1$ecs_count * admin_lev1$pop_percent # Normalise ecs_count using places of worship counts (using ArcGIS method) @@ -386,13 +407,12 @@ admin_lev2$ecs_count_pownorm_scaled <- admin_lev2$ecs_count_pownorm*(sum(admin_l Perhaps the first important question to ask of these groups is, where are they? I calculated the spread of eco-congregations and transition groups across each of the 32 council areas in Scotland. Every council area in Scotland has at least one eco-congregation or transition group). The most are located in `r as.character(admin_lev1$NAME_2[which.max(admin_lev1$ecs_count)])`, with `r max(admin_lev1$ecs_count)`, whereas the mean among all the 32 council areas is `r mean(admin_lev1$ecs_count)`, with a median of `r median(admin_lev1$ecs_count)`, standard deviation of `r sd(admin_lev1$ecs_count)`, and interquartile range of `r IQR(admin_lev1$ecs_count)`. The following choropleth maps show the relative concentration of eco-congregations (indicated by yellow to red). -Though there are too few eco-congregations and transition groups for a numerically significant representation in any of the intermediate geographies, mapping the concentration of sites by agricultural parishes allows for a more granular visual and I include this for comparison sake. Note, for the sake of a more accurate visual communication, we have also marked out areas of Scotland that are uninhabited with hash marks on the map of agricultural parishes. (*TODO: this will be done in the final draft, once I get my image masking fixed!*).[^15571030] +Though there are too few eco-congregations and transition groups for a numerically significant representation in any of the intermediate geographies, mapping the concentration of sites by agricultural parishes allows for a more granular visual and I include this for comparison sake.[^15571030] ## Eco-Congregation Scotland groups shown by concentration in administrative regions (NUTS3) ```{r 01_admin_ecs_choropleth, fig.width=4, fig.cap="Figure 1"} # Note: for more information on EU administrative levels, see here: https://ec.europa.eu/eurostat/web/nuts/national-structures-eu -# TODO: clip choropleth polygons to buildings shapefile (possibly superceded by pverlay on lev2) # Draw initial choropleth map of ECS concentration (using tmap and sf below by default) # Revising re: CRS inset maps complete to here @@ -405,7 +425,7 @@ tm_shape(admin_lev2) + # also consider scaling text size using area # quick plot example: # qtm(World, fill = "income_grp", text = "iso_a3", text.size = "AREA") # use "World$" to see the two attributes: income_grp and iso_a3, text.size= area: text is sized increasingly with coutry area size. # tm_text("name", size=.2, shadow=TRUE, bg.color="white", bg.alpha=.25) + -# tm_shape(ecs_sf) + +# tm_shape(ecs) + # tm_dots("red", size = .05, alpha = .4) + # tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + @@ -486,7 +506,7 @@ admin_lev1$ecs_dtas_factor <- ((admin_lev1$ecs_percent - admin_lev1$dtas_percent We can use this data to normalise our figures regarding Eco-Congregation Scotland communities and this draws the presence in Edinburgh of ECS communities into even sharper relief, as Edinburgh, though ranked second in terms of population and fifth in terms of places of worship, ranks first for the presence of all ECS congregations and awarded ECS congregations. However, taking population as the basis for normalisation first, we find that Edinburgh is far from the most prominent outlier. In trying to communicate this difference for a lay-audience, we have chosen to list this difference as a multiplier (i.e. there are 2.x times as many congregations as their share of population and an average figure of congregations might allow for) as this conveys the difference in a straight-forward way. Outliers where the disparity between their relative share of the total ECS footprint and their relative share of population is different by a positive ratio of more than double include the Orkney Islands (3.7 times more eco-congregations than their expected average share based on population), Argyll and Bute (`admin_lev1[CODE=S12000023]$ecs_pop_factor` 4.2x), Stirling (2.76x), and Perthshire and Kinross (2.18x). Interestingly, there are no outliers whose relative share of the total footprint of ECS is double or more in the negative direction (see Appendix A chart for full numbers). -Turning to the total of `r length(pow_pointX)` "places of worship" in Scotland, we find a slightly different picture of the relative concentration of Eco-Congregations in Scotland. In this case, the outliers are +Turning to the total of `r length(pow_pointX)` "places of worship" in Scotland, we find a slightly different picture of the relative concentration of Eco-Congregations in Scotland. In this case, the outliers are *TODO: add code here!* Whereas our initial measurements indicated a prominent lead for Edinburgh, by normalising our data in this way we can highlight the stronger-than-expected presence of several others that might otherwise escape notice because they lie in a region with significantly lower population or numerically less places of worship. Taking the PointX data on "places of worship" in Scotland, we find a less dramatic picture, but also a slightly different one. The positive outliers include East Renfrewshire (3.4x) Edinburgh (2.9x), Stirling (2.2), West Lothian (1.9x) and Aberdeen (1.5x). Again, negative outliers are far less dramatic, with only Midlothian possessing a ratio of more than 100% negative difference from the number of "places of worship" at 1.5x *fewer*. @@ -532,7 +552,7 @@ admin_lev2_scotland_ecs_plot <- tm_borders(alpha=.5, lwd=0.1) + tm_shape(admin_lev1) + tm_borders(lwd=0.6) + - tm_shape(ecs_sf) + + tm_shape(ecs) + tm_dots("red", size = .02, alpha = .2) + tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + @@ -552,12 +572,12 @@ admin_lev2_scotland_ecs_plot <- # Build smaller central belt plot for inset: -admin_lev2_centralbelt <- st_crop(admin_lev2_sf, centralbelt_region) +admin_lev2_centralbelt <- st_crop(admin_lev2, centralbelt_region) admin_lev2_centralbelt_ecs_plot <- tm_shape(admin_lev2_centralbelt) + tm_fill(col = "ecs_count", palette = "Oranges", n = 5) + - tm_shape(ecs_sf_centralbelt) + tm_dots("red", size = .05, alpha = .4) + + tm_shape(ecs_centralbelt) + tm_dots("red", size = .05, alpha = .4) + tm_legend(show=FALSE) # Stitch together maps using grid() @@ -578,7 +598,7 @@ tm_shape(admin_lev2) + tm_borders(alpha=.5, lwd=0.1) + tm_shape(admin_lev1) + tm_borders(lwd=0.6) + - tm_shape(transition_sf) + + tm_shape(transition) + tm_dots("red", size = .02, alpha = .2) + tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + @@ -601,7 +621,7 @@ tm_shape(admin_lev2) + tm_borders(alpha=.5, lwd=0.1) + tm_shape(admin_lev1) + tm_borders(lwd=0.6) + - tm_shape(dtas_sf) + + tm_shape(dtas) + tm_dots("red", size = .02, alpha = .2) + tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + @@ -625,7 +645,7 @@ tm_shape(admin_lev2) + tm_borders(alpha=.5, lwd=0.1) + tm_shape(admin_lev1) + tm_borders(lwd=0.6) + - tm_shape(permaculture_sf) + + tm_shape(permaculture) + tm_dots("red", size = .02, alpha = .2) + tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + @@ -691,28 +711,25 @@ download.file("http://sedsh127.sedsh.gov.uk/Atom_data/ScotGov/ZippedShapefiles/S destfile = "data/SG_UrbanRural_2016.zip") unzip("data/SG_UrbanRural_2016.zip", exdir = "data") } -# Todo: remove sp datasets when sf revisions are complete. Currently running in parallel -urbanrural <- readOGR("./data", "SG_UrbanRural_2016") -proj4string(urbanrural) <- bng -urbanrural_sf <- st_read("data/SG_UrbanRural_2016.shp") %>% st_transform(paste0("+init=epsg:",27700)) -urbanrural_sf_simplified <- st_simplify(urbanrural_sf) +urbanrural <- st_read("data/SG_UrbanRural_2016.shp") %>% st_transform(paste0("+init=epsg:",27700)) +urbanrural_simplified <- st_simplify(urbanrural) # TODO: worth considering uploading data to zenodo for long-term reproducibility as ScotGov shuffles this stuff around periodically breaking URLs # This code will generate a table of frequencies for each spatialpointsdataframe in urbanrural # calculate count of ECS for fields in urbanrural -urbanrural$ecs_count <- poly.counts(ecs,urbanrural) +urbanrural$ecs_count <- lengths(st_covers(urbanrural, ecs)) urbanrural$ecs_percent<- prop.table(urbanrural$ecs_count) # calculate count of places of worship in PointX db for fields in urbanrural and provide percentages -urbanrural$pow_count <- poly.counts(pow_pointX,urbanrural) +urbanrural$pow_count <- lengths(st_covers(urbanrural, pow_pointX)) urbanrural$pow_percent<- prop.table(urbanrural$pow_count) # calculate count of Transition for fields in urbanrural -urbanrural$transition_count <- poly.counts(transition,urbanrural) +urbanrural$transition_count <- lengths(st_covers(urbanrural, transition)) urbanrural$transition_percent<- prop.table(urbanrural$transition_count) # calculate count of dtas for fields in urbanrural -urbanrural$dtas_count <- poly.counts(dtas,urbanrural) +urbanrural$dtas_count <- lengths(st_covers(urbanrural, dtas)) urbanrural$dtas_percent<- prop.table(urbanrural$dtas_count) # calculate count of permaculture for fields in urbanrural -urbanrural$permaculture_count <- poly.counts(permaculture,urbanrural) +urbanrural$permaculture_count <- lengths(st_covers(urbanrural, permaculture)) urbanrural$permaculture_percent<- prop.table(urbanrural$permaculture_count) ``` @@ -752,17 +769,15 @@ ggplot(urbanrural_gathered, ```{r 08_urbanrural_ecs_chart_choropleth, message=FALSE, warning=FALSE, fig.width=4, fig.cap="Figure 8"} -# TODO: Clip shapes to buildings shapefile (use OSM or OS?), using st_difference # TODO: Double check data licenses for tm_credits - # Generate code for inset map of central belt # First build large plot using National level view # TODO: add inner margin to layout to provide adequate spce for inset map at bottom -urbanrural_uk_ecs_choropleth_plot <- tm_shape(urbanrural_sf_simplified) + +urbanrural_uk_ecs_choropleth_plot <- tm_shape(urbanrural_simplified) + tm_polygons(col = "UR8FOLD", palette = "BrBG", lwd=0.001, n=9, title = "UrbanRural 8 Fold Scale") + - tm_shape(ecs_sf) + + tm_shape(ecs) + tm_dots("red", size = .05, alpha = .2) + tm_scale_bar(position = c("left", "bottom")) + tm_style("gray") + @@ -783,12 +798,12 @@ urbanrural_uk_ecs_choropleth_plot <- tm_shape(urbanrural_sf_simplified) + # Next build smaller central belt plot for inset: -urbanrural_sf_simplified_centralbelt <- st_crop(urbanrural_sf_simplified, centralbelt_region) +urbanrural_simplified_centralbelt <- st_crop(urbanrural_simplified, centralbelt_region) urbanrural_centralbelt_ecs_choropleth_plot <- - tm_shape(urbanrural_sf_simplified_centralbelt) + + tm_shape(urbanrural_simplified_centralbelt) + tm_polygons(col = "UR8FOLD", palette = "BrBG") + - tm_shape(ecs_sf_centralbelt) + tm_dots("red", size = .05, alpha = .4) + + tm_shape(ecs_centralbelt) + tm_dots("red", size = .05, alpha = .4) + tm_legend(show=FALSE) # Stitch together maps using grid() @@ -812,8 +827,8 @@ print(urbanrural_centralbelt_ecs_choropleth_plot, vp = vp_urbanrural_centralbelt # TODO: change basemap # tmap_mode("view") -# tm_shape(urbanrural_sf_simplified) + tm_polygons(col = "UR8FOLD", palette = "BrBG") + -# tm_shape(ecs_sf) + +# tm_shape(urbanrural_simplified) + tm_polygons(col = "UR8FOLD", palette = "BrBG") + +# tm_shape(ecs) + # tm_dots("red", size = .05, alpha = .4, popup.vars = TRUE) + # tm_view(alpha = 1, basemaps = "Esri.WorldTopoMap") @@ -1006,11 +1021,11 @@ scenicareas_simplified <- st_simplify(scenicareas) # Set symmetrical CRS for analysis below (inserted here in order to correct errors, may be deprecated later) # st_crs(sssi) <- 27700 -# st_crs(ecs_sf) <- 27700 -# st_crs(pow_pointX_sf) <- 27700 -# st_crs(dtas_sf) <- 27700 -# st_crs(transition_sf) <- 27700 -# st_crs(permaculture_sf) <- 27700 +# st_crs(ecs) <- 27700 +# st_crs(pow_pointX) <- 27700 +# st_crs(dtas) <- 27700 +# st_crs(transition) <- 27700 +# st_crs(permaculture) <- 27700 # Define buffer and measure number of ECS groups within 0.5 miles of all SSSI # CRS uses meters for units, so a buffer for 0.5 miles would use 805 meters) @@ -1061,46 +1076,55 @@ scenicareas_buf500 <- st_buffer(scenicareas_simplified, dist = 500) # plot(lnd[ sel, ], col = "turquoise", add = TRUE) # add selected zones to map # from https://gotellilab.github.io/Bio381/StudentPresentations/SpatialDataTutorial.html -ecs_sf_sssi <- st_within(ecs_sf, sssi_simplified) -ecs_sf_sssi50m <- st_within(ecs_sf, sssi_buf50) -ecs_sf_sssi500m <- st_within(ecs_sf, sssi_buf500) -ecs_sf_sssibeyond500m <- !(st_within(ecs_sf, sssi_buf500)) +# TODO: integrate pre-calc here into calculations further down which are still recalculating these figures +ecs_sssi <- st_within(ecs, sssi_simplified) +ecs_sssi50m <- st_within(ecs, sssi_buf50) +ecs_sssi500m <- st_within(ecs, sssi_buf500) +ecs_sssibeyond500m <- !(st_within(ecs, sssi_buf500)) -ecs_sf_wildland <- st_within(ecs_sf, wildland_simplified) -ecs_sf_wildland50m <- st_within(ecs_sf, wildland_buf50) -ecs_sf_wildland500m <- st_within(ecs_sf, wildland_buf500) -ecs_sf_wildlandbeyond500m <- !(st_within(ecs_sf, wildland_buf500)) +ecs_wildland <- st_within(ecs, wildland_simplified) +ecs_wildland50m <- st_within(ecs, wildland_buf50) +ecs_wildland500m <- st_within(ecs, wildland_buf500) +ecs_wildlandbeyond500m <- !(st_within(ecs, wildland_buf500)) -ecs_sf_forestinv <- st_within(ecs_sf, forestinv_simplified) -ecs_sf_forestinv50m <- st_within(ecs_sf, forestinv_buf50) -ecs_sf_forestinv500m <- st_within(ecs_sf, forestinv_buf500) -ecs_sf_forestinvbeyond500m <- !(st_within(ecs_sf, forestinv_buf500)) +ecs_forestinv <- st_within(ecs, forestinv_simplified) +ecs_forestinv50m <- st_within(ecs, forestinv_buf50) +ecs_forestinv500m <- st_within(ecs, forestinv_buf500) +ecs_forestinvbeyond500m <- !(st_within(ecs, forestinv_buf500)) -ecs_sf_scenicareas <- st_within(ecs_sf, scenicareas_simplified) -ecs_sf_scenicareas50m <- st_within(ecs_sf, scenicareas_buf50) -ecs_sf_scenicareas500m <- st_within(ecs_sf, scenicareas_buf500) -ecs_sf_scenicareasbeyond500m <- !(st_within(ecs_sf, scenicareas_buf500)) +ecs_scenicareas <- st_within(ecs, scenicareas_simplified) +ecs_scenicareas50m <- st_within(ecs, scenicareas_buf50) +ecs_scenicareas500m <- st_within(ecs, scenicareas_buf500) +ecs_scenicareasbeyond500m <- !(st_within(ecs, scenicareas_buf500)) # TODO: implement more efficient code using do.call() function or sapply() as here https://stackoverflow.com/questions/3642535/creating-an-r-dataframe-row-by-row +# TODO: implement parallel computing to distribute execution of loopable calculations below +# See: https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html # Generate dataframe based on SSSI buffers # Calculate incidence of ecs within SSSI and within buffers at 50/500m -ecs_sssi_row <- c(sum(apply(st_within(ecs_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, sssi_buf500, sparse=FALSE), 1, any))) +ecs_sssi_row <- c(sum(apply(st_within(ecs, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs, sssi_buf500, sparse=FALSE), 1, any))) -pow_sssi_row <- c(sum(apply(st_within(pow_pointX_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, sssi_buf500, sparse=FALSE), 1, any))) +pow_sssi_row <- c(sum(apply(st_within(pow_pointX, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX, sssi_buf500, sparse=FALSE), 1, any))) -dtas_sssi_row <- c(sum(apply(st_within(dtas_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, sssi_buf500, sparse=FALSE), 1, any))) +dtas_sssi_row <- c(sum(apply(st_within(dtas, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas, sssi_buf500, sparse=FALSE), 1, any))) -transition_sssi_row <- c(sum(apply(st_within(transition_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, sssi_buf500, sparse=FALSE), 1, any))) +transition_sssi_row <- c(sum(apply(st_within(transition, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition, sssi_buf500, sparse=FALSE), 1, any))) -permaculture_sssi_row <- c(sum(apply(st_within(permaculture_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, sssi_buf500, sparse=FALSE), 1, any))) +permaculture_sssi_row <- c(sum(apply(st_within(permaculture, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture, sssi_buf500, sparse=FALSE), 1, any))) + +grocery_sssi_row <- c(sum(apply(st_within(poi_grocery, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery, sssi_buf500, sparse=FALSE), 1, any))) + +pubs_sssi_row <- c(sum(apply(st_within(poi_pubs, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs, sssi_buf500, sparse=FALSE), 1, any))) # Generate dataframe from rows based on counts sssi_counts <- rbind(ecs_sssi_row, pow_sssi_row) sssi_counts <- rbind(sssi_counts, dtas_sssi_row) sssi_counts <- rbind(sssi_counts, transition_sssi_row) sssi_counts <- rbind(sssi_counts, permaculture_sssi_row) +sssi_counts <- rbind(sssi_counts, grocery_sssi_row) +sssi_counts <- rbind(sssi_counts, pubs_sssi_row) sssi_counts <- as.data.frame(sssi_counts) colnames(sssi_counts) <- c("Within SSSIs", "...50m", "...500m") @@ -1111,11 +1135,15 @@ pow_sssi_row_pct <- pow_sssi_row/length(pow_pointX) dtas_sssi_row_pct <- dtas_sssi_row/length(dtas) transition_sssi_row_pct <- transition_sssi_row/length(transition) permaculture_sssi_row_pct <- permaculture_sssi_row/length(permaculture) +grocery_sssi_row_pct <- grocery_sssi_row/length(poi_grocery) +pubs_sssi_row_pct <- pubs_sssi_row/length(poi_pubs) sssi_counts_pct <- rbind(ecs_sssi_row_pct, pow_sssi_row_pct) sssi_counts_pct <- rbind(sssi_counts_pct, dtas_sssi_row_pct) sssi_counts_pct <- rbind(sssi_counts_pct, transition_sssi_row_pct) sssi_counts_pct <- rbind(sssi_counts_pct, permaculture_sssi_row_pct) +sssi_counts_pct <- rbind(sssi_counts_pct, grocery_sssi_row_pct) +sssi_counts_pct <- rbind(sssi_counts_pct, pubs_sssi_row_pct) colnames(sssi_counts_pct) <- c("% Within SSSIs", "% within 50m", "% within 500m") # Merge into larger dataframe @@ -1123,34 +1151,44 @@ sssi_counts_merged <- cbind(sssi_counts, sssi_counts_pct) # Generate dataframe based on wildland buffers -ecs_wildland_row <- c(sum(apply(st_within(ecs_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, wildland_buf500, sparse=FALSE), 1, any))) +ecs_wildland_row <- c(sum(apply(st_within(ecs, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs, wildland_buf500, sparse=FALSE), 1, any))) -pow_wildland_row <- c(sum(apply(st_within(pow_pointX_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, wildland_buf500, sparse=FALSE), 1, any))) +pow_wildland_row <- c(sum(apply(st_within(pow_pointX, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX, wildland_buf500, sparse=FALSE), 1, any))) wildland_counts <- rbind(ecs_wildland_row, pow_wildland_row) -dtas_wildland_row <- c(sum(apply(st_within(dtas_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, wildland_buf500, sparse=FALSE), 1, any))) +dtas_wildland_row <- c(sum(apply(st_within(dtas, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas, wildland_buf500, sparse=FALSE), 1, any))) wildland_counts <- rbind(wildland_counts, dtas_wildland_row) -transition_wildland_row <- c(sum(apply(st_within(transition_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, wildland_buf500, sparse=FALSE), 1, any))) +transition_wildland_row <- c(sum(apply(st_within(transition, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition, wildland_buf500, sparse=FALSE), 1, any))) wildland_counts <- rbind(wildland_counts, transition_wildland_row) -permaculture_wildland_row <- c(sum(apply(st_within(permaculture_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, wildland_buf500, sparse=FALSE), 1, any))) +permaculture_wildland_row <- c(sum(apply(st_within(permaculture, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture, wildland_buf500, sparse=FALSE), 1, any))) wildland_counts <- rbind(wildland_counts, permaculture_wildland_row) +grocery_wildland_row <- c(sum(apply(st_within(poi_grocery, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery, wildland_buf500, sparse=FALSE), 1, any))) +wildland_counts <- rbind(wildland_counts, grocery_wildland_row) + +pubs_wildland_row <- c(sum(apply(st_within(poi_pubs, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs, wildland_buf500, sparse=FALSE), 1, any))) +wildland_counts <- rbind(wildland_counts, pubs_wildland_row) + colnames(wildland_counts) <- c("Within Wildland Areas", "...50m", "...500m") # Generate dataframe from rows based on percentages of totals -ecs_wildland_row_pct <- ecs_wildland_row/length(ecs_sf) +ecs_wildland_row_pct <- ecs_wildland_row/length(ecs) pow_wildland_row_pct <- pow_wildland_row/length(pow_pointX) dtas_wildland_row_pct <- dtas_wildland_row/length(dtas) transition_wildland_row_pct <- transition_wildland_row/length(transition) permaculture_wildland_row_pct <- permaculture_wildland_row/length(permaculture) +grocery_wildland_row_pct <- grocery_wildland_row/length(poi_grocery) +pubs_wildland_row_pct <- pubs_wildland_row/length(poi_pubs) wildland_counts_pct <- rbind(ecs_wildland_row_pct, pow_wildland_row_pct) wildland_counts_pct <- rbind(wildland_counts_pct, dtas_wildland_row_pct) wildland_counts_pct <- rbind(wildland_counts_pct, transition_wildland_row_pct) wildland_counts_pct <- rbind(wildland_counts_pct, permaculture_wildland_row_pct) +wildland_counts_pct <- rbind(wildland_counts_pct, grocery_wildland_row_pct) +wildland_counts_pct <- rbind(wildland_counts_pct, pubs_wildland_row_pct) colnames(wildland_counts_pct) <- c("% Within wildlands", "% within 50m", "% within 500m") # Merge into larger dataframe @@ -1158,33 +1196,44 @@ wildland_counts_merged <- cbind(wildland_counts, wildland_counts_pct) # Generate dataframe based on forestinv buffers -ecs_forestinv_row <- c(sum(apply(st_within(ecs_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, forestinv_buf500, sparse=FALSE), 1, any))) +ecs_forestinv_row <- c(sum(apply(st_within(ecs, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs, forestinv_buf500, sparse=FALSE), 1, any))) -pow_forestinv_row <- c(sum(apply(st_within(pow_pointX_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, forestinv_buf500, sparse=FALSE), 1, any))) +pow_forestinv_row <- c(sum(apply(st_within(pow_pointX, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX, forestinv_buf500, sparse=FALSE), 1, any))) forestinv_counts <- rbind(ecs_forestinv_row, pow_forestinv_row) -dtas_forestinv_row <- c(sum(apply(st_within(dtas_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, forestinv_buf500, sparse=FALSE), 1, any))) +dtas_forestinv_row <- c(sum(apply(st_within(dtas, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas, forestinv_buf500, sparse=FALSE), 1, any))) forestinv_counts <- rbind(forestinv_counts, dtas_forestinv_row) -transition_forestinv_row <- c(sum(apply(st_within(transition_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, forestinv_buf500, sparse=FALSE), 1, any))) +transition_forestinv_row <- c(sum(apply(st_within(transition, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition, forestinv_buf500, sparse=FALSE), 1, any))) forestinv_counts <- rbind(forestinv_counts, transition_forestinv_row) -permaculture_forestinv_row <- c(sum(apply(st_within(permaculture_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, forestinv_buf500, sparse=FALSE), 1, any))) +permaculture_forestinv_row <- c(sum(apply(st_within(permaculture, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture, forestinv_buf500, sparse=FALSE), 1, any))) forestinv_counts <- rbind(forestinv_counts, permaculture_forestinv_row) +grocery_forestinv_row <- c(sum(apply(st_within(poi_grocery, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery, forestinv_buf500, sparse=FALSE), 1, any))) +forestinv_counts <- rbind(forestinv_counts, grocery_forestinv_row) + +pubs_forestinv_row <- c(sum(apply(st_within(poi_pubs, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs, forestinv_buf500, sparse=FALSE), 1, any))) +forestinv_counts <- rbind(forestinv_counts, pubs_forestinv_row) + colnames(forestinv_counts) <- c("Within Woodlands", "...50m", "...500m") # Generate dataframe from rows based on percentages of totals -ecs_forestinv_row_pct <- ecs_forestinv_row/length(ecs_sf) +# TODO: fix error generated by ecs_forestinv_row_pct using ecs as sf(). +ecs_forestinv_row_pct <- ecs_forestinv_row/length(ecs) pow_forestinv_row_pct <- pow_forestinv_row/length(pow_pointX) dtas_forestinv_row_pct <- dtas_forestinv_row/length(dtas) transition_forestinv_row_pct <- transition_forestinv_row/length(transition) permaculture_forestinv_row_pct <- permaculture_forestinv_row/length(permaculture) +grocery_forestinv_row_pct <- grocery_forestinv_row/length(poi_grocery) +pubs_forestinv_row_pct <- pubs_forestinv_row/length(poi_pubs) forestinv_counts_pct <- rbind(ecs_forestinv_row_pct, pow_forestinv_row_pct) forestinv_counts_pct <- rbind(forestinv_counts_pct, dtas_forestinv_row_pct) forestinv_counts_pct <- rbind(forestinv_counts_pct, transition_forestinv_row_pct) forestinv_counts_pct <- rbind(forestinv_counts_pct, permaculture_forestinv_row_pct) +forestinv_counts_pct <- rbind(forestinv_counts_pct, grocery_forestinv_row_pct) +forestinv_counts_pct <- rbind(forestinv_counts_pct, pubs_forestinv_row_pct) colnames(forestinv_counts_pct) <- c("% Within Woodlands", "% within 50m", "% within 500m") # Merge into larger dataframe @@ -1192,34 +1241,44 @@ forestinv_counts_merged <- cbind(forestinv_counts, forestinv_counts_pct) # Generate dataframe based on scenicareas buffers -ecs_scenicareas_row <- c(sum(apply(st_within(ecs_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, scenicareas_buf500, sparse=FALSE), 1, any))) +ecs_scenicareas_row <- c(sum(apply(st_within(ecs, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs, scenicareas_buf500, sparse=FALSE), 1, any))) -pow_scenicareas_row <- c(sum(apply(st_within(pow_pointX_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, scenicareas_buf500, sparse=FALSE), 1, any))) +pow_scenicareas_row <- c(sum(apply(st_within(pow_pointX, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX, scenicareas_buf500, sparse=FALSE), 1, any))) scenicareas_counts <- rbind(ecs_scenicareas_row, pow_scenicareas_row) -dtas_scenicareas_row <- c(sum(apply(st_within(dtas_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, scenicareas_buf500, sparse=FALSE), 1, any))) +dtas_scenicareas_row <- c(sum(apply(st_within(dtas, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas, scenicareas_buf500, sparse=FALSE), 1, any))) scenicareas_counts <- rbind(scenicareas_counts, dtas_scenicareas_row) -transition_scenicareas_row <- c(sum(apply(st_within(transition_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, scenicareas_buf500, sparse=FALSE), 1, any))) +transition_scenicareas_row <- c(sum(apply(st_within(transition, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition, scenicareas_buf500, sparse=FALSE), 1, any))) scenicareas_counts <- rbind(scenicareas_counts, transition_scenicareas_row) -permaculture_scenicareas_row <- c(sum(apply(st_within(permaculture_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, scenicareas_buf500, sparse=FALSE), 1, any))) +permaculture_scenicareas_row <- c(sum(apply(st_within(permaculture, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture, scenicareas_buf500, sparse=FALSE), 1, any))) scenicareas_counts <- rbind(scenicareas_counts, permaculture_scenicareas_row) +grocery_scenicareas_row <- c(sum(apply(st_within(poi_grocery, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery, scenicareas_buf500, sparse=FALSE), 1, any))) +scenicareas_counts <- rbind(scenicareas_counts, grocery_scenicareas_row) + +pubs_scenicareas_row <- c(sum(apply(st_within(poi_pubs, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs, scenicareas_buf500, sparse=FALSE), 1, any))) +scenicareas_counts <- rbind(scenicareas_counts, pubs_scenicareas_row) + colnames(scenicareas_counts) <- c("Within Scenic Areas", "...50m", "...500m") # Generate dataframe from rows based on percentages of totals -ecs_scenicareas_row_pct <- ecs_scenicareas_row/length(ecs_sf) +ecs_scenicareas_row_pct <- ecs_scenicareas_row/length(ecs) pow_scenicareas_row_pct <- pow_scenicareas_row/length(pow_pointX) dtas_scenicareas_row_pct <- dtas_scenicareas_row/length(dtas) transition_scenicareas_row_pct <- transition_scenicareas_row/length(transition) permaculture_scenicareas_row_pct <- permaculture_scenicareas_row/length(permaculture) +grocery_scenicareas_row_pct <- grocery_scenicareas_row/length(poi_grocery) +pubs_scenicareas_row_pct <- pubs_scenicareas_row/length(poi_pubs) scenicareas_counts_pct <- rbind(ecs_scenicareas_row_pct, pow_scenicareas_row_pct) scenicareas_counts_pct <- rbind(scenicareas_counts_pct, dtas_scenicareas_row_pct) scenicareas_counts_pct <- rbind(scenicareas_counts_pct, transition_scenicareas_row_pct) scenicareas_counts_pct <- rbind(scenicareas_counts_pct, permaculture_scenicareas_row_pct) +scenicareas_counts_pct <- rbind(scenicareas_counts_pct, grocery_scenicareas_row_pct) +scenicareas_counts_pct <- rbind(scenicareas_counts_pct, pubs_scenicareas_row_pct) colnames(scenicareas_counts_pct) <- c("% Within scenicareass", "% within 50m", "% within 500m") # Merge into larger dataframe @@ -1234,7 +1293,7 @@ Chasing down a curiosity, I decided to try and calculate whether proximity to "w Proximity to these areas was the next concern, because many of these designations deliberately exclude human habitat, so it was necessary to measure the number of sites within proximity. There is a question which lies here regarding aesthetics, namely, what sort of proximity might generate an affective connection? From my own experience, I decided upon the distance represented by a short walk, i.e. a half-kilometre. However, with the more generic measurements, such as SSSI and forestation, this wouldn't do, as there are so many of these sites that a buffer of 500 meters encapsulates almost all of inhabited Scotland. So for these sites I also calculated a count within 50 metres. -So what did I discover? The results were inconclusive. First, it is important to note that on the whole, Eco-Congregations tend to be more urban than place of worship taken generally at a rate of nearly 3:1 (5.4% of Eco-Congregations lie in areas currently designated as "Very Remote Rural Areas" whereas nearly 15% of places of worship lie in these areas), so what I was testing for was whether this gap was smaller when specifying these various forms of "wild" remoteness. For our narrowest measurements, there were so few sites captured as to render measurement unreliable. There are, for obvious reasons, `st_within(ecs_sf, wildland)` sites located within any of SNG's core wild areas. Similarly, there are very few of our activist communities located within SSSI's (only `st_within(pow_pointX_sf, sssi)` places of worship out of `r length(pow_pointX)`, `st_within(transition_sf, sssi)` transition towns, (or 2%) and `st_within(dtas_sf, sssi)` community development trusts (3%)). However, expanding this out makes things a bit more interesting, within 50 metres of SSSI's in Scotland lie `st_within(ecs_sf, sssi_buf50)` Eco-Congregations (or just under 1%), which compares favourably with the `st_within(pow_pointX_sf, sssi_buf50)` places of worship (or just 1.5%) far exceeding our ratio (1:1.5 vs. 1:3). This is the same with our more anachronistic measure of "scenic areas," there are 7 eco-congregations within these areas, and 175 places of worship, making for a ratio of nearly 1:2 (2.1% vs. 4.3%). Taking our final measure, of forested areas, this is hard to calculate, as only `st_within(ecs_sf, forestinv)` Eco-Congregation lies within either native or generally forested land. +So what did I discover? The results were inconclusive. First, it is important to note that on the whole, Eco-Congregations tend to be more urban than place of worship taken generally at a rate of nearly 3:1 (5.4% of Eco-Congregations lie in areas currently designated as "Very Remote Rural Areas" whereas nearly 15% of places of worship lie in these areas), so what I was testing for was whether this gap was smaller when specifying these various forms of "wild" remoteness. For our narrowest measurements, there were so few sites captured as to render measurement unreliable. There are, for obvious reasons, `st_within(ecs, wildland)` sites located within any of SNG's core wild areas. Similarly, there are very few of our activist communities located within SSSI's (only `st_within(pow_pointX, sssi)` places of worship out of `r length(pow_pointX)`, `st_within(transition, sssi)` transition towns, (or 2%) and `st_within(dtas, sssi)` community development trusts (3%)). However, expanding this out makes things a bit more interesting, within 50 metres of SSSI's in Scotland lie `st_within(ecs, sssi_buf50)` Eco-Congregations (or just under 1%), which compares favourably with the `st_within(pow_pointX, sssi_buf50)` places of worship (or just 1.5%) far exceeding our ratio (1:1.5 vs. 1:3). This is the same with our more anachronistic measure of "scenic areas," there are 7 eco-congregations within these areas, and 175 places of worship, making for a ratio of nearly 1:2 (2.1% vs. 4.3%). Taking our final measure, of forested areas, this is hard to calculate, as only `st_within(ecs, forestinv)` Eco-Congregation lies within either native or generally forested land. ```{r 13_wilderness_tables} @@ -1276,11 +1335,11 @@ tm_shape(sssi_simplified, bbox = scotland) + title = "Sites of Special Scientific Interest and ECS Groups") + # tm_shape(sssi_buf50) + tm_borders(lwd=0.001) + # tm_shape(sssi_buf500) + tm_borders(lwd=0.001) - tm_shape(admin_lev1_sf) + tm_borders(lwd=0.01) + - tm_shape(ecs_sf) + tm_dots("red", size = .02, alpha = .4) + -# tm_shape(ecs_sf_sssi50m) + tm_dots("yellow", size = .5, alpha = .4) + -# tm_shape(ecs_sf_sssi500m) + tm_dots("orange", size = .5, alpha = .4) + -# tm_shape(ecs_sf_sssibeyond500m) + tm_dots("red", size = .5, alpha = .4) + tm_shape(admin_lev1) + tm_borders(lwd=0.01) + + tm_shape(ecs) + tm_dots("red", size = .02, alpha = .4) + +# tm_shape(ecs_sssi50m) + tm_dots("yellow", size = .5, alpha = .4) + +# tm_shape(ecs_sssi500m) + tm_dots("orange", size = .5, alpha = .4) + +# tm_shape(ecs_sssibeyond500m) + tm_dots("red", size = .5, alpha = .4) # tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + tm_credits("Data: UK Data Service (OGL)\n& Jeremy H. Kidwell,\nGraphic is CC-by-SA 4.0", @@ -1307,11 +1366,11 @@ tm_shape(sssi_simplified, bbox = scotland) + tm_fill(col = "blue", alpha = 0.4, tm_shape(scenicareas_simplified, bbox = scotland) + tm_fill(col = "orange", alpha = 0.4, lwd=0.01) + tm_shape(admin_lev1) + tm_borders(lwd=0.01) + # tm_scale_bar(breaks = c(0, 100, 200), size = 1) + - tm_shape(ecs_sf) + tm_dots("red", size = .02, alpha = .4) + - tm_shape(pow_pointX_sf) + tm_dots("orange", size = .01, alpha = .2) + - tm_shape(dtas_sf) + tm_dots("blue", size = .02, alpha = .4) + - tm_shape(transition_sf) + tm_dots("green", size = .02, alpha = .4) + - tm_shape(permaculture_sf) + tm_dots("pink", size = .025, alpha = .4) + + tm_shape(ecs) + tm_dots("red", size = .02, alpha = .4) + + tm_shape(pow_pointX) + tm_dots("orange", size = .01, alpha = .2) + + tm_shape(dtas) + tm_dots("blue", size = .02, alpha = .4) + + tm_shape(transition) + tm_dots("green", size = .02, alpha = .4) + + tm_shape(permaculture) + tm_dots("pink", size = .025, alpha = .4) + # tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + tm_credits("Data: UK Data Service (OGL)\n& Jeremy H. Kidwell,\nGraphic is CC-by-SA 4.0", @@ -1384,7 +1443,7 @@ urbanrural_table %>% [^15541312]: This research was jointly funded by the AHRC/ESRC under project numnbers AH/K005456/1 and AH/P005063/1. [^158261118]: This is not to say that there have been no collaborations before 2000, noteworthy in this respect is the WWF who helped to found the Alliance of Religion and Conservation (ARC) in 1985. [^159141043]: This suggestion should be qualified - RSPB would greatly exceed ECS both in terms of the number of individual subscribers and budget. The RSPB trustee's report for 2013-2014 suggests that their member base was 1,114,938 people across Britain with a net income of £127m - the latter of which exceeds the Church of Scotland. If we adjust this based on the Scottish share of the population of the United Kingdom as of the 2011 census (8.3%) this leaves us with an income of £9.93m. The British charity commission requires charities to self-report the number of volunteers and staff, and from their most recent statistics we learn that RSPB engaged with 17,600 volunteers and employed 2,110 members of staff. Again, adjusted for population, this leaves 1,460 volunteers in Scotland and 176 staff. However, if we measure environmental groups based on the number of sites they maintain, RSPB has only 40 reserves with varying levels of local community engagement. For comparison, as of Sep 14 2015, Friends of the Earth Scotland had only 10 local groups (concentrated mostly in large urban areas). Depending on how one measures "volunteerism," it may be possible that ECS has more engaged volunteers in Scotland as well - if each ECS group had only 4 "volunteers" then this would exceed RSPB. -[^15541313]: Kidwell, Jeremy. (2016). Eco-Congregation Scotland, 2014-2016. University of Edinburgh. http://dx.doi.org/10.7488/ds/1357. +[^15541313]: Kidwell, Jeremy. (2016). Eco-Congregation Scotland, 2014-2016. University of Edinburgh. http://dx.doi.org/10.7488/ds/1357. Include citation here to RDJ article [^15541342]:My dataset on transition towns will be made available later in 2016. Initial data was aquired from the Transition Scotland website http://www.transitionscotland.org/transition-in-scotland on December 10, 2014. We are currently in the process of collaboratively generating a more up-to-date dataset which will reflect their collaboration with SCCAN. [^177171536]: For further detail on Dataset generation, see Kidwell, Forthcoming, 2018. [^158261232]:Data was acquired from the Development Trusts Association website, http://www.dtascot.org.uk, accessed on 20 July 2015. As above, we are currently in the process of active collaboration with volunteers from the DTAS to co-generate a new dataset. @@ -1394,5 +1453,5 @@ urbanrural_table %>% [^1554162]:From the Transition map key, "Green pins are 'official' groups Blue pins are active communities who are connected to the Scottish Transition network Yellow pins show interest in this area" [^15571030]:This was calculated by calculating a 10m wide footprint for every postcode in Scotland, areas which are not within 10m of a postcode (as of May 2014) are counted as uninhabited. -[^159142242]: Fiona Tweedie, *Ecumenical Audit: Questionnaire Findings* (2014). +[^159142242]: Fiona Tweedie, *Ecumenical Audit: Questionnaire Findings* (2014). [^footnote1]: Ref. IA article [^15914204]:See note above regarding the data used from the PointX POI database. Note, for our research,we filtered out religious groups not represented within the Eco-Congregation footprint. We discuss representation by tradition and religion further below.adition and religion further below. diff --git a/mapping_draft-hpc_optimised_wilderness.Rmd b/mapping_draft-hpc_optimised_wilderness.Rmd deleted file mode 100644 index baaff2c..0000000 --- a/mapping_draft-hpc_optimised_wilderness.Rmd +++ /dev/null @@ -1,764 +0,0 @@ ---- -title: "Mapping Environmental Action in Scotland" -abstract: -# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: `r format(Sys.time(), '%B %d, %Y')` -style: jeremy1 -author: "[Jeremy H. Kidwell](http://jeremykidwell.info)" -affiliation: University of Birmingham -institute: University of Birmingham -e-mail: "[j.kidwell@bham.ac.uk](mailto:j.kidwell@bham.ac.uk)" -date: "`r Sys.Date()`" -bibliography: biblio.bib -linkcolor: black -geometry: margin=1in -# fontfamily: mathpazo -fontsize: 11pt -output: - html_document: - theme: readable - keep_md: true - code_folding: hide - self_contained: true - toc: true - toc_depth: 2 - number_sections: true - fig_caption: true - fig_retina: 1 - pdf_document: - toc: false - keep_tex: true - number_sections: true - fig_caption: true - citation_package: natbib - latex_engine: xelatex - always_allow_html: yes - ---- - -```{R setup, include=FALSE} -require(knitr) -require(kableExtra) -knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FALSE, dpi=300, fig.width=7) -# TODO: consider implementing knitcitations - https://github.com/cboettig/knitcitations -# TODO: fix simultaneous output towards PDF, see here: https://stackoverflow.com/questions/23621012/display-and-save-the-plot-simultaneously-in-r-rstudio -``` - -```{R load_packages, message=FALSE, warning=FALSE, include=FALSE} -## Default repo -# setwd("/Users/jeremy/gits/mapping_environmental_action") -# setwd("/Users/kidwellj/OneDrive\ -\ bham.ac.uk/writing/201708_mapping_environmental_action") - -# Set repository to be new standard, e.g. cloud server. -# This will avoid a dialogue box if packages are to be installed for below on first run. -local({r <- getOption("repos") - r["CRAN"] <- "https://cloud.r-project.org" - options(repos=r) -}) -# TODO: remove sp etc. once sf is fully implemented -# TODO: automatically test for packages below on given execution environment and run install.packages() as needed. -require(RCurl) # used for fetching reproducible datasets -require(sf) # new simplefeature data class, supercedes sp in many ways -require(sp) # needed for proj4string, deprecated by sf() -require(rgdal) # deprecated by sf() -require(GISTools) # deprecated by sf() -require(rgeos) # deprecated by sf() -require(maptools) -require(ggplot2) -require(tmap) # using as an alternative to base r graphics and ggplot for geospatial plots -require(tmaptools) # for get_asp_ratio below -require(grid) # using for inset maps on tmap -require(broom) # required for tidying SPDF to data.frame for ggplot2 -require(tidyr) # using for grouped bar plot -require(plyr) -require(dplyr) -require(reshape2) # using for grouped bar plot -require(scales) -# require(sqldf) # using sqldf to filter before loading very large data sets -require(plotly) # allows for export of plots to dynamic web pages -require(gtable) # more powerful package for multi-plot layouts, not necessary for knitr -require(showtext) # for loading in fonts -require(extrafont) # font support - -# Set up local workspace: -if (dir.exists("data") == FALSE) { - dir.create("data") -} -if (dir.exists("figures") == FALSE) { - dir.create("figures") -} -if (dir.exists("derivedData") == FALSE) { - dir.create("derivedData") -} - -# Define Coordinate Reference Systems (CRS) for later use -# Note: I've used British National Grid (27000) in this paper, but have found that -# it is falling out of use in many cases, so will be defaulting to WGS84 in future -# data-sets and papers. - -# Working with EPSG codes for spatialfeature CRS given the usage of this approach with sf() -bng <- CRS("+init=epsg:27700") -wgs84 <- CRS("+init=epsg:4326") - -# Configure fonts for plots below - -## Loading Google fonts (http://www.google.com/fonts) -# font_add_google("Merriweather", "merriweather") -# The following will load in system fonts (uncomment and run as needed on first execution) -# font_import(pattern="[A/a]rial", prompt=FALSE) -``` - -# Introduction[^15541312] - -Until recently, environmentalism has been treated by governments and environmental charities as a largely secular concern. In spite of the well-developed tradition of "eco-theology" which began in earnest in the UK in the mid-twentieth century (and which has many precursors in previous centuries), third-sector groups and governments, particularly in Britain and Europe, have largely ignored religious groups as they have gone about their business crafting agendas for behaviour change, developing funding programmes, and developing platforms to mitigate ecological harm, motivate consumers and create regulation regimes. That this has changed is evidenced by the fact that several prominent non-religious environmental groups have commissioned studies and crafted outreach programmes to persons with a particular faith tradition or to "spiritual communities" including RSPB (2013) and the Sierra Club USA (2008).[^158261118] Further, since 2008, the Scottish Government has provided a significant portion of funding for the ecumenical charity, Eco-Congregation Scotland, which works to promote literacy on environmental issues in religious communities in Scotland and helps to certify congregations under their award programme. What is not well known, however, even by these religious environmental groups themselves, is whether or how their membership might be different from other environmental groups. This study represents an attempt to illuminate this new interest with some more concrete data about religious groups in Scotland and how they may differ from non-religious counterparts. - -# Eco-Congregation Scotland: The Basics - -```{r load_ecs_data, message=FALSE, warning=FALSE} -# read in Eco-Congregation Scotland data and------------------- -# ...turn it into a SpatialPointsDataFrame--------------------- -# TODO: upload ECS-GIS-Locations_3.0.csv to zenodo repository, i.e. -ecs <- read.csv("data/ECS-GIS-Locations_3.0.csv", comment.char="#") -# unnecessary with advent of sf (above) -coordinates(ecs) <- c("X", "Y") -# Modified to use EPSG code directly 27 Feb 2019 -proj4string(ecs) <- bng -ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=paste0("+init=epsg:",27700)) -``` - -There are `r length(ecs)` eco-congregations in Scotland. By some measurements, particularly in terms of individual sites and possibly also with regards to volunteers, this makes Eco-Congregation Scotland one of the largest environmental third-sector groups in Scotland.[^159141043] - -In seeking to conduct GIS and statistical analysis of ECS, it is important to note that there some ways in which these sites are statistically opaque. Our research conducted through interviews at a sampling of sites and analysis of a variety of documents suggests that there is a high level of diversity both in terms of the number of those participating in environmental action and the types of action underway at specific sites. Work at a particular site can also ebb and flow over the course of time. Of course, as research into other forms of activism and secular environmental NGOs has shown, this is no different from any other third sector volunteer group. Variability is a regular feature of groups involved in activism and/or environmental concern. - -For the sake of this analysis, we took each Eco-Congregation Scotland site to represent a point of analysis as if each specific site represented a community group which had "opted-in" on environmental concern. On this basis, in this section, in the tradition of human geography, we "map" environmental action among religious communities in Scotland a variety of ways. This is the first major geographical analysis of this kind conducted to date in Europe. We measure the frequency and location of ECS sites against a variety of standard geo-referenced statistical data sets, seeking to provide a statistical and geographically based assessment of the participation of religious groups in relation to the following: - -- Location within Scotland -- Religious affiliation -- Relation to the Scottish Index of Multiple Deprivation (SIMD) -- Relation to the 8-Fold Scottish Government Urban-Rural Scale -- Proximity to "wilderness" (based on several different designations) - -For the sake of comparison, we also measured the geographical footprint of two other forms of community group in Scotland, (1) Transition Towns (taking into account their recent merge with Scotland Communities Climate Action Network) and (2) member groups of the Development Trust Association Scotland ("DTAS"). These two groups provide a helpful basis for comparison as they are not centralised and thus have a significant geographical dispersion across Scotland. They also provide a useful comparison as transition is a (mostly) non-religious environmental movement, and community development trusts are not explicitly linked to environmental conservation (though this is often part of their remit), so we have a non-religious point of comparison in Transition and a non-environmental point of comparison with DTAS - -# Technical Background - -Analysis was conducted using QGIS 2.8 and R `r getRversion()`, and data-sets were generated in CSV format.[^15541313] To begin with, I assembled a data set consisting of x and y coordinates for each congregation in Scotland and collated this against a variety of other specific data. Coordinates were checked by matching UK postcodes of individual congregations against geo-referencing data in the Office for National Statistics postcode database. In certain instances a single "congregation" is actually a series of sites which have joined together under one administrative unit. In these cases, each site was treated as a separate data point if worship was held at that site at least once a month, but all joined sites shared a single unique identifier. As noted above, two other datasets were generated for the sake of comparative analysis.[^177171536] These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);[^15541342] and another community-based NGO, Scottish Community Development Trusts.[^158261232] As this report will detail, these three overlap in certain instances both literally and in terms of their aims, but each also has a separate identity and footprint in Scotland. Finally, in order to normalise data, we utilised the PointX POI dataset which maintains a complete database of Places of Worship in Scotland.[^15541614] - -# Background and History of Eco-Congregation Scotland - -Eco-Congregation Scotland began a year before the official launch of Eco-Congregation England and Wales, in 1999, as part of an effort by Kippen Environment Centre (later renamed to Forth Environment Link, or "FEL") a charity devoted to environmental education in central Scotland[^158261210] to broaden the scope of its environmental outreach to churches in central Scotland.[^15826124] Initial funding was provided, through Kippen Environment Centre by way of a "sustainable action grant" (with funds drawn from a government landfill tax) through a government programme called Keep Scotland Beautiful (the Scottish cousin of Keep Britain Tidy). After this initial pilot project concluded, the Church of Scotland provided additional funding for the project in the form of staff time and office space. Additional funding a few years later from the Scottish Government helped subsidise the position of a business manager, and in 2011 the United Reformed Church contributed additional funding which subsidised the position of a full-time environmental chaplain for a 5-year term, bringing the total staff to five. - -```{r calculate_ecs_by_year, message=FALSE, warning=FALSE} -# Tidy up date fields and convert to date data type -ecs$registration <- as.Date(ecs$registration, "%Y-%m-%d") -# TODO: Fix issues here with R complaining that "character string is not in a std... -# ecs$award1 <- as.Date(ecs$award1) -# ecs$award2 <- as.Date(ecs$award2) -# ecs$award3 <- as.Date(ecs$award3) -# ecs$award4 <- as.Date(ecs$award4) -# TODO: add "R" to command in paragraph below once this is resolved, do search for all non 'r instances -ecs_complete_cases <- ecs[complete.cases(ecs$year_begun),] -``` - -The programme launched officially in 2001 at Dunblane Cathedral in Stirling and by 2005 the project had `r length(ecs_complete_cases[ecs_complete_cases$year_begun < 2006, ])` congregations registered to be a part of the programme and 25 which had completed the curriculum successfully and received an Eco-Congregation award. By 2011, the number of registrations had tripled to `r length(ecs_complete_cases[ecs_complete_cases$year_begun < 2012, ])` and the number of awarded congregations had quadrupled to `sum(ecs$award1 < "01/01/2012", na.rm=TRUE)`. This process of taking registrations and using a tiered award or recognition scheme is common to many voluntary organisations. The ECS curriculum was developed in part by consulting the Eco-Congregation England and Wales materials which had been released just a year earlier in 1999, though it has been subsequently revised, particularly with a major redesign in 2010. In the USA, a number of similar groups take a similar approach including Earth Ministry (earthministry.org) and Green Faith (greenfaith.org). - -In the case of Eco-Congregation Scotland, congregations are invited to begin by "registering" their interest in the programme by completing a basic one-sided form. The next step requires the completion of an award application, which includes a facilitated curriculum called a "church check-up" and after an application is submitted, the site is visited and assessed by third-party volunteer assessors. Sites are invited to complete additional applications for further awards which are incremental (as is the application process). Transition communities, at least in the period reflected on their map, go through a similar process (though this does not involve the use of a supplied curriculum) by which they are marked first as "interested," become "active" and then gain "official" status.[^1554162] - -# Representation by Regional Authorities (Council Areas) {.tabset} - -```{r import_admin_data, message=FALSE, warning=FALSE, include=FALSE} -# read in polygon for Scottish admin boundaries -# TODO: upload bundle of admin data to new zenodo repository and alter below to use new URLs -# TODO: need to remove readOGR below once st_read is confirmed to be working as sf - -if (file.exists("data/scotland_ca_2010.shp") == FALSE) { -download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/Scotland_ca_2010.zip", - destfile = "data/Scotland_ca_2010.zip") -unzip("data/Scotland_ca_2010.zip", exdir = "data") -} -admin_lev1 <- readOGR("./data", "scotland_ca_2010") -admin_lev1_sf <- st_read("data/scotland_ca_2010.shp") %>% st_transform(paste0("+init=epsg:",27700)) - -# read in polygon for intermediate admin boundary layers -if (file.exists("data/scotland_parlcon_2011.shp") == FALSE) { -download.file("http://census.edina.ac.uk/ukborders/easy_download/prebuilt/shape/Scotland_parlcon_2011.zip", - destfile = "data/Scotland_parlcon_2011.zip") -unzip("data/Scotland_parlcon_2011.zip", exdir = "data") -} -admin_lev2 <- readOGR("./data", "scotland_parlcon_2011") -admin_lev2_sf <- st_read("data/scotland_parlcon_2011.shp") %>% st_transform(paste0("+init=epsg:",27700)) - -# Set CRS using epsg code on spdf for symmetry with datasets below -proj4string(admin_lev1) <- bng -proj4string(admin_lev2) <- bng - -# Generate new sf shape using bounding box for central belt for map insets below -# Note: coordinates use BNG as CRS (EPSG: 27700) - -scotland <- st_bbox(c(xmin = 5513.0000, xmax = 470332.0000, - ymin = 530249.0000, ymax = 1220301.5000), - crs = st_crs("+init=epsg:27700")) %>% - st_as_sfc() - -centralbelt_region <- st_bbox(c(xmin = 234841, xmax = 346309, - ymin = 653542, ymax = 686722), - crs = st_crs("+init=epsg:27700")) %>% - st_as_sfc() - -centralbelt_ratio <- get_asp_ratio(centralbelt_region) -scotland_ratio<- get_asp_ratio(scotland) -``` - -```{r import_groups_data, message=FALSE, warning=FALSE, include=FALSE} -# read in Transition Towns data and turn it into a SpatialPointsDataFrame -transition_wgs <- read.csv(text=getURL("https://zenodo.org/record/165519/files/SCCAN_1.4.csv")) -coordinates(transition_wgs) <- c("X", "Y") -proj4string(transition_wgs) <- CRS("+init=epsg:4326") -transition <- spTransform(transition_wgs, bng) -transition_sf <- st_as_sf(transition, coords = c("X", "Y"), crs=27700) - -# read in all_churches data (data set generated by Jeremy Kidwell to replace PointX data used from Ordnance Survey) -# TODO: need to remove all data points which are outside BNG area to resolve error -# also need to make symmetrical with ECS denominations, add Methodist -# churches, remove nazarene and salvation army - -# churches_all <- read.csv("data/all_churches_0.9.csv") -# churches_all_clean<-churches_all[complete.cases(churches_all),] -# churches_all_null<-churches_all[!complete.cases(churches_all),] -# coordinates(churches_all) <- c("X", "Y") -# proj4string(churches_all) <- proj4string(admin_lev1) - -# read in pointX data and turn it into a SpatialPointsDataFrame -pow_pointX <- read.csv("./data/poi_2015_12_scot06340459.csv", sep="|") -coordinates(pow_pointX) <- c("feature_easting", "feature_northing") -# TODO: need to alter to draw from wgs84 or bng defined in preamble above -proj4string(pow_pointX) <- CRS("+init=epsg:27700") -pow_pointX_sf <- st_as_sf(pow_pointX, coords = c("X", "Y"), crs=27700) - -# read in Scottish Community Dev. trust data and turn it into a SpatialPointsDataFrame -dtas <- read.csv("data/community-dev-trusts-2.6.csv") -coordinates(dtas) <- c("X", "Y") -proj4string(dtas) <- CRS("+init=epsg:27700") -dtas_sf <- st_as_sf(dtas, coords = c("X", "Y"), crs=27700) - -# read in permaculture data and turn it into a SpatialPointsDataFrame -permaculture <- read.csv("data/permaculture_scot-0.8.csv") -coordinates(permaculture) <- c("X", "Y") -proj4string(permaculture) <- CRS("+init=epsg:27700") -permaculture_sf <- st_as_sf(permaculture, coords = c("X", "Y"), crs=27700) - -# subset point datasets for inset maps below -ecs_sf_centralbelt <- st_intersection(ecs_sf, centralbelt_region) -dtas_sf_centralbelt <- st_intersection(dtas_sf, centralbelt_region) -transition_sf_centralbelt <- st_intersection(transition_sf, centralbelt_region) -pow_sf_centralbelt <- st_intersection(pow_pointX_sf, centralbelt_region) -permaculture_sf_centralbelt <- st_intersection(permaculture_sf, centralbelt_region) - -``` - -```{r process_admin_data} -# Augment existing dataframes to run calculations and add columns with point counts per polygon, -# percentages, and normalising data. - -# This code will generate a table of frequencies for each spatialpointsdataframe in admin -# calculate count of ECS for fields in admin and provide percentages -# JK Note: need to convert from poly.counts, which uses sp() to st_covers() which uses sf() - cf. https://stackoverflow.com/questions/45314094/equivalent-of-poly-counts-to-count-lat-long-pairs-falling-inside-of-polygons-w#45337050 - -admin_lev1$ecs_count <- poly.counts(ecs,admin_lev1) -admin_lev1$ecs_percent<- prop.table(admin_lev1$ecs_count) -# calculate count of places of worship in PointX db for fields in admin and provide percentages -admin_lev1$pow_count <- poly.counts(pow_pointX,admin_lev1) -admin_lev1$pow_percent<- prop.table(admin_lev1$pow_count) -# calculate count of Transition for fields in admin and provide percentages -admin_lev1$transition_count <- poly.counts(transition,admin_lev1) -admin_lev1$transition_percent<- prop.table(admin_lev1$transition_count) -# calculate count of dtas for fields in admin and provide percentages -admin_lev1$dtas_count <- poly.counts(dtas,admin_lev1) -admin_lev1$dtas_percent<- prop.table(admin_lev1$dtas_count) -# calculate count of permaculture for fields in admin and provide percentages -admin_lev1$permaculture_count <- poly.counts(permaculture,admin_lev1) -admin_lev1$permaculture_percent<- prop.table(admin_lev1$permaculture_count) - -# run totals for intermediate boundaries level 2 -# This code will generate a table of frequencies for each spatialpointsdataframe in admin_lev2 -# calculate count of ECS for fields in admin_lev2 and provide percentages -admin_lev2$ecs_count <- poly.counts(ecs,admin_lev2) -admin_lev2$ecs_percent<- prop.table(admin_lev2$ecs_count) -# calculate count of places of worship in PointX db for fields in admin_lev2 and provide percentages -admin_lev2$pow_count <- poly.counts(pow_pointX,admin_lev2) -admin_lev2$pow_percent<- prop.table(admin_lev2$pow_count) -# calculate count of Transition for fields in admin_lev2 and provide percentages -admin_lev2$transition_count <- poly.counts(transition,admin_lev2) -admin_lev2$transition_percent<- prop.table(admin_lev2$transition_count) -# calculate count of dtas for fields in admin_lev2 and provide percentages -admin_lev2$dtas_count <- poly.counts(dtas,admin_lev2) -admin_lev2$dtas_percent<- prop.table(admin_lev2$dtas_count) -# calculate count of permaculture for fields in admin_lev2 and provide percentages -admin_lev2$permaculture_count <- poly.counts(permaculture,admin_lev2) -admin_lev2$permaculture_percent<- prop.table(admin_lev2$permaculture_count) - -# calculate count of ECS for fields in admin_lev2_sf -# TODO: for future migration to sf throughout, remove above content and swap out references. -admin_lev2_sf$ecs_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(ecs_sf, coords = c("long", "lat"), crs = st_crs(27700)))) -admin_lev2_sf$pow_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(pow_pointX_sf, coords = c("long", "lat"), crs = st_crs(27700)))) -admin_lev2_sf$transition_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(transition_sf, coords = c("long", "lat"), crs = st_crs(27700)))) -admin_lev2_sf$dtas_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(dtas_sf, coords = c("long", "lat"), crs = st_crs(27700)))) -admin_lev2_sf$permaculture_count <- lengths(st_covers(admin_lev2_sf, st_as_sf(permaculture_sf, coords = c("long", "lat"), crs = st_crs(27700)))) - -# Import csv with population data for each level of administrative subdivision and join to spatialdataframe - -# Placeholder for England parish data for later implementation -# download.file("http://census.edina.ac.uk/ukborders/easy_download/prebuilt/shape/England_cp_1991.zip", destfile = "parishes/parishes-1991.zip") -# unzip("parishes/parishes-1991.zip", exdir = "parishes") -# parishes <- rgdal::readOGR(dsn = "parishes", "england_cp_1991") - -# TODO - consider adapting to use ONS mid-year statistics: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland - -# Load population statistics for normalising data by population and merge with admin_lev1 -admin_lev1_pop <- read.csv("./data/scotland_admin_2011pop.csv") -admin_lev1 <- merge(x=admin_lev1, y=admin_lev1_pop, by.x = "code", by.y = "CODE") -# Convert number stored as string with a comma to an integer -admin_lev1$X2011_pop <- as.numeric(gsub(",", "", admin_lev1@data$X2011_pop)) -# Calculate counts as percentages of total for normalising below -admin_lev1$pop_percent<- prop.table(admin_lev1$X2011_pop) -admin_lev1$pow_percent<- prop.table(admin_lev1$pow_count) -# Normalise ecs_count using population figures (using ArcGIS method) -admin_lev1$ecs_count_popnorm <- admin_lev1$ecs_count * admin_lev1$pop_percent -# Normalise ecs_count using places of worship counts (using ArcGIS method) -admin_lev1$ecs_count_pownorm <- admin_lev1$ecs_count * admin_lev1$pow_percent - -# Preserve scale -admin_lev1$ecs_count_popnorm_scaled <- admin_lev1$ecs_count_popnorm*(sum(admin_lev1$ecs_count)/sum(admin_lev1$ecs_count_popnorm)) -admin_lev1$ecs_count_pownorm_scaled <- admin_lev1$ecs_count_pownorm*(sum(admin_lev1$ecs_count)/sum(admin_lev1$ecs_count_pownorm)) - -# Load population statistics for normalising data by population on admin_lev2 -# TODO: source this data file in a replicable way (e.g. zenodo) -admin_lev2_pop <- read.csv("./data/scotland_and_wales_const_scotland_2011pop.csv") -admin_lev2 <- merge(x=admin_lev2, y=admin_lev2_pop, by.x = "code", by.y = "CODE") -# Convert number stored as string with a comma to an integer (using data.zone data) -admin_lev2$Data.zone.Population <- as.numeric(gsub(",", "", admin_lev2$Data.zone.Population)) -# Calculate counts as percentages of total for normalising below -admin_lev2$pop_percent <- prop.table(admin_lev2$Data.zone.Population) -admin_lev2$pow_percent <- prop.table(admin_lev2$pow_count) -# Normalise ecs_count using population figures (using ArcGIS method) -admin_lev2$ecs_count_popnorm <- admin_lev2$ecs_count * admin_lev2$pop_percent -# Normalise ecs_count using places of worship counts (using ArcGIS method) -admin_lev2$ecs_count_pownorm <- admin_lev2$ecs_count * admin_lev2$pow_percent - -# Preserve scale -admin_lev2$ecs_count_popnorm_scaled <- admin_lev2$ecs_count_popnorm*(sum(admin_lev2$ecs_count)/sum(admin_lev2$ecs_count_popnorm)) -admin_lev2$ecs_count_pownorm_scaled <- admin_lev2$ecs_count_pownorm*(sum(admin_lev2$ecs_count)/sum(admin_lev2$ecs_count_pownorm)) -``` - -```{r additional_poi_data} -# Load in updated DTAS data set -dtas_new <- read.csv("data/dtas_4.0.csv") -coordinates(dtas_new) <- c("X", "Y") -proj4string(dtas_new) <- CRS("+init=epsg:27700") - -# Load in retail data from geolytics dataset -# from here: https://geolytix.co.uk/?retail_points -poi_grocery_wgs <- read.csv("data/retailpoints_version11_dec17.txt", sep = "\t") -# select useful columns -poi_grocery_wgs <- subset(poi_grocery_wgs, select = c("retailer", "store_name", "long_wgs", "lat_wgs")) -# convert to spdf -coordinates(poi_grocery_wgs) <- c("long_wgs", "lat_wgs") -proj4string(poi_grocery_wgs) <- CRS("+init=epsg:4326") -poi_grocery <- spTransform(poi_grocery_wgs, proj4string(admin_lev1)) -# filter out non-Scottish data -poi_grocery <- poi_grocery[!is.na(over(poi_grocery, geometry(admin_lev1))),] -poi_grocery_sf <- st_as_sf(poi_grocery, coords = c("long_wgs", "lat_wgs"), crs=paste0("+init=epsg:",27700)) - -# Load in British pubs from Ordnance survey dataset -poi_pubs <- read.csv("data/poi_pubs.csv", header = FALSE, sep = "|") -# select useful columns -poi_pubs <- subset(poi_pubs, select = c("V1", "V2", "V3", "V4", "V5")) -# rename columns to tidier names -colnames(poi_pubs) <- c("refnum", "name", "code", "x", "y") -coordinates(poi_pubs) <- c("x", "y") -proj4string(poi_pubs) <- proj4string(admin_lev1) -# filter out non-Scottish pubs -poi_pubs <- poi_pubs[!is.na(over(poi_pubs, geometry(admin_lev1))),] -poi_pubs_sf <- st_as_sf(poi_pubs, coords = c("x", "y"), crs=paste0("+init=epsg:",27700)) -``` - -```{r wilderness_data_prep} - -# 1. Download data for SSSI: - -if (file.exists("data/SSSI_SCOTLAND.shp") == FALSE) { -# TODO: upload data to zenodo, uncomment below -# http://gateway.snh.gov.uk/natural-spaces/dataset.jsp?dsid=SSSI -# download.file("", destfile = "data/SSSI_SCOTLAND_ESRI.zip") -unzip("data/SSSI_SCOTLAND_ESRI.zip", exdir = "data") -} -sssi <- st_read("data/SSSI_SCOTLAND.shp") %>% st_transform(paste0("+init=epsg:",27700)) -sssi_sp <- readOGR("./data", "SSSI_SCOTLAND") -# Generate simplified polygon for plots below -sssi_simplified <- st_simplify(sssi) -# sssi_simplified_sp <- rgeos::gSimplify(sssi_sp, tol=3) - -# 2. Download wild land areas: - -if (file.exists("data/WILDLAND_SCOTLAND.shp") == FALSE) { -# TODO: upload data to zenodo, uncomment below -# https://gateway.snh.gov.uk/natural-spaces/dataset.jsp?dsid=WILDLAND -# download.file("", destfile = "data/WILDLAND_SCOTLAND_ESRI.zip") -unzip("data/WILDLAND_SCOTLAND_ESRI.zip", exdir = "data") -} - -wildland <- st_read("data/WILDLAND_SCOTLAND.shp") %>% st_transform(paste0("+init=epsg:",27700)) -# Generate simplified polygon for plots below -wildland_simplified <- st_simplify(wildland) - -# 3. Download data for National Forest Inventory: - -# Note: UK-wide data is here: https://opendata.arcgis.com/datasets/bcd6742a2add4b68962aec073ab44138_0.zip?outSR=%7B%22wkid%22%3A27700%2C%22latestWkid%22%3A27700%7D - -if (file.exists("data/National_Forest_Inventory_Woodland_Scotland_2017.shp") == FALSE) { -download.file("https://opendata.arcgis.com/datasets/3cb1abc185a247a48b9d53e4c4a8be87_0.zip?outSR=%7B%22wkid%22%3A27700%2C%22latestWkid%22%3A27700%7D", - destfile = "data/National_Forest_Inventory_Woodland_Scotland_2017.zip") -unzip("data/National_Forest_Inventory_Woodland_Scotland_2017.zip", exdir = "data") -} - -forestinv <- st_read("data/National_Forest_Inventory_Woodland_Scotland_2017.shp") %>% st_transform(paste0("+init=epsg:",27700)) -# Generate simplified polygon for plots below -forestinv_simplified <- st_simplify(forestinv) - -# Download data for scenic areas -# https://opendata.arcgis.com/datasets/d7f6b987c7224a72a185ce012258d500_23.zip -# International Union for Conservation of Nature (IUCN), Category V Protected Landscapes -# for England: https://environment.data.gov.uk/DefraDataDownload/?mapService=NE/AreasOfOutstandingNaturalBeautyEngland&Mode=spatial - -# download.file("https://opendata.arcgis.com/datasets/d7f6b987c7224a72a185ce012258d500_23.zip", destfile = "data/ScenicAreas.zip") -# unzip("data/ScenicAreas.zip", exdir = "data") - -scenicareas <- st_read("data/SG_NationalScenicAreas_1998.shp") %>% st_transform(paste0("+init=epsg:",27700)) -# Generate simplified polygon for plots below -scenicareas_simplified <- st_simplify(scenicareas) - -# Set symmetrical CRS for analysis below (inserted here in order to correct errors, may be deprecated later) -# st_crs(sssi) <- 27700 -# st_crs(ecs_sf) <- 27700 -# st_crs(pow_pointX_sf) <- 27700 -# st_crs(dtas_sf) <- 27700 -# st_crs(transition_sf) <- 27700 -# st_crs(permaculture_sf) <- 27700 - -# Define buffer and measure number of ECS groups within 0.5 miles of all SSSI -# CRS uses meters for units, so a buffer for 0.5 miles would use 805 meters) - -# Define buffers as lines and polygons for reuse below (as some of these operations take > 10 minutes) -# TODO: Working with simplified polygons here to optimise execution, need to confirm calculations are the same as polygons without simplification (likely!) - -sssi_buf50 <- st_buffer(sssi_simplified, dist = 50) -sssi_buf500 <- st_buffer(sssi_simplified, dist = 500) -# Lines offer even more optimised representation suitable for plotting -# TODO: resolve error: -# Error in st_cast.sfc(., to = "LINESTRING") : -# use smaller steps for st_cast; first cast to MULTILINESTRING or POLYGON? -# sssi_buf50_lines <- st_union(sssi_simplified) %>% st_buffer(50) %>% -# st_cast(to = "MULTILINESTRING") %>% -# st_cast(to = "LINESTRING") -# sssi_buf500_lines <- st_union(sssi_simplified) %>% st_buffer(500) %>% -# st_cast(to = "MULTILINESTRING") %>% -# st_cast(to = "LINESTRING") - -wildland_buf50 <- st_buffer(wildland_simplified, dist = 50) -wildland_buf500 <- st_buffer(wildland_simplified, dist = 500) -# Lines offer even more optimised representation suitable for plotting -# wildland_buf50_lines = st_union(wildland_simplified) %>% st_buffer(50) %>% -# st_cast(to = "LINESTRING") -# wildland_buf500_lines = st_union(wildland_simplified) %>% st_buffer(500) %>% -# st_cast(to = "LINESTRING") - -forestinv_buf50 <- st_buffer(forestinv_simplified, dist = 50) -forestinv_buf500 <- st_buffer(forestinv_simplified, dist = 500) -# Lines offer even more optimised representation suitable for plotting -# forestinv_buf50_lines = st_union(forestinv_simplified) %>% st_buffer(50) %>% -# st_cast(to = "LINESTRING") -# forestinv_buf500_lines = st_union(forestinv_simplified) %>% st_buffer(500) %>% -# st_cast(to = "LINESTRING") - -scenicareas_buf50 <- st_buffer(scenicareas_simplified, dist = 50) -scenicareas_buf500 <- st_buffer(scenicareas_simplified, dist = 500) - -# Calculate number of groups within polygons - -# calculate coincidence of ecs points within each polygons and buffers for each -# TODO: possibly use st_difference(sssi_buf50, sssi) -# TODO: consider subsetting instead here, e.g.: - -# plot(lnd, col = "lightgrey") # plot the london_sport object -# sel <- lnd$Partic_Per > 25 -# plot(lnd[ sel, ], col = "turquoise", add = TRUE) # add selected zones to map -# from https://gotellilab.github.io/Bio381/StudentPresentations/SpatialDataTutorial.html - -# TODO: integrate pre-calc here into calculations further down which are still recalculating these figures -ecs_sf_sssi <- st_within(ecs_sf, sssi_simplified) -ecs_sf_sssi50m <- st_within(ecs_sf, sssi_buf50) -ecs_sf_sssi500m <- st_within(ecs_sf, sssi_buf500) -ecs_sf_sssibeyond500m <- !(st_within(ecs_sf, sssi_buf500)) - -ecs_sf_wildland <- st_within(ecs_sf, wildland_simplified) -ecs_sf_wildland50m <- st_within(ecs_sf, wildland_buf50) -ecs_sf_wildland500m <- st_within(ecs_sf, wildland_buf500) -ecs_sf_wildlandbeyond500m <- !(st_within(ecs_sf, wildland_buf500)) - -ecs_sf_forestinv <- st_within(ecs_sf, forestinv_simplified) -ecs_sf_forestinv50m <- st_within(ecs_sf, forestinv_buf50) -ecs_sf_forestinv500m <- st_within(ecs_sf, forestinv_buf500) -ecs_sf_forestinvbeyond500m <- !(st_within(ecs_sf, forestinv_buf500)) - -ecs_sf_scenicareas <- st_within(ecs_sf, scenicareas_simplified) -ecs_sf_scenicareas50m <- st_within(ecs_sf, scenicareas_buf50) -ecs_sf_scenicareas500m <- st_within(ecs_sf, scenicareas_buf500) -ecs_sf_scenicareasbeyond500m <- !(st_within(ecs_sf, scenicareas_buf500)) - -# TODO: implement more efficient code using do.call() function or sapply() as here https://stackoverflow.com/questions/3642535/creating-an-r-dataframe-row-by-row -# TODO: implement parallel computing to distribute execution of loopable calculations below -# See: https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html - -# Generate dataframe based on SSSI buffers - -# Calculate incidence of ecs within SSSI and within buffers at 50/500m -ecs_sssi_row <- c(sum(apply(st_within(ecs_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, sssi_buf500, sparse=FALSE), 1, any))) - -pow_sssi_row <- c(sum(apply(st_within(pow_pointX_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, sssi_buf500, sparse=FALSE), 1, any))) - -dtas_sssi_row <- c(sum(apply(st_within(dtas_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, sssi_buf500, sparse=FALSE), 1, any))) - -transition_sssi_row <- c(sum(apply(st_within(transition_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, sssi_buf500, sparse=FALSE), 1, any))) - -permaculture_sssi_row <- c(sum(apply(st_within(permaculture_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, sssi_buf500, sparse=FALSE), 1, any))) - -grocery_sssi_row <- c(sum(apply(st_within(poi_grocery_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery_sf, sssi_buf500, sparse=FALSE), 1, any))) - -pubs_sssi_row <- c(sum(apply(st_within(poi_pubs_sf, sssi_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, sssi_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, sssi_buf500, sparse=FALSE), 1, any))) - - -# Generate dataframe from rows based on counts -sssi_counts <- rbind(ecs_sssi_row, pow_sssi_row) -sssi_counts <- rbind(sssi_counts, dtas_sssi_row) -sssi_counts <- rbind(sssi_counts, transition_sssi_row) -sssi_counts <- rbind(sssi_counts, permaculture_sssi_row) -sssi_counts <- rbind(sssi_counts, grocery_sssi_row) -sssi_counts <- rbind(sssi_counts, pubs_sssi_row) -sssi_counts <- as.data.frame(sssi_counts) -colnames(sssi_counts) <- c("Within SSSIs", "...50m", "...500m") - -# Generate dataframe from rows based on percentages of totals -# TODO: Work with ecs_sf etc., but first sort out why length is different between shapefiles -ecs_sssi_row_pct <- ecs_sssi_row/length(ecs) -pow_sssi_row_pct <- pow_sssi_row/length(pow_pointX) -dtas_sssi_row_pct <- dtas_sssi_row/length(dtas) -transition_sssi_row_pct <- transition_sssi_row/length(transition) -permaculture_sssi_row_pct <- permaculture_sssi_row/length(permaculture) -grocery_sssi_row_pct <- grocery_sssi_row/length(poi_grocery) -pubs_sssi_row_pct <- pubs_sssi_row/length(poi_pubs) - -sssi_counts_pct <- rbind(ecs_sssi_row_pct, pow_sssi_row_pct) -sssi_counts_pct <- rbind(sssi_counts_pct, dtas_sssi_row_pct) -sssi_counts_pct <- rbind(sssi_counts_pct, transition_sssi_row_pct) -sssi_counts_pct <- rbind(sssi_counts_pct, permaculture_sssi_row_pct) -sssi_counts_pct <- rbind(sssi_counts_pct, grocery_sssi_row_pct) -sssi_counts_pct <- rbind(sssi_counts_pct, pubs_sssi_row_pct) -colnames(sssi_counts_pct) <- c("% Within SSSIs", "% within 50m", "% within 500m") - -# Merge into larger dataframe -sssi_counts_merged <- cbind(sssi_counts, sssi_counts_pct) - -# Generate dataframe based on wildland buffers - -ecs_wildland_row <- c(sum(apply(st_within(ecs_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, wildland_buf500, sparse=FALSE), 1, any))) - -pow_wildland_row <- c(sum(apply(st_within(pow_pointX_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, wildland_buf500, sparse=FALSE), 1, any))) -wildland_counts <- rbind(ecs_wildland_row, pow_wildland_row) - -dtas_wildland_row <- c(sum(apply(st_within(dtas_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, wildland_buf500, sparse=FALSE), 1, any))) -wildland_counts <- rbind(wildland_counts, dtas_wildland_row) - -transition_wildland_row <- c(sum(apply(st_within(transition_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, wildland_buf500, sparse=FALSE), 1, any))) -wildland_counts <- rbind(wildland_counts, transition_wildland_row) - -permaculture_wildland_row <- c(sum(apply(st_within(permaculture_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, wildland_buf500, sparse=FALSE), 1, any))) -wildland_counts <- rbind(wildland_counts, permaculture_wildland_row) - -grocery_wildland_row <- c(sum(apply(st_within(poi_grocery_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery_sf, wildland_buf500, sparse=FALSE), 1, any))) -wildland_counts <- rbind(wildland_counts, grocery_wildland_row) - -pubs_wildland_row <- c(sum(apply(st_within(poi_pubs_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, wildland_buf500, sparse=FALSE), 1, any))) -wildland_counts <- rbind(wildland_counts, pubs_wildland_row) - -colnames(wildland_counts) <- c("Within Wildland Areas", "...50m", "...500m") - -# Generate dataframe from rows based on percentages of totals - -ecs_wildland_row_pct <- ecs_wildland_row/length(ecs) -pow_wildland_row_pct <- pow_wildland_row/length(pow_pointX) -dtas_wildland_row_pct <- dtas_wildland_row/length(dtas) -transition_wildland_row_pct <- transition_wildland_row/length(transition) -permaculture_wildland_row_pct <- permaculture_wildland_row/length(permaculture) -grocery_wildland_row_pct <- grocery_wildland_row/length(poi_grocery) -pubs_wildland_row_pct <- pubs_wildland_row/length(poi_pubs) - -wildland_counts_pct <- rbind(ecs_wildland_row_pct, pow_wildland_row_pct) -wildland_counts_pct <- rbind(wildland_counts_pct, dtas_wildland_row_pct) -wildland_counts_pct <- rbind(wildland_counts_pct, transition_wildland_row_pct) -wildland_counts_pct <- rbind(wildland_counts_pct, permaculture_wildland_row_pct) -wildland_counts_pct <- rbind(wildland_counts_pct, grocery_wildland_row_pct) -wildland_counts_pct <- rbind(wildland_counts_pct, pubs_wildland_row_pct) - -colnames(wildland_counts_pct) <- c("% Within wildlands", "% within 50m", "% within 500m") - -# Merge into larger dataframe -wildland_counts_merged <- cbind(wildland_counts, wildland_counts_pct) - -# Generate dataframe based on forestinv buffers - -ecs_forestinv_row <- c(sum(apply(st_within(ecs_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, forestinv_buf500, sparse=FALSE), 1, any))) - -pow_forestinv_row <- c(sum(apply(st_within(pow_pointX_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, forestinv_buf500, sparse=FALSE), 1, any))) -forestinv_counts <- rbind(ecs_forestinv_row, pow_forestinv_row) - -dtas_forestinv_row <- c(sum(apply(st_within(dtas_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, forestinv_buf500, sparse=FALSE), 1, any))) -forestinv_counts <- rbind(forestinv_counts, dtas_forestinv_row) - -transition_forestinv_row <- c(sum(apply(st_within(transition_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, forestinv_buf500, sparse=FALSE), 1, any))) -forestinv_counts <- rbind(forestinv_counts, transition_forestinv_row) - -permaculture_forestinv_row <- c(sum(apply(st_within(permaculture_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, forestinv_buf500, sparse=FALSE), 1, any))) -forestinv_counts <- rbind(forestinv_counts, permaculture_forestinv_row) - -grocery_forestinv_row <- c(sum(apply(st_within(poi_grocery_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery_sf, forestinv_buf500, sparse=FALSE), 1, any))) -forestinv_counts <- rbind(forestinv_counts, grocery_forestinv_row) - -pubs_forestinv_row <- c(sum(apply(st_within(poi_pubs_sf, forestinv_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, forestinv_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, forestinv_buf500, sparse=FALSE), 1, any))) -forestinv_counts <- rbind(forestinv_counts, pubs_forestinv_row) - -colnames(forestinv_counts) <- c("Within Woodlands", "...50m", "...500m") - -# Generate dataframe from rows based on percentages of totals -# TODO: fix error generated by ecs_forestinv_row_pct using ecs_sf. Migrate all these to sf, but check for errors. -ecs_forestinv_row_pct <- ecs_forestinv_row/length(ecs) -pow_forestinv_row_pct <- pow_forestinv_row/length(pow_pointX) -dtas_forestinv_row_pct <- dtas_forestinv_row/length(dtas) -transition_forestinv_row_pct <- transition_forestinv_row/length(transition) -permaculture_forestinv_row_pct <- permaculture_forestinv_row/length(permaculture) -grocery_forestinv_row_pct <- grocery_forestinv_row/length(poi_grocery) -pubs_forestinv_row_pct <- pubs_forestinv_row/length(poi_pubs) - -forestinv_counts_pct <- rbind(ecs_forestinv_row_pct, pow_forestinv_row_pct) -forestinv_counts_pct <- rbind(forestinv_counts_pct, dtas_forestinv_row_pct) -forestinv_counts_pct <- rbind(forestinv_counts_pct, transition_forestinv_row_pct) -forestinv_counts_pct <- rbind(forestinv_counts_pct, permaculture_forestinv_row_pct) -forestinv_counts_pct <- rbind(forestinv_counts_pct, grocery_forestinv_row_pct) -forestinv_counts_pct <- rbind(forestinv_counts_pct, pubs_forestinv_row_pct) - -colnames(forestinv_counts_pct) <- c("% Within Woodlands", "% within 50m", "% within 500m") - -# Merge into larger dataframe -forestinv_counts_merged <- cbind(forestinv_counts, forestinv_counts_pct) - -# Generate dataframe based on scenicareas buffers - -ecs_scenicareas_row <- c(sum(apply(st_within(ecs_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, scenicareas_buf500, sparse=FALSE), 1, any))) - -pow_scenicareas_row <- c(sum(apply(st_within(pow_pointX_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, scenicareas_buf500, sparse=FALSE), 1, any))) -scenicareas_counts <- rbind(ecs_scenicareas_row, pow_scenicareas_row) - -dtas_scenicareas_row <- c(sum(apply(st_within(dtas_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, scenicareas_buf500, sparse=FALSE), 1, any))) -scenicareas_counts <- rbind(scenicareas_counts, dtas_scenicareas_row) - -transition_scenicareas_row <- c(sum(apply(st_within(transition_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, scenicareas_buf500, sparse=FALSE), 1, any))) -scenicareas_counts <- rbind(scenicareas_counts, transition_scenicareas_row) - -permaculture_scenicareas_row <- c(sum(apply(st_within(permaculture_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, scenicareas_buf500, sparse=FALSE), 1, any))) -scenicareas_counts <- rbind(scenicareas_counts, permaculture_scenicareas_row) - -grocery_scenicareas_row <- c(sum(apply(st_within(poi_grocery_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_grocery_sf, scenicareas_buf500, sparse=FALSE), 1, any))) -scenicareas_counts <- rbind(scenicareas_counts, grocery_scenicareas_row) - -pubs_scenicareas_row <- c(sum(apply(st_within(poi_pubs_sf, scenicareas_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, scenicareas_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, scenicareas_buf500, sparse=FALSE), 1, any))) -scenicareas_counts <- rbind(scenicareas_counts, pubs_scenicareas_row) - -colnames(scenicareas_counts) <- c("Within Scenic Areas", "...50m", "...500m") - -# Generate dataframe from rows based on percentages of totals - -ecs_scenicareas_row_pct <- ecs_scenicareas_row/length(ecs) -pow_scenicareas_row_pct <- pow_scenicareas_row/length(pow_pointX) -dtas_scenicareas_row_pct <- dtas_scenicareas_row/length(dtas) -transition_scenicareas_row_pct <- transition_scenicareas_row/length(transition) -permaculture_scenicareas_row_pct <- permaculture_scenicareas_row/length(permaculture) -grocery_scenicareas_row_pct <- grocery_scenicareas_row/length(poi_grocery) -pubs_scenicareas_row_pct <- pubs_scenicareas_row/length(poi_pubs) - -scenicareas_counts_pct <- rbind(ecs_scenicareas_row_pct, pow_scenicareas_row_pct) -scenicareas_counts_pct <- rbind(scenicareas_counts_pct, dtas_scenicareas_row_pct) -scenicareas_counts_pct <- rbind(scenicareas_counts_pct, transition_scenicareas_row_pct) -scenicareas_counts_pct <- rbind(scenicareas_counts_pct, permaculture_scenicareas_row_pct) -scenicareas_counts_pct <- rbind(scenicareas_counts_pct, grocery_scenicareas_row_pct) -scenicareas_counts_pct <- rbind(scenicareas_counts_pct, pubs_scenicareas_row_pct) -colnames(scenicareas_counts_pct) <- c("% Within scenicareass", "% within 50m", "% within 500m") - -# Merge into larger dataframe -scenicareas_counts_merged <- cbind(scenicareas_counts, scenicareas_counts_pct) - - -``` - -# Proximity to "Wilderness" - -Chasing down a curiosity, I decided to try and calculate whether proximity to "wilderness" or "scenic nature" or just trees might have some impact on generating more mobilised communities. I realised that there would be several problems with this kind of calculation up front, first being that "nature" is a deeply problematic construct, reviled by geographers and philosophers alike. With this in mind, I identified several different ways of reckoning wilderness, starting with the highly anachronistic "Scenic Land" designation from the 1970s. Then I pursued the more carefully calculated "core wild areas" generated by SNH just a few years ago. However, even the core wile areas concept has been criticised heavily, so I also expanded out my search to include all sites of special scientific interest and then went even wider to include the Scottish Forestry Service's "Native Woodland" and finally, the most generic possible measurement, any land identified as forested at the last Forest Inventory. - -Proximity to these areas was the next concern, because many of these designations deliberately exclude human habitat, so it was necessary to measure the number of sites within proximity. There is a question which lies here regarding aesthetics, namely, what sort of proximity might generate an affective connection? From my own experience, I decided upon the distance represented by a short walk, i.e. a half-kilometre. However, with the more generic measurements, such as SSSI and forestation, this wouldn't do, as there are so many of these sites that a buffer of 500 meters encapsulates almost all of inhabited Scotland. So for these sites I also calculated a count within 50 metres. - -So what did I discover? The results were inconclusive. First, it is important to note that on the whole, Eco-Congregations tend to be more urban than place of worship taken generally at a rate of nearly 3:1 (5.4% of Eco-Congregations lie in areas currently designated as "Very Remote Rural Areas" whereas nearly 15% of places of worship lie in these areas), so what I was testing for was whether this gap was smaller when specifying these various forms of "wild" remoteness. For our narrowest measurements, there were so few sites captured as to render measurement unreliable. There are, for obvious reasons, `st_within(ecs_sf, wildland)` sites located within any of SNG's core wild areas. Similarly, there are very few of our activist communities located within SSSI's (only `st_within(pow_pointX_sf, sssi)` places of worship out of `r length(pow_pointX)`, `st_within(transition_sf, sssi)` transition towns, (or 2%) and `st_within(dtas_sf, sssi)` community development trusts (3%)). However, expanding this out makes things a bit more interesting, within 50 metres of SSSI's in Scotland lie `st_within(ecs_sf, sssi_buf50)` Eco-Congregations (or just under 1%), which compares favourably with the `st_within(pow_pointX_sf, sssi_buf50)` places of worship (or just 1.5%) far exceeding our ratio (1:1.5 vs. 1:3). This is the same with our more anachronistic measure of "scenic areas," there are 7 eco-congregations within these areas, and 175 places of worship, making for a ratio of nearly 1:2 (2.1% vs. 4.3%). Taking our final measure, of forested areas, this is hard to calculate, as only `st_within(ecs_sf, forestinv)` Eco-Congregation lies within either native or generally forested land. - - -```{r 13_wilderness_tables} - -# Output mmd tables using kable - -sssi_counts_merged %>% - kable(format = "html", col.names = colnames(sssi_counts_merged), caption = "Group counts within SSSIs") %>% - kable_styling(bootstrap_options = c("striped", "hover", "condensed", full_width = F, "responsive")) - -wildland_counts_merged %>% - kable(format = "html", col.names = colnames(wildland_counts_merged), caption = "Group counts within Wildland Areas") %>% - kable_styling(bootstrap_options = c("striped", "hover", "condensed", full_width = F, "responsive")) - -forestinv_counts_merged %>% - kable(format = "html", col.names = colnames(forestinv_counts_merged), caption = "Group counts within Woodlands") %>% - kable_styling(bootstrap_options = c("striped", "hover", "condensed", full_width = F, "responsive")) - -scenicareas_counts_merged %>% - kable(format = "html", col.names = colnames(scenicareas_counts_merged), caption = "Group counts within Scenic Areas") %>% - kable_styling(bootstrap_options = c("striped", "hover", "condensed", full_width = F, "responsive")) - - -# Output CSV files for tables above -write.csv(sssi_counts_merged, "derivedData/sssi_counts_merged.csv", row.names=TRUE) -write.csv(wildland_counts_merged, "derivedData/wildland_counts_merged.csv", row.names=TRUE) -write.csv(forestinv_counts_merged, "derivedData/forestinv_counts_merged.csv", row.names=TRUE) -write.csv(scenicareas_counts_merged, "derivedData/scenicareas_counts_merged.csv", row.names=TRUE) - -``` - -# Citations - -[^15541312]: This research was jointly funded by the AHRC/ESRC under project numnbers AH/K005456/1 and AH/P005063/1. -[^158261118]: This is not to say that there have been no collaborations before 2000, noteworthy in this respect is the WWF who helped to found the Alliance of Religion and Conservation (ARC) in 1985. -[^159141043]: This suggestion should be qualified - RSPB would greatly exceed ECS both in terms of the number of individual subscribers and budget. The RSPB trustee's report for 2013-2014 suggests that their member base was 1,114,938 people across Britain with a net income of £127m - the latter of which exceeds the Church of Scotland. If we adjust this based on the Scottish share of the population of the United Kingdom as of the 2011 census (8.3%) this leaves us with an income of £9.93m. The British charity commission requires charities to self-report the number of volunteers and staff, and from their most recent statistics we learn that RSPB engaged with 17,600 volunteers and employed 2,110 members of staff. Again, adjusted for population, this leaves 1,460 volunteers in Scotland and 176 staff. However, if we measure environmental groups based on the number of sites they maintain, RSPB has only 40 reserves with varying levels of local community engagement. For comparison, as of Sep 14 2015, Friends of the Earth Scotland had only 10 local groups (concentrated mostly in large urban areas). Depending on how one measures "volunteerism," it may be possible that ECS has more engaged volunteers in Scotland as well - if each ECS group had only 4 "volunteers" then this would exceed RSPB. -[^15541313]: Kidwell, Jeremy. (2016). Eco-Congregation Scotland, 2014-2016. University of Edinburgh. http://dx.doi.org/10.7488/ds/1357. -[^15541342]:My dataset on transition towns will be made available later in 2016. Initial data was aquired from the Transition Scotland website http://www.transitionscotland.org/transition-in-scotland on December 10, 2014. We are currently in the process of collaboratively generating a more up-to-date dataset which will reflect their collaboration with SCCAN. -[^177171536]: For further detail on Dataset generation, see Kidwell, Forthcoming, 2018. -[^158261232]:Data was acquired from the Development Trusts Association website, http://www.dtascot.org.uk, accessed on 20 July 2015. As above, we are currently in the process of active collaboration with volunteers from the DTAS to co-generate a new dataset. -[^15541614]:PointX data for "Landscape Data" items is sourced from Ordnance Survey Land-Line and MasterMap(R) and the data points are augmented with additional information provided through research by PointX staff, and data aquired from unidentified "local data companie(s)" and the "118 Information" database (see: http://www.118information.co.uk). This data is under license and cannot be made available for use. It is important to note that I became aware of inaccuracies in this dataset over the course of use and subsequently generated my own dataset in collaboration with churches in Scotland. This will be made available later in 2016. I am in active conversation with OS about improving the quality of the data in PointX regarding places of worship. -[^15826124]:Interview with Margaret Warnock, 29 Aug 2014. -[^158261210]:From http://www.forthenvironmentlink.org, accessed 12 July 2015. -[^1554162]:From the Transition map key, "Green pins are 'official' groups -Blue pins are active communities who are connected to the Scottish Transition network Yellow pins show interest in this area" -[^15571030]:This was calculated by calculating a 10m wide footprint for every postcode in Scotland, areas which are not within 10m of a postcode (as of May 2014) are counted as uninhabited. -[^159142242]: Fiona Tweedie, *Ecumenical Audit: Questionnaire Findings* (2014). -[^15914204]:See note above regarding the data used from the PointX POI database. Note, for our research,we filtered out religious groups not represented within the Eco-Congregation footprint. We discuss representation by tradition and religion further below.adition and religion further below. diff --git a/renv/.gitignore b/renv/.gitignore new file mode 100644 index 0000000..82740ba --- /dev/null +++ b/renv/.gitignore @@ -0,0 +1,3 @@ +library/ +python/ +staging/ diff --git a/renv/activate.R b/renv/activate.R new file mode 100644 index 0000000..4baa934 --- /dev/null +++ b/renv/activate.R @@ -0,0 +1,185 @@ + +local({ + + # the requested version of renv + version <- "0.9.3" + + # avoid recursion + if (!is.na(Sys.getenv("RENV_R_INITIALIZING", unset = NA))) + return(invisible(TRUE)) + + # signal that we're loading renv during R startup + Sys.setenv("RENV_R_INITIALIZING" = "true") + on.exit(Sys.unsetenv("RENV_R_INITIALIZING"), add = TRUE) + + # signal that we've consented to use renv + options(renv.consent = TRUE) + + # load the 'utils' package eagerly -- this ensures that renv shims, which + # mask 'utils' packages, will come first on the search path + library(utils, lib.loc = .Library) + + # check to see if renv has already been loaded + if ("renv" %in% loadedNamespaces()) { + + # if renv has already been loaded, and it's the requested version of renv, + # nothing to do + spec <- .getNamespaceInfo(.getNamespace("renv"), "spec") + if (identical(spec[["version"]], version)) + return(invisible(TRUE)) + + # otherwise, unload and attempt to load the correct version of renv + unloadNamespace("renv") + + } + + # construct path to renv in library + libpath <- local({ + + root <- Sys.getenv("RENV_PATHS_LIBRARY", unset = "renv/library") + prefix <- paste("R", getRversion()[1, 1:2], sep = "-") + + # include SVN revision for development versions of R + # (to avoid sharing platform-specific artefacts with released versions of R) + devel <- + identical(R.version[["status"]], "Under development (unstable)") || + identical(R.version[["nickname"]], "Unsuffered Consequences") + + if (devel) + prefix <- paste(prefix, R.version[["svn rev"]], sep = "-r") + + file.path(root, prefix, R.version$platform) + + }) + + # try to load renv from the project library + if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { + + # warn if the version of renv loaded does not match + loadedversion <- utils::packageDescription("renv", fields = "Version") + if (version != loadedversion) { + + # assume four-component versions are from GitHub; three-component + # versions are from CRAN + components <- strsplit(loadedversion, "[.-]")[[1]] + remote <- if (length(components) == 4L) + paste("rstudio/renv", loadedversion, sep = "@") + else + paste("renv", loadedversion, sep = "@") + + fmt <- paste( + "renv %1$s was loaded from project library, but renv %2$s is recorded in lockfile.", + "Use `renv::record(\"%3$s\")` to record this version in the lockfile.", + "Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library.", + sep = "\n" + ) + + msg <- sprintf(fmt, loadedversion, version, remote) + warning(msg, call. = FALSE) + + } + + # load the project + return(renv::load()) + + } + + # failed to find renv locally; we'll try to install from GitHub. + # first, set up download options as appropriate (try to use GITHUB_PAT) + install_renv <- function() { + + message("Failed to find installation of renv -- attempting to bootstrap...") + + # ensure .Rprofile doesn't get executed + rpu <- Sys.getenv("R_PROFILE_USER", unset = NA) + Sys.setenv(R_PROFILE_USER = "") + on.exit({ + if (is.na(rpu)) + Sys.unsetenv("R_PROFILE_USER") + else + Sys.setenv(R_PROFILE_USER = rpu) + }, add = TRUE) + + # prepare download options + pat <- Sys.getenv("GITHUB_PAT") + if (nzchar(Sys.which("curl")) && nzchar(pat)) { + fmt <- "--location --fail --header \"Authorization: token %s\"" + extra <- sprintf(fmt, pat) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "curl", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } else if (nzchar(Sys.which("wget")) && nzchar(pat)) { + fmt <- "--header=\"Authorization: token %s\"" + extra <- sprintf(fmt, pat) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "wget", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } + + # fix up repos + repos <- getOption("repos") + on.exit(options(repos = repos), add = TRUE) + repos[repos == "@CRAN@"] <- "https://cloud.r-project.org" + options(repos = repos) + + # check for renv on CRAN matching this version + db <- as.data.frame(available.packages(), stringsAsFactors = FALSE) + if ("renv" %in% rownames(db)) { + entry <- db["renv", ] + if (identical(entry$Version, version)) { + message("* Installing renv ", version, " ... ", appendLF = FALSE) + dir.create(libpath, showWarnings = FALSE, recursive = TRUE) + utils::install.packages("renv", lib = libpath, quiet = TRUE) + message("Done!") + return(TRUE) + } + } + + # try to download renv + message("* Downloading renv ", version, " ... ", appendLF = FALSE) + prefix <- "https://api.github.com" + url <- file.path(prefix, "repos/rstudio/renv/tarball", version) + destfile <- tempfile("renv-", fileext = ".tar.gz") + on.exit(unlink(destfile), add = TRUE) + utils::download.file(url, destfile = destfile, mode = "wb", quiet = TRUE) + message("Done!") + + # attempt to install it into project library + message("* Installing renv ", version, " ... ", appendLF = FALSE) + dir.create(libpath, showWarnings = FALSE, recursive = TRUE) + + # invoke using system2 so we can capture and report output + bin <- R.home("bin") + exe <- if (Sys.info()[["sysname"]] == "Windows") "R.exe" else "R" + r <- file.path(bin, exe) + args <- c("--vanilla", "CMD", "INSTALL", "-l", shQuote(libpath), shQuote(destfile)) + output <- system2(r, args, stdout = TRUE, stderr = TRUE) + message("Done!") + + # check for successful install + status <- attr(output, "status") + if (is.numeric(status) && !identical(status, 0L)) { + text <- c("Error installing renv", "=====================", output) + writeLines(text, con = stderr()) + } + + + } + + try(install_renv()) + + # try again to load + if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { + message("Successfully installed and loaded renv ", version, ".") + return(renv::load()) + } + + # failed to download or load renv; warn the user + msg <- c( + "Failed to find an renv installation: the project will not be loaded.", + "Use `renv::activate()` to re-initialize the project." + ) + + warning(paste(msg, collapse = "\n"), call. = FALSE) + +}) diff --git a/renv/settings.dcf b/renv/settings.dcf new file mode 100644 index 0000000..bba46f4 --- /dev/null +++ b/renv/settings.dcf @@ -0,0 +1,6 @@ +external.libraries: +ignored.packages: +package.dependency.fields: Imports, Depends, LinkingTo +snapshot.type: packrat +use.cache: TRUE +vcs.ignore.library: TRUE