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tweaking calculations
This commit is contained in:
Jeremy Kidwell
parent
e74a9b7447
commit
d353964221
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@ -960,21 +960,16 @@ st_crs(permaculture_sf) <- 27700
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# Calculate incidence of ecs within SSSI and within buffers at 50/500m
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ecs_sssi_row <- c(sum(apply(st_within(ecs_sf, sssi, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, st_buffer(sssi, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, st_buffer(sssi, dist = 500), sparse=FALSE), 1, any)))
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# Calculate row based on percentage of total
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ecs_sssi_row_pct <- ecs_sssi_row/length(ecs)
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pow_sssi_row <- c(sum(apply(st_within(pow_pointX_sf, sssi, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, st_buffer(sssi, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, st_buffer(sssi, dist = 500), sparse=FALSE), 1, any)))
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pow_sssi_row_pct <- pow_sssi_row/length(pow_pointX)
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dtas_sssi_row <- c(sum(apply(st_within(dtas_sf, sssi, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, st_buffer(sssi, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, st_buffer(sssi, dist = 500), sparse=FALSE), 1, any)))
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dtas_sssi_row_pct <- dtas_sssi_row/length(dtas)
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transition_sssi_row <- c(sum(apply(st_within(transition_sf, sssi, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, st_buffer(sssi, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, st_buffer(sssi, dist = 500), sparse=FALSE), 1, any)))
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transition_sssi_row_pct <- transition_sssi_row/length(transition)
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permaculture_sssi_row <- c(sum(apply(st_within(permaculture_sf, sssi, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, st_buffer(sssi, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, st_buffer(sssi, dist = 500), sparse=FALSE), 1, any)))
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permaculture_sssi_row_pct <- permaculture_sssi_row/length(permaculture)
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# Generate dataframe from rows based on counts
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sssi_counts <- rbind(ecs_sssi_row, pow_sssi_row)
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sssi_counts <- rbind(sssi_counts, dtas_sssi_row)
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sssi_counts <- rbind(sssi_counts, transition_sssi_row)
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@ -982,12 +977,20 @@ sssi_counts <- rbind(sssi_counts, permaculture_sssi_row)
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sssi_counts <- as.data.frame(sssi_counts)
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colnames(sssi_counts) <- c("Within SSSIs", "...50m", "...500m")
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# Generate dataframe from rows based on percentages of totals
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ecs_sssi_row_pct <- ecs_sssi_row/length(ecs)
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pow_sssi_row_pct <- pow_sssi_row/length(pow_pointX)
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dtas_sssi_row_pct <- dtas_sssi_row/length(dtas)
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transition_sssi_row_pct <- transition_sssi_row/length(transition)
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permaculture_sssi_row_pct <- permaculture_sssi_row/length(permaculture)
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sssi_counts_pct <- rbind(ecs_sssi_row_pct, pow_sssi_row_pct)
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sssi_counts_pct <- rbind(sssi_counts_pct, dtas_sssi_row_pct)
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sssi_counts_pct <- rbind(sssi_counts_pct, transition_sssi_row_pct)
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sssi_counts_pct <- rbind(sssi_counts_pct, permaculture_sssi_row_pct)
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colnames(sssi_counts_pct) <- c("% Within SSSIs", "% within 50m", "% within 500m")
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# Merge into larger dataframe
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sssi_counts_merged <- cbind(sssi_counts, sssi_counts_pct)
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@ -996,50 +999,70 @@ sssi_counts_merged <- cbind(sssi_counts, sssi_counts_pct)
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titles <- c("Within Wildland Areas", "...50m", "...500m")
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ecs_wildland_row <- c(sum(apply(st_within(ecs_sf, wildland, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, st_buffer(wildland, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, st_buffer(wildland, dist = 500), sparse=FALSE), 1, any)))
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# TODO: add additional column with counts converted to percentages - use prop.table()?
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ecs_wildland_row <- rbind(titles, ecs_wildland_row)
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pow_wildland_row <- c(sum(apply(st_within(pow_pointX_sf, wildland, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, st_buffer(wildland, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, st_buffer(wildland, dist = 500), sparse=FALSE), 1, any)))
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wildland_counts <- rbind(ecs_wildland_row, pow_wildland_row)
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dtas_wildland_row <- c(sum(apply(st_within(dtas_sf, wildland, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, st_buffer(wildland, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, st_buffer(wildland, dist = 500), sparse=FALSE), 1, any)))
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wildland_counts <- rbind(wildland_counts, dtas_wildland_row)
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transition_wildland_row <- c(sum(apply(st_within(transition_sf, wildland, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, st_buffer(wildland, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, st_buffer(wildland, dist = 500), sparse=FALSE), 1, any)))
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wildland_counts <- rbind(wildland_counts, transition_wildland_row)
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permaculture_wildland_row <- c(sum(apply(st_within(permaculture_sf, wildland, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, st_buffer(wildland, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, st_buffer(wildland, dist = 500), sparse=FALSE), 1, any)))
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wildland_counts <- rbind(wildland_counts, permaculture_wildland_row)
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# Generate dataframe from rows based on percentages of totals
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ecs_wildland_row_pct <- ecs_wildland_row/length(ecs)
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pow_wildland_row_pct <- pow_wildland_row/length(pow_pointX)
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dtas_wildland_row_pct <- dtas_wildland_row/length(dtas)
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transition_wildland_row_pct <- transition_wildland_row/length(transition)
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permaculture_wildland_row_pct <- permaculture_wildland_row/length(permaculture)
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wildland_counts_pct <- rbind(ecs_wildland_row_pct, pow_wildland_row_pct)
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wildland_counts_pct <- rbind(wildland_counts_pct, dtas_wildland_row_pct)
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wildland_counts_pct <- rbind(wildland_counts_pct, transition_wildland_row_pct)
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wildland_counts_pct <- rbind(wildland_counts_pct, permaculture_wildland_row_pct)
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colnames(wildland_counts_pct) <- c("% Within wildlands", "% within 50m", "% within 500m")
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# Merge into larger dataframe
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wildland_counts_merged <- cbind(wildland_counts, wildland_counts_pct)
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# Generate dataframe based on forest_inventory buffers
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titles <- c("Within woodlands", "...50m", "...500m")
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ecs_forest_inventory_row <- c(sum(apply(st_within(ecs_sf, forest_inventory, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, st_buffer(forest_inventory, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, st_buffer(forest_inventory, dist = 500), sparse=FALSE), 1, any)))
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# TODO: add additional column with counts converted to percentages - use prop.table()?
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ecs_forest_inventory_row <- rbind(titles, ecs_forest_inventory_row)
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pow_forest_inventory_row <- c(sum(apply(st_within(pow_pointX_sf, forest_inventory, sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, st_buffer(forest_inventory, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(pow_pointX_sf, st_buffer(forest_inventory, dist = 500), sparse=FALSE), 1, any)))
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forest_inventory_counts <- rbind(ecs_forest_inventory_row, pow_forest_inventory_row)
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dtas_forest_inventory_row <- c(sum(apply(st_within(dtas_sf, forest_inventory, sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, st_buffer(forest_inventory, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(dtas_sf, st_buffer(forest_inventory, dist = 500), sparse=FALSE), 1, any)))
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forest_inventory_counts <- rbind(forest_inventory_counts, dtas_forest_inventory_row)
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transition_forest_inventory_row <- c(sum(apply(st_within(transition_sf, forest_inventory, sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, st_buffer(forest_inventory, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(transition_sf, st_buffer(forest_inventory, dist = 500), sparse=FALSE), 1, any)))
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forest_inventory_counts <- rbind(forest_inventory_counts, transition_forest_inventory_row)
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permaculture_forest_inventory_row <- c(sum(apply(st_within(permaculture_sf, forest_inventory, sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, st_buffer(forest_inventory, dist = 50), sparse=FALSE), 1, any)), sum(apply(st_within(permaculture_sf, st_buffer(forest_inventory, dist = 500), sparse=FALSE), 1, any)))
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forest_inventory_counts <- rbind(forest_inventory_counts, permaculture_forest_inventory_row)
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forest_inventory_counts <- rbind(forest_inventory_counts, permaculture_wildland_row)
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# Generate dataframe from rows based on percentages of totals
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ecs_forest_inventory_row_pct <- ecs_forest_inventory_row/length(as.data.frame(ecs))
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pow_forest_inventory_row_pct <- pow_forest_inventory_row/length(pow_pointX)
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dtas_forest_inventory_row_pct <- dtas_forest_inventory_row/length(dtas)
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transition_forest_inventory_row_pct <- transition_forest_inventory_row/length(transition)
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permaculture_forest_inventory_row_pct <- permaculture_forest_inventory_row/length(permaculture)
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forest_inventory_counts_pct <- rbind(ecs_forest_inventory_row_pct, pow_forest_inventory_row_pct)
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forest_inventory_counts_pct <- rbind(forest_inventory_counts_pct, dtas_forest_inventory_row_pct)
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forest_inventory_counts_pct <- rbind(forest_inventory_counts_pct, transition_forest_inventory_row_pct)
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forest_inventory_counts_pct <- rbind(forest_inventory_counts_pct, permaculture_forest_inventory_row_pct)
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colnames(forest_inventory_counts_pct) <- c("% Within Woodlands", "% within 50m", "% within 500m")
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# Merge into larger dataframe
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forest_inventory_counts_merged <- cbind(forest_inventory_counts, forest_inventory_counts_pct)
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```
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@ -1055,18 +1078,23 @@ So what did I discover? The results were inconclusive. First, it is important to
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# Output mmd tables using kable
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sssi_counts %>%
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kable(format = "html", col.names = colnames(sssi_counts), "Group counts within SSSIs") %>%
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sssi_counts_merged %>%
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kable(format = "html", col.names = colnames(sssi_counts_merged), caption = "Group counts within SSSIs") %>%
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kable_styling(bootstrap_options = c("striped", "hover", "condensed", full_width = F, "responsive"))
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wildland_counts %>%
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kable(format = "html", col.names = colnames(wildland_counts), caption = "Group counts within Wildland Areas") %>%
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wildland_counts_merged %>%
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kable(format = "html", col.names = colnames(wildland_counts_merged), caption = "Group counts within Wildland Areas") %>%
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kable_styling(bootstrap_options = c("striped", "hover", "condensed", full_width = F, "responsive"))
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forest_inventory_counts %>%
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kable(format = "html", col.names = colnames(forest_inventory_counts), caption = "Group counts within woodlands") %>%
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forest_inventory_counts_merged %>%
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kable(format = "html", col.names = colnames(forest_inventory_counts_merged), caption = "Group counts within woodlands") %>%
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kable_styling(bootstrap_options = c("striped", "hover", "condensed", full_width = F, "responsive"))
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# Output CSV files for tables above
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write.csv(sssi_counts_merged, "derivedData/sssi_counts_merged.csv", row.names=TRUE)
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write.csv(wildland_counts_merged, "derivedData/wildland_counts_merged.csv", row.names=TRUE)
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write.csv(forest_inventory_counts_merged, "derivedData/forest_inventory_counts_merged.csv", row.names=TRUE)
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```
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@ -3,12 +3,11 @@ require(sf) # new simplefeature data class, supercedes sp in many ways
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# using GEOS 3.6.1, GDAL 2.1.3, PROJ 4.9.3
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require(sp) # needed for proj4string, deprecated by sf()
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require(rgdal) # version version: 1.3-6
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require(rgeos) # used for buffering below
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require(devtools)
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# Set up local workspace and load data
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if (dir.exists("data") == FALSE) {
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dir.create("data")
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}
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setwd("~/Downloads/test")
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# load data
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transition_wgs <- read.csv(text=getURL("https://zenodo.org/record/165519/files/SCCAN_1.4.csv"))
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coordinates(transition_wgs) <- c("X", "Y")
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@ -16,37 +15,50 @@ proj4string(transition_wgs) <- CRS(wgs84)
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transition_sp <- spTransform(transition_wgs, bng)
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transition_sf <- st_as_sf(transition, coords = c("X", "Y"), crs=27700)
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# Download data as ESRI Shapefile from page at: https://gateway.snh.gov.uk/natural-spaces/dataset.jsp?dsid=SSSI
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if (file.exists("data/SSSI_SCOTLAND.shp") == FALSE) {
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# TODO: get reliable URL for data download
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# http://gateway.snh.gov.uk/natural-spaces/dataset.jsp?dsid=SSSI
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# download.file("", destfile = "data/SSSI_SCOTLAND_ESRI.zip")
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unzip("data/SSSI_SCOTLAND_ESRI.zip", exdir = "data")
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}
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unzip("SSSI_SCOTLAND_ESRI.zip")
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if (file.exists("data/National_Forest_Inventory_Woodland_Scotland_2017.shp") == FALSE) {
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download.file("https://opendata.arcgis.com/datasets/3cb1abc185a247a48b9d53e4c4a8be87_0.zip", destfile = "data/National_Forest_Inventory_Woodland_Scotland_2017.zip")
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# # Download data as ESRI Shapefile from page at: http://data-forestry.opendata.arcgis.com/datasets/3cb1abc185a247a48b9d53e4c4a8be87_0
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unzip("data/National_Forest_Inventory_Woodland_Scotland_2017.zip", exdir = "data")
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}
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unzip("National_Forest_Inventory_Woodland_Scotland_2017.zip")
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forest_inventory_sf <- st_read("data/National_Forest_Inventory_Woodland_Scotland_2017.shp")
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forest_inventory_sp <- readOGR("./data", "National_Forest_Inventory_Woodland_Scotland_2017")
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sssi_sf <- st_read("SSSI_SCOTLAND.shp")
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sssi_sp <- readOGR("./", "SSSI_SCOTLAND")
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st_crs(sssi) <- 27700
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st_crs(ecs_sf) <- 27700
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forest_inventory_sf <- st_read("National_Forest_Inventory_Woodland_Scotland_2017.shp")
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forest_inventory_sp <- readOGR("./", "National_Forest_Inventory_Woodland_Scotland_2017")
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# First test out plots using spatialfeatures and spdf with core R
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system.time(
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ggplot() +
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geom_sf(data = forest_inventory_sf)
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plot(sssi_sf)
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)
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system.time(
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ggplot() +
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geom_polygon(data = forest_inventory_sp)
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plot(sssi_sp)
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)
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count_data <- sum(apply(st_within(points_sf, st_buffer(sssi, dist = 50), sparse=FALSE), 1, any))
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# First test out plots using spatialfeatures and spdf with ggplot2
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system.time(
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ggplot() +
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geom_sf(data = sssi_sf)
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)
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system.time(
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ggplot() +
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geom_polygon(data = sssi_sp)
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)
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# Now try to run a count within a buffer:
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st_crs(sssi_sf) <- 27700
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st_crs(transition_sf) <- 27700
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# CRS uses meters for units, so buffer here should be a modest 50m:
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count_data_sf <- sum(apply(st_within(points_sf, st_buffer(sssi, dist = 50), sparse=FALSE), 1, any))
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# count_data_sf <- sum(apply(gWithin(points_sf, gBuffer(sssi,width=50)
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sessioninfo::session_info()
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