From ec980c423d6bac637549332433741ec68a55478e Mon Sep 17 00:00:00 2001
From: Jeremy Kidwell <j.kidwell@bham.ac.uk>
Date: Thu, 28 Mar 2019 09:36:31 +0000
Subject: [PATCH] fixed error in ecs sssi table, added calc for pubs and
 grocery stores to other wilderness categories

---
 mapping_draft-hpc_optimised_wilderness.Rmd |  30 ++++++++++++++++++---
 wilderness_layers.qgz                      | Bin 10443 -> 11202 bytes
 2 files changed, 27 insertions(+), 3 deletions(-)

diff --git a/mapping_draft-hpc_optimised_wilderness.Rmd b/mapping_draft-hpc_optimised_wilderness.Rmd
index 9286f2f..4cd32c3 100644
--- a/mapping_draft-hpc_optimised_wilderness.Rmd
+++ b/mapping_draft-hpc_optimised_wilderness.Rmd
@@ -493,6 +493,7 @@ 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
 
+# 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)
@@ -514,6 +515,8 @@ 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
 
@@ -581,23 +584,30 @@ permaculture_wildland_row <- c(sum(apply(st_within(permaculture_sf, wildland_sim
 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_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
@@ -620,22 +630,30 @@ permaculture_forestinv_row <- c(sum(apply(st_within(permaculture_sf, forestinv_s
 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
-ecs_forestinv_row_pct <- ecs_forestinv_row/length(ecs_sf)
+# 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
@@ -658,23 +676,29 @@ permaculture_scenicareas_row <- c(sum(apply(st_within(permaculture_sf, scenicare
 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_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
diff --git a/wilderness_layers.qgz b/wilderness_layers.qgz
index bee41ef58478df4c96c173c1f52d78ac1a25b057..a9aee007d76683d4598aa3dd43d26041eba10d76 100644
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