code/accessibility-indices-leeds.R
In cyipt/acton: Active Transport Options for New Developments
library(sf)
library(mapview)
library(pct)
library(tidyverse)
library(acton)
library(tmap)
tmap_mode("view")
reszone = pct::get_pct_zones(region = "west-yorkshire", geography = "lsoa", purpose = "commute") %>% rename(LA_Code = lad11cd) %>% st_set_crs(4326)
mzone = pct::get_pct_zones(region = "west-yorkshire", geography = "msoa", purpose = "commute") %>% rename(LA_Code = lad11cd) %>% st_set_crs(4326)
which(st_is_valid(reszone) == FALSE)
reszone = lwgeom::st_make_valid(reszone)
which(st_is_valid(reszone) == FALSE)
c = pct::get_pct_centroids(region = "west-yorkshire", geography = "lsoa") %>% rename(LA_Code = lad11cd) %>% st_set_crs(4326)
m = pct::get_pct_centroids(region = "west-yorkshire", geography = "msoa") %>% rename(LA_Code = lad11cd) %>% st_set_crs(4326)
##Filter to only include Leeds. But Tyersall is actually closest to a centroid in Bradford
# reszone = reszone %>% filter(LA_Code == "E08000035")
# c = c %>% filter(LA_Code == "E08000035")
# Accessibility stats -----------------------------------------------------
# access_town = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_town.Rds")
# access_food = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_food.Rds")
# access_employ = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_employ.Rds")
# access_primary = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_primary.Rds")
# access_secondary = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_secondary.Rds")
# access_gp = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_gp.Rds")
# wy = unique(reszone$lad11cd)
# access_town_wy = access_town[access_town$LA_Code %in% wy,]
access_employ = get_jts_data("jts0501", 2017)
access_town = get_jts_data("jts0508", 2017)#error
access_food = get_jts_data("jts0507", 2017)
access_primary = get_jts_data("jts0502", 2017)
access_secondary = get_jts_data("jts0503", 2017)
access_gp = get_jts_data("jts0505", 2017)#error
# Weighted employment figures ---------------------------------
access_employ$weightedJobsPTt = apply(
X = access_employ[c("Jobs100EmpPTt", "Jobs500EmpPTt", "Jobs5000EmpPTt")],
MARGIN = 1,
FUN = weighted.mean,
w = c(100, 500, 5000)
)
access_employ$weightedJobsCyct = apply(
X = access_employ[c("Jobs100EmpCyct", "Jobs500EmpCyct", "Jobs5000EmpCyct")],
MARGIN = 1,
FUN = weighted.mean,
w = c(100, 500, 5000)
)
access_employ$weightedJobsCart = apply(
X = access_employ[c("Jobs100EmpCart", "Jobs500EmpCart", "Jobs5000EmpCart")],
MARGIN = 1,
FUN = weighted.mean,
w = c(100, 500, 5000)
)
# Join the data tables ----------------------------------------------------
#these need to be joined so they show on the maps
reszone = inner_join(reszone,access_town,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_food,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_employ,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_primary,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_secondary,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_gp,by = c("geo_code" = "LSOA_code","LA_Code"))
#these need to be joined to extract the data for the sites
c = inner_join(c,access_town,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_food,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_employ,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_primary,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_secondary,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_gp,by = c("geo_code" = "LSOA_code","LA_Code"))
# Index of overall accessibility ------------------------------------------
reszone = reszone %>%
mutate(index_PT = (weightedJobsPTt+TownPTt+FoodPTt+PSPTt+SSPTt+GPPTt)/6,
index_Cyc = (weightedJobsCyct+TownCyct+FoodCyct+PSCyct+SSCyct+GPCyct)/6,
index_Car = (weightedJobsCart+TownCart+FoodCart+PSCart+SSCart+GPCart)/6)
c = c %>%
mutate(index_PT = (weightedJobsPTt+TownPTt+FoodPTt+PSPTt+SSPTt+GPPTt)/6,
index_Cyc = (weightedJobsCyct+TownCyct+FoodCyct+PSCyct+SSCyct+GPCyct)/6,
index_Car = (weightedJobsCart+TownCart+FoodCart+PSCart+SSCart+GPCart)/6)
# PlanIt data for Allerton Bywater and other sites--------------------------------------------
# ?get_planit_data #####should work once I can download the acton package
# https://www.planit.org.uk/planapplic/13/05235/FU@Leeds/geojson ## the full URL for alleron bywater in planit
#
# base = "https://www.planit.org.uk/"
# endpoint = "planapplic/13/05235/FU@Leeds/geojson"
# call1 = paste(base,endpoint, sep = "")
#
# library(geojsonsf)
#
# planit_ab = geojson_sf(call1)
# head(planit_ab)
#
# planit_ab_proj = st_transform(planit_ab,27700)
# get_planit_data(bbox = NULL, pcode = "LS2 9JT", limit = 2) # data from specific postcode
# library(httr)
# library(jsonlite)
#
# get_ab = GET(call1)
#
# get_ab_text = content(get_ab, "text")
# get_ab_json = fromJSON(get_ab_text, flatten = TRUE)
#
# get_ab_df = as.data.frame(get_ab_json$properties)
# st_as_sf(get_ab_df, coords = c(get_ab_df$lat, get_ab_df$lng))#fails
##########
p1 = get_planit_data(bbox = NULL, query_type = "planapplic", query_value = "13/05235/FU@Leeds", base_url = "https://www.planit.org.uk/")
p2 = get_planit_data(bbox = NULL, query_type = "planapplic", query_value = "15/04151/FU@Leeds", base_url = "https://www.planit.org.uk/")
p3 = get_planit_data(bbox = NULL, query_type = "planapplic", query_value = "15/01973/FU@Leeds", base_url = "https://www.planit.org.uk/")
p4 = get_planit_data(bbox = NULL, query_type = "planapplic", query_value = "15/00415/FU@Leeds", base_url = "https://www.planit.org.uk/")
p4 = p4[,-9]
# Link the sites with the closest centroid --------------------------------
# # this joins the sites with the LSOA they sit within, but I think it would be better to join them with the closest LSOA centroid instead, since it is the centroids that are used as the origin points for the accessibility stats
# reszone_proj = reszone %>% st_transform(27700)
#
# sites = sites %>%
# st_transform(27700)
# sites = st_join(sites,reszone_proj,join = st_within) %>%
# st_transform(4326)
sites = rbind(p1,p2,p3,p4)
sites$place = c("AllertonBywater", "Tyersal", "Micklefield", "LeedsClimateInnovationDistrict")
sites$id = 1:dim(sites)[1]
sites = sites %>%
select(id, place, everything())
# this finds the nearest centroid to each site
c_proj = c %>% st_transform(27700)
sites = sites %>%
st_transform(27700)
sites = st_join(sites,c_proj,join = st_nearest_feature) %>%
st_transform(4326)
# sites$nearest_centroid = sites %>%
# st_transform(27700)
# st_nearest_feature(near,c_proj) %>%
# st_transform(4326)
#find nearest MSOA centroid to each site
m_proj = m %>% st_transform(27700)
m_code = m_proj %>%
select(msoa_code = geo_code)
sites = sites %>%
st_transform(27700)
sites = st_join(sites,m_code,join = st_nearest_feature) %>%
st_transform(4326)
sites = sites %>%
select(id, place, geo_code, msoa_code, everything())
sites$geo_code
sites$place
sites$index_PT
sites$index_Cyc
sites$index_Car
write_sf(sites,"./data/leeds-sites.geojson")
# write_sf(reszone,"./data/wy-zones.geojson")
# piggyback::pb_upload("./data/wy-zones.geojson")
# piggyback::pb_download_url("wy-zones.geojson")
# Maps --------------------------------------------------------------------
sites = read_sf("./data/leeds-sites.geojson")
reszone = read_sf("https://github.com/cyipt/acton/releases/download/0.0.1/wy-zones.geojson")
# tmaptools::palette_explorer()
reszone_leeds = reszone %>%
filter(lad_name == "Leeds")
applications_case_study$site = as.factor(c("Tyersal","Micklefield","Leeds Climate Innovation District","Allerton Bywater"))
tm_shape(applications_case_study) +
tm_dots(col = "place", size = 0.1) +
tm_basemap(server = "CartoDB.Positron")
# map sites and nearest centroids
mapview(reszone) +
mapview(sites) +
mapview(c[c$geo_code %in% sites$geo_code,])
mapview(mzone) +
mapview(sites) +
mapview(m[m$geo_code %in% sites$msoa_code,])
mapview(mzone) +
mapview(sites) +
mapview(m)
#Warning - geometry is not valid on row 359
# reszone[which(st_is_valid(reszone) == FALSE),]
#Accessibility stat examples
tm_shape(reszone) +
tm_polygons(c("index_PT", "index_Cyc")) +
tm_format("reszone")
tmap_mode("view")
tm_shape(reszone_leeds) +
tm_polygons(c("index_PT","index_Cyc","index_Car"),palette="-viridis",title = c("Walk/PT","Cycle","Car")) +
tm_shape(sites) +
tm_dots(size=0.2) +
tm_facets(nrow = 1)
tm_shape(reszone_leeds) +
tm_polygons(c("weightedJobsPTt","weightedJobsCyct","weightedJobsCart"),palette="-cividis",title = c("Walk/PT","Cycle","Car")) +
tm_shape(sites) +
tm_dots(size=0.2) +
tm_facets(nrow = 1)
# tm_shape(reszone) +
# tm_polygons(col = "index_Cyc") +
# tm_shape(sites) +
# tm_dots()
#
# tm_shape(reszone) +
# tm_polygons(col = "index_Car") +
# tm_shape(sites) +
# tm_dots()
tm_shape(reszone_leeds) +
tm_polygons(c("FoodPTt","FoodCyct","FoodCart"),palette="-inferno",title = c("Walk/PT","Cycle","Car")) +
tm_shape(sites) +
tm_dots(size=0.2) +
tm_facets(nrow = 1)
tm_shape(reszone_leeds) +
tm_polygons(c("GPPTt","GPCyct","GPCart"),palette="-magma",title = c("Walk/PT","Cycle","Car")) +
tm_shape(sites) +
tm_dots(size=0.2) +
tm_facets(nrow = 1)
tm_shape(reszone) +
tm_polygons(col = "weightedJobsPTt") +
tm_shape(sites) +
tm_dots()
tm_shape(reszone) +
tm_polygons(col = "weightedJobsCyct") +
tm_shape(sites) +
tm_dots()
tm_shape(reszone) +
tm_polygons(col = "weightedJobsCart") +
tm_shape(sites) +
tm_dots()
# Allerton Bywater Millennium Community polygon ---------------------------
ab = st_sfc(st_polygon(list(cbind(c(441926,441865,442023,442362,442610,442613,442238,442297,442294,442197,442140,441968,441926),c(428055,427794,427620,427630,427579,427690,427906,428008,428094,428085,427954,428061,428055)))))
abc = st_sf(ab,crs = 27700)
abc$n_homes = 562
# additional variables
abc_4326 = st_transform(abc, 4326)
#
lwgeom::st_geod_area(abc_4326)
st_area(abc_4326)
st_area(abc) # why are these not the same?
# abc_buffer = st_buffer(abc, 500)
# abc_buffer2 = abc_4326 %>%
# st_transform(27700) %>%
# st_buffer(500) %>%
# st_transform(4326)
#
# abc_buffer3 = stplanr::geo_projected(abc_4326, st_buffer, dist = 500)
#
# mapview(abc_buffer) + mapview(abc_buffer2) + mapview(abc_buffer3)
mapview(abc) + mapview(reszone)
cyipt/acton documentation built on Jan. 29, 2021, 7:30 a.m.
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library(sf)
library(mapview)
library(pct)
library(tidyverse)
library(acton)
library(tmap)
tmap_mode("view")
reszone = pct::get_pct_zones(region = "west-yorkshire", geography = "lsoa", purpose = "commute") %>% rename(LA_Code = lad11cd) %>% st_set_crs(4326)
mzone = pct::get_pct_zones(region = "west-yorkshire", geography = "msoa", purpose = "commute") %>% rename(LA_Code = lad11cd) %>% st_set_crs(4326)
which(st_is_valid(reszone) == FALSE)
reszone = lwgeom::st_make_valid(reszone)
which(st_is_valid(reszone) == FALSE)
c = pct::get_pct_centroids(region = "west-yorkshire", geography = "lsoa") %>% rename(LA_Code = lad11cd) %>% st_set_crs(4326)
m = pct::get_pct_centroids(region = "west-yorkshire", geography = "msoa") %>% rename(LA_Code = lad11cd) %>% st_set_crs(4326)
##Filter to only include Leeds. But Tyersall is actually closest to a centroid in Bradford
# reszone = reszone %>% filter(LA_Code == "E08000035")
# c = c %>% filter(LA_Code == "E08000035")
# Accessibility stats -----------------------------------------------------
# access_town = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_town.Rds")
# access_food = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_food.Rds")
# access_employ = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_employ.Rds")
# access_primary = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_primary.Rds")
# access_secondary = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_secondary.Rds")
# access_gp = readRDS("~/NewDevelopmentsCycling/data/accessibility/access_gp.Rds")
# wy = unique(reszone$lad11cd)
# access_town_wy = access_town[access_town$LA_Code %in% wy,]
access_employ = get_jts_data("jts0501", 2017)
access_town = get_jts_data("jts0508", 2017)#error
access_food = get_jts_data("jts0507", 2017)
access_primary = get_jts_data("jts0502", 2017)
access_secondary = get_jts_data("jts0503", 2017)
access_gp = get_jts_data("jts0505", 2017)#error
# Weighted employment figures ---------------------------------
access_employ$weightedJobsPTt = apply(
X = access_employ[c("Jobs100EmpPTt", "Jobs500EmpPTt", "Jobs5000EmpPTt")],
MARGIN = 1,
FUN = weighted.mean,
w = c(100, 500, 5000)
)
access_employ$weightedJobsCyct = apply(
X = access_employ[c("Jobs100EmpCyct", "Jobs500EmpCyct", "Jobs5000EmpCyct")],
MARGIN = 1,
FUN = weighted.mean,
w = c(100, 500, 5000)
)
access_employ$weightedJobsCart = apply(
X = access_employ[c("Jobs100EmpCart", "Jobs500EmpCart", "Jobs5000EmpCart")],
MARGIN = 1,
FUN = weighted.mean,
w = c(100, 500, 5000)
)
# Join the data tables ----------------------------------------------------
#these need to be joined so they show on the maps
reszone = inner_join(reszone,access_town,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_food,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_employ,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_primary,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_secondary,by = c("geo_code" = "LSOA_code","LA_Code"))
reszone = inner_join(reszone,access_gp,by = c("geo_code" = "LSOA_code","LA_Code"))
#these need to be joined to extract the data for the sites
c = inner_join(c,access_town,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_food,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_employ,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_primary,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_secondary,by = c("geo_code" = "LSOA_code","LA_Code"))
c = inner_join(c,access_gp,by = c("geo_code" = "LSOA_code","LA_Code"))
# Index of overall accessibility ------------------------------------------
reszone = reszone %>%
mutate(index_PT = (weightedJobsPTt+TownPTt+FoodPTt+PSPTt+SSPTt+GPPTt)/6,
index_Cyc = (weightedJobsCyct+TownCyct+FoodCyct+PSCyct+SSCyct+GPCyct)/6,
index_Car = (weightedJobsCart+TownCart+FoodCart+PSCart+SSCart+GPCart)/6)
c = c %>%
mutate(index_PT = (weightedJobsPTt+TownPTt+FoodPTt+PSPTt+SSPTt+GPPTt)/6,
index_Cyc = (weightedJobsCyct+TownCyct+FoodCyct+PSCyct+SSCyct+GPCyct)/6,
index_Car = (weightedJobsCart+TownCart+FoodCart+PSCart+SSCart+GPCart)/6)
# PlanIt data for Allerton Bywater and other sites--------------------------------------------
# ?get_planit_data #####should work once I can download the acton package
# https://www.planit.org.uk/planapplic/13/05235/FU@Leeds/geojson ## the full URL for alleron bywater in planit
#
# base = "https://www.planit.org.uk/"
# endpoint = "planapplic/13/05235/FU@Leeds/geojson"
# call1 = paste(base,endpoint, sep = "")
#
# library(geojsonsf)
#
# planit_ab = geojson_sf(call1)
# head(planit_ab)
#
# planit_ab_proj = st_transform(planit_ab,27700)
# get_planit_data(bbox = NULL, pcode = "LS2 9JT", limit = 2) # data from specific postcode
# library(httr)
# library(jsonlite)
#
# get_ab = GET(call1)
#
# get_ab_text = content(get_ab, "text")
# get_ab_json = fromJSON(get_ab_text, flatten = TRUE)
#
# get_ab_df = as.data.frame(get_ab_json$properties)
# st_as_sf(get_ab_df, coords = c(get_ab_df$lat, get_ab_df$lng))#fails
##########
p1 = get_planit_data(bbox = NULL, query_type = "planapplic", query_value = "13/05235/FU@Leeds", base_url = "https://www.planit.org.uk/")
p2 = get_planit_data(bbox = NULL, query_type = "planapplic", query_value = "15/04151/FU@Leeds", base_url = "https://www.planit.org.uk/")
p3 = get_planit_data(bbox = NULL, query_type = "planapplic", query_value = "15/01973/FU@Leeds", base_url = "https://www.planit.org.uk/")
p4 = get_planit_data(bbox = NULL, query_type = "planapplic", query_value = "15/00415/FU@Leeds", base_url = "https://www.planit.org.uk/")
p4 = p4[,-9]
# Link the sites with the closest centroid --------------------------------
# # this joins the sites with the LSOA they sit within, but I think it would be better to join them with the closest LSOA centroid instead, since it is the centroids that are used as the origin points for the accessibility stats
# reszone_proj = reszone %>% st_transform(27700)
#
# sites = sites %>%
# st_transform(27700)
# sites = st_join(sites,reszone_proj,join = st_within) %>%
# st_transform(4326)
sites = rbind(p1,p2,p3,p4)
sites$place = c("AllertonBywater", "Tyersal", "Micklefield", "LeedsClimateInnovationDistrict")
sites$id = 1:dim(sites)[1]
sites = sites %>%
select(id, place, everything())
# this finds the nearest centroid to each site
c_proj = c %>% st_transform(27700)
sites = sites %>%
st_transform(27700)
sites = st_join(sites,c_proj,join = st_nearest_feature) %>%
st_transform(4326)
# sites$nearest_centroid = sites %>%
# st_transform(27700)
# st_nearest_feature(near,c_proj) %>%
# st_transform(4326)
#find nearest MSOA centroid to each site
m_proj = m %>% st_transform(27700)
m_code = m_proj %>%
select(msoa_code = geo_code)
sites = sites %>%
st_transform(27700)
sites = st_join(sites,m_code,join = st_nearest_feature) %>%
st_transform(4326)
sites = sites %>%
select(id, place, geo_code, msoa_code, everything())
sites$geo_code
sites$place
sites$index_PT
sites$index_Cyc
sites$index_Car
write_sf(sites,"./data/leeds-sites.geojson")
# write_sf(reszone,"./data/wy-zones.geojson")
# piggyback::pb_upload("./data/wy-zones.geojson")
# piggyback::pb_download_url("wy-zones.geojson")
# Maps --------------------------------------------------------------------
sites = read_sf("./data/leeds-sites.geojson")
reszone = read_sf("https://github.com/cyipt/acton/releases/download/0.0.1/wy-zones.geojson")
# tmaptools::palette_explorer()
reszone_leeds = reszone %>%
filter(lad_name == "Leeds")
applications_case_study$site = as.factor(c("Tyersal","Micklefield","Leeds Climate Innovation District","Allerton Bywater"))
tm_shape(applications_case_study) +
tm_dots(col = "place", size = 0.1) +
tm_basemap(server = "CartoDB.Positron")
# map sites and nearest centroids
mapview(reszone) +
mapview(sites) +
mapview(c[c$geo_code %in% sites$geo_code,])
mapview(mzone) +
mapview(sites) +
mapview(m[m$geo_code %in% sites$msoa_code,])
mapview(mzone) +
mapview(sites) +
mapview(m)
#Warning - geometry is not valid on row 359
# reszone[which(st_is_valid(reszone) == FALSE),]
#Accessibility stat examples
tm_shape(reszone) +
tm_polygons(c("index_PT", "index_Cyc")) +
tm_format("reszone")
tmap_mode("view")
tm_shape(reszone_leeds) +
tm_polygons(c("index_PT","index_Cyc","index_Car"),palette="-viridis",title = c("Walk/PT","Cycle","Car")) +
tm_shape(sites) +
tm_dots(size=0.2) +
tm_facets(nrow = 1)
tm_shape(reszone_leeds) +
tm_polygons(c("weightedJobsPTt","weightedJobsCyct","weightedJobsCart"),palette="-cividis",title = c("Walk/PT","Cycle","Car")) +
tm_shape(sites) +
tm_dots(size=0.2) +
tm_facets(nrow = 1)
# tm_shape(reszone) +
# tm_polygons(col = "index_Cyc") +
# tm_shape(sites) +
# tm_dots()
#
# tm_shape(reszone) +
# tm_polygons(col = "index_Car") +
# tm_shape(sites) +
# tm_dots()
tm_shape(reszone_leeds) +
tm_polygons(c("FoodPTt","FoodCyct","FoodCart"),palette="-inferno",title = c("Walk/PT","Cycle","Car")) +
tm_shape(sites) +
tm_dots(size=0.2) +
tm_facets(nrow = 1)
tm_shape(reszone_leeds) +
tm_polygons(c("GPPTt","GPCyct","GPCart"),palette="-magma",title = c("Walk/PT","Cycle","Car")) +
tm_shape(sites) +
tm_dots(size=0.2) +
tm_facets(nrow = 1)
tm_shape(reszone) +
tm_polygons(col = "weightedJobsPTt") +
tm_shape(sites) +
tm_dots()
tm_shape(reszone) +
tm_polygons(col = "weightedJobsCyct") +
tm_shape(sites) +
tm_dots()
tm_shape(reszone) +
tm_polygons(col = "weightedJobsCart") +
tm_shape(sites) +
tm_dots()
# Allerton Bywater Millennium Community polygon ---------------------------
ab = st_sfc(st_polygon(list(cbind(c(441926,441865,442023,442362,442610,442613,442238,442297,442294,442197,442140,441968,441926),c(428055,427794,427620,427630,427579,427690,427906,428008,428094,428085,427954,428061,428055)))))
abc = st_sf(ab,crs = 27700)
abc$n_homes = 562
# additional variables
abc_4326 = st_transform(abc, 4326)
#
lwgeom::st_geod_area(abc_4326)
st_area(abc_4326)
st_area(abc) # why are these not the same?
# abc_buffer = st_buffer(abc, 500)
# abc_buffer2 = abc_4326 %>%
# st_transform(27700) %>%
# st_buffer(500) %>%
# st_transform(4326)
#
# abc_buffer3 = stplanr::geo_projected(abc_4326, st_buffer, dist = 500)
#
# mapview(abc_buffer) + mapview(abc_buffer2) + mapview(abc_buffer3)
mapview(abc) + mapview(reszone)
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