5. Map Accessibility
Two ways to map the estimates:
5.1 Choropleth maps
Each point is the centroid of a fine-resolution H3 hexagon, so we retrieve the hexagon polygons and join the accessibility estimates to them.
# retrieve polygons of H3 spatial grid
grid <- h3jsr::cell_to_polygon(points$id, simple = FALSE)
# merge accessibility estimates
access_sf <- left_join(grid, access1, by = c('h3_address'='id'))
# plot
ggplot() +
geom_sf(data = access_sf, aes(fill = accessibility), color= NA) +
scale_fill_viridis_c(direction = -1, option = 'B') +
labs(fill = "Number of\nfacilities within\n20 minutes") +
theme_minimal() +
theme(axis.title = element_blank()) +
facet_wrap(~opportunity) +
theme_void()
5.2 Spatial interpolation
Alternatively, interpolate the point estimates to get a smoother surface:
# interpolate estimates to get spatially smooth result
access_schools <- access1 %>%
filter(opportunity == "schools") %>%
inner_join(points, by='id') %>%
with(interp::interp(lon, lat, accessibility)) %>%
with(cbind(acc=as.vector(z), # Column-major order
x=rep(x, times=length(y)),
y=rep(y, each=length(x)))) %>% as.data.frame() %>% na.omit() %>%
mutate(opportunity = "schools")
access_health <- access1 %>%
filter(opportunity == "healthcare") %>%
inner_join(points, by='id') %>%
with(interp::interp(lon, lat, accessibility)) %>%
with(cbind(acc=as.vector(z), # Column-major order
x=rep(x, times=length(y)),
y=rep(y, each=length(x)))) %>% as.data.frame() %>% na.omit() %>%
mutate(opportunity = "healthcare")
access.interp <- rbind(access_schools, access_health)
# find results' bounding box to crop the map
bb_x <- c(min(access.interp$x), max(access.interp$x))
bb_y <- c(min(access.interp$y), max(access.interp$y))
# extract OSM network, to plot over map
street_net <- street_network_to_sf(r5r_network)
# plot
ggplot(na.omit(access.interp)) +
geom_sf(data = street_net$edges, color = "gray55", size=0.01, alpha = 0.7) +
geom_contour_filled(aes(x=x, y=y, z=acc), alpha=.7) +
scale_fill_viridis_d(direction = -1, option = 'B') +
scale_x_continuous(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
coord_sf(xlim = bb_x, ylim = bb_y, datum = NA) +
labs(fill = "Number of\nfacilities within\n20 minutes") +
theme_void() +
facet_wrap(~opportunity)
Cleaning up after usage
Stop the network and run Java’s garbage collector to free the memory it used:
If you have any suggestions or want to report an error, please visit the package GitHub page.
References
Levinson, David, and et al. 2020.
Transport Access Manual: A Guide for Measuring Connection Between
People and Places. January 1. https://hdl.handle.net/2123/23733.