7093_HW6

Create point layer and project into CRS 2278

Mapshot

OpenStreetMap - dots

Leaflet - markers

Mean Center of the points

mean_feature <- apply(st_coordinates(results.proj), MARGIN = 2, FUN = mean)

mean_feature <- data.frame(place = "meanfeature", x = mean_feature[1], y = mean_feature[2])

mean_feature <- st_as_sf(mean_feature, coords = c("x", "y"), crs = 2278)

tmap_mode("view")

tm_basemap("OpenStreetMap.Mapnik")+
  tm_shape(results.proj)+
  tm_dots(col = "green", size = .1)+
  tm_shape(mean_feature)+
  tm_dots(col = "red", size = .5)

Convex Hull of the points

chull <- st_convex_hull(st_union(results.proj))

tmap_mode("view")

tm_basemap("OpenStreetMap.Mapnik")+
  tm_shape(results.proj)+
  tm_dots(col = "green")+
  tm_shape(chull)+
  tm_polygons(col = "brown", alpha = .25)

Spatial Join of the points - bars per 1000 people

library(tidycensus)

sa_acs <- get_acs(geography = "tract",
                  state = "TX",
                  county = "Bexar", 
                  year = 2019,
                  variables = c("DP05_0001E"),
                  geometry = T, 
                  output = "wide")

sa_acs2 <- sa_acs %>%
  mutate(totpop = DP05_0001E) %>%
    dplyr::select(totpop)

sa_acs2 <- st_transform(sa_acs2, crs = 2278)

# sa_trol <- st_cast(sa_acs2, "MULTILINESTRING")

spjoin <- st_join(results.proj, sa_acs2)

sa_acs2$nbars <- lengths(st_intersects(sa_acs2, results.proj))

sa_acs2$bars_per_capita <- 1000*(sa_acs2$nbars / sa_acs2$totpop)

tmap_mode("plot")

nbarspc_map <- tm_basemap("OpenStreetMap.Mapnik")+
  tm_shape(sa_acs2)+
  tm_polygons("bars_per_capita")+
  tm_shape(spjoin, is.master = T)+
  tm_dots(size = .2)

nbarspc_map

Nearest Neighbor Analysis of the bar layer

library(spatstat)

bars.pp <- as.ppp(as(results.proj, "Spatial"))

plot(nearest.neighbour(bars.pp))

options(qgisprocess.path = "C://Program Files//QGIS 3.16.16//bin//qgis_process-qgis-ltr.bat")

library(qgisprocess)

qgis_configure()

bars_nn <- qgis_run_algorithm(alg="native:nearestneighbouranalysis",
                              INPUT=results.proj,
                              OUTPUT_HTML_FILE=file.path(tempdir(), "barsnn.html"),
                              load_output = TRUE)

bars_nn

## <Result of `qgis_run_algorithm("native:nearestneighbouranalysis", ...)`>
## List of 6
##  $ EXPECTED_MD     : num 1885
##  $ NN_INDEX        : num 0.963
##  $ OBSERVED_MD     : num 1815
##  $ OUTPUT_HTML_FILE: 'qgis_outputHtml' chr "C:\\Users\\r_lab\\AppData\\Local\\Temp\\RtmpeYV0Qz/barsnn.html"
##  $ POINT_COUNT     : num 25
##  $ Z_SCORE         : num -0.352

Numeric summary of the analysis: The nearest neighbor index is less than 1 (0.963), indicating the pattern is clustered.

However, the graph generally indicates dispersion at the shorter distances (observed distances greater than expected distances) and then changes to clustering at the higher distances (observed distances less than expected distances).