In-Class Exercise 5: Advanced Spatial Point Patterns Analysis

# sfdep => for working with sf
# will be using colocation quotients (CLQs), focusing on local (theres also global and pairwise)
pacman::p_load(sf, tmap, tidyverse, sfdep)

Import Data

studyArea <- st_read(dsn = "data",
                     layer="study_area") %>%
  st_transform(crs = 3829) # national projection system of taiwan

Reading layer `study_area' from data source 
  `/Users/michelle/Desktop/IS415/shelle-mim/IS415-GAA/In-class_Exercise/Wk5/data' 
  using driver `ESRI Shapefile'
Simple feature collection with 7 features and 7 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 121.4836 ymin: 25.00776 xmax: 121.592 ymax: 25.09288
Geodetic CRS:  TWD97

stores <- st_read(dsn = "data",
                     layer="stores") %>%
  st_transform(crs = 3829)

Reading layer `stores' from data source 
  `/Users/michelle/Desktop/IS415/shelle-mim/IS415-GAA/In-class_Exercise/Wk5/data' 
  using driver `ESRI Shapefile'
Simple feature collection with 1409 features and 4 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 121.4902 ymin: 25.01257 xmax: 121.5874 ymax: 25.08557
Geodetic CRS:  TWD97

Visualise the layers

tmap_mode("view")
tm_shape(studyArea) +
  tm_polygons()+ #always plot polygon before line
  tm_shape(stores)+
  tm_dots(col = "Name",
          size = 0.01,
          border.col = "black",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(12, 16))

Local Colocation Quotients (LCLQ)

nb <- include_self(
  st_knn(st_geometry(stores), 6) # search for the 6 nearest neighbour => each list have 6
) # why 6? => so will not have 50-50 (since we include_self, so total 7)

wt <- st_kernel_weights(nb, # calculate weight metrics
                        stores, # target: all stores => convert into a weight metrics
                        "gaussian",
                        adaptive = TRUE) # use adaptive method

FamilyMart <- stores %>%
  filter(Name == "Family Mart")
A <- FamilyMart$Name

SevenEleven <- stores %>%
  filter(Name == "7-Eleven")
B <- SevenEleven$Name

# A: target, B: neighbour that we want to find out is colocated or not
LCLQ <- local_colocation(A, B, nb, wt, 49)

LCLQ_stores <- cbind(stores, LCLQ) # cannot sort lclq, if not will not match with original data

tmap_mode("view")
tm_shape(studyArea)+
  tm_polygons() +
  tm_shape(LCLQ_stores)+
  tm_dots(col="X7.Eleven",
          size = 0.01,
          border.col = "black",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(12, 16))