::p_load(tidyverse, tmap, sf, sfdep) pacman
In-Class Exercise 5: Advanced Spatial Point Patterns Analysis: Local Co-Location Quotient
Getting Started
Importing Module
Importing Data
<- st_read(dsn="data",
studyArea layer="study_area") %>%
st_transform(crs = 3829)
Reading layer `study_area' from data source
`C:\Users\la935\Desktop\IS415 - GAA\IS415 - GAA (New)\In-Class_Ex\In-Class_Ex05\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
<- st_read(dsn="data",
stores layer = "stores") %>%
st_transform(crs = 3829)
Reading layer `stores' from data source
`C:\Users\la935\Desktop\IS415 - GAA\IS415 - GAA (New)\In-Class_Ex\In-Class_Ex05\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
Visualising The SF Layers
tmap_mode("view")
tm_shape(studyArea) +
tm_polygons() +
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)
<- include_self(
nb st_knn(st_geometry(stores), 6))
#Use even number to avoid 50/50 results
<- st_kernel_weights(nb,
wt
stores,"gaussian",
adaptive = TRUE)
#Higher the weight, the nearer the point
<- stores %>%
FamilyMart filter(Name == "Family Mart")
<- FamilyMart$Name A
<- stores %>%
SevenEleven filter(Name == "7-Eleven")
<- SevenEleven$Name B
<- local_colocation(A, B, nb, wt, 49)
LCLQ
#Running 50 simulation
<- cbind(stores, LCLQ)
LCLQ_stores
#cbind only works when the table structure is not changed (aka filter)
tmap_mode("view")
tm_shape(studyArea) +
tm_polygons() +
tm_shape(LCLQ_stores) +
tm_dots(col = "X7.Eleven", size=0.01) +
tm_view(set.zoom.limits = c(12,14))