In class exercise 5

Published

February 6, 2023

Modified

Invalid Date

Install and Load Packages

pacman::p_load(sfdep, sf, tidyverse, tmap)

New package used is sfdep, used mainly for Local Co-Location Quotient

Importing Data

The national projection system of Taiwan is EPSG::3829

studyArea <- st_read(dsn = "data/", 
                     layer = "study_area") %>%
  st_transform(crs = 3829)
Reading layer `study_area' from data source 
  `/Users/junhaoteo/Documents/junhao2309/IS415/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
stores <- st_read(dsn = "data/", 
                  layer = "stores") %>%
  st_transform(crs = 3829)
Reading layer `stores' from data source 
  `/Users/junhaoteo/Documents/junhao2309/IS415/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 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))
+−
Name
7-Eleven
Family Mart
Leaflet | Tiles © Esri — Esri, DeLorme, NAVTEQ
tmap_mode("plot")

local_colocation(A, B, nb, wt, nsim), where - A

Local Colocation Quotients (LCLQ)

nb <- include_self(
  st_knn(st_geometry(stores), 6))
# The number 6 indicates getting the 6 nearest stores (nearest neighbor)

# List of lengths in nb showcases the numbers of the neighbors of a particular point
wt <- st_kernel_weights(nb,
                        stores,
                        "gaussian",
                        adaptive = TRUE)
# List of lengths in wt showcases the weights: 
# The closer, the higher the weight, the further, the lower the weight

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

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

LCLQ <- local_colocation(A, B, nb, wt, 49)

LCLQ_stores <- cbind(stores, LCLQ) %>%
  na.exclude()


# Place stores first as the function takes on the structure of the first input.
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_shape(LCLQ_stores) +
  tm_dots(col = "p_sim_7.Eleven",
          size = 0.01,
          border.col = "black",
          border.lwd = 0.5)
+−
X7.Eleven
0.999
p_sim_7.Eleven
0.02 to 0.04
0.04 to 0.06
0.06 to 0.08
0.08 to 0.10
0.10 to 0.12
Leaflet | Tiles © Esri — Esri, DeLorme, NAVTEQ
  tm_view(set.zoom.limits = c(12,16))
$tm_layout
$tm_layout$set.zoom.limits
[1] 12 16

$tm_layout$style
[1] NA


attr(,"class")
[1] "tm"