In Class Exercise 5

Getting Started

Importing Module

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

Importing Data

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

Reading layer `study_area' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-GAA\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

Note

CRS 3829 = Taiwan’s projection map

Can refer to EPSG.io

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

Reading layer `stores' from data source 
  `C:\Users\Daniel\Desktop\Github\IS419\IS415-GAA\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

Visualizing the SF layer

Note

The sequence of the layout matters if overlapping

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)

Note

Taking only 6 neighbors. Include self will make it 7 so that there will never be a 50-50 change

nb = include_self(
  st_knn(st_geometry(stores), 6))

Note

Converting into distance weight matrix

Nearer target, higher weight, lower = lesser

wt <- st_kernel_weights(nb,
                        stores,
                        "gaussian",
                        adaptive = TRUE)

Note

Making it a vector

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

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

Note

A = Interest

B = neighbour to check colocation

nb = location (circle to search)

wt = weight

49 = simulation (will convert to P value after that)

NA = No results, not near each other

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

Note

Remember not to sort. Sequence is important

LCLQ_stores <- cbind(stores, LCLQ)

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(12,18))