In-class Exercise 6: Spatial Weights and Applications

Published

February 13, 2023

Modified

April 3, 2023

1 Goals

Learn how to compute spatial weights

2 Datasets

Hunan county boundary layer: This is a geospatial data set in ESRI shapefile format.
Hunan_2012.csv: This csv file contains selected Hunan’s local development indicators in 2012.

3 R Packages

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

4 Import Data

hunan <- st_read(dsn = "data/geospatial", 
                 layer = "Hunan")

Reading layer `Hunan' from data source 
  `C:\deadline2359\IS415-GAA\In-class_Ex\In-class_Ex06\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 88 features and 7 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 108.7831 ymin: 24.6342 xmax: 114.2544 ymax: 30.12812
Geodetic CRS:  WGS 84

head(hunan, 5)

Simple feature collection with 5 features and 7 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 111.2145 ymin: 28.61762 xmax: 112.3013 ymax: 29.95847
Geodetic CRS:  WGS 84
   NAME_2  ID_3  NAME_3   ENGTYPE_3 Shape_Leng Shape_Area  County
1 Changde 21098 Anxiang      County   1.869074 0.10056190 Anxiang
2 Changde 21100 Hanshou      County   2.360691 0.19978745 Hanshou
3 Changde 21101  Jinshi County City   1.425620 0.05302413  Jinshi
4 Changde 21102      Li      County   3.474325 0.18908121      Li
5 Changde 21103   Linli      County   2.289506 0.11450357   Linli
                        geometry
1 POLYGON ((112.0625 29.75523...
2 POLYGON ((112.2288 29.11684...
3 POLYGON ((111.8927 29.6013,...
4 POLYGON ((111.3731 29.94649...
5 POLYGON ((111.6324 29.76288...

4.2 Hunan’s Local Development Indicators (2012)

Importing
Data

hunan2012 <- read_csv("data/aspatial/Hunan_2012.csv")

head(hunan2012, 5)

# A tibble: 5 × 29
  County  City    avg_w…¹ depos…²   FAI Gov_Rev Gov_Exp    GDP GDPPC   GIO  Loan
  <chr>   <chr>     <dbl>   <dbl> <dbl>   <dbl>   <dbl>  <dbl> <dbl> <dbl> <dbl>
1 Anhua   Yiyang    30544  10967  6832.    457.   2703  13225  14567 9277. 3955.
2 Anren   Chenzh…   28058   4599. 6386.    221.   1455.  4941. 12761 4189. 2555.
3 Anxiang Changde   31935   5517. 3541     244.   1780. 12482  23667 5109. 2807.
4 Baojing Hunan …   30843   2250  1005.    193.   1379.  4088. 14563 3624. 1254.
5 Chaling Zhuzhou   31251   8241. 6508.    620.   1947  11585  20078 9158. 4287.
# … with 18 more variables: NIPCR <dbl>, Bed <dbl>, Emp <dbl>, EmpR <dbl>,
#   EmpRT <dbl>, Pri_Stu <dbl>, Sec_Stu <dbl>, Household <dbl>,
#   Household_R <dbl>, NOIP <dbl>, Pop_R <dbl>, RSCG <dbl>, Pop_T <dbl>,
#   Agri <dbl>, Service <dbl>, Disp_Inc <dbl>, RORP <dbl>, ROREmp <dbl>, and
#   abbreviated variable names ¹avg_wage, ²deposite

4.3 Performing Relational Join

Update the attribute table of hunan’s SpatialPolygonsDataFrame with the attribute fields of hunan2012 dataframe. This is performed by using left_join() of dplyr package.

Code
Data

hunan_GDPPC <- left_join(hunan,hunan2012)%>%
  select(1:4, 7, 15)
colnames(hunan_GDPPC)

[1] "NAME_2"    "ID_3"      "NAME_3"    "ENGTYPE_3" "County"    "GDPPC"    
[7] "geometry"

head(hunan_GDPPC, 5)

Simple feature collection with 5 features and 6 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 111.2145 ymin: 28.61762 xmax: 112.3013 ymax: 29.95847
Geodetic CRS:  WGS 84
   NAME_2  ID_3  NAME_3   ENGTYPE_3  County GDPPC
1 Changde 21098 Anxiang      County Anxiang 23667
2 Changde 21100 Hanshou      County Hanshou 20981
3 Changde 21101  Jinshi County City  Jinshi 34592
4 Changde 21102      Li      County      Li 24473
5 Changde 21103   Linli      County   Linli 25554
                        geometry
1 POLYGON ((112.0625 29.75523...
2 POLYGON ((112.2288 29.11684...
3 POLYGON ((111.8927 29.6013,...
4 POLYGON ((111.3731 29.94649...
5 POLYGON ((111.6324 29.76288...

Warning

In order to retain the geospatial properties, the left dataframe must be a sf dataframe (i.e., hunan in our case)

In addition, left_join() does not allow joining columns where their names are of different cases.

5 Visualising Regional Development Indicator

Now, we will create a choropleth map showing the distribution of GDPPC 2012.

Using tmap
Using qtm()

tm_shape(hunan_GDPPC) +
  tm_fill("GDPPC",
          style = "quantile",
          palette = "Blues",
          title = "GDP Per Capita") +
  tm_layout(main.title = "Distribution of GDP Per Capita by distribution",
            main.title.position = "center",
            main.title.size = 1.2,
            legend.height = 0.45,
            legend.width = 0.35,
            frame = TRUE) +
  tm_borders(alpha = 0.5) +
  tm_compass(type = "8star", size = 2) +
  tm_scale_bar() +
  tm_grid(alpha = 0.2)

basemap <- tm_shape(hunan_GDPPC) +
  tm_polygons() +
  tm_text("NAME_3", size=0.5) # add text labels

gdppc <- qtm(hunan_GDPPC, "GDPPC")
tmap_arrange(basemap, gdppc, asp=1, ncol=2)

6 Computing Contiguity Spatial Weights

st_contiguity() is used to derive a contiguity neighoubour list. st_contiguity() wraps poly2nb() in spdep.

It will create a new column “nb” (i.e., neighbours). Each row will contain a list for areas adjacent to a region.

st_weights() then using nb to create a list of weights.

Queen
Rook

wm_queen <- hunan_GDPPC %>% 
  mutate(nb = st_contiguity(geometry), 
         wt = st_weights(nb),
         .before = 1) # put it as the first column
head(wm_queen, 5)

Simple feature collection with 5 features and 8 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 111.2145 ymin: 28.61762 xmax: 112.3013 ymax: 29.95847
Geodetic CRS:  WGS 84
                 nb                      wt  NAME_2  ID_3  NAME_3   ENGTYPE_3
1   2, 3, 4, 57, 85 0.2, 0.2, 0.2, 0.2, 0.2 Changde 21098 Anxiang      County
2 1, 57, 58, 78, 85 0.2, 0.2, 0.2, 0.2, 0.2 Changde 21100 Hanshou      County
3       1, 4, 5, 85  0.25, 0.25, 0.25, 0.25 Changde 21101  Jinshi County City
4        1, 3, 5, 6  0.25, 0.25, 0.25, 0.25 Changde 21102      Li      County
5       3, 4, 6, 85  0.25, 0.25, 0.25, 0.25 Changde 21103   Linli      County
   County GDPPC                       geometry
1 Anxiang 23667 POLYGON ((112.0625 29.75523...
2 Hanshou 20981 POLYGON ((112.2288 29.11684...
3  Jinshi 34592 POLYGON ((111.8927 29.6013,...
4      Li 24473 POLYGON ((111.3731 29.94649...
5   Linli 25554 POLYGON ((111.6324 29.76288...

Include the parameter queen = FALSE to change to rook’s method.

cn_rook <- hunan_GDPPC %>% 
  mutate(nb = st_contiguity(geometry), 
         wt = st_weights(nb),
         queen = FALSE,
         .before = 1)
head(cn_rook, 5)

Simple feature collection with 5 features and 9 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 111.2145 ymin: 28.61762 xmax: 112.3013 ymax: 29.95847
Geodetic CRS:  WGS 84
                 nb                      wt queen  NAME_2  ID_3  NAME_3
1   2, 3, 4, 57, 85 0.2, 0.2, 0.2, 0.2, 0.2 FALSE Changde 21098 Anxiang
2 1, 57, 58, 78, 85 0.2, 0.2, 0.2, 0.2, 0.2 FALSE Changde 21100 Hanshou
3       1, 4, 5, 85  0.25, 0.25, 0.25, 0.25 FALSE Changde 21101  Jinshi
4        1, 3, 5, 6  0.25, 0.25, 0.25, 0.25 FALSE Changde 21102      Li
5       3, 4, 6, 85  0.25, 0.25, 0.25, 0.25 FALSE Changde 21103   Linli
    ENGTYPE_3  County GDPPC                       geometry
1      County Anxiang 23667 POLYGON ((112.0625 29.75523...
2      County Hanshou 20981 POLYGON ((112.2288 29.11684...
3 County City  Jinshi 34592 POLYGON ((111.8927 29.6013,...
4      County      Li 24473 POLYGON ((111.3731 29.94649...
5      County   Linli 25554 POLYGON ((111.6324 29.76288...