Hands-on Exercise 4: Spatial Weights and Applications

Author

Shubham Sinha

Published

September 14, 2024

Modified

September 20, 2025

1 Overview

In this hands-on exercise, we will learn how to compute spatial weights using R. We will learn to compute spatial weights, visualize spatial distributions, and create spatially lagged variables using various functions from R packages such as sf,spdep, and tmap

2 The Data

We will use following geospatial datasets in this exercise:

Dataset Description Format
Hunan Hunan county boundary layer geospatial data ESRI shapefile
Hunan_2012 Contains selected Hunan’s local development indicators in 2012 CSV file

3 Installing and launching the R packages

We will use following packages in this exercise:

Package Description
sf Provides functions to manage, processing, and manipulate Simple Features, a formal geospatial data standard that specifies a storage and access model of spatial geometries such as points, lines, and polygons.
spdep Provides collection of functions to create spatial weights matrix objects from polygon ‘contiguities’, from point patterns by distance and tessellations.
tidyverse Provides collection of functions for performing data science task such as importing, tidying, wrangling data and visualising data.
tmap Provides functions for plotting cartographic quality static point patterns maps or interactive maps by using leaflet API
knitr Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.

To install and launch the four R packages.

pacman::p_load(sf, spdep, tmap, tidyverse, knitr)

4 Data Import and Preparation

We use st_read() of sf package to import Hunan shapefile into R. The imported shapefile will be simple features Object of sf.

hunan <- st_read(dsn = "data/geospatial", 
                 layer = "Hunan")
Reading layer `Hunan' from data source 
  `D:\ssinha8752\ISSS608-VAA\Hands-on_Ex\Hands-on_Ex04\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
Rows: 88
Columns: 8
$ NAME_2     <chr> "Changde", "Changde", "Changde", "Changde", "Changde", "Cha…
$ ID_3       <int> 21098, 21100, 21101, 21102, 21103, 21104, 21109, 21110, 211…
$ NAME_3     <chr> "Anxiang", "Hanshou", "Jinshi", "Li", "Linli", "Shimen", "L…
$ ENGTYPE_3  <chr> "County", "County", "County City", "County", "County", "Cou…
$ Shape_Leng <dbl> 1.869074, 2.360691, 1.425620, 3.474325, 2.289506, 4.171918,…
$ Shape_Area <dbl> 0.10056190, 0.19978745, 0.05302413, 0.18908121, 0.11450357,…
$ County     <chr> "Anxiang", "Hanshou", "Jinshi", "Li", "Linli", "Shimen", "L…
$ geometry   <POLYGON [°]> POLYGON ((112.0625 29.75523..., POLYGON ((112.2288 …

Then we will import Hunan_2012.csv into R by using read_csv() of readr package. The output is R dataframe class.

hunan2012 <- read_csv("data/aspatial/Hunan_2012.csv")
Rows: 88 Columns: 29
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): County, City
dbl (27): avg_wage, deposite, FAI, Gov_Rev, Gov_Exp, GDP, GDPPC, GIO, Loan, ...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 88
Columns: 29
$ County      <chr> "Anhua", "Anren", "Anxiang", "Baojing", "Chaling", "Changn…
$ City        <chr> "Yiyang", "Chenzhou", "Changde", "Hunan West", "Zhuzhou", …
$ avg_wage    <dbl> 30544, 28058, 31935, 30843, 31251, 28518, 54540, 28597, 33…
$ deposite    <dbl> 10967.0, 4598.9, 5517.2, 2250.0, 8241.4, 10860.0, 24332.0,…
$ FAI         <dbl> 6831.7, 6386.1, 3541.0, 1005.4, 6508.4, 7920.0, 33624.0, 1…
$ Gov_Rev     <dbl> 456.72, 220.57, 243.64, 192.59, 620.19, 769.86, 5350.00, 1…
$ Gov_Exp     <dbl> 2703.0, 1454.7, 1779.5, 1379.1, 1947.0, 2631.6, 7885.5, 11…
$ GDP         <dbl> 13225.0, 4941.2, 12482.0, 4087.9, 11585.0, 19886.0, 88009.…
$ GDPPC       <dbl> 14567, 12761, 23667, 14563, 20078, 24418, 88656, 10132, 17…
$ GIO         <dbl> 9276.90, 4189.20, 5108.90, 3623.50, 9157.70, 37392.00, 513…
$ Loan        <dbl> 3954.90, 2555.30, 2806.90, 1253.70, 4287.40, 4242.80, 4053…
$ NIPCR       <dbl> 3528.3, 3271.8, 7693.7, 4191.3, 3887.7, 9528.0, 17070.0, 3…
$ Bed         <dbl> 2718, 970, 1931, 927, 1449, 3605, 3310, 582, 2170, 2179, 1…
$ Emp         <dbl> 494.310, 290.820, 336.390, 195.170, 330.290, 548.610, 670.…
$ EmpR        <dbl> 441.4, 255.4, 270.5, 145.6, 299.0, 415.1, 452.0, 127.6, 21…
$ EmpRT       <dbl> 338.0, 99.4, 205.9, 116.4, 154.0, 273.7, 219.4, 94.4, 174.…
$ Pri_Stu     <dbl> 54.175, 33.171, 19.584, 19.249, 33.906, 81.831, 59.151, 18…
$ Sec_Stu     <dbl> 32.830, 17.505, 17.819, 11.831, 20.548, 44.485, 39.685, 7.…
$ Household   <dbl> 290.4, 104.6, 148.1, 73.2, 148.7, 211.2, 300.3, 76.1, 139.…
$ Household_R <dbl> 234.5, 121.9, 135.4, 69.9, 139.4, 211.7, 248.4, 59.6, 110.…
$ NOIP        <dbl> 101, 34, 53, 18, 106, 115, 214, 17, 55, 70, 44, 84, 74, 17…
$ Pop_R       <dbl> 670.3, 243.2, 346.0, 184.1, 301.6, 448.2, 475.1, 189.6, 31…
$ RSCG        <dbl> 5760.60, 2386.40, 3957.90, 768.04, 4009.50, 5220.40, 22604…
$ Pop_T       <dbl> 910.8, 388.7, 528.3, 281.3, 578.4, 816.3, 998.6, 256.7, 45…
$ Agri        <dbl> 4942.253, 2357.764, 4524.410, 1118.561, 3793.550, 6430.782…
$ Service     <dbl> 5414.5, 3814.1, 14100.0, 541.8, 5444.0, 13074.6, 17726.6, …
$ Disp_Inc    <dbl> 12373, 16072, 16610, 13455, 20461, 20868, 183252, 12379, 1…
$ RORP        <dbl> 0.7359464, 0.6256753, 0.6549309, 0.6544614, 0.5214385, 0.5…
$ ROREmp      <dbl> 0.8929619, 0.8782065, 0.8041262, 0.7460163, 0.9052651, 0.7…

We will also 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.

colnames(hunan)
[1] "NAME_2"     "ID_3"       "NAME_3"     "ENGTYPE_3"  "Shape_Leng"
[6] "Shape_Area" "County"     "geometry"  
colnames(hunan2012)
 [1] "County"      "City"        "avg_wage"    "deposite"    "FAI"        
 [6] "Gov_Rev"     "Gov_Exp"     "GDP"         "GDPPC"       "GIO"        
[11] "Loan"        "NIPCR"       "Bed"         "Emp"         "EmpR"       
[16] "EmpRT"       "Pri_Stu"     "Sec_Stu"     "Household"   "Household_R"
[21] "NOIP"        "Pop_R"       "RSCG"        "Pop_T"       "Agri"       
[26] "Service"     "Disp_Inc"    "RORP"        "ROREmp"     
hunan <- left_join(hunan,hunan2012) %>%
  select(1:4, 7, 15)
Joining with `by = join_by(County)`
Rows: 88
Columns: 7
$ NAME_2    <chr> "Changde", "Changde", "Changde", "Changde", "Changde", "Chan…
$ ID_3      <int> 21098, 21100, 21101, 21102, 21103, 21104, 21109, 21110, 2111…
$ NAME_3    <chr> "Anxiang", "Hanshou", "Jinshi", "Li", "Linli", "Shimen", "Li…
$ ENGTYPE_3 <chr> "County", "County", "County City", "County", "County", "Coun…
$ County    <chr> "Anxiang", "Hanshou", "Jinshi", "Li", "Linli", "Shimen", "Li…
$ GDPPC     <dbl> 23667, 20981, 34592, 24473, 25554, 27137, 63118, 62202, 7066…
$ geometry  <POLYGON [°]> POLYGON ((112.0625 29.75523..., POLYGON ((112.2288 2…

5 Visualising Regional Development Indicator

We will prepare a basemap and a choropleth map showing the distribution of GDPPC 2012 by using qtm() of tmap package.

basemap <- tm_shape(hunan) +
  tm_polygons() +
  tm_text("NAME_3", size=0.25)

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

6 Computing Contiguity Spatial Weights

6.1 Computing contiguity using poly2nb()

poly2nb

We will explore how to use the poly2nb() function from the spdep package to compute contiguity weight matrices for the study area. This function generates a neighbors list based on regions with shared boundaries.

Key Parameters

  • queen:
    Determines the contiguity criteria:

    • TRUE (default): Implements the Queen contiguity, where regions are considered neighbors if they share either a border or a corner.
    • FALSE: Implements the Rook contiguity, where regions are neighbors only if they share a border.

Note: If we do not specify the queen argument, it defaults to TRUE, meaning the function will return a list of first-order neighbors using the Queen contiguity rule.

wm_q <- poly2nb(hunan, queen=TRUE)
summary(wm_q)
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 448 
Percentage nonzero weights: 5.785124 
Average number of links: 5.090909 
Link number distribution:

 1  2  3  4  5  6  7  8  9 11 
 2  2 12 16 24 14 11  4  2  1 
2 least connected regions:
30 65 with 1 link
1 most connected region:
85 with 11 links

We found out that:

  • There are 88 area units in Hunan.
  • The most connected area unit has 11 neighbours.
  • There are 2 area units with only one neighbours.
wm_r <- poly2nb(hunan, queen=FALSE)
summary(wm_r)
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 440 
Percentage nonzero weights: 5.681818 
Average number of links: 5 
Link number distribution:

 1  2  3  4  5  6  7  8  9 10 
 2  2 12 20 21 14 11  3  2  1 
2 least connected regions:
30 65 with 1 link
1 most connected region:
85 with 10 links

We found out that:

  • There are 88 area units in Hunan.
  • The most connected area unit has 10 neighbours.
  • There are 2 area units with only one neighbours.

6.1.1 Display neighbour(s) of a county

For each polygon in our polygon object, wm_q lists all neighboring polygons. For example, to see the neighbors for the first polygon in the object, we can type:

wm_q[[1]]
[1]  2  3  4 57 85

We see that Polygon 1 has 5 neighbors. The numbers represent the polygon IDs as stored in hunan SpatialPolygonsDataFrame class.

6.1.2 Retrieve County Name by polygon ID

We can retrive the county name of Polygon ID=1 by using following code:

hunan$County[1]
[1] "Anxiang"

The output reveals that Polygon ID=1 is Anxiang county.

To reveal the county names of the five neighboring polygons, we can use following code:

hunan$NAME_3[c(2,3,4,57,85)]
[1] "Hanshou" "Jinshi"  "Li"      "Nan"     "Taoyuan"

6.1.3 Retrieve GDPPC

We can retrieve the GDPPC of these five countries by using following code:

nb1 <- wm_q[[1]]
nb1 <- hunan$GDPPC[nb1]
nb1
[1] 20981 34592 24473 21311 22879

6.1.4 Display complete weight matrix using str()

str(wm_q)
List of 88
 $ : int [1:5] 2 3 4 57 85
 $ : int [1:5] 1 57 58 78 85
 $ : int [1:4] 1 4 5 85
 $ : int [1:4] 1 3 5 6
 $ : int [1:4] 3 4 6 85
 $ : int [1:5] 4 5 69 75 85
 $ : int [1:4] 67 71 74 84
 $ : int [1:7] 9 46 47 56 78 80 86
 $ : int [1:6] 8 66 68 78 84 86
 $ : int [1:8] 16 17 19 20 22 70 72 73
 $ : int [1:3] 14 17 72
 $ : int [1:5] 13 60 61 63 83
 $ : int [1:4] 12 15 60 83
 $ : int [1:3] 11 15 17
 $ : int [1:4] 13 14 17 83
 $ : int [1:5] 10 17 22 72 83
 $ : int [1:7] 10 11 14 15 16 72 83
 $ : int [1:5] 20 22 23 77 83
 $ : int [1:6] 10 20 21 73 74 86
 $ : int [1:7] 10 18 19 21 22 23 82
 $ : int [1:5] 19 20 35 82 86
 $ : int [1:5] 10 16 18 20 83
 $ : int [1:7] 18 20 38 41 77 79 82
 $ : int [1:5] 25 28 31 32 54
 $ : int [1:5] 24 28 31 33 81
 $ : int [1:4] 27 33 42 81
 $ : int [1:3] 26 29 42
 $ : int [1:5] 24 25 33 49 54
 $ : int [1:3] 27 37 42
 $ : int 33
 $ : int [1:8] 24 25 32 36 39 40 56 81
 $ : int [1:8] 24 31 50 54 55 56 75 85
 $ : int [1:5] 25 26 28 30 81
 $ : int [1:3] 36 45 80
 $ : int [1:6] 21 41 47 80 82 86
 $ : int [1:6] 31 34 40 45 56 80
 $ : int [1:4] 29 42 43 44
 $ : int [1:4] 23 44 77 79
 $ : int [1:5] 31 40 42 43 81
 $ : int [1:6] 31 36 39 43 45 79
 $ : int [1:6] 23 35 45 79 80 82
 $ : int [1:7] 26 27 29 37 39 43 81
 $ : int [1:6] 37 39 40 42 44 79
 $ : int [1:4] 37 38 43 79
 $ : int [1:6] 34 36 40 41 79 80
 $ : int [1:3] 8 47 86
 $ : int [1:5] 8 35 46 80 86
 $ : int [1:5] 50 51 52 53 55
 $ : int [1:4] 28 51 52 54
 $ : int [1:5] 32 48 52 54 55
 $ : int [1:3] 48 49 52
 $ : int [1:5] 48 49 50 51 54
 $ : int [1:3] 48 55 75
 $ : int [1:6] 24 28 32 49 50 52
 $ : int [1:5] 32 48 50 53 75
 $ : int [1:7] 8 31 32 36 78 80 85
 $ : int [1:6] 1 2 58 64 76 85
 $ : int [1:5] 2 57 68 76 78
 $ : int [1:4] 60 61 87 88
 $ : int [1:4] 12 13 59 61
 $ : int [1:7] 12 59 60 62 63 77 87
 $ : int [1:3] 61 77 87
 $ : int [1:4] 12 61 77 83
 $ : int [1:2] 57 76
 $ : int 76
 $ : int [1:5] 9 67 68 76 84
 $ : int [1:4] 7 66 76 84
 $ : int [1:5] 9 58 66 76 78
 $ : int [1:3] 6 75 85
 $ : int [1:3] 10 72 73
 $ : int [1:3] 7 73 74
 $ : int [1:5] 10 11 16 17 70
 $ : int [1:5] 10 19 70 71 74
 $ : int [1:6] 7 19 71 73 84 86
 $ : int [1:6] 6 32 53 55 69 85
 $ : int [1:7] 57 58 64 65 66 67 68
 $ : int [1:7] 18 23 38 61 62 63 83
 $ : int [1:7] 2 8 9 56 58 68 85
 $ : int [1:7] 23 38 40 41 43 44 45
 $ : int [1:8] 8 34 35 36 41 45 47 56
 $ : int [1:6] 25 26 31 33 39 42
 $ : int [1:5] 20 21 23 35 41
 $ : int [1:9] 12 13 15 16 17 18 22 63 77
 $ : int [1:6] 7 9 66 67 74 86
 $ : int [1:11] 1 2 3 5 6 32 56 57 69 75 ...
 $ : int [1:9] 8 9 19 21 35 46 47 74 84
 $ : int [1:4] 59 61 62 88
 $ : int [1:2] 59 87
 - attr(*, "class")= chr "nb"
 - attr(*, "region.id")= chr [1:88] "1" "2" "3" "4" ...
 - attr(*, "call")= language poly2nb(pl = hunan, queen = TRUE)
 - attr(*, "type")= chr "queen"
 - attr(*, "snap")= num 9e-08
 - attr(*, "sym")= logi TRUE
 - attr(*, "ncomp")=List of 2
  ..$ nc     : int 1
  ..$ comp.id: int [1:88] 1 1 1 1 1 1 1 1 1 1 ...

6.2 Visualising contiguity weights

A connectivity graph connects each point to its neighboring points with lines. Since we are working with polygons, we need to derive points to create these graphs. The most common approach is to use polygon centroids.

  • Calculate these in the sf package before moving onto the graphs.
  • Get Latitude and Longitude of Polygon Centroids.
  • It is a little more complicated than just running st_centroid on the sfobject, because we need to extract the latitude and longitude coordinates into a separate data frame.
  • We also need map_dbl() function as a mapping function from the purrr package. This function applies a given function to each element of a vector and returns a vector of the same length. The argument will be:
    • Input Vector: The geometry column.
    • Function: st_centroid()

This process will give us the latitude and longitude of the centroids in a format we can use for further analysis.

For more details: Refer to the purrr map documentation.

6.2.1 Get Latitude and Longitude of Polygon Centroids

To get our longitude values we map the st_centroid function over the geometry column of us.bound and access the longitude value through double bracket notation [[]] and 1. This allows us to get only the longitude, which is the first value in each centroid.

longitude <- map_dbl(hunan$geometry, ~st_centroid(.x)[[1]])

We do the same for latitude with one key difference. We access the second value per each centroid.

latitude <- map_dbl(hunan$geometry, ~st_centroid(.x)[[2]])

Now that we have latitude and longitude, we use cbind() to put longitude and latitude into the same object.

coords <- cbind(longitude, latitude)

Let’s verify the first few observations to see if coords are formatted correctly.

head(coords)
     longitude latitude
[1,]  112.1531 29.44362
[2,]  112.0372 28.86489
[3,]  111.8917 29.47107
[4,]  111.7031 29.74499
[5,]  111.6138 29.49258
[6,]  111.0341 29.79863

6.2.2 Plotting contiguity-based neighbours map

par(mfrow=c(1,2))
plot(hunan$geometry, border="lightgrey", main="Queen Contiguity")
plot(wm_q, coords, pch = 19, cex = 0.6, add = TRUE, col= "red")
plot(hunan$geometry, border="lightgrey", main="Rook Contiguity")
plot(wm_r, coords, pch = 19, cex = 0.6, add = TRUE, col = "red")

7 Computing distance based neighbours

We will learn how to derive distance-based weight matrices by using dnearneigh() of spdep package.

The function identifies neighbours of region points by Euclidean distance with a distance band with lower d1= and upper d2= bounds controlled by the bounds= argument.

If unprojected coordinates are used and either specified in the coordinates object x or with x as a two column matrix and longlat=TRUE, great circle distances in km will be calculated assuming the WGS84 reference ellipsoid.

7.1 Determine the cut-off distance

We need to determine the upper limit for distance band by using following steps:

Step Description Function
1 Return a matrix with the indices of points belonging to the set of the k-nearest neighbours (knn) of each other. knearneigh() of spdep
2 Convert the knn object into a neighbours list of class nb with a list of integer vectors containing neighbour region number ids. knn2nb() of spdep
3 Return the length of neighbour relationship edges. nbdists() of spdep
4 Remove the list structure of the returned object. unlist() 
#coords <- coordinates(hunan)
k1 <- knn2nb(knearneigh(coords))
Warning in knn2nb(knearneigh(coords)): neighbour object has 25 sub-graphs
k1dists <- unlist(nbdists(k1, coords, longlat = TRUE))
summary(k1dists)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  24.79   32.57   38.01   39.07   44.52   61.79

The summary report shows that the largest first nearest neighbour distance is 61.79 km, so using this as the upper threshold gives certainty that all units will have at least one neighbour.

7.2 Computing fixed distance weight matrix

We will compute the distance weight matrix by using dnearneigh() function

wm_d62 <- dnearneigh(coords, 0, 62, longlat = TRUE)
wm_d62
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 324 
Percentage nonzero weights: 4.183884 
Average number of links: 3.681818

The “Average number of links: 3.681818” means that, on average, each region is connected to approximately 3.68 neighboring regions based on the specified distance criteria.

We’ll display the content of wm_d62 weight matrix.

str(wm_d62)
List of 88
 $ : int [1:5] 3 4 5 57 64
 $ : int [1:4] 57 58 78 85
 $ : int [1:4] 1 4 5 57
 $ : int [1:3] 1 3 5
 $ : int [1:4] 1 3 4 85
 $ : int 69
 $ : int [1:2] 67 84
 $ : int [1:4] 9 46 47 78
 $ : int [1:4] 8 46 68 84
 $ : int [1:4] 16 22 70 72
 $ : int [1:3] 14 17 72
 $ : int [1:5] 13 60 61 63 83
 $ : int [1:4] 12 15 60 83
 $ : int [1:2] 11 17
 $ : int 13
 $ : int [1:4] 10 17 22 83
 $ : int [1:3] 11 14 16
 $ : int [1:3] 20 22 63
 $ : int [1:5] 20 21 73 74 82
 $ : int [1:5] 18 19 21 22 82
 $ : int [1:6] 19 20 35 74 82 86
 $ : int [1:4] 10 16 18 20
 $ : int [1:3] 41 77 82
 $ : int [1:4] 25 28 31 54
 $ : int [1:4] 24 28 33 81
 $ : int [1:4] 27 33 42 81
 $ : int [1:2] 26 29
 $ : int [1:6] 24 25 33 49 52 54
 $ : int [1:2] 27 37
 $ : int 33
 $ : int [1:2] 24 36
 $ : int 50
 $ : int [1:5] 25 26 28 30 81
 $ : int [1:3] 36 45 80
 $ : int [1:6] 21 41 46 47 80 82
 $ : int [1:5] 31 34 45 56 80
 $ : int [1:2] 29 42
 $ : int [1:3] 44 77 79
 $ : int [1:4] 40 42 43 81
 $ : int [1:3] 39 45 79
 $ : int [1:5] 23 35 45 79 82
 $ : int [1:5] 26 37 39 43 81
 $ : int [1:3] 39 42 44
 $ : int [1:2] 38 43
 $ : int [1:6] 34 36 40 41 79 80
 $ : int [1:5] 8 9 35 47 86
 $ : int [1:5] 8 35 46 80 86
 $ : int [1:5] 50 51 52 53 55
 $ : int [1:4] 28 51 52 54
 $ : int [1:6] 32 48 51 52 54 55
 $ : int [1:4] 48 49 50 52
 $ : int [1:6] 28 48 49 50 51 54
 $ : int [1:2] 48 55
 $ : int [1:5] 24 28 49 50 52
 $ : int [1:4] 48 50 53 75
 $ : int 36
 $ : int [1:5] 1 2 3 58 64
 $ : int [1:5] 2 57 64 66 68
 $ : int [1:3] 60 87 88
 $ : int [1:4] 12 13 59 61
 $ : int [1:5] 12 60 62 63 87
 $ : int [1:4] 61 63 77 87
 $ : int [1:5] 12 18 61 62 83
 $ : int [1:4] 1 57 58 76
 $ : int 76
 $ : int [1:5] 58 67 68 76 84
 $ : int [1:2] 7 66
 $ : int [1:4] 9 58 66 84
 $ : int [1:2] 6 75
 $ : int [1:3] 10 72 73
 $ : int [1:2] 73 74
 $ : int [1:3] 10 11 70
 $ : int [1:4] 19 70 71 74
 $ : int [1:5] 19 21 71 73 86
 $ : int [1:2] 55 69
 $ : int [1:3] 64 65 66
 $ : int [1:3] 23 38 62
 $ : int [1:2] 2 8
 $ : int [1:4] 38 40 41 45
 $ : int [1:5] 34 35 36 45 47
 $ : int [1:5] 25 26 33 39 42
 $ : int [1:6] 19 20 21 23 35 41
 $ : int [1:4] 12 13 16 63
 $ : int [1:4] 7 9 66 68
 $ : int [1:2] 2 5
 $ : int [1:4] 21 46 47 74
 $ : int [1:4] 59 61 62 88
 $ : int [1:2] 59 87
 - attr(*, "class")= chr "nb"
 - attr(*, "region.id")= chr [1:88] "1" "2" "3" "4" ...
 - attr(*, "call")= language dnearneigh(x = coords, d1 = 0, d2 = 62, longlat = TRUE)
 - attr(*, "dnn")= num [1:2] 0 62
 - attr(*, "bounds")= chr [1:2] "GE" "LE"
 - attr(*, "nbtype")= chr "distance"
 - attr(*, "sym")= logi TRUE
 - attr(*, "ncomp")=List of 2
  ..$ nc     : int 1
  ..$ comp.id: int [1:88] 1 1 1 1 1 1 1 1 1 1 ...
table(hunan$County, card(wm_d62))
               
                1 2 3 4 5 6
  Anhua         1 0 0 0 0 0
  Anren         0 0 0 1 0 0
  Anxiang       0 0 0 0 1 0
  Baojing       0 0 0 0 1 0
  Chaling       0 0 1 0 0 0
  Changning     0 0 1 0 0 0
  Changsha      0 0 0 1 0 0
  Chengbu       0 1 0 0 0 0
  Chenxi        0 0 0 1 0 0
  Cili          0 1 0 0 0 0
  Dao           0 0 0 1 0 0
  Dongan        0 0 1 0 0 0
  Dongkou       0 0 0 1 0 0
  Fenghuang     0 0 0 1 0 0
  Guidong       0 0 1 0 0 0
  Guiyang       0 0 0 1 0 0
  Guzhang       0 0 0 0 0 1
  Hanshou       0 0 0 1 0 0
  Hengdong      0 0 0 0 1 0
  Hengnan       0 0 0 0 1 0
  Hengshan      0 0 0 0 0 1
  Hengyang      0 0 0 0 0 1
  Hongjiang     0 0 0 0 1 0
  Huarong       0 0 0 1 0 0
  Huayuan       0 0 0 1 0 0
  Huitong       0 0 0 1 0 0
  Jiahe         0 0 0 0 1 0
  Jianghua      0 0 1 0 0 0
  Jiangyong     0 1 0 0 0 0
  Jingzhou      0 1 0 0 0 0
  Jinshi        0 0 0 1 0 0
  Jishou        0 0 0 0 0 1
  Lanshan       0 0 0 1 0 0
  Leiyang       0 0 0 1 0 0
  Lengshuijiang 0 0 1 0 0 0
  Li            0 0 1 0 0 0
  Lianyuan      0 0 0 0 1 0
  Liling        0 1 0 0 0 0
  Linli         0 0 0 1 0 0
  Linwu         0 0 0 1 0 0
  Linxiang      1 0 0 0 0 0
  Liuyang       0 1 0 0 0 0
  Longhui       0 0 1 0 0 0
  Longshan      0 1 0 0 0 0
  Luxi          0 0 0 0 1 0
  Mayang        0 0 0 0 0 1
  Miluo         0 0 0 0 1 0
  Nan           0 0 0 0 1 0
  Ningxiang     0 0 0 1 0 0
  Ningyuan      0 0 0 0 1 0
  Pingjiang     0 1 0 0 0 0
  Qidong        0 0 1 0 0 0
  Qiyang        0 0 1 0 0 0
  Rucheng       0 1 0 0 0 0
  Sangzhi       0 1 0 0 0 0
  Shaodong      0 0 0 0 1 0
  Shaoshan      0 0 0 0 1 0
  Shaoyang      0 0 0 1 0 0
  Shimen        1 0 0 0 0 0
  Shuangfeng    0 0 0 0 0 1
  Shuangpai     0 0 0 1 0 0
  Suining       0 0 0 0 1 0
  Taojiang      0 1 0 0 0 0
  Taoyuan       0 1 0 0 0 0
  Tongdao       0 1 0 0 0 0
  Wangcheng     0 0 0 1 0 0
  Wugang        0 0 1 0 0 0
  Xiangtan      0 0 0 1 0 0
  Xiangxiang    0 0 0 0 1 0
  Xiangyin      0 0 0 1 0 0
  Xinhua        0 0 0 0 1 0
  Xinhuang      1 0 0 0 0 0
  Xinning       0 1 0 0 0 0
  Xinshao       0 0 0 0 0 1
  Xintian       0 0 0 0 1 0
  Xupu          0 1 0 0 0 0
  Yanling       0 0 1 0 0 0
  Yizhang       1 0 0 0 0 0
  Yongshun      0 0 0 1 0 0
  Yongxing      0 0 0 1 0 0
  You           0 0 0 1 0 0
  Yuanjiang     0 0 0 0 1 0
  Yuanling      1 0 0 0 0 0
  Yueyang       0 0 1 0 0 0
  Zhijiang      0 0 0 0 1 0
  Zhongfang     0 0 0 1 0 0
  Zhuzhou       0 0 0 0 1 0
  Zixing        0 0 1 0 0 0

Let us the number of disjoint connected subgraphs in the graph.

n_comp <- n.comp.nb(wm_d62)
n_comp$nc
[1] 1
table(n_comp$comp.id)

 1 
88

7.2.1 Plotting fixed distance weight matrix

plot(hunan$geometry, border="lightgrey")
plot(wm_d62, coords, add=TRUE)
plot(k1, coords, add=TRUE, col="red", length=0.08)

The red lines show the links of 1st nearest neighbours and the black lines show the links of neighbours within the cut-off distance of 62km.

Alternatively, we can plot both of them next to each other.

par(mfrow=c(1,2))
plot(hunan$geometry, border="lightgrey", main="1st nearest neighbours")
plot(k1, coords, add=TRUE, col="red", length=0.08)
plot(hunan$geometry, border="lightgrey", main="Distance link")
plot(wm_d62, coords, add=TRUE, pch = 19, cex = 0.6)

7.3 Computing adaptive distance weight matrix

For fixed distance weight matrix, more densely settled areas (usually urban areas) tend to have more neighbours and the less densely settled areas (usually rural counties) tend to have lesser neighbours.

Having many neighbours smoothes the neighbour relationship across more neighbours.

It is possible to control the numbers of neighbours directly using k-nearest neighbours, either accepting asymmetric neighbours or imposing symmetry.

knn6 <- knn2nb(knearneigh(coords, k=6))
knn6
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 528 
Percentage nonzero weights: 6.818182 
Average number of links: 6 
Non-symmetric neighbours list

Notice that each county has at most 6 neighbours.

str(knn6)
List of 88
 $ : int [1:6] 2 3 4 5 57 64
 $ : int [1:6] 1 3 57 58 78 85
 $ : int [1:6] 1 2 4 5 57 85
 $ : int [1:6] 1 3 5 6 69 85
 $ : int [1:6] 1 3 4 6 69 85
 $ : int [1:6] 3 4 5 69 75 85
 $ : int [1:6] 9 66 67 71 74 84
 $ : int [1:6] 9 46 47 78 80 86
 $ : int [1:6] 8 46 66 68 84 86
 $ : int [1:6] 16 19 22 70 72 73
 $ : int [1:6] 10 14 16 17 70 72
 $ : int [1:6] 13 15 60 61 63 83
 $ : int [1:6] 12 15 60 61 63 83
 $ : int [1:6] 11 15 16 17 72 83
 $ : int [1:6] 12 13 14 17 60 83
 $ : int [1:6] 10 11 17 22 72 83
 $ : int [1:6] 10 11 14 16 72 83
 $ : int [1:6] 20 22 23 63 77 83
 $ : int [1:6] 10 20 21 73 74 82
 $ : int [1:6] 18 19 21 22 23 82
 $ : int [1:6] 19 20 35 74 82 86
 $ : int [1:6] 10 16 18 19 20 83
 $ : int [1:6] 18 20 41 77 79 82
 $ : int [1:6] 25 28 31 52 54 81
 $ : int [1:6] 24 28 31 33 54 81
 $ : int [1:6] 25 27 29 33 42 81
 $ : int [1:6] 26 29 30 37 42 81
 $ : int [1:6] 24 25 33 49 52 54
 $ : int [1:6] 26 27 37 42 43 81
 $ : int [1:6] 26 27 28 33 49 81
 $ : int [1:6] 24 25 36 39 40 54
 $ : int [1:6] 24 31 50 54 55 56
 $ : int [1:6] 25 26 28 30 49 81
 $ : int [1:6] 36 40 41 45 56 80
 $ : int [1:6] 21 41 46 47 80 82
 $ : int [1:6] 31 34 40 45 56 80
 $ : int [1:6] 26 27 29 42 43 44
 $ : int [1:6] 23 43 44 62 77 79
 $ : int [1:6] 25 40 42 43 44 81
 $ : int [1:6] 31 36 39 43 45 79
 $ : int [1:6] 23 35 45 79 80 82
 $ : int [1:6] 26 27 37 39 43 81
 $ : int [1:6] 37 39 40 42 44 79
 $ : int [1:6] 37 38 39 42 43 79
 $ : int [1:6] 34 36 40 41 79 80
 $ : int [1:6] 8 9 35 47 78 86
 $ : int [1:6] 8 21 35 46 80 86
 $ : int [1:6] 49 50 51 52 53 55
 $ : int [1:6] 28 33 48 51 52 54
 $ : int [1:6] 32 48 51 52 54 55
 $ : int [1:6] 28 48 49 50 52 54
 $ : int [1:6] 28 48 49 50 51 54
 $ : int [1:6] 48 50 51 52 55 75
 $ : int [1:6] 24 28 49 50 51 52
 $ : int [1:6] 32 48 50 52 53 75
 $ : int [1:6] 32 34 36 78 80 85
 $ : int [1:6] 1 2 3 58 64 68
 $ : int [1:6] 2 57 64 66 68 78
 $ : int [1:6] 12 13 60 61 87 88
 $ : int [1:6] 12 13 59 61 63 87
 $ : int [1:6] 12 13 60 62 63 87
 $ : int [1:6] 12 38 61 63 77 87
 $ : int [1:6] 12 18 60 61 62 83
 $ : int [1:6] 1 3 57 58 68 76
 $ : int [1:6] 58 64 66 67 68 76
 $ : int [1:6] 9 58 67 68 76 84
 $ : int [1:6] 7 65 66 68 76 84
 $ : int [1:6] 9 57 58 66 78 84
 $ : int [1:6] 4 5 6 32 75 85
 $ : int [1:6] 10 16 19 22 72 73
 $ : int [1:6] 7 19 73 74 84 86
 $ : int [1:6] 10 11 14 16 17 70
 $ : int [1:6] 10 19 21 70 71 74
 $ : int [1:6] 19 21 71 73 84 86
 $ : int [1:6] 6 32 50 53 55 69
 $ : int [1:6] 58 64 65 66 67 68
 $ : int [1:6] 18 23 38 61 62 63
 $ : int [1:6] 2 8 9 46 58 68
 $ : int [1:6] 38 40 41 43 44 45
 $ : int [1:6] 34 35 36 41 45 47
 $ : int [1:6] 25 26 28 33 39 42
 $ : int [1:6] 19 20 21 23 35 41
 $ : int [1:6] 12 13 15 16 22 63
 $ : int [1:6] 7 9 66 68 71 74
 $ : int [1:6] 2 3 4 5 56 69
 $ : int [1:6] 8 9 21 46 47 74
 $ : int [1:6] 59 60 61 62 63 88
 $ : int [1:6] 59 60 61 62 63 87
 - attr(*, "region.id")= chr [1:88] "1" "2" "3" "4" ...
 - attr(*, "call")= language knearneigh(x = coords, k = 6)
 - attr(*, "sym")= logi FALSE
 - attr(*, "type")= chr "knn"
 - attr(*, "knn-k")= num 6
 - attr(*, "class")= chr "nb"
 - attr(*, "ncomp")=List of 2
  ..$ nc     : int 1
  ..$ comp.id: int [1:88] 1 1 1 1 1 1 1 1 1 1 ...

We can plot the weight matrix using following code:

plot(hunan$geometry, border="lightgrey")
plot(knn6, coords, pch = 19, cex = 0.6, add = TRUE, col = "red")

8 Weights based on Inversed Distance Weights (IDW)

8.1 Step 1: Compute the distance between areas using nbdists() of spdep.

dist <- nbdists(wm_q, coords, longlat = TRUE)
ids <- lapply(dist, function(x) 1/(x))
ids
[[1]]
[1] 0.01535405 0.03916350 0.01820896 0.02807922 0.01145113

[[2]]
[1] 0.01535405 0.01764308 0.01925924 0.02323898 0.01719350

[[3]]
[1] 0.03916350 0.02822040 0.03695795 0.01395765

[[4]]
[1] 0.01820896 0.02822040 0.03414741 0.01539065

[[5]]
[1] 0.03695795 0.03414741 0.01524598 0.01618354

[[6]]
[1] 0.015390649 0.015245977 0.021748129 0.011883901 0.009810297

[[7]]
[1] 0.01708612 0.01473997 0.01150924 0.01872915

[[8]]
[1] 0.02022144 0.03453056 0.02529256 0.01036340 0.02284457 0.01500600 0.01515314

[[9]]
[1] 0.02022144 0.01574888 0.02109502 0.01508028 0.02902705 0.01502980

[[10]]
[1] 0.02281552 0.01387777 0.01538326 0.01346650 0.02100510 0.02631658 0.01874863
[8] 0.01500046

[[11]]
[1] 0.01882869 0.02243492 0.02247473

[[12]]
[1] 0.02779227 0.02419652 0.02333385 0.02986130 0.02335429

[[13]]
[1] 0.02779227 0.02650020 0.02670323 0.01714243

[[14]]
[1] 0.01882869 0.01233868 0.02098555

[[15]]
[1] 0.02650020 0.01233868 0.01096284 0.01562226

[[16]]
[1] 0.02281552 0.02466962 0.02765018 0.01476814 0.01671430

[[17]]
[1] 0.01387777 0.02243492 0.02098555 0.01096284 0.02466962 0.01593341 0.01437996

[[18]]
[1] 0.02039779 0.02032767 0.01481665 0.01473691 0.01459380

[[19]]
[1] 0.01538326 0.01926323 0.02668415 0.02140253 0.01613589 0.01412874

[[20]]
[1] 0.01346650 0.02039779 0.01926323 0.01723025 0.02153130 0.01469240 0.02327034

[[21]]
[1] 0.02668415 0.01723025 0.01766299 0.02644986 0.02163800

[[22]]
[1] 0.02100510 0.02765018 0.02032767 0.02153130 0.01489296

[[23]]
[1] 0.01481665 0.01469240 0.01401432 0.02246233 0.01880425 0.01530458 0.01849605

[[24]]
[1] 0.02354598 0.01837201 0.02607264 0.01220154 0.02514180

[[25]]
[1] 0.02354598 0.02188032 0.01577283 0.01949232 0.02947957

[[26]]
[1] 0.02155798 0.01745522 0.02212108 0.02220532

[[27]]
[1] 0.02155798 0.02490625 0.01562326

[[28]]
[1] 0.01837201 0.02188032 0.02229549 0.03076171 0.02039506

[[29]]
[1] 0.02490625 0.01686587 0.01395022

[[30]]
[1] 0.02090587

[[31]]
[1] 0.02607264 0.01577283 0.01219005 0.01724850 0.01229012 0.01609781 0.01139438
[8] 0.01150130

[[32]]
[1] 0.01220154 0.01219005 0.01712515 0.01340413 0.01280928 0.01198216 0.01053374
[8] 0.01065655

[[33]]
[1] 0.01949232 0.01745522 0.02229549 0.02090587 0.01979045

[[34]]
[1] 0.03113041 0.03589551 0.02882915

[[35]]
[1] 0.01766299 0.02185795 0.02616766 0.02111721 0.02108253 0.01509020

[[36]]
[1] 0.01724850 0.03113041 0.01571707 0.01860991 0.02073549 0.01680129

[[37]]
[1] 0.01686587 0.02234793 0.01510990 0.01550676

[[38]]
[1] 0.01401432 0.02407426 0.02276151 0.01719415

[[39]]
[1] 0.01229012 0.02172543 0.01711924 0.02629732 0.01896385

[[40]]
[1] 0.01609781 0.01571707 0.02172543 0.01506473 0.01987922 0.01894207

[[41]]
[1] 0.02246233 0.02185795 0.02205991 0.01912542 0.01601083 0.01742892

[[42]]
[1] 0.02212108 0.01562326 0.01395022 0.02234793 0.01711924 0.01836831 0.01683518

[[43]]
[1] 0.01510990 0.02629732 0.01506473 0.01836831 0.03112027 0.01530782

[[44]]
[1] 0.01550676 0.02407426 0.03112027 0.01486508

[[45]]
[1] 0.03589551 0.01860991 0.01987922 0.02205991 0.02107101 0.01982700

[[46]]
[1] 0.03453056 0.04033752 0.02689769

[[47]]
[1] 0.02529256 0.02616766 0.04033752 0.01949145 0.02181458

[[48]]
[1] 0.02313819 0.03370576 0.02289485 0.01630057 0.01818085

[[49]]
[1] 0.03076171 0.02138091 0.02394529 0.01990000

[[50]]
[1] 0.01712515 0.02313819 0.02551427 0.02051530 0.02187179

[[51]]
[1] 0.03370576 0.02138091 0.02873854

[[52]]
[1] 0.02289485 0.02394529 0.02551427 0.02873854 0.03516672

[[53]]
[1] 0.01630057 0.01979945 0.01253977

[[54]]
[1] 0.02514180 0.02039506 0.01340413 0.01990000 0.02051530 0.03516672

[[55]]
[1] 0.01280928 0.01818085 0.02187179 0.01979945 0.01882298

[[56]]
[1] 0.01036340 0.01139438 0.01198216 0.02073549 0.01214479 0.01362855 0.01341697

[[57]]
[1] 0.028079221 0.017643082 0.031423501 0.029114131 0.013520292 0.009903702

[[58]]
[1] 0.01925924 0.03142350 0.02722997 0.01434859 0.01567192

[[59]]
[1] 0.01696711 0.01265572 0.01667105 0.01785036

[[60]]
[1] 0.02419652 0.02670323 0.01696711 0.02343040

[[61]]
[1] 0.02333385 0.01265572 0.02343040 0.02514093 0.02790764 0.01219751 0.02362452

[[62]]
[1] 0.02514093 0.02002219 0.02110260

[[63]]
[1] 0.02986130 0.02790764 0.01407043 0.01805987

[[64]]
[1] 0.02911413 0.01689892

[[65]]
[1] 0.02471705

[[66]]
[1] 0.01574888 0.01726461 0.03068853 0.01954805 0.01810569

[[67]]
[1] 0.01708612 0.01726461 0.01349843 0.01361172

[[68]]
[1] 0.02109502 0.02722997 0.03068853 0.01406357 0.01546511

[[69]]
[1] 0.02174813 0.01645838 0.01419926

[[70]]
[1] 0.02631658 0.01963168 0.02278487

[[71]]
[1] 0.01473997 0.01838483 0.03197403

[[72]]
[1] 0.01874863 0.02247473 0.01476814 0.01593341 0.01963168

[[73]]
[1] 0.01500046 0.02140253 0.02278487 0.01838483 0.01652709

[[74]]
[1] 0.01150924 0.01613589 0.03197403 0.01652709 0.01342099 0.02864567

[[75]]
[1] 0.011883901 0.010533736 0.012539774 0.018822977 0.016458383 0.008217581

[[76]]
[1] 0.01352029 0.01434859 0.01689892 0.02471705 0.01954805 0.01349843 0.01406357

[[77]]
[1] 0.014736909 0.018804247 0.022761507 0.012197506 0.020022195 0.014070428
[7] 0.008440896

[[78]]
[1] 0.02323898 0.02284457 0.01508028 0.01214479 0.01567192 0.01546511 0.01140779

[[79]]
[1] 0.01530458 0.01719415 0.01894207 0.01912542 0.01530782 0.01486508 0.02107101

[[80]]
[1] 0.01500600 0.02882915 0.02111721 0.01680129 0.01601083 0.01982700 0.01949145
[8] 0.01362855

[[81]]
[1] 0.02947957 0.02220532 0.01150130 0.01979045 0.01896385 0.01683518

[[82]]
[1] 0.02327034 0.02644986 0.01849605 0.02108253 0.01742892

[[83]]
[1] 0.023354289 0.017142433 0.015622258 0.016714303 0.014379961 0.014593799
[7] 0.014892965 0.018059871 0.008440896

[[84]]
[1] 0.01872915 0.02902705 0.01810569 0.01361172 0.01342099 0.01297994

[[85]]
 [1] 0.011451133 0.017193502 0.013957649 0.016183544 0.009810297 0.010656545
 [7] 0.013416965 0.009903702 0.014199260 0.008217581 0.011407794

[[86]]
[1] 0.01515314 0.01502980 0.01412874 0.02163800 0.01509020 0.02689769 0.02181458
[8] 0.02864567 0.01297994

[[87]]
[1] 0.01667105 0.02362452 0.02110260 0.02058034

[[88]]
[1] 0.01785036 0.02058034

8.2 Step 2: Row-standardised Weights Matrix

Then we need to assign weights to each neighboring polygon. In our case, each neighboring polygon will be assigned equal weight (style=“W”). This is accomplished by assigning the fraction 1/(# of neighbors) to each neighboring county then summing the weighted income values.

While this is the most intuitive way to summaries the neighbors’ values it has one drawback in that polygons along the edges of the study area will base their lagged values on fewer polygons thus potentially over- or under-estimating the true nature of the spatial autocorrelation in the data.

For this example, we’ll use style=“W” option for simplicity’s sake but more robust options are available, notably style=“B”.

rswm_q <- nb2listw(wm_q, style="W", zero.policy = TRUE)
rswm_q
Characteristics of weights list object:
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 448 
Percentage nonzero weights: 5.785124 
Average number of links: 5.090909 

Weights style: W 
Weights constants summary:
   n   nn S0       S1       S2
W 88 7744 88 37.86334 365.9147

zero.policy = TRUE option allows for lists of non-neighbors. This should be used with caution since the user may not be aware of missing neighbors in their dataset however, a zero.policy = FALSE would return an error.

To see the weight of the first polygon’s eight neighbors:

rswm_q$weights[10]
[[1]]
[1] 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125

Each neighbor is assigned a 0.125 of the total weight. This means that when R computes the average neighboring income values, each neighbor’s income will be multiplied by 0.125 before being tallied.

Using the same method, we can also derive a row standardised distance weight matrix by using following code:

rswm_ids <- nb2listw(wm_q, glist=ids, style="B", zero.policy=TRUE)
rswm_ids
Characteristics of weights list object:
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 448 
Percentage nonzero weights: 5.785124 
Average number of links: 5.090909 

Weights style: B 
Weights constants summary:
   n   nn       S0        S1     S2
B 88 7744 8.786867 0.3776535 3.8137
rswm_ids$weights[1]
[[1]]
[1] 0.01535405 0.03916350 0.01820896 0.02807922 0.01145113
summary(unlist(rswm_ids$weights))
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.008218 0.015088 0.018739 0.019614 0.022823 0.040338

9 Application of Spatial Weight Matrix

In this section, we will learn how to create four different spatial lagged variables, they are:

  • spatial lag with row-standardized weights.
  • spatial lag as a sum of neighbouring values.
  • spatial window average.
  • spatial window sum.

9.1 Spatial lag with row-standardized weights

We will compute the average neighbor GDPPC value for each polygon. These values are often referred to as spatially lagged values.

GDPPC.lag <- lag.listw(rswm_q, hunan$GDPPC)
GDPPC.lag
 [1] 24847.20 22724.80 24143.25 27737.50 27270.25 21248.80 43747.00 33582.71
 [9] 45651.17 32027.62 32671.00 20810.00 25711.50 30672.33 33457.75 31689.20
[17] 20269.00 23901.60 25126.17 21903.43 22718.60 25918.80 20307.00 20023.80
[25] 16576.80 18667.00 14394.67 19848.80 15516.33 20518.00 17572.00 15200.12
[33] 18413.80 14419.33 24094.50 22019.83 12923.50 14756.00 13869.80 12296.67
[41] 15775.17 14382.86 11566.33 13199.50 23412.00 39541.00 36186.60 16559.60
[49] 20772.50 19471.20 19827.33 15466.80 12925.67 18577.17 14943.00 24913.00
[57] 25093.00 24428.80 17003.00 21143.75 20435.00 17131.33 24569.75 23835.50
[65] 26360.00 47383.40 55157.75 37058.00 21546.67 23348.67 42323.67 28938.60
[73] 25880.80 47345.67 18711.33 29087.29 20748.29 35933.71 15439.71 29787.50
[81] 18145.00 21617.00 29203.89 41363.67 22259.09 44939.56 16902.00 16930.00

Then we can append the spatially lag GDPPC values onto hunan sf data frame.

lag.list <- list(hunan$NAME_3, lag.listw(rswm_q, hunan$GDPPC))
lag.res <- as.data.frame(lag.list)
colnames(lag.res) <- c("NAME_3", "lag GDPPC")
hunan <- left_join(hunan,lag.res)
Joining with `by = join_by(NAME_3)`

The following table shows the average neighboring income values (stored in the Inc.lag object) for each county.

head(hunan)
Simple feature collection with 6 features and 7 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 110.4922 ymin: 28.61762 xmax: 112.3013 ymax: 30.12812
Geodetic CRS:  WGS 84
   NAME_2  ID_3  NAME_3   ENGTYPE_3  County GDPPC lag GDPPC
1 Changde 21098 Anxiang      County Anxiang 23667  24847.20
2 Changde 21100 Hanshou      County Hanshou 20981  22724.80
3 Changde 21101  Jinshi County City  Jinshi 34592  24143.25
4 Changde 21102      Li      County      Li 24473  27737.50
5 Changde 21103   Linli      County   Linli 25554  27270.25
6 Changde 21104  Shimen      County  Shimen 27137  21248.80
                        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...
6 POLYGON ((110.8825 30.11675...

Next, we will plot both the GDPPC and spatial lag GDPPC for comparison.

gdppc <- qtm(hunan, "GDPPC")
lag_gdppc <- qtm(hunan, "lag GDPPC")
tmap_arrange(gdppc, lag_gdppc, asp=1, ncol=2)

9.2 Spatial lag as a sum of neighboring values

We can calculate spatial lag as a sum of neighboring values by assigning binary weights. This requires us to go back to our neighbors list, then apply a function that will assign binary weights, then we use glist = in the nb2listw function to explicitly assign these weights.

We start by applying a function that will assign a value of 1 per each neighbor. This is done with lapply, which we have been using to manipulate the neighbors structure throughout the past notebooks. Basically it applies a function across each value in the neighbors structure.

b_weights <- lapply(wm_q, function(x) 0*x + 1)
b_weights2 <- nb2listw(wm_q, 
                       glist = b_weights, 
                       style = "B")
b_weights2
Characteristics of weights list object:
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 448 
Percentage nonzero weights: 5.785124 
Average number of links: 5.090909 

Weights style: B 
Weights constants summary:
   n   nn  S0  S1    S2
B 88 7744 448 896 10224

With the proper weights assigned, we can use lag.listw to compute a lag variable from our weight and GDPPC.

lag_sum <- list(hunan$NAME_3, lag.listw(b_weights2, hunan$GDPPC))
lag.res <- as.data.frame(lag_sum)
colnames(lag.res) <- c("NAME_3", "lag_sum GDPPC")
lag_sum
[[1]]
 [1] "Anxiang"       "Hanshou"       "Jinshi"        "Li"           
 [5] "Linli"         "Shimen"        "Liuyang"       "Ningxiang"    
 [9] "Wangcheng"     "Anren"         "Guidong"       "Jiahe"        
[13] "Linwu"         "Rucheng"       "Yizhang"       "Yongxing"     
[17] "Zixing"        "Changning"     "Hengdong"      "Hengnan"      
[21] "Hengshan"      "Leiyang"       "Qidong"        "Chenxi"       
[25] "Zhongfang"     "Huitong"       "Jingzhou"      "Mayang"       
[29] "Tongdao"       "Xinhuang"      "Xupu"          "Yuanling"     
[33] "Zhijiang"      "Lengshuijiang" "Shuangfeng"    "Xinhua"       
[37] "Chengbu"       "Dongan"        "Dongkou"       "Longhui"      
[41] "Shaodong"      "Suining"       "Wugang"        "Xinning"      
[45] "Xinshao"       "Shaoshan"      "Xiangxiang"    "Baojing"      
[49] "Fenghuang"     "Guzhang"       "Huayuan"       "Jishou"       
[53] "Longshan"      "Luxi"          "Yongshun"      "Anhua"        
[57] "Nan"           "Yuanjiang"     "Jianghua"      "Lanshan"      
[61] "Ningyuan"      "Shuangpai"     "Xintian"       "Huarong"      
[65] "Linxiang"      "Miluo"         "Pingjiang"     "Xiangyin"     
[69] "Cili"          "Chaling"       "Liling"        "Yanling"      
[73] "You"           "Zhuzhou"       "Sangzhi"       "Yueyang"      
[77] "Qiyang"        "Taojiang"      "Shaoyang"      "Lianyuan"     
[81] "Hongjiang"     "Hengyang"      "Guiyang"       "Changsha"     
[85] "Taoyuan"       "Xiangtan"      "Dao"           "Jiangyong"    

[[2]]
 [1] 124236 113624  96573 110950 109081 106244 174988 235079 273907 256221
[11]  98013 104050 102846  92017 133831 158446 141883 119508 150757 153324
[21] 113593 129594 142149 100119  82884  74668  43184  99244  46549  20518
[31] 140576 121601  92069  43258 144567 132119  51694  59024  69349  73780
[41]  94651 100680  69398  52798 140472 118623 180933  82798  83090  97356
[51]  59482  77334  38777 111463  74715 174391 150558 122144  68012  84575
[61] 143045  51394  98279  47671  26360 236917 220631 185290  64640  70046
[71] 126971 144693 129404 284074 112268 203611 145238 251536 108078 238300
[81] 108870 108085 262835 248182 244850 404456  67608  33860

Next, we will append the lag_sum GDPPC field into hunan sf data frame.

hunan <- left_join(hunan, lag.res)
Joining with `by = join_by(NAME_3)`

Now, we can plot both the GDPPC and Spatial Lag Sum GDPPC for comparison.

gdppc <- qtm(hunan, "GDPPC")
lag_sum_gdppc <- qtm(hunan, "lag_sum GDPPC")
tmap_arrange(gdppc, lag_sum_gdppc, asp=1, ncol=2)

9.3 Spatial window average

The spatial window average uses row-standardized weights and includes the diagonal element. To do this in R, we need to go back to the neighbors structure and add the diagonal element before assigning weights.

To add the diagonal element to the neighbour list, we just need to use include.self() from spdep.

wm_qs <- include.self(wm_q)
wm_qs
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 536 
Percentage nonzero weights: 6.921488 
Average number of links: 6.090909

Notice that the Number of nonzero links, Percentage nonzero weights and Average number of links are 536, 6.921488 and 6.090909 respectively as compared to wm_q of 448, 5.785124 and 5.090909.

Let us take a good look at the neighbour list of area [1].

wm_qs[[1]]
[1]  1  2  3  4 57 85

Notice that now [1] has six neighbours instead of five.

Now we obtain weights with nb2listw()

wm_qs <- nb2listw(wm_qs)
wm_qs
Characteristics of weights list object:
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 536 
Percentage nonzero weights: 6.921488 
Average number of links: 6.090909 

Weights style: W 
Weights constants summary:
   n   nn S0       S1       S2
W 88 7744 88 30.90265 357.5308

Again, we use nb2listw() and glist() to explicitly assign weight values.

Lastly, we just need to create the lag variable from our weight structure and GDPPC variable.

lag_w_avg_gpdpc <- lag.listw(wm_qs, 
                             hunan$GDPPC)
lag_w_avg_gpdpc
 [1] 24650.50 22434.17 26233.00 27084.60 26927.00 22230.17 47621.20 37160.12
 [9] 49224.71 29886.89 26627.50 22690.17 25366.40 25825.75 30329.00 32682.83
[17] 25948.62 23987.67 25463.14 21904.38 23127.50 25949.83 20018.75 19524.17
[25] 18955.00 17800.40 15883.00 18831.33 14832.50 17965.00 17159.89 16199.44
[33] 18764.50 26878.75 23188.86 20788.14 12365.20 15985.00 13764.83 11907.43
[41] 17128.14 14593.62 11644.29 12706.00 21712.29 43548.25 35049.00 16226.83
[49] 19294.40 18156.00 19954.75 18145.17 12132.75 18419.29 14050.83 23619.75
[57] 24552.71 24733.67 16762.60 20932.60 19467.75 18334.00 22541.00 26028.00
[65] 29128.50 46569.00 47576.60 36545.50 20838.50 22531.00 42115.50 27619.00
[73] 27611.33 44523.29 18127.43 28746.38 20734.50 33880.62 14716.38 28516.22
[81] 18086.14 21244.50 29568.80 48119.71 22310.75 43151.60 17133.40 17009.33

Next, we will convert the lag variable listw object into a data.frame by using as.data.frame().

lag.list.wm_qs <- list(hunan$NAME_3, lag.listw(wm_qs, hunan$GDPPC))
lag_wm_qs.res <- as.data.frame(lag.list.wm_qs)
colnames(lag_wm_qs.res) <- c("NAME_3", "lag_window_avg GDPPC")

Note: The third command line on the code chunk above renames the field names of lag_wm_q1.res object into NAME_3 and lag_window_avg GDPPC respectively.

Next, the code chunk below will be used to append lag_window_avg GDPPC values onto hunan sf data.frame by using left_join() of dplyr package.

hunan <- left_join(hunan, lag_wm_qs.res)
Joining with `by = join_by(NAME_3)`

To compare the values of lag GDPPC and Spatial window average, kable() of Knitr package is used to prepare a table using the code chunk below.

hunan %>%
  select("County", 
         "lag GDPPC", 
         "lag_window_avg GDPPC") %>%
  kable()
County lag GDPPC lag_window_avg GDPPC geometry
Anxiang 24847.20 24650.50 POLYGON ((112.0625 29.75523…
Hanshou 22724.80 22434.17 POLYGON ((112.2288 29.11684…
Jinshi 24143.25 26233.00 POLYGON ((111.8927 29.6013,…
Li 27737.50 27084.60 POLYGON ((111.3731 29.94649…
Linli 27270.25 26927.00 POLYGON ((111.6324 29.76288…
Shimen 21248.80 22230.17 POLYGON ((110.8825 30.11675…
Liuyang 43747.00 47621.20 POLYGON ((113.9905 28.5682,…
Ningxiang 33582.71 37160.12 POLYGON ((112.7181 28.38299…
Wangcheng 45651.17 49224.71 POLYGON ((112.7914 28.52688…
Anren 32027.62 29886.89 POLYGON ((113.1757 26.82734…
Guidong 32671.00 26627.50 POLYGON ((114.1799 26.20117…
Jiahe 20810.00 22690.17 POLYGON ((112.4425 25.74358…
Linwu 25711.50 25366.40 POLYGON ((112.5914 25.55143…
Rucheng 30672.33 25825.75 POLYGON ((113.6759 25.87578…
Yizhang 33457.75 30329.00 POLYGON ((113.2621 25.68394…
Yongxing 31689.20 32682.83 POLYGON ((113.3169 26.41843…
Zixing 20269.00 25948.62 POLYGON ((113.7311 26.16259…
Changning 23901.60 23987.67 POLYGON ((112.6144 26.60198…
Hengdong 25126.17 25463.14 POLYGON ((113.1056 27.21007…
Hengnan 21903.43 21904.38 POLYGON ((112.7599 26.98149…
Hengshan 22718.60 23127.50 POLYGON ((112.607 27.4689, …
Leiyang 25918.80 25949.83 POLYGON ((112.9996 26.69276…
Qidong 20307.00 20018.75 POLYGON ((111.7818 27.0383,…
Chenxi 20023.80 19524.17 POLYGON ((110.2624 28.21778…
Zhongfang 16576.80 18955.00 POLYGON ((109.9431 27.72858…
Huitong 18667.00 17800.40 POLYGON ((109.9419 27.10512…
Jingzhou 14394.67 15883.00 POLYGON ((109.8186 26.75842…
Mayang 19848.80 18831.33 POLYGON ((109.795 27.98008,…
Tongdao 15516.33 14832.50 POLYGON ((109.9294 26.46561…
Xinhuang 20518.00 17965.00 POLYGON ((109.227 27.43733,…
Xupu 17572.00 17159.89 POLYGON ((110.7189 28.30485…
Yuanling 15200.12 16199.44 POLYGON ((110.9652 28.99895…
Zhijiang 18413.80 18764.50 POLYGON ((109.8818 27.60661…
Lengshuijiang 14419.33 26878.75 POLYGON ((111.5307 27.81472…
Shuangfeng 24094.50 23188.86 POLYGON ((112.263 27.70421,…
Xinhua 22019.83 20788.14 POLYGON ((111.3345 28.19642…
Chengbu 12923.50 12365.20 POLYGON ((110.4455 26.69317…
Dongan 14756.00 15985.00 POLYGON ((111.4531 26.86812…
Dongkou 13869.80 13764.83 POLYGON ((110.6622 27.37305…
Longhui 12296.67 11907.43 POLYGON ((110.985 27.65983,…
Shaodong 15775.17 17128.14 POLYGON ((111.9054 27.40254…
Suining 14382.86 14593.62 POLYGON ((110.389 27.10006,…
Wugang 11566.33 11644.29 POLYGON ((110.9878 27.03345…
Xinning 13199.50 12706.00 POLYGON ((111.0736 26.84627…
Xinshao 23412.00 21712.29 POLYGON ((111.6013 27.58275…
Shaoshan 39541.00 43548.25 POLYGON ((112.5391 27.97742…
Xiangxiang 36186.60 35049.00 POLYGON ((112.4549 28.05783…
Baojing 16559.60 16226.83 POLYGON ((109.7015 28.82844…
Fenghuang 20772.50 19294.40 POLYGON ((109.5239 28.19206…
Guzhang 19471.20 18156.00 POLYGON ((109.8968 28.74034…
Huayuan 19827.33 19954.75 POLYGON ((109.5647 28.61712…
Jishou 15466.80 18145.17 POLYGON ((109.8375 28.4696,…
Longshan 12925.67 12132.75 POLYGON ((109.6337 29.62521…
Luxi 18577.17 18419.29 POLYGON ((110.1067 28.41835…
Yongshun 14943.00 14050.83 POLYGON ((110.0003 29.29499…
Anhua 24913.00 23619.75 POLYGON ((111.6034 28.63716…
Nan 25093.00 24552.71 POLYGON ((112.3232 29.46074…
Yuanjiang 24428.80 24733.67 POLYGON ((112.4391 29.1791,…
Jianghua 17003.00 16762.60 POLYGON ((111.6461 25.29661…
Lanshan 21143.75 20932.60 POLYGON ((112.2286 25.61123…
Ningyuan 20435.00 19467.75 POLYGON ((112.0715 26.09892…
Shuangpai 17131.33 18334.00 POLYGON ((111.8864 26.11957…
Xintian 24569.75 22541.00 POLYGON ((112.2578 26.0796,…
Huarong 23835.50 26028.00 POLYGON ((112.9242 29.69134…
Linxiang 26360.00 29128.50 POLYGON ((113.5502 29.67418…
Miluo 47383.40 46569.00 POLYGON ((112.9902 29.02139…
Pingjiang 55157.75 47576.60 POLYGON ((113.8436 29.06152…
Xiangyin 37058.00 36545.50 POLYGON ((112.9173 28.98264…
Cili 21546.67 20838.50 POLYGON ((110.8822 29.69017…
Chaling 23348.67 22531.00 POLYGON ((113.7666 27.10573…
Liling 42323.67 42115.50 POLYGON ((113.5673 27.94346…
Yanling 28938.60 27619.00 POLYGON ((113.9292 26.6154,…
You 25880.80 27611.33 POLYGON ((113.5879 27.41324…
Zhuzhou 47345.67 44523.29 POLYGON ((113.2493 28.02411…
Sangzhi 18711.33 18127.43 POLYGON ((110.556 29.40543,…
Yueyang 29087.29 28746.38 POLYGON ((113.343 29.61064,…
Qiyang 20748.29 20734.50 POLYGON ((111.5563 26.81318…
Taojiang 35933.71 33880.62 POLYGON ((112.0508 28.67265…
Shaoyang 15439.71 14716.38 POLYGON ((111.5013 27.30207…
Lianyuan 29787.50 28516.22 POLYGON ((111.6789 28.02946…
Hongjiang 18145.00 18086.14 POLYGON ((110.1441 27.47513…
Hengyang 21617.00 21244.50 POLYGON ((112.7144 26.98613…
Guiyang 29203.89 29568.80 POLYGON ((113.0811 26.04963…
Changsha 41363.67 48119.71 POLYGON ((112.9421 28.03722…
Taoyuan 22259.09 22310.75 POLYGON ((112.0612 29.32855…
Xiangtan 44939.56 43151.60 POLYGON ((113.0426 27.8942,…
Dao 16902.00 17133.40 POLYGON ((111.498 25.81679,…
Jiangyong 16930.00 17009.33 POLYGON ((111.3659 25.39472…

Lastly, qtm() of tmap package is used to plot the lag_gdppc and w_ave_gdppc maps next to each other for quick comparison.

w_avg_gdppc <- qtm(hunan, "lag_window_avg GDPPC")
tmap_arrange(lag_gdppc, w_avg_gdppc, asp=1, ncol=2)

Note: For more effective comparison, it is advisable to use the core tmap mapping functions.

9.4 Spatial window sum

The spatial window sum is the counter part of the window average, but without using row-standardized weights.

To add the diagonal element to the neighbour list, we just need to use include.self() from spdep.

wm_qs <- include.self(wm_q)
wm_qs
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 536 
Percentage nonzero weights: 6.921488 
Average number of links: 6.090909

Next, we will assign binary weights to the neighbour structure that includes the diagonal element.

b_weights <- lapply(wm_qs, function(x) 0*x + 1)
b_weights[1]
[[1]]
[1] 1 1 1 1 1 1

Notice that now [1] has six neighbours instead of five.

Again, we use nb2listw() and glist() to explicitly assign weight values.

b_weights2 <- nb2listw(wm_qs, 
                       glist = b_weights, 
                       style = "B")
b_weights2
Characteristics of weights list object:
Neighbour list object:
Number of regions: 88 
Number of nonzero links: 536 
Percentage nonzero weights: 6.921488 
Average number of links: 6.090909 

Weights style: B 
Weights constants summary:
   n   nn  S0   S1    S2
B 88 7744 536 1072 14160

With our new weight structure, we can compute the lag variable with lag.listw().

w_sum_gdppc <- list(hunan$NAME_3, lag.listw(b_weights2, hunan$GDPPC))
w_sum_gdppc
[[1]]
 [1] "Anxiang"       "Hanshou"       "Jinshi"        "Li"           
 [5] "Linli"         "Shimen"        "Liuyang"       "Ningxiang"    
 [9] "Wangcheng"     "Anren"         "Guidong"       "Jiahe"        
[13] "Linwu"         "Rucheng"       "Yizhang"       "Yongxing"     
[17] "Zixing"        "Changning"     "Hengdong"      "Hengnan"      
[21] "Hengshan"      "Leiyang"       "Qidong"        "Chenxi"       
[25] "Zhongfang"     "Huitong"       "Jingzhou"      "Mayang"       
[29] "Tongdao"       "Xinhuang"      "Xupu"          "Yuanling"     
[33] "Zhijiang"      "Lengshuijiang" "Shuangfeng"    "Xinhua"       
[37] "Chengbu"       "Dongan"        "Dongkou"       "Longhui"      
[41] "Shaodong"      "Suining"       "Wugang"        "Xinning"      
[45] "Xinshao"       "Shaoshan"      "Xiangxiang"    "Baojing"      
[49] "Fenghuang"     "Guzhang"       "Huayuan"       "Jishou"       
[53] "Longshan"      "Luxi"          "Yongshun"      "Anhua"        
[57] "Nan"           "Yuanjiang"     "Jianghua"      "Lanshan"      
[61] "Ningyuan"      "Shuangpai"     "Xintian"       "Huarong"      
[65] "Linxiang"      "Miluo"         "Pingjiang"     "Xiangyin"     
[69] "Cili"          "Chaling"       "Liling"        "Yanling"      
[73] "You"           "Zhuzhou"       "Sangzhi"       "Yueyang"      
[77] "Qiyang"        "Taojiang"      "Shaoyang"      "Lianyuan"     
[81] "Hongjiang"     "Hengyang"      "Guiyang"       "Changsha"     
[85] "Taoyuan"       "Xiangtan"      "Dao"           "Jiangyong"    

[[2]]
 [1] 147903 134605 131165 135423 134635 133381 238106 297281 344573 268982
[11] 106510 136141 126832 103303 151645 196097 207589 143926 178242 175235
[21] 138765 155699 160150 117145 113730  89002  63532 112988  59330  35930
[31] 154439 145795 112587 107515 162322 145517  61826  79925  82589  83352
[41] 119897 116749  81510  63530 151986 174193 210294  97361  96472 108936
[51]  79819 108871  48531 128935  84305 188958 171869 148402  83813 104663
[61] 155742  73336 112705  78084  58257 279414 237883 219273  83354  90124
[71] 168462 165714 165668 311663 126892 229971 165876 271045 117731 256646
[81] 126603 127467 295688 336838 267729 431516  85667  51028

Next, we will convert the lag variable listw object into a data.frame by using as.data.frame().

w_sum_gdppc.res <- as.data.frame(w_sum_gdppc)
colnames(w_sum_gdppc.res) <- c("NAME_3", "w_sum GDPPC")

Note: The second command line on the code chunk above renames the field names ofw_sum_gdppc.res object into NAME_3 and w_sum GDPPC respectively.

Next, the code chunk below will be used to append w_sum GDPPC values onto hunan sf data.frame by using left_join() of dplyr package.

hunan <- left_join(hunan, w_sum_gdppc.res)
Joining with `by = join_by(NAME_3)`

To compare the values of lag GDPPC and Spatial window average, kable() of Knitr package is used to prepare a table using the code chunk below.

hunan %>%
  select("County", "lag_sum GDPPC", "w_sum GDPPC") %>%
  kable()
County lag_sum GDPPC w_sum GDPPC geometry
Anxiang 124236 147903 POLYGON ((112.0625 29.75523…
Hanshou 113624 134605 POLYGON ((112.2288 29.11684…
Jinshi 96573 131165 POLYGON ((111.8927 29.6013,…
Li 110950 135423 POLYGON ((111.3731 29.94649…
Linli 109081 134635 POLYGON ((111.6324 29.76288…
Shimen 106244 133381 POLYGON ((110.8825 30.11675…
Liuyang 174988 238106 POLYGON ((113.9905 28.5682,…
Ningxiang 235079 297281 POLYGON ((112.7181 28.38299…
Wangcheng 273907 344573 POLYGON ((112.7914 28.52688…
Anren 256221 268982 POLYGON ((113.1757 26.82734…
Guidong 98013 106510 POLYGON ((114.1799 26.20117…
Jiahe 104050 136141 POLYGON ((112.4425 25.74358…
Linwu 102846 126832 POLYGON ((112.5914 25.55143…
Rucheng 92017 103303 POLYGON ((113.6759 25.87578…
Yizhang 133831 151645 POLYGON ((113.2621 25.68394…
Yongxing 158446 196097 POLYGON ((113.3169 26.41843…
Zixing 141883 207589 POLYGON ((113.7311 26.16259…
Changning 119508 143926 POLYGON ((112.6144 26.60198…
Hengdong 150757 178242 POLYGON ((113.1056 27.21007…
Hengnan 153324 175235 POLYGON ((112.7599 26.98149…
Hengshan 113593 138765 POLYGON ((112.607 27.4689, …
Leiyang 129594 155699 POLYGON ((112.9996 26.69276…
Qidong 142149 160150 POLYGON ((111.7818 27.0383,…
Chenxi 100119 117145 POLYGON ((110.2624 28.21778…
Zhongfang 82884 113730 POLYGON ((109.9431 27.72858…
Huitong 74668 89002 POLYGON ((109.9419 27.10512…
Jingzhou 43184 63532 POLYGON ((109.8186 26.75842…
Mayang 99244 112988 POLYGON ((109.795 27.98008,…
Tongdao 46549 59330 POLYGON ((109.9294 26.46561…
Xinhuang 20518 35930 POLYGON ((109.227 27.43733,…
Xupu 140576 154439 POLYGON ((110.7189 28.30485…
Yuanling 121601 145795 POLYGON ((110.9652 28.99895…
Zhijiang 92069 112587 POLYGON ((109.8818 27.60661…
Lengshuijiang 43258 107515 POLYGON ((111.5307 27.81472…
Shuangfeng 144567 162322 POLYGON ((112.263 27.70421,…
Xinhua 132119 145517 POLYGON ((111.3345 28.19642…
Chengbu 51694 61826 POLYGON ((110.4455 26.69317…
Dongan 59024 79925 POLYGON ((111.4531 26.86812…
Dongkou 69349 82589 POLYGON ((110.6622 27.37305…
Longhui 73780 83352 POLYGON ((110.985 27.65983,…
Shaodong 94651 119897 POLYGON ((111.9054 27.40254…
Suining 100680 116749 POLYGON ((110.389 27.10006,…
Wugang 69398 81510 POLYGON ((110.9878 27.03345…
Xinning 52798 63530 POLYGON ((111.0736 26.84627…
Xinshao 140472 151986 POLYGON ((111.6013 27.58275…
Shaoshan 118623 174193 POLYGON ((112.5391 27.97742…
Xiangxiang 180933 210294 POLYGON ((112.4549 28.05783…
Baojing 82798 97361 POLYGON ((109.7015 28.82844…
Fenghuang 83090 96472 POLYGON ((109.5239 28.19206…
Guzhang 97356 108936 POLYGON ((109.8968 28.74034…
Huayuan 59482 79819 POLYGON ((109.5647 28.61712…
Jishou 77334 108871 POLYGON ((109.8375 28.4696,…
Longshan 38777 48531 POLYGON ((109.6337 29.62521…
Luxi 111463 128935 POLYGON ((110.1067 28.41835…
Yongshun 74715 84305 POLYGON ((110.0003 29.29499…
Anhua 174391 188958 POLYGON ((111.6034 28.63716…
Nan 150558 171869 POLYGON ((112.3232 29.46074…
Yuanjiang 122144 148402 POLYGON ((112.4391 29.1791,…
Jianghua 68012 83813 POLYGON ((111.6461 25.29661…
Lanshan 84575 104663 POLYGON ((112.2286 25.61123…
Ningyuan 143045 155742 POLYGON ((112.0715 26.09892…
Shuangpai 51394 73336 POLYGON ((111.8864 26.11957…
Xintian 98279 112705 POLYGON ((112.2578 26.0796,…
Huarong 47671 78084 POLYGON ((112.9242 29.69134…
Linxiang 26360 58257 POLYGON ((113.5502 29.67418…
Miluo 236917 279414 POLYGON ((112.9902 29.02139…
Pingjiang 220631 237883 POLYGON ((113.8436 29.06152…
Xiangyin 185290 219273 POLYGON ((112.9173 28.98264…
Cili 64640 83354 POLYGON ((110.8822 29.69017…
Chaling 70046 90124 POLYGON ((113.7666 27.10573…
Liling 126971 168462 POLYGON ((113.5673 27.94346…
Yanling 144693 165714 POLYGON ((113.9292 26.6154,…
You 129404 165668 POLYGON ((113.5879 27.41324…
Zhuzhou 284074 311663 POLYGON ((113.2493 28.02411…
Sangzhi 112268 126892 POLYGON ((110.556 29.40543,…
Yueyang 203611 229971 POLYGON ((113.343 29.61064,…
Qiyang 145238 165876 POLYGON ((111.5563 26.81318…
Taojiang 251536 271045 POLYGON ((112.0508 28.67265…
Shaoyang 108078 117731 POLYGON ((111.5013 27.30207…
Lianyuan 238300 256646 POLYGON ((111.6789 28.02946…
Hongjiang 108870 126603 POLYGON ((110.1441 27.47513…
Hengyang 108085 127467 POLYGON ((112.7144 26.98613…
Guiyang 262835 295688 POLYGON ((113.0811 26.04963…
Changsha 248182 336838 POLYGON ((112.9421 28.03722…
Taoyuan 244850 267729 POLYGON ((112.0612 29.32855…
Xiangtan 404456 431516 POLYGON ((113.0426 27.8942,…
Dao 67608 85667 POLYGON ((111.498 25.81679,…
Jiangyong 33860 51028 POLYGON ((111.3659 25.39472…

Lastly, qtm() of tmap package is used to plot the lag_sum GDPPC and w_sum_gdppc maps next to each other for quick comparison.

w_sum_gdppc <- qtm(hunan, "w_sum GDPPC")
tmap_arrange(lag_sum_gdppc, w_sum_gdppc, asp=1, ncol=2)

Note: For more effective comparison, it is advisable to use the core tmap mapping functions.

10 Reference

Kam, T. S. Spatial Weights and Applications. R for Geospatial Data Science and Analytics. https://r4gdsa.netlify.app/chap08.html