pacman::p_load(spdep, sp, tmap, sf, ClustGeo,
ggpubr, cluster, factoextra, NbClust,
heatmaply, corrplot, psych, tidyverse, GGally)Geospatial Analysis3 - Spatially Constrained Clustering: ClustGeo method
Loading R packages
ACLED_MMR <- read_csv("data/MMR.csv")mmr_shp_mimu_2 <- st_read(dsn = "data/geospatial3",
layer = "mmr_polbnda_adm2_250k_mimu")Reading layer `mmr_polbnda_adm2_250k_mimu' from data source
`C:\imranmi\ISSS608-VAA\Take-home-ex\Take-home-Ex4f\data\geospatial3'
using driver `ESRI Shapefile'
Simple feature collection with 80 features and 7 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 92.1721 ymin: 9.696844 xmax: 101.17 ymax: 28.54554
Geodetic CRS: WGS 84ACLED_MMR_1 <- ACLED_MMR %>%
mutate(admin1 = case_when(
admin1 == "Bago-East" ~ "Bago (East)",
admin1 == "Bago-West" ~ "Bago (West)",
admin1 == "Shan-North" ~ "Shan (North)",
admin1 == "Shan-South" ~ "Shan (South)",
admin1 == "Shan-East" ~ "Shan (East)",
TRUE ~ as.character(admin1)
))ACLED_MMR_1 <- ACLED_MMR_1 %>%
mutate(admin2 = case_when(
admin2 == "Yangon-East" ~ "Yangon (East)",
admin2 == "Yangon-West" ~ "Yangon (West)",
admin2 == "Yangon-North" ~ "Yangon (North)",
admin2 == "Yangon-South" ~ "Yangon (South)",
admin2 == "Mong Pawk (Wa SAD)" ~ "Tachileik",
admin2 == "Nay Pyi Taw" ~ "Det Khi Na",
admin2 == "Yangon" ~ "Yangon (West)",
TRUE ~ as.character(admin2)
))ACLED_MMR_1 <- ACLED_MMR_1 %>%
filter(year >= 2020 & year <= 2023)Data2 <- ACLED_MMR_1 %>%
group_by(year, admin2, event_type) %>%
summarise(Incidents = n(),
Fatalities = sum(fatalities, na.rm = TRUE)) %>%
ungroup()library(tidyr)
years <- unique(Data2$year)
event_types <- unique(Data2$event_type)
districts <- unique(Data2$admin2)
# Using complete() to create all combinations of year, admin2, and event_type
# and replacing NA values with 0 for Incidents and Fatalities
Data2_complete <- Data2 %>%
complete(year = years, admin2 = districts, event_type = event_types, fill = list(Incidents = 0, Fatalities = 0))library(dplyr)
library(tidyr)
data2_summary <- Data2_complete %>%
group_by(admin2, event_type, year) %>%
summarise(Incidents = sum(Incidents), Fatalities = sum(Fatalities), .groups = 'drop') # Summarize and drop grouping
# Now we'll spread this into a wider format
data2_long <- data2_summary %>%
pivot_wider(
names_from = c(event_type, year),
values_from = c(Incidents, Fatalities),
names_glue = "{event_type}_{year}_{.value}"
)Filtering the data set for just “Battles_2022_Incidents”
selected <- data2_long %>%
select(admin2, Battles_2022_Incidents)selected# A tibble: 80 × 2
admin2 Battles_2022_Incidents
<chr> <int>
1 Bago 12
2 Bawlake 16
3 Bhamo 54
4 Danu Self-Administered Zone 15
5 Dawei 141
6 Det Khi Na 0
7 Falam 59
8 Gangaw 143
9 Hakha 43
10 Hinthada 6
# ℹ 70 more rowsSpatially Constrained Clustering: ClustGeo Method
Computing proximity matrix
In R, many packages provide functions to calculate distance matrix. We will compute the proximity matrix by using dist() of R.
dist() supports six distance proximity calculations, they are: euclidean, maximum, manhattan, canberra, binary and minkowski. The default is euclidean proximity matrix.
The code chunk below is used to compute the proximity matrix using euclidean method.
proxmat <- dist(selected, method = 'euclidean')proxmat 1 2 3 4 5 6 7
2 5.656854
3 59.396970 53.740115
4 4.242641 1.414214 55.154329
5 182.433550 176.776695 123.036580 178.190909
6 16.970563 22.627417 76.367532 21.213203 199.404112
7 66.468037 60.811183 7.071068 62.225397 115.965512 83.438600
8 185.261977 179.605122 125.865007 181.019336 2.828427 202.232539 118.793939
9 43.840620 38.183766 15.556349 39.597980 138.592929 60.811183 22.627417
10 8.485281 14.142136 67.882251 12.727922 190.918831 8.485281 74.953319
11 2.828427 8.485281 62.225397 7.071068 185.261977 14.142136 69.296465
12 16.970563 22.627417 76.367532 21.213203 199.404112 0.000000 83.438600
13 21.213203 15.556349 38.183766 16.970563 161.220346 38.183766 45.254834
14 38.183766 32.526912 21.213203 33.941125 144.249783 55.154329 28.284271
15 213.546248 207.889394 154.149278 209.303607 31.112698 230.516811 147.078210
16 31.112698 25.455844 28.284271 26.870058 151.320851 48.083261 35.355339
17 206.475180 200.818326 147.078210 202.232539 24.041631 223.445743 140.007143
18 108.894444 103.237590 49.497475 104.651804 73.539105 125.865007 42.426407
19 43.840620 38.183766 15.556349 39.597980 138.592929 60.811183 22.627417
20 1.414214 4.242641 57.982756 2.828427 181.019336 18.384776 65.053824
21 16.970563 22.627417 76.367532 21.213203 199.404112 0.000000 83.438600
22 8.485281 14.142136 67.882251 12.727922 190.918831 8.485281 74.953319
23 38.183766 32.526912 21.213203 33.941125 144.249783 55.154329 28.284271
24 7.071068 12.727922 66.468037 11.313708 189.504617 9.899495 73.539105
25 1.414214 4.242641 57.982756 2.828427 181.019336 18.384776 65.053824
26 14.142136 19.798990 73.539105 18.384776 196.575685 2.828427 80.610173
27 12.727922 18.384776 72.124892 16.970563 195.161472 4.242641 79.195959
28 11.313708 5.656854 48.083261 7.071068 171.119841 28.284271 55.154329
29 359.210245 353.553391 299.813275 354.967604 176.776695 376.180808 292.742207
30 5.656854 11.313708 65.053824 9.899495 188.090404 11.313708 72.124892
31 5.656854 11.313708 65.053824 9.899495 188.090404 11.313708 72.124892
32 46.669048 41.012193 12.727922 42.426407 135.764502 63.639610 19.798990
33 16.970563 22.627417 76.367532 21.213203 199.404112 0.000000 83.438600
34 97.580736 91.923882 38.183766 93.338095 84.852814 114.551299 31.112698
35 14.142136 19.798990 73.539105 18.384776 196.575685 2.828427 80.610173
36 67.882251 62.225397 8.485281 63.639610 114.551299 84.852814 1.414214
37 14.142136 8.485281 45.254834 9.899495 168.291414 31.112698 52.325902
38 108.894444 103.237590 49.497475 104.651804 73.539105 125.865007 42.426407
39 14.142136 19.798990 73.539105 18.384776 196.575685 2.828427 80.610173
40 0.000000 5.656854 59.396970 4.242641 182.433550 16.970563 66.468037
41 77.781746 72.124892 18.384776 73.539105 104.651804 94.752309 11.313708
42 70.710678 65.053824 11.313708 66.468037 111.722871 87.681241 4.242641
43 15.556349 21.213203 74.953319 19.798990 197.989899 1.414214 82.024387
44 1.414214 4.242641 57.982756 2.828427 181.019336 18.384776 65.053824
45 195.161472 189.504617 135.764502 190.918831 12.727922 212.132034 128.693434
46 8.485281 2.828427 50.911688 4.242641 173.948268 25.455844 57.982756
47 142.835570 137.178716 83.438600 138.592929 39.597980 159.806133 76.367532
48 15.556349 21.213203 74.953319 19.798990 197.989899 1.414214 82.024387
49 135.764502 130.107648 76.367532 131.521861 46.669048 152.735065 69.296465
50 179.605122 173.948268 120.208153 175.362482 2.828427 196.575685 113.137085
51 52.325902 46.669048 7.071068 48.083261 130.107648 69.296465 14.142136
52 41.012193 35.355339 18.384776 36.769553 141.421356 57.982756 25.455844
53 16.970563 22.627417 76.367532 21.213203 199.404112 0.000000 83.438600
54 11.313708 16.970563 70.710678 15.556349 193.747258 5.656854 77.781746
55 16.970563 22.627417 76.367532 21.213203 199.404112 0.000000 83.438600
56 7.071068 1.414214 52.325902 2.828427 175.362482 24.041631 59.396970
57 9.899495 15.556349 69.296465 14.142136 192.333044 7.071068 76.367532
58 299.813275 294.156421 240.416306 295.570635 117.379726 316.783838 233.345238
59 11.313708 16.970563 70.710678 15.556349 193.747258 5.656854 77.781746
60 25.455844 19.798990 33.941125 21.213203 156.977705 42.426407 41.012193
61 14.142136 19.798990 73.539105 18.384776 196.575685 2.828427 80.610173
62 1.414214 4.242641 57.982756 2.828427 181.019336 18.384776 65.053824
63 48.083261 42.426407 11.313708 43.840620 134.350288 65.053824 18.384776
64 107.480231 101.823376 48.083261 103.237590 74.953319 124.450793 41.012193
65 275.771645 270.114790 216.374675 271.529004 93.338095 292.742207 209.303607
66 4.242641 1.414214 55.154329 0.000000 178.190909 21.213203 62.225397
67 16.970563 22.627417 76.367532 21.213203 199.404112 0.000000 83.438600
68 42.426407 36.769553 16.970563 38.183766 140.007143 59.396970 24.041631
69 90.509668 84.852814 31.112698 86.267027 91.923882 107.480231 24.041631
70 52.325902 46.669048 7.071068 48.083261 130.107648 69.296465 14.142136
71 12.727922 18.384776 72.124892 16.970563 195.161472 4.242641 79.195959
72 131.521861 125.865007 72.124892 127.279221 50.911688 148.492424 65.053824
73 12.727922 7.071068 46.669048 8.485281 169.705627 29.698485 53.740115
74 5.656854 11.313708 65.053824 9.899495 188.090404 11.313708 72.124892
75 16.970563 22.627417 76.367532 21.213203 199.404112 0.000000 83.438600
76 5.656854 0.000000 53.740115 1.414214 176.776695 22.627417 60.811183
77 7.071068 12.727922 66.468037 11.313708 189.504617 9.899495 73.539105
78 2.828427 2.828427 56.568542 1.414214 179.605122 19.798990 63.639610
79 2.828427 8.485281 62.225397 7.071068 185.261977 14.142136 69.296465
80 282.842712 277.185858 223.445743 278.600072 100.409163 299.813275 216.374675
8 9 10 11 12 13 14
2
3
4
5
6
7
8
9 141.421356
10 193.747258 52.325902
11 188.090404 46.669048 5.656854
12 202.232539 60.811183 8.485281 14.142136
13 164.048773 22.627417 29.698485 24.041631 38.183766
14 147.078210 5.656854 46.669048 41.012193 55.154329 16.970563
15 28.284271 169.705627 222.031529 216.374675 230.516811 192.333044 175.362482
16 154.149278 12.727922 39.597980 33.941125 48.083261 9.899495 7.071068
17 21.213203 162.634560 214.960461 209.303607 223.445743 185.261977 168.291414
18 76.367532 65.053824 117.379726 111.722871 125.865007 87.681241 70.710678
19 141.421356 0.000000 52.325902 46.669048 60.811183 22.627417 5.656854
20 183.847763 42.426407 9.899495 4.242641 18.384776 19.798990 36.769553
21 202.232539 60.811183 8.485281 14.142136 0.000000 38.183766 55.154329
22 193.747258 52.325902 0.000000 5.656854 8.485281 29.698485 46.669048
23 147.078210 5.656854 46.669048 41.012193 55.154329 16.970563 0.000000
24 192.333044 50.911688 1.414214 4.242641 9.899495 28.284271 45.254834
25 183.847763 42.426407 9.899495 4.242641 18.384776 19.798990 36.769553
26 199.404112 57.982756 5.656854 11.313708 2.828427 35.355339 52.325902
27 197.989899 56.568542 4.242641 9.899495 4.242641 33.941125 50.911688
28 173.948268 32.526912 19.798990 14.142136 28.284271 9.899495 26.870058
29 173.948268 315.369624 367.695526 362.038672 376.180808 337.997041 321.026479
30 190.918831 49.497475 2.828427 2.828427 11.313708 26.870058 43.840620
31 190.918831 49.497475 2.828427 2.828427 11.313708 26.870058 43.840620
32 138.592929 2.828427 55.154329 49.497475 63.639610 25.455844 8.485281
33 202.232539 60.811183 8.485281 14.142136 0.000000 38.183766 55.154329
34 87.681241 53.740115 106.066017 100.409163 114.551299 76.367532 59.396970
35 199.404112 57.982756 5.656854 11.313708 2.828427 35.355339 52.325902
36 117.379726 24.041631 76.367532 70.710678 84.852814 46.669048 29.698485
37 171.119841 29.698485 22.627417 16.970563 31.112698 7.071068 24.041631
38 76.367532 65.053824 117.379726 111.722871 125.865007 87.681241 70.710678
39 199.404112 57.982756 5.656854 11.313708 2.828427 35.355339 52.325902
40 185.261977 43.840620 8.485281 2.828427 16.970563 21.213203 38.183766
41 107.480231 33.941125 86.267027 80.610173 94.752309 56.568542 39.597980
42 114.551299 26.870058 79.195959 73.539105 87.681241 49.497475 32.526912
43 200.818326 59.396970 7.071068 12.727922 1.414214 36.769553 53.740115
44 183.847763 42.426407 9.899495 4.242641 18.384776 19.798990 36.769553
45 9.899495 151.320851 203.646753 197.989899 212.132034 173.948268 156.977705
46 176.776695 35.355339 16.970563 11.313708 25.455844 12.727922 29.698485
47 42.426407 98.994949 151.320851 145.663997 159.806133 121.622366 104.651804
48 200.818326 59.396970 7.071068 12.727922 1.414214 36.769553 53.740115
49 49.497475 91.923882 144.249783 138.592929 152.735065 114.551299 97.580736
50 5.656854 135.764502 188.090404 182.433550 196.575685 158.391919 141.421356
51 132.936075 8.485281 60.811183 55.154329 69.296465 31.112698 14.142136
52 144.249783 2.828427 49.497475 43.840620 57.982756 19.798990 2.828427
53 202.232539 60.811183 8.485281 14.142136 0.000000 38.183766 55.154329
54 196.575685 55.154329 2.828427 8.485281 5.656854 32.526912 49.497475
55 202.232539 60.811183 8.485281 14.142136 0.000000 38.183766 55.154329
56 178.190909 36.769553 15.556349 9.899495 24.041631 14.142136 31.112698
57 195.161472 53.740115 1.414214 7.071068 7.071068 31.112698 48.083261
58 114.551299 255.972655 308.298557 302.641702 316.783838 278.600072 261.629509
59 196.575685 55.154329 2.828427 8.485281 5.656854 32.526912 49.497475
60 159.806133 18.384776 33.941125 28.284271 42.426407 4.242641 12.727922
61 199.404112 57.982756 5.656854 11.313708 2.828427 35.355339 52.325902
62 183.847763 42.426407 9.899495 4.242641 18.384776 19.798990 36.769553
63 137.178716 4.242641 56.568542 50.911688 65.053824 26.870058 9.899495
64 77.781746 63.639610 115.965512 110.308658 124.450793 86.267027 69.296465
65 90.509668 231.931024 284.256926 278.600072 292.742207 254.558441 237.587878
66 181.019336 39.597980 12.727922 7.071068 21.213203 16.970563 33.941125
67 202.232539 60.811183 8.485281 14.142136 0.000000 38.183766 55.154329
68 142.835570 1.414214 50.911688 45.254834 59.396970 21.213203 4.242641
69 94.752309 46.669048 98.994949 93.338095 107.480231 69.296465 52.325902
70 132.936075 8.485281 60.811183 55.154329 69.296465 31.112698 14.142136
71 197.989899 56.568542 4.242641 9.899495 4.242641 33.941125 50.911688
72 53.740115 87.681241 140.007143 134.350288 148.492424 110.308658 93.338095
73 172.534055 31.112698 21.213203 15.556349 29.698485 8.485281 25.455844
74 190.918831 49.497475 2.828427 2.828427 11.313708 26.870058 43.840620
75 202.232539 60.811183 8.485281 14.142136 0.000000 38.183766 55.154329
76 179.605122 38.183766 14.142136 8.485281 22.627417 15.556349 32.526912
77 192.333044 50.911688 1.414214 4.242641 9.899495 28.284271 45.254834
78 182.433550 41.012193 11.313708 5.656854 19.798990 18.384776 35.355339
79 188.090404 46.669048 5.656854 0.000000 14.142136 24.041631 41.012193
80 97.580736 239.002092 291.327994 285.671140 299.813275 261.629509 244.658946
15 16 17 18 19 20 21
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16 182.433550
17 7.071068 175.362482
18 104.651804 77.781746 97.580736
19 169.705627 12.727922 162.634560 65.053824
20 212.132034 29.698485 205.060967 107.480231 42.426407
21 230.516811 48.083261 223.445743 125.865007 60.811183 18.384776
22 222.031529 39.597980 214.960461 117.379726 52.325902 9.899495 8.485281
23 175.362482 7.071068 168.291414 70.710678 5.656854 36.769553 55.154329
24 220.617316 38.183766 213.546248 115.965512 50.911688 8.485281 9.899495
25 212.132034 29.698485 205.060967 107.480231 42.426407 0.000000 18.384776
26 227.688384 45.254834 220.617316 123.036580 57.982756 15.556349 2.828427
27 226.274170 43.840620 219.203102 121.622366 56.568542 14.142136 4.242641
28 202.232539 19.798990 195.161472 97.580736 32.526912 9.899495 28.284271
29 145.663997 328.097546 152.735065 250.315801 315.369624 357.796031 376.180808
30 219.203102 36.769553 212.132034 114.551299 49.497475 7.071068 11.313708
31 219.203102 36.769553 212.132034 114.551299 49.497475 7.071068 11.313708
32 166.877200 15.556349 159.806133 62.225397 2.828427 45.254834 63.639610
33 230.516811 48.083261 223.445743 125.865007 60.811183 18.384776 0.000000
34 115.965512 66.468037 108.894444 11.313708 53.740115 96.166522 114.551299
35 227.688384 45.254834 220.617316 123.036580 57.982756 15.556349 2.828427
36 145.663997 36.769553 138.592929 41.012193 24.041631 66.468037 84.852814
37 199.404112 16.970563 192.333044 94.752309 29.698485 12.727922 31.112698
38 104.651804 77.781746 97.580736 0.000000 65.053824 107.480231 125.865007
39 227.688384 45.254834 220.617316 123.036580 57.982756 15.556349 2.828427
40 213.546248 31.112698 206.475180 108.894444 43.840620 1.414214 16.970563
41 135.764502 46.669048 128.693434 31.112698 33.941125 76.367532 94.752309
42 142.835570 39.597980 135.764502 38.183766 26.870058 69.296465 87.681241
43 229.102597 46.669048 222.031529 124.450793 59.396970 16.970563 1.414214
44 212.132034 29.698485 205.060967 107.480231 42.426407 0.000000 18.384776
45 18.384776 164.048773 11.313708 86.267027 151.320851 193.747258 212.132034
46 205.060967 22.627417 197.989899 100.409163 35.355339 7.071068 25.455844
47 70.710678 111.722871 63.639610 33.941125 98.994949 141.421356 159.806133
48 229.102597 46.669048 222.031529 124.450793 59.396970 16.970563 1.414214
49 77.781746 104.651804 70.710678 26.870058 91.923882 134.350288 152.735065
50 33.941125 148.492424 26.870058 70.710678 135.764502 178.190909 196.575685
51 161.220346 21.213203 154.149278 56.568542 8.485281 50.911688 69.296465
52 172.534055 9.899495 165.462987 67.882251 2.828427 39.597980 57.982756
53 230.516811 48.083261 223.445743 125.865007 60.811183 18.384776 0.000000
54 224.859956 42.426407 217.788889 120.208153 55.154329 12.727922 5.656854
55 230.516811 48.083261 223.445743 125.865007 60.811183 18.384776 0.000000
56 206.475180 24.041631 199.404112 101.823376 36.769553 5.656854 24.041631
57 223.445743 41.012193 216.374675 118.793939 53.740115 11.313708 7.071068
58 86.267027 268.700577 93.338095 190.918831 255.972655 298.399062 316.783838
59 224.859956 42.426407 217.788889 120.208153 55.154329 12.727922 5.656854
60 188.090404 5.656854 181.019336 83.438600 18.384776 24.041631 42.426407
61 227.688384 45.254834 220.617316 123.036580 57.982756 15.556349 2.828427
62 212.132034 29.698485 205.060967 107.480231 42.426407 0.000000 18.384776
63 165.462987 16.970563 158.391919 60.811183 4.242641 46.669048 65.053824
64 106.066017 76.367532 98.994949 1.414214 63.639610 106.066017 124.450793
65 62.225397 244.658946 69.296465 166.877200 231.931024 274.357431 292.742207
66 209.303607 26.870058 202.232539 104.651804 39.597980 2.828427 21.213203
67 230.516811 48.083261 223.445743 125.865007 60.811183 18.384776 0.000000
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22 23 24 25 26 27 28
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23 46.669048
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29 30 31 32 33 34 35
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30 364.867099
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36 37 38 39 40 41 42
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37 53.740115
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43 44 45 46 47 48 49
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44 16.970563
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51 127.279221
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74 185.261977 57.982756 46.669048 11.313708 5.656854 11.313708 12.727922
75 196.575685 69.296465 57.982756 0.000000 5.656854 0.000000 24.041631
76 173.948268 46.669048 35.355339 22.627417 16.970563 22.627417 1.414214
77 186.676190 59.396970 48.083261 9.899495 4.242641 9.899495 14.142136
78 176.776695 49.497475 38.183766 19.798990 14.142136 19.798990 4.242641
79 182.433550 55.154329 43.840620 14.142136 8.485281 14.142136 9.899495
80 103.237590 230.516811 241.830519 299.813275 294.156421 299.813275 275.771645
57 58 59 60 61 62 63
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58 309.712770
59 1.414214 311.126984
60 35.355339 274.357431 36.769553
61 4.242641 313.955411 2.828427 39.597980
62 11.313708 298.399062 12.727922 24.041631 15.556349
63 57.982756 251.730014 59.396970 22.627417 62.225397 46.669048
64 117.379726 192.333044 118.793939 82.024387 121.622366 106.066017 59.396970
65 285.671140 24.041631 287.085353 250.315801 289.913780 274.357431 227.688384
66 14.142136 295.570635 15.556349 21.213203 18.384776 2.828427 43.840620
67 7.071068 316.783838 5.656854 42.426407 2.828427 18.384776 65.053824
68 52.325902 257.386868 53.740115 16.970563 56.568542 41.012193 5.656854
69 100.409163 209.303607 101.823376 65.053824 104.651804 89.095454 42.426407
70 62.225397 247.487373 63.639610 26.870058 66.468037 50.911688 4.242641
71 2.828427 312.541197 1.414214 38.183766 1.414214 14.142136 60.811183
72 141.421356 168.291414 142.835570 106.066017 145.663997 130.107648 83.438600
73 22.627417 287.085353 24.041631 12.727922 26.870058 11.313708 35.355339
74 4.242641 305.470129 5.656854 31.112698 8.485281 7.071068 53.740115
75 7.071068 316.783838 5.656854 42.426407 2.828427 18.384776 65.053824
76 15.556349 294.156421 16.970563 19.798990 19.798990 4.242641 42.426407
77 2.828427 306.884343 4.242641 32.526912 7.071068 8.485281 55.154329
78 12.727922 296.984848 14.142136 22.627417 16.970563 1.414214 45.254834
79 7.071068 302.641702 8.485281 28.284271 11.313708 4.242641 50.911688
80 292.742207 16.970563 294.156421 257.386868 296.984848 281.428499 234.759451
64 65 66 67 68 69 70
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66 103.237590 271.529004
67 124.450793 292.742207 21.213203
68 65.053824 233.345238 38.183766 59.396970
69 16.970563 185.261977 86.267027 107.480231 48.083261
70 55.154329 223.445743 48.083261 69.296465 9.899495 38.183766
71 120.208153 288.499567 16.970563 4.242641 55.154329 103.237590 65.053824
72 24.041631 144.249783 127.279221 148.492424 89.095454 41.012193 79.195959
73 94.752309 263.043723 8.485281 29.698485 29.698485 77.781746 39.597980
74 113.137085 281.428499 9.899495 11.313708 48.083261 96.166522 57.982756
75 124.450793 292.742207 21.213203 0.000000 59.396970 107.480231 69.296465
76 101.823376 270.114790 1.414214 22.627417 36.769553 84.852814 46.669048
77 114.551299 282.842712 11.313708 9.899495 49.497475 97.580736 59.396970
78 104.651804 272.943218 1.414214 19.798990 39.597980 87.681241 49.497475
79 110.308658 278.600072 7.071068 14.142136 45.254834 93.338095 55.154329
80 175.362482 7.071068 278.600072 299.813275 240.416306 192.333044 230.516811
71 72 73 74 75 76 77
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73 25.455844 118.793939
74 7.071068 137.178716 18.384776
75 4.242641 148.492424 29.698485 11.313708
76 18.384776 125.865007 7.071068 11.313708 22.627417
77 5.656854 138.592929 19.798990 1.414214 9.899495 12.727922
78 15.556349 128.693434 9.899495 8.485281 19.798990 2.828427 9.899495
79 9.899495 134.350288 15.556349 2.828427 14.142136 8.485281 4.242641
80 295.570635 151.320851 270.114790 288.499567 299.813275 277.185858 289.913780
78 79
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80 280.014285 285.671140A short note about ClustGeo package
ClustGeo package is an R package specially designed to support the need of performing spatially constrained cluster analysis. More specifically, it provides a Ward-like hierarchical clustering algorithm called hclustgeo() including spatial/geographical constraints.
In a nutshell, the algorithm uses two dissimilarity matrices D0 and D1 along with a mixing parameter alpha, whereby the value of alpha must be a real number between [0, 1]. D0 can be non-Euclidean and the weights of the observations can be non-uniform. It gives the dissimilarities in the attribute/clustering variable space. D1, on the other hand, gives the dissimilarities in the constraint space. The criterion minimised at each stage is a convex combination of the homogeneity criterion calculated with D0 and the homogeneity criterion calculated with D1.
The idea is then to determine a value of alpha which increases the spatial contiguity without deteriorating too much the quality of the solution based on the variables of interest. This need is supported by a function called choicealpha().
Ward-like hierarchical clustering: ClustGeo
ClustGeo package provides function called hclustgeo() to perform a typical Ward-like hierarchical clustering just like hclust() you learned in previous section.
To perform non-spatially constrained hierarchical clustering, we only need to provide the function a dissimilarity matrix as shown in the code chunk below.
nongeo_cluster <- hclustgeo(proxmat)
plot(nongeo_cluster, cex = 0.5)
rect.hclust(nongeo_cluster,
k = 6,
border = 2:5)
class(nongeo_cluster)[1] "hclust"Mapping the clusters formed
groups <- as.factor(cutree(nongeo_cluster, k=6))myanmar_ngeo_cluster <- cbind(mmr_shp_mimu_2, as.matrix(groups)) %>%
rename(`CLUSTER` = `as.matrix.groups.`)qtm(myanmar_ngeo_cluster, "CLUSTER")
Spatially Constrained Hierarchical Clustering
Before we can perform spatially constrained hierarchical clustering, a spatial distance matrix will be derived by using st_distance() of sf package.
dist <- st_distance(mmr_shp_mimu_2, mmr_shp_mimu_2)
distmat <- as.dist(dist)Notice that as.dist() is used to convert the data frame into matrix.
Next, choicealpha() will be used to determine a suitable value for the mixing parameter alpha as shown in the code chunk below.
cr <- choicealpha(proxmat, distmat, range.alpha = seq(0, 1, 0.1), K=6, graph = TRUE)
With reference to the graphs above, alpha = 0.6 will be used as shown in the code chunk below.
clustG <- hclustgeo(proxmat, distmat, alpha = 0.6)Next, cutree() is used to derive the cluster object.
groups <- as.factor(cutree(clustG, k=6))We will then join back the group list with mmr_shp_mimu polygon feature data frame by using the code chunk below.
MMR_sf_Gcluster <- cbind(mmr_shp_mimu_2, as.matrix(groups)) %>%
rename(`CLUSTER` = `as.matrix.groups.`)We can now plot the map of the newly delineated spatially constrained clusters.
qtm(MMR_sf_Gcluster, "CLUSTER")
Visual Interpretation of Clusters
Visualising individual clustering variable
Code chunk below is used to reveal the distribution of a clustering variable (i.e Battles_2022_Incidents) by cluster.
#ggplot(data = myanmar_ngeo_cluster,
# aes(x = CLUSTER, y = Battles_2022_Incidents)) +
# geom_boxplot()References
Main reference: Kam, T.S. (2024). Geographical Segmentation with Spatially Constrained Clustering Techniques