pacman::p_load(sf, tidyverse, tmap, spdep, funModeling,readr)Take Home Ex 1 : Nigeria Water Functional & Non Functional Water Points
1. Overview.
Water is an important resource to mankind. Clean and accessible water is critical to human health. It provides a healthy environment, a sustainable economy, reduces poverty and ensures peace and security. Yet over 40% of the global population does not have access to sufficient clean water. By 2025, 1.8 billion people will be living in countries or regions with absolute water scarcity, according to UN-Water. The lack of water poses a major threat to several sectors, including food security. Agriculture uses about 70% of the world’s accessible freshwater.
Developing countries are most affected by water shortages and poor water quality. Up to 80% of illnesses in the developing world are linked to inadequate water supply and sanitation. Despite technological advancement, providing clean water to the rural community is still a major development issue in many countries globally, especially countries in the Africa continent.
To address the issue of providing clean and sustainable water supply to the rural community, a global Water Point Data Exchange (WPdx) project has been initiated. The main aim of this initiative is to collect water point related data from rural areas at the water point or small water scheme level and share the data via WPdx Data Repository, a cloud-based data library. What is so special of this project is that data are collected based on WPDx Data Standard.
2. Objective.
Geospatial analytics hold tremendous potential to address complex problems facing society. In this study, I am tasked to apply appropriate global and local measures of spatial association techniques to reveals the spatial patterns of ‘Not Functional’ water points. For the purpose of this study, Nigeria will be used as the study country.
3. Importing Geospatial Data.
3.1 Import the data.
Here, we use pacman command to load following packages -
sf (simple feature) - Used for importing and handling geospatial data in R
tidyverse - Used for data wrangling in R
spdep - Used to compute spatial weights, global & local spatial autocorrelation statistics
funmodeling - This package contains a set of functions related to exploratory data analysis, data preparation, and model performance
We are using following two datasets -
- Aspatial Data -
In following code chunk we use read_csv command of readr package to read comma separated file.
The output R object is called water_point_csv and it is a tibble data frame.
water_point_csv <- read_csv('C:/Yogendra345/ISSS624_A01/InClass_Ex02/Africa_Water/data/Water_Point.csv')After running code chunk above, we can observe that there are 406566 rows and 70 columns. This helps us to tally with source data and check if all records are correctly imported in R or not.
After importing the data file into R, it is important for us to examine if the data file has been imported correctly. The code chunk below shows list() of Base R instead of glimpse() is used to do the job.
list(water_point_csv)The output reveals that listing tibble data frame consists of 406566 rows and 70 columns. Two useful fields we are going to use in the next phase are latitude(#lat_deg) and longitude(#lon_deg). Note that they are in decimal degree format. As a best guess, we will assume that the data is in wgs84 Geographic Coordinate System.
- Geospatial Data -
geoBoundaries-NGA-ADM2 - Nigeria Level-2 Administrative Boundary (also known as Local Government Area) polygon features GIS data will be used in this take-home exercise. The data can be downloaded either from The Humanitarian Data Exchange portal or geoBoundaries.
The code chunk below uses st_read() function of sf package to import geoBoundaries-NGA-ADM2 shapefile into R as a polygon feature data frame. Note that when the input geospatial data is in shapefile format, two arguments will be used, namely:
dsnto define the data path andlayerto provide the shapefile name. Also note that no extension such as .shp, .dbf, .prj and .shx are needed.wp = st_read(dsn = "C:/Yogendra345/ISSS624_A01/InClass_Ex02/Africa_Water/data/new_data1", layer = "geo_Export", crs = 4326)%>% filter(clean_coun == "Nigeria")
3.2 Quick Visualization of Imported Data.
In geospatial data science, by looking at the feature information is not enough. We are also interested to visualize the geospatial features. Here we will use basic command plot() of R Graphic that comes in very handy as shown in the code chunk below.
plot(wp)plot(st_geometry(wp))plot(wp)The default plot of an sf object is a multi-plot of all attributes, up to a reasonable maximum as shown above. We can, however, choose to plot only the geometry by using the code chunk below.
plot(st_geometry(wp))3.3 Assigning EPSG code to a simple feature data frame
One of the common issue that can happen during importing geospatial data into R is that the coordinate system of the source data was either missing (such as due to missing .proj for ESRI shapefile) or wrongly assigned during the importing process.
This is an example the coordinate system of wp2_nga simple feature data frame by using st_crs() of sf package as shown in the code chunk below.
st_crs(wp)3.4 Create rds data format file.
Next, write_rds() of readr package is used to save the extracted sf data table (i.e. wp) into an output file in rds data format. The output file is called .rds and it is saved in new_data1 sub-folder.
write_rds(wp, "C:/Yogendra345/ISSS624_A01/InClass_Ex02/Africa_Water/data/new_data1/RDS/wp_nga.rds")3.5 Transforming the projection of wp2_nga from wgs84 to EPSG 26391.
Projected Coordinate Systems of Nigeria, they are: EPSG: 26391, 26392, and 26303. We can use any one of them.
wp2_nga_EPSG26391 <- st_transform(wp, 26391)st_crs(wp)st_crs(wp2_nga_EPSG26391)3.6 Importing Nigeria LGA boundary data.
Now, we are going to import the LGA boundary data into R environment by using the code chunk below.
nga <- st_read(dsn = "C:/Yogendra345/ISSS624_A01/InClass_Ex02/Africa_Water/data/new_data1",layer = "geo_export",crs = 4326)Thing to learn from the code chunk above.
st_read()of sf package is used to import geoBoundaries-NGA-ADM2 shapefile into R environment and save the imported geospatial data into simple feature data table.
4. Data Wrangling.
4.1 Recoding NA values into string.
In the code chunk below, replace_na() is used to re-code all the NA values in status_cle field into Unknown.
#|eval: false
wp_nga <- read_rds("C:/Yogendra345/ISSS624_A01/InClass_Ex02/Africa_Water/data/new_data1/RDS/wp_nga.rds") %>%
mutate(status_cle = replace_na(status_cle, "Unknown"))4.2 Exploratory Data Analysis (EDA).
In the code chunk below, freq() of funModeling package is used to display the distribution of status_cle field in wp_nga.
freq(data=wp_nga,
input = 'status_cle')Above bar plot shows us that, we have 48.29% functional water points and 30.93% non-functional water points.
Total number of water points 7 + 175 + 234 + 1686 + 2403 + 4579 + 10656 + 29385 + 45883 = 95008.
5. Extracting Water Point Data.
In this section, we will extract the water point records by using classes in status_cle field. If we see the source data, we observe that water points are classified as -
Functional
Functional but not in use
Functional but needs repair
Unknown
Non-functional
Non-functional due to dry season
Abandoned / Decommissioned
5.1 Extracting funtional water point.
wpt_functional <- wp_nga %>%
filter(status_cle %in%
c("Functional",
"Functional but not in use",
"Functional but needs repair"))Let us draw a bar plot graph to visualize water points using freq function of funmodeling package.
freq(data=wpt_functional,
input = 'status_cle')5.2 Extracting ‘Non-funtional’ water point.
In the code chunk below, filter() of dplyr is used to select non-functional water points.
wpt_nonfunctional <- wp_nga %>%
filter(status_cle %in%
c("Abandoned/Decommissioned",
"Abandoned",
"Non-Functional",
"Non functional due to dry season",
"Non-Functional due to dry season"))freq(data=wpt_nonfunctional,
input = 'status_cle')5.4 Extracting water point with ‘Unknown’ class.
In the code chunk below, filter() of dplyr is used to select water points with unknown status.
wpt_unknown <- wp_nga %>%
filter(status_cle == "Unknown")5.4 Performing Point-in-Polygon Count.
nga_wp <- nga %>%
mutate(`total wpt` = lengths(
st_intersects(nga, wp_nga))) %>%
mutate(`wpt functional` = lengths(
st_intersects(nga, wpt_functional))) %>%
mutate(`wpt non-functional` = lengths(
st_intersects(nga, wpt_nonfunctional))) %>%
mutate(`wpt unknown` = lengths(
st_intersects(nga, wpt_unknown)))5.5 Saving the Analytical Data Table.
nga_wp <- nga_wp %>%
mutate(pct_functional = `wpt functional`/`total wpt`) %>%
mutate(`pct_non-functional` = `wpt non-functional`/`total wpt`) %>%
select(3:4, 9:10, 18:23)Things to learn from the code chunk above:
mutate()of dplyr package is used to derive two fields namely pct_functional and pct_non-functional.to keep the file size small,
select()of dplyr is used to retain only field 3,4,9,10, 18,19,20,21,22,and 23.
Now, we have the tidy sf data table subsequent analysis. We will save the sf data table into rds format.
write_rds(nga_wp, "C:/Yogendra345/ISSS624_A01/InClass_Ex02/Africa_Water/data/new_data1/RDS/nga_wp.rds")6. Visualizing the spatial distribution of water points.
nga_wp <- read_rds("C:/Yogendra345/ISSS624_A01/InClass_Ex02/Africa_Water/data/new_data1/RDS/nga_wp1.rds")
total <- qtm(nga_wp, "total wpt")+
tm_layout(legend.outside = FALSE,
legend.stack = "vertical",
legend.text.size =0.30,
legend.title.size=0.4)
wp_functional <- qtm(nga_wp, "wpt functional")+
tm_layout(legend.outside = FALSE,
legend.stack = "vertical",
legend.text.size =0.3,
legend.title.size=0.4)
wp_nonfunctional <- qtm(nga_wp, "wpt non-functional")+
tm_layout(legend.outside = FALSE,
legend.stack = "vertical",
legend.text.size =0.3,
legend.title.size=0.4)
unknown <- qtm(nga_wp, "wpt unknown")+
tm_layout(legend.outside = FALSE,
legend.stack = "vertical",
legend.text.size =0.35,
legend.title.size=0.4)
tmap_arrange(total, wp_functional, wp_nonfunctional, unknown, asp=1, ncol=2)
tm_shape(nga_wp)+
tm_fill(c("wpt non-functional", "wpt functional"),
style = "equal",
palette = "Blues") +
tm_layout(legend.position = c("right", "bottom")) +
tm_borders(alpha = 0.7) +
tmap_style("classic")tmap style set to "classic"other available styles are: "white", "gray", "natural", "cobalt", "col_blind", "albatross", "beaver", "bw", "watercolor" 
7. Computing Contiguity Spatial Weights.
In this section we will further analyse using poly2nb() of spdep package to compute contiguity weight matrices for the study area. This function builds a neighbours list based on regions with contiguous boundaries.
IMPORTANT NOTE - We can pass a “queen” argument that takes TRUE or FALSE as options. If we do not specify this argument the default is set to TRUE, that is, if we don’t specify queen = FALSE this function will return a list of first order neighbours using the Queen criteria.
7.1 Computing (QUEEN) contiguity based neighbors.
The code chunk below is used to compute Queen contiguity weight matrix.
wm_q <- poly2nb(nga_wp, queen=TRUE)
summary(wm_q)Neighbour list object:
Number of regions: 774
Number of nonzero links: 4440
Percentage nonzero weights: 0.7411414
Average number of links: 5.736434
1 region with no links:
86
Link number distribution:
0 1 2 3 4 5 6 7 8 9 10 11 12 14
1 2 14 57 125 182 140 122 72 41 12 4 1 1
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 linksThe summary report above shows that there are 774 area units in Nigeria.
The most connected unit has 14 neighbors and there are 2 regions with just one neighbor.
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, type:
wm_q[[1]][1] 2 548 624 721Polygon 1 has 4 neighbors.
wm_q[[508]] [1] 20 106 123 171 174 239 402 419 468 471 494 511 644 753Polygon 508 has 14 neighbors.
nga_wp$shapeName[508][1] "Mokwa"nga_wp$shapeName[c(20,106,123,171,174,239,402,419,468,471,494,511,644,753)] [1] "Agaie" "Bida" "Borgu" "Edati" "Edu" "Gbako"
[7] "Kaiama" "Katcha" "Lavun" "Lokoja" "Mashegu" "Moro"
[13] "Pategi" "Wushishi"We can retrieve the Functional Water Points of these 14 regions by using the code chunk below.
nb1 <- wm_q[[508]]
nb1 <- nga_wp$`wpt functional`[nb1]
nb1 [1] 47 211 80 103 126 101 114 22 65 41 18 101 154 72str(wm_q)List of 774
$ : int [1:4] 2 548 624 721
$ : int [1:3] 1 624 721
$ : int [1:3] 261 447 507
$ : int [1:7] 257 263 436 446 454 466 709
$ : int [1:5] 203 208 331 617 738
$ : int [1:7] 170 217 218 337 379 553 758
$ : int [1:6] 8 176 214 281 349 555
$ : int [1:4] 7 214 544 555
$ : int [1:5] 18 104 337 601 757
$ : int [1:7] 25 216 325 364 365 528 632
$ : int [1:7] 26 27 43 157 191 524 565
$ : int [1:8] 135 263 417 446 520 690 695 709
$ : int [1:5] 31 37 471 583 584
$ : int [1:8] 170 362 363 546 577 581 589 626
$ : int [1:7] 49 82 177 297 306 352 580
$ : int [1:5] 30 187 328 357 360
$ : int [1:3] 35 638 639
$ : int [1:5] 9 19 104 576 601
$ : int [1:6] 18 103 104 376 574 576
$ : int [1:5] 419 466 471 508 641
$ : int [1:5] 61 162 269 520 596
$ : int [1:3] 49 297 326
$ : int [1:5] 54 291 537 618 619
$ : int [1:4] 123 527 673 761
$ : int [1:7] 10 181 216 314 325 366 552
$ : int [1:4] 11 27 191 562
$ : int [1:5] 11 26 562 565 762
$ : int [1:7] 29 173 300 315 316 358 369
$ : int [1:7] 28 173 182 358 378 460 591
$ : int [1:8] 16 38 39 186 192 329 357 360
$ : int [1:6] 13 94 211 471 561 584
$ : int [1:3] 51 62 693
$ : int [1:6] 166 227 238 655 743 750
$ : int [1:7] 42 104 213 330 553 559 757
$ : int [1:7] 17 275 295 378 460 638 639
$ : int [1:8] 50 107 164 247 408 432 455 759
$ : int [1:11] 13 38 40 211 212 320 570 583 584 620 ...
$ : int [1:7] 30 37 39 40 41 192 320
$ : int [1:4] 30 38 186 320
$ : int [1:4] 37 38 41 620
$ : int [1:5] 38 40 192 620 634
$ : int [1:4] 34 136 137 559
$ : int [1:3] 11 157 524
$ : int [1:6] 45 290 303 328 360 634
$ : int [1:3] 44 290 303
$ : int [1:4] 438 521 668 742
$ : int [1:5] 166 234 238 698 750
$ : int [1:4] 113 265 386 701
$ : int [1:7] 15 22 51 297 326 580 623
$ : int [1:8] 36 98 107 409 416 432 681 696
$ : int [1:8] 32 49 62 207 461 580 623 693
$ : int [1:6] 53 78 80 165 602 636
$ : int [1:8] 52 80 199 280 602 621 622 739
$ : int [1:7] 23 79 293 294 532 537 618
$ : int [1:3] 122 430 605
$ : int [1:5] 77 376 533 576 728
$ : int [1:4] 58 199 322 621
$ : int [1:7] 57 322 323 522 523 621 622
$ : int [1:5] 88 128 493 700 714
$ : int [1:7] 61 158 561 578 592 596 626
$ : int [1:5] 21 60 269 596 626
$ : int [1:6] 32 51 207 461 462 693
$ : int [1:6] 90 237 384 416 467 765
$ : int [1:8] 65 74 109 113 131 148 251 407
$ : int [1:5] 64 74 113 265 701
$ : int [1:6] 103 104 288 351 559 574
$ : int [1:7] 304 348 511 594 609 640 694
$ : int [1:2] 157 191
$ : int [1:9] 115 140 146 248 273 274 473 500 512
$ : int [1:5] 71 301 341 343 610
$ : int [1:9] 70 173 298 299 301 343 344 550 625
$ : int [1:8] 73 361 594 607 609 638 639 665
$ : int [1:6] 72 361 374 377 665 666
$ : int [1:6] 64 65 109 683 701 754
$ : int [1:7] 272 398 422 433 485 501 768
$ : int [1:8] 254 287 427 459 547 647 677 751
$ : int [1:6] 56 533 534 579 716 728
$ : int [1:7] 52 79 80 165 215 532 579
$ : int [1:5] 54 78 532 579 618
$ : int [1:5] 52 53 78 215 739
$ : int [1:5] 99 145 233 426 689
$ : int [1:3] 15 352 580
$ : int [1:4] 132 258 383 414
$ : int [1:5] 123 148 437 673 692
$ : int [1:7] 105 156 394 654 675 707 712
$ : int 0
$ : int [1:6] 151 221 226 399 410 486
$ : int [1:6] 59 150 489 648 700 714
$ : int [1:7] 260 408 416 463 674 681 759
$ : int [1:9] 63 163 232 236 237 452 497 710 765
$ : int [1:4] 160 271 406 440
$ : int [1:6] 119 390 392 487 656 668
$ : int [1:6] 123 354 402 607 665 666
$ : int [1:8] 31 158 436 471 520 561 596 709
$ : int [1:6] 391 392 405 469 656 708
$ : int [1:7] 97 139 389 403 420 451 653
$ : int [1:5] 96 389 451 662 773
$ : int [1:5] 50 231 432 696 708
$ : int [1:5] 81 426 689 760 769
[list output truncated]
- attr(*, "class")= chr "nb"
- attr(*, "region.id")= chr [1:774] "1" "2" "3" "4" ...
- attr(*, "call")= language poly2nb(pl = nga_wp, queen = TRUE)
- attr(*, "type")= chr "queen"
- attr(*, "sym")= logi TRUE7.2 Creating (ROOK) contiguity based neighbors.
The code chunk below is used to compute Rook contiguity weight matrix.
wm_r <- poly2nb(nga_wp, queen=FALSE)
summary(wm_r)Neighbour list object:
Number of regions: 774
Number of nonzero links: 4420
Percentage nonzero weights: 0.7378029
Average number of links: 5.710594
1 region with no links:
86
Link number distribution:
0 1 2 3 4 5 6 7 8 9 10 11 12 14
1 2 14 59 127 181 141 124 66 42 11 4 1 1
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 linksThe summary report above shows that there are 774 area units in Nigeria. The most connect area unit which is 508, has 14 neighbors. There are two area units with only one neighbors.
7.3 Visualizing contiguity weights.
A connectivity graph takes a point and displays a line to each neighboring point. We are working with polygons at the moment, so we will need to get points in order to make our connectivity graphs. The most typically method for this will be polygon centroids. We will calculate these in the sf package before moving onto the graphs. Getting Latitude and Longitude of Polygon Centroids.
longitude <- map_dbl(nga_wp$geometry, ~st_centroid(.x)[[1]])We do the same for latitude with one key difference. We access the second value per each centroid with [[2]].
latitude <- map_dbl(nga_wp$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)We check the first few observations to see if things are formatted correctly.
head(coords) longitude latitude
[1,] 7.372450 5.113107
[2,] 7.352131 5.083219
[3,] 13.322900 13.428835
[4,] 6.847325 8.825812
[5,] 7.771541 5.022061
[6,] 8.219654 6.2598457.3.1 Plotting Queen contiguity based neighbors map.
plot(nga_wp$geometry, border="lightgrey")
plot(wm_q, coords, pch = 19, cex = 0.36, add = TRUE, col= "red")
7.3.2 Plotting Rook contiguity based neighbors map.
plot(nga_wp$geometry, border="lightgrey")
plot(wm_r, coords, pch = 19, cex = 0.36, add = TRUE, col = "red")
7.3.3 Plotting both Queen and Rook contiguity based neighbors maps.
par(mfrow=c(1,2))
plot(nga_wp$geometry, border="lightgrey")
plot(wm_q, coords, pch = 19, cex = 0.26, add = TRUE, col= "red", main="Queen Contiguity")
plot(nga_wp$geometry, border="lightgrey")
plot(wm_r, coords, pch = 19, cex = 0.26, add = TRUE, col = "red", main="Rook Contiguity")
7.4 Computing distance based neighbors.
In this section, we will derive distance-based weight matrices by using dnearneigh() of spdep package.
The function identifies neighbors 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.
7.4.1 Determine the cut-off distance
Firstly, we need to determine the upper limit for distance band by using the steps below:
Return a matrix with the indices of points belonging to the set of the k nearest neighbors of each other by using knearneigh() of spdep.
Convert the knn object returned by knearneigh() into a neighbors list of class nb with a list of integer vectors containing neighbour region number ids by using knn2nb().
Return the length of neighbour relationship edges by using nbdists() of spdep. The function returns in the units of the coordinates if the coordinates are projected, in km otherwise.
Remove the list structure of the returned object by using unlist().
#coords <- coordinates(hunan)
k1 <- knn2nb(knearneigh(coords))
k1dists <- unlist(nbdists(k1, coords, longlat = TRUE))
summary(k1dists) Min. 1st Qu. Median Mean 3rd Qu. Max.
2.662 12.815 20.242 22.031 27.706 71.661 The summary report shows that the largest first nearest neighbor distance is 71.661 km, so using this as the upper threshold gives certainty that all units will have at least one neighbor.
7.4.2 Computing fixed distance weight matrix.
Now, we will compute the distance weight matrix by using dnearneigh() as shown in the code chunk below.
wm_d72 <- dnearneigh(coords, 0, 72, longlat = TRUE)
wm_d72Neighbour list object:
Number of regions: 774
Number of nonzero links: 18112
Percentage nonzero weights: 3.023323
Average number of links: 23.40052 Next, we will use str() to display the content of wm_d72 weight matrix.
str(wm_d72)List of 774
$ : int [1:63] 2 5 10 25 55 66 68 103 122 181 ...
$ : int [1:62] 1 5 10 25 55 66 68 103 122 181 ...
$ : int [1:2] 261 447
$ : int [1:10] 12 20 257 263 446 454 466 641 690 695
$ : int [1:56] 1 2 55 66 104 136 137 169 184 202 ...
$ : int [1:21] 9 14 18 19 56 170 217 218 330 337 ...
$ : int [1:19] 8 15 22 176 177 214 281 282 283 295 ...
$ : int [1:32] 7 15 22 49 176 177 214 275 276 277 ...
$ : int [1:26] 6 18 19 56 66 77 103 104 217 218 ...
$ : int [1:63] 1 2 23 25 66 103 181 191 203 204 ...
$ : int [1:22] 26 27 43 68 126 157 190 191 204 336 ...
$ : int [1:11] 4 135 257 263 401 417 429 446 454 690 ...
$ : int [1:13] 31 37 38 40 94 211 320 393 436 471 ...
$ : int [1:24] 6 170 193 194 195 217 309 310 311 362 ...
$ : int [1:27] 7 8 22 32 49 51 62 82 176 177 ...
$ : int [1:37] 30 38 39 41 44 45 70 71 120 124 ...
$ : int [1:34] 28 29 35 72 172 173 178 179 182 275 ...
$ : int [1:29] 6 9 19 56 66 77 103 104 217 218 ...
$ : int [1:41] 6 9 18 25 56 66 77 103 104 181 ...
$ : int [1:7] 4 106 239 263 419 454 466
$ : int [1:9] 60 61 162 269 484 520 578 596 626
$ : int [1:31] 7 8 15 32 49 51 62 82 176 177 ...
$ : int [1:64] 10 25 52 53 54 56 58 77 78 79 ...
$ : int [1:5] 123 476 527 673 761
$ : int [1:68] 1 2 10 19 23 54 56 66 77 103 ...
$ : int [1:30] 11 27 43 68 157 190 191 204 336 370 ...
$ : int [1:24] 11 26 43 68 157 191 204 336 370 371 ...
$ : int [1:43] 17 29 35 70 71 124 172 173 178 179 ...
$ : int [1:45] 17 28 35 70 71 124 172 173 178 179 ...
$ : int [1:30] 16 38 39 40 41 44 45 175 185 186 ...
$ : int [1:13] 13 37 94 158 210 211 212 289 308 561 ...
$ : int [1:28] 15 22 49 51 62 82 177 196 207 214 ...
$ : int [1:29] 47 111 130 142 145 155 166 219 227 233 ...
$ : int [1:11] 42 86 104 136 137 213 375 553 559 733 ...
$ : int [1:32] 17 28 29 172 173 178 179 182 275 276 ...
$ : int [1:8] 50 107 247 408 432 455 681 759
$ : int [1:21] 13 31 38 39 40 41 186 192 197 198 ...
$ : int [1:25] 13 16 30 37 39 40 41 44 186 192 ...
$ : int [1:27] 16 30 37 38 40 41 44 185 186 192 ...
$ : int [1:21] 13 30 37 38 39 41 44 186 192 211 ...
$ : int [1:22] 16 30 37 38 39 40 44 45 186 192 ...
$ : int [1:20] 34 86 136 137 184 202 285 286 375 499 ...
$ : int [1:19] 11 26 27 68 122 126 157 190 191 246 ...
$ : int [1:27] 16 30 38 39 40 41 45 70 175 186 ...
$ : int [1:27] 16 30 41 44 70 175 187 188 192 290 ...
$ : int [1:12] 119 380 387 417 423 429 438 459 521 656 ...
$ : int [1:24] 33 111 127 130 155 166 227 234 238 242 ...
$ : int [1:12] 64 65 74 113 131 265 386 407 428 482 ...
$ : int [1:30] 8 15 22 32 51 62 82 176 177 207 ...
$ : int [1:4] 36 107 409 432
$ : int [1:27] 15 22 32 49 62 82 177 207 214 284 ...
$ : int [1:47] 23 53 54 57 58 77 78 79 80 165 ...
$ : int [1:37] 23 52 54 57 58 78 79 80 165 189 ...
$ : int [1:58] 23 25 52 53 56 57 58 77 78 79 ...
$ : int [1:33] 1 2 5 68 122 157 169 184 190 208 ...
$ : int [1:51] 6 9 18 19 23 25 54 66 77 78 ...
$ : int [1:34] 52 53 54 58 78 79 80 165 189 197 ...
$ : int [1:37] 23 52 53 54 57 78 79 165 189 197 ...
$ : int [1:5] 128 129 493 700 748
$ : int [1:14] 21 61 158 269 310 311 561 563 578 589 ...
$ : int [1:11] 21 60 162 268 269 484 578 589 592 596 ...
$ : int [1:28] 15 22 32 49 51 82 177 196 207 214 ...
$ : int [1:5] 384 416 467 765 772
$ : int [1:7] 48 65 74 113 131 265 407
$ : int [1:11] 48 64 74 109 113 265 386 407 683 701 ...
$ : int [1:47] 1 2 5 9 10 18 19 25 56 103 ...
$ : int [1:25] 72 120 124 179 182 304 305 346 347 348 ...
$ : int [1:30] 1 2 11 26 27 43 55 122 157 190 ...
$ : int [1:8] 140 146 248 274 473 500 512 513
$ : int [1:44] 16 28 29 44 45 71 120 124 172 173 ...
$ : int [1:49] 16 28 29 70 120 124 172 173 175 178 ...
$ : int [1:18] 17 67 182 361 374 378 404 566 567 568 ...
$ : int [1:6] 361 374 377 404 665 666
$ : int [1:14] 48 64 65 109 113 116 251 265 672 683 ...
$ : int [1:15] 110 229 255 258 272 373 382 398 422 433 ...
$ : int [1:9] 254 287 427 459 470 547 647 677 751
$ : int [1:55] 9 18 19 23 25 52 54 56 78 79 ...
$ : int [1:51] 23 52 53 54 56 57 58 77 79 80 ...
$ : int [1:57] 23 52 53 54 56 57 58 77 78 80 ...
$ : int [1:39] 23 52 53 54 57 77 78 79 165 189 ...
$ : int [1:19] 99 145 227 233 242 255 270 426 449 483 ...
$ : int [1:21] 15 22 32 49 51 62 177 207 214 297 ...
$ : int [1:6] 132 258 383 414 529 767
$ : int [1:3] 148 437 692
$ : int [1:38] 101 105 130 142 145 155 156 219 235 242 ...
$ : int [1:17] 34 42 136 137 184 202 285 286 499 538 ...
$ : int [1:19] 147 149 151 221 226 245 267 399 410 415 ...
$ : int [1:5] 150 489 648 700 714
$ : int [1:12] 100 107 159 260 408 458 463 542 674 676 ...
$ : int 237
$ : int [1:3] 160 271 406
$ : int [1:11] 95 119 390 391 392 423 487 642 656 668 ...
$ : int [1:3] 354 607 665
$ : int [1:7] 13 31 158 436 561 596 709
$ : int [1:10] 92 390 391 392 405 423 469 656 708 770
$ : int [1:17] 97 108 139 167 168 350 389 403 412 420 ...
$ : int [1:13] 96 108 114 139 147 168 389 403 420 451 ...
$ : int [1:4] 153 231 432 696
$ : int [1:18] 81 145 154 167 227 233 255 270 426 449 ...
[list output truncated]
- attr(*, "class")= chr "nb"
- attr(*, "region.id")= chr [1:774] "1" "2" "3" "4" ...
- attr(*, "call")= language dnearneigh(x = coords, d1 = 0, d2 = 72, longlat = TRUE)
- attr(*, "dnn")= num [1:2] 0 72
- attr(*, "bounds")= chr [1:2] "GE" "LE"
- attr(*, "nbtype")= chr "distance"
- attr(*, "sym")= logi TRUEAnother way to display the structure of the weight matrix is to combine table() and card() of spdep.
table(nga_wp$shapeName, card(wm_d72))
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Aba North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aba South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abadam 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abaji 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Abak 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abakaliki 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Abeokuta North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Abeokuta South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aboh-Mbaise 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abua/Odual 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Abuja Municipal 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Adavi 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ado 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado-Odo/Ota 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado Ekiti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Afijio 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Afikpo North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Afikpo South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agaie 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agatu 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Agege 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aguata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agwara 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahiazu-Mbaise 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahoada East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahoada West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyedade 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyedire 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyekire (Gbonyin) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ajaokuta 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ajeromi-Ifelodun 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ajingi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akamkpa 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Akinyele 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akko 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko-Edo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Akoko North East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko North West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko South East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Akoko South West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Akpabuyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Akuku Toru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Akure North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akure South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akwanga 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Albasu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aleiro 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Alimosho 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Alkaleri 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Amuwo-Odofin 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anambra East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anambra West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anaocha 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Andoni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aninri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aniocha North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aniocha South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anka 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ankpa 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Apa 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Apapa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ardo-Kola 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Arewa-Dandi 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Argungu 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Arochukwu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Asa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Asari-Toru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Askira/Uba 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atakumosa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atakumosa West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atiba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Atigbo 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Augie 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Auyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Awe 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Awgu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awka North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awka South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ayamelum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Babura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Badagry 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Bade 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bagudo 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bagwai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bakassi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Bakori 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Bakura 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Balanga 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Bali 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bama 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Barikin Ladi 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Baruten 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bassa 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Batagarawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Batsari 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Bauchi 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Baure 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Bayo 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Bebeji 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bekwara 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Bende 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Biase 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bichi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bida 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Billiri 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bindawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Binji 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Biriniwa 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Birni Kudu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Birnin-Gwari 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Birnin Kebbi 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Birnin Magaji 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Biu 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bodinga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Bogoro 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Boki 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bokkos 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Boluwaduro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bomadi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Bonny 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Borgu 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Boripe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bosso 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Brass 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Buji 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Bukkuyum 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bungudu 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bunkure 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bunza 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bursari 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Buruku 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Burutu 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Bwari 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Calabar-Municipal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Calabar South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Chanchaga 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Charanchi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Chibok 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Chikun 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dala 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Damaturu 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Damban 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dambatta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Damboa 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dan Musa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Dandi 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dandume 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Dange-Shuni 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Danja 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Darazo 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dass 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Daura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Dawakin Kudu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dawakin Tofa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Degema 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dekina 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Demsa 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Dikwa 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Doguwa 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Doma 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Donga 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dukku 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dunukofia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dutse 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Dutsi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dutsin-Ma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Eastern Obolo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ebonyi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Edati 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ede North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ede South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Edu 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Efon 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Egbado North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Egbado South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Egbeda 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Egbedore 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Egor 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ehime-Mbano 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ejigbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekeremor 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Eket 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekiti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekiti East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekiti South West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekiti West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekwusigo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Eleme 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Emohua 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Emure 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Enugu East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Enugu North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Enugu South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Epe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Esan Central 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Esan North East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Esan South East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Esan West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Ese-Odo 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Esit - Eket 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Essien Udim 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Etche 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ethiope East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Ethiope West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Eti-Osa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Etim Ekpo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Etinan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Etsako Central 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Etsako East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Etsako West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Etung 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ewekoro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ezeagu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ezinihitte 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ezza North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ezza South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fagge 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fakai 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Faskari 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Fika 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fufore 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Funakaye 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Fune 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Funtua 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Gabasawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gada 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gagarawa 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Gamawa 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Ganjuwa 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ganye 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Garki 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Garko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Garum Mallam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gashaka 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gassol 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gaya 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gbako 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Gboko 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Geidam 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gezawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Giade 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Girei 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Giwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Gokana 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gombe 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Gombi 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Goronyo 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Gubio 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gudu 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gujba 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gulani 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Guma 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Gumel 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Gummi 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gurara 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Guri 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Gusau 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Guyuk 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Guzamala 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gwadabawa 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Gwagwalada 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
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Ogbadibo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Ogbaru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogbia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Ogbomosho North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogbomosho South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogo Oluwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogoja 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Ogori/Magongo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Ogu/Bolo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogun waterside 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Oguta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohafia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohaji/Egbema 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohaozara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohaukwu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohimini 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Oji-River 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ojo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oju 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Oke-Ero 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okehi 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Okene 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Okigwe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okitipupa 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Okobo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okpe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Okpokwu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Okrika 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ola-oluwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Olamabolo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Olorunda 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Olorunsogo 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Oluyole 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Omala 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Omumma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ona-Ara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ondo East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ondo West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onicha 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onitsha North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onitsha South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onna 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Opobo/Nkoro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oredo 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Orelope 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orhionmwon 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ori Ire 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Oriade 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orlu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orolu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oron 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orsu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oru East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oru West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oruk Anam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orumba North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orumba South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ose 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Oshimili North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oshimili South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oshodi-Isolo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Osisioma Ngwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Osogbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oturkpo 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ovia North East 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ovia South West 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Owan East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Owan West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Owerri-Municipal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owerri North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owerri West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Oye 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyigbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyo East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyo West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyun 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Paikoro 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Pankshin 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Patani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Pategi 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Port-Harcourt 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Potiskum 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Qua'an Pan 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Rabah 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Rafi 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rano 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Remo North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rijau 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rimi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Rimin Gado 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ringim 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Riyom 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Rogo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Roni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sabon-Gari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Sabon Birni 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sabuwa 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Safana 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Sagbama 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Sakaba 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Saki East 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Saki West 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sandamu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Sanga 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Sapele 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Sardauna 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shagamu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shagari 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Shanga 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shani 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Shanono 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shelleng 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Shendam 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shinkafi 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shira 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Shiroro 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomgom 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomolu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Silame 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Soba 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Sokoto North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Sokoto South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Song 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Southern Ijaw 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Sule-Tankarkar 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Suleja 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Sumaila 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Suru 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Surulere 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tafa 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Tafawa-Balewa 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Takai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Takum 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Talata Mafara 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tambuwal 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Tangaza 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Tarauni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarka 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Tarmua 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Taura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Tofa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toro 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toto 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toungo 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tsafe 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Tsanyawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tudun Wada 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Tureta 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Udenu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Udi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Udung Uko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ughelli North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ughelli South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Ugwunagbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uhunmwonde 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Ukanafun 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukum 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwa West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwuani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umu-Nneochi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umuahia North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umuahia South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ungogo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Unuimo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uruan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Urue-Offong/Oruko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ushongo 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Ussa 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uvwie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Uyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uzo-Uwani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Vandeikya 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Wamako 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Wamba 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Warawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warji 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warri North 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Warri South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Warri South West 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Wasagu/Danko 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wase 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wudil 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wukari 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wurno 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Wushishi 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yabo 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Yagba East 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Yagba West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Yakurr 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Yala 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Yamaltu/Deba 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Yankwashi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Yauri 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yenegoa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Yola North 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yola South 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Yorro 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yunusari 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yusufari 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zaki 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Zango 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Zango-Kataf 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Zaria 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Zing 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Zurmi 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zuru 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Aba North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aba South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abadam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abaji 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abak 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abakaliki 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abeokuta North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abeokuta South 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Abi 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aboh-Mbaise 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abua/Odual 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abuja Municipal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Adavi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado-Odo/Ota 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado Ekiti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Afijio 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Afikpo North 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Afikpo South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Agaie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agatu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agege 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Aguata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agwara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahiazu-Mbaise 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahoada East 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Ahoada West 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyedade 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyedire 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyekire (Gbonyin) 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Ajaokuta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ajeromi-Ifelodun 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Ajingi 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Akamkpa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akinyele 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Akko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko-Edo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko North East 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko North West 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko South East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko South West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akpabuyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akuku Toru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akure North 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akure South 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akwanga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Albasu 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aleiro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Alimosho 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Alkaleri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Amuwo-Odofin 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anambra East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anambra West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Anaocha 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Andoni 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Aninri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aniocha North 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Aniocha South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Anka 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ankpa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Apa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Apapa 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Ardo-Kola 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Arewa-Dandi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Argungu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Arochukwu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Asa 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Asari-Toru 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Askira/Uba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atakumosa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atakumosa West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atiba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atigbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Augie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Auyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awgu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awka North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awka South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ayamelum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Babura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Badagry 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bade 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bagudo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bagwai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Bakassi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bakori 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bakura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Balanga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bali 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bama 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Barikin Ladi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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Dawakin Kudu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dawakin Tofa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Degema 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Sakaba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Saki East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Saki West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sandamu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sanga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sapele 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sardauna 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shagamu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shagari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shanga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shanono 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shelleng 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shendam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shinkafi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shira 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shiroro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomgom 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomolu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Silame 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Soba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sokoto North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sokoto South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Song 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Southern Ijaw 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sule-Tankarkar 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Suleja 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sumaila 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Suru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Surulere 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tafa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tafawa-Balewa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Takai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Takum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Talata Mafara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tambuwal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tangaza 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarauni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarka 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarmua 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Taura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tofa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toto 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toungo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tsafe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tsanyawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tudun Wada 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tureta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udenu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udung Uko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ughelli North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ughelli South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ugwunagbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Uhunmwonde 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukanafun 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Ukum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Ukwa West 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Ukwuani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umu-Nneochi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umuahia North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umuahia South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ungogo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Unuimo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uruan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Urue-Offong/Oruko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ushongo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ussa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uvwie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uyo 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Uzo-Uwani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Vandeikya 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wamako 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wamba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warji 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warri North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warri South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warri South West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wasagu/Danko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wase 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wudil 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wukari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wurno 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wushishi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yabo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yagba East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yagba West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yakurr 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yala 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yamaltu/Deba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yankwashi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yauri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yenegoa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yola North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yola South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yorro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yunusari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yusufari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zaki 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zango 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zango-Kataf 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zaria 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zurmi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zuru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
61 62 63 64 65 67 68 70
Aba North 0 0 1 0 0 0 0 0
Aba South 0 1 0 0 0 0 0 0
Abadam 0 0 0 0 0 0 0 0
Abaji 0 0 0 0 0 0 0 0
Abak 0 0 0 0 0 0 0 0
Abakaliki 0 0 0 0 0 0 0 0
Abeokuta North 0 0 0 0 0 0 0 0
Abeokuta South 0 0 0 0 0 0 0 0
Abi 0 0 0 0 0 0 0 0
Aboh-Mbaise 0 0 1 0 0 0 0 0
Abua/Odual 0 0 0 0 0 0 0 0
Abuja Municipal 0 0 0 0 0 0 0 0
Adavi 0 0 0 0 0 0 0 0
Ado 0 0 0 0 0 0 0 0
Ado-Odo/Ota 0 0 0 0 0 0 0 0
Ado Ekiti 0 0 0 0 0 0 0 0
Afijio 0 0 0 0 0 0 0 0
Afikpo North 0 0 0 0 0 0 0 0
Afikpo South 0 0 0 0 0 0 0 0
Agaie 0 0 0 0 0 0 0 0
Agatu 0 0 0 0 0 0 0 0
Agege 0 0 0 0 0 0 0 0
Aguata 0 0 0 1 0 0 0 0
Agwara 0 0 0 0 0 0 0 0
Ahiazu-Mbaise 0 0 0 0 0 0 1 0
Ahoada East 0 0 0 0 0 0 0 0
Ahoada West 0 0 0 0 0 0 0 0
Aiyedade 0 0 0 0 0 0 0 0
Aiyedire 0 0 0 0 0 0 0 0
Aiyekire (Gbonyin) 0 0 0 0 0 0 0 0
Ajaokuta 0 0 0 0 0 0 0 0
Ajeromi-Ifelodun 0 0 0 0 0 0 0 0
Ajingi 0 0 0 0 0 0 0 0
Akamkpa 0 0 0 0 0 0 0 0
Akinyele 0 0 0 0 0 0 0 0
Akko 0 0 0 0 0 0 0 0
Akoko-Edo 0 0 0 0 0 0 0 0
Akoko North East 0 0 0 0 0 0 0 0
Akoko North West 0 0 0 0 0 0 0 0
Akoko South East 0 0 0 0 0 0 0 0
Akoko South West 0 0 0 0 0 0 0 0
Akpabuyo 0 0 0 0 0 0 0 0
Akuku Toru 0 0 0 0 0 0 0 0
Akure North 0 0 0 0 0 0 0 0
Akure South 0 0 0 0 0 0 0 0
Akwanga 0 0 0 0 0 0 0 0
Albasu 0 0 0 0 0 0 0 0
Aleiro 0 0 0 0 0 0 0 0
Alimosho 0 0 0 0 0 0 0 0
Alkaleri 0 0 0 0 0 0 0 0
Amuwo-Odofin 0 0 0 0 0 0 0 0
Anambra East 0 0 0 0 0 0 0 0
Anambra West 0 0 0 0 0 0 0 0
Anaocha 0 0 0 0 0 0 0 0
Andoni 0 0 0 0 0 0 0 0
Aninri 0 0 0 0 0 0 0 0
Aniocha North 0 0 0 0 0 0 0 0
Aniocha South 0 0 0 0 0 0 0 0
Anka 0 0 0 0 0 0 0 0
Ankpa 0 0 0 0 0 0 0 0
Apa 0 0 0 0 0 0 0 0
Apapa 0 0 0 0 0 0 0 0
Ardo-Kola 0 0 0 0 0 0 0 0
Arewa-Dandi 0 0 0 0 0 0 0 0
Argungu 0 0 0 0 0 0 0 0
Arochukwu 0 0 0 0 0 0 0 0
Asa 0 0 0 0 0 0 0 0
Asari-Toru 0 0 0 0 0 0 0 0
Askira/Uba 0 0 0 0 0 0 0 0
Atakumosa East 0 0 0 0 0 0 0 0
Atakumosa West 0 0 0 0 0 0 0 0
Atiba 0 0 0 0 0 0 0 0
Atigbo 0 0 0 0 0 0 0 0
Augie 0 0 0 0 0 0 0 0
Auyo 0 0 0 0 0 0 0 0
Awe 0 0 0 0 0 0 0 0
Awgu 0 0 0 0 0 0 0 0
Awka North 0 0 0 0 0 0 0 0
Awka South 0 0 0 0 0 0 0 0
Ayamelum 0 0 0 0 0 0 0 0
Babura 0 0 0 0 0 0 0 0
Badagry 0 0 0 0 0 0 0 0
Bade 0 0 0 0 0 0 0 0
Bagudo 0 0 0 0 0 0 0 0
Bagwai 0 0 0 0 0 0 0 0
Bakassi 0 0 0 0 0 0 0 0
Bakori 0 0 0 0 0 0 0 0
Bakura 0 0 0 0 0 0 0 0
Balanga 0 0 0 0 0 0 0 0
Bali 0 0 0 0 0 0 0 0
Bama 0 0 0 0 0 0 0 0
Barikin Ladi 0 0 0 0 0 0 0 0
Baruten 0 0 0 0 0 0 0 0
Bassa 0 0 0 0 0 0 0 0
Batagarawa 0 0 0 0 0 0 0 0
Batsari 0 0 0 0 0 0 0 0
Bauchi 0 0 0 0 0 0 0 0
Baure 0 0 0 0 0 0 0 0
Bayo 0 0 0 0 0 0 0 0
Bebeji 0 0 0 0 0 0 0 0
Bekwara 0 0 0 0 0 0 0 0
Bende 0 0 0 0 0 0 0 0
Biase 0 0 0 0 0 0 0 0
Bichi 0 0 0 0 0 0 0 0
Bida 0 0 0 0 0 0 0 0
Billiri 0 0 0 0 0 0 0 0
Bindawa 0 0 0 0 0 0 0 0
Binji 0 0 0 0 0 0 0 0
Biriniwa 0 0 0 0 0 0 0 0
Birni Kudu 0 0 0 0 0 0 0 0
Birnin-Gwari 0 0 0 0 0 0 0 0
Birnin Kebbi 0 0 0 0 0 0 0 0
Birnin Magaji 0 0 0 0 0 0 0 0
Biu 0 0 0 0 0 0 0 0
Bodinga 0 0 0 0 0 0 0 0
Bogoro 0 0 0 0 0 0 0 0
Boki 0 0 0 0 0 0 0 0
Bokkos 0 0 0 0 0 0 0 0
Boluwaduro 0 0 0 0 0 0 0 0
Bomadi 0 0 0 0 0 0 0 0
Bonny 0 0 0 0 0 0 0 0
Borgu 0 0 0 0 0 0 0 0
Boripe 0 0 0 0 0 0 0 0
Bosso 0 0 0 0 0 0 0 0
Brass 0 0 0 0 0 0 0 0
Buji 0 0 0 0 0 0 0 0
Bukkuyum 0 0 0 0 0 0 0 0
Bungudu 0 0 0 0 0 0 0 0
Bunkure 0 0 0 0 0 0 0 0
Bunza 0 0 0 0 0 0 0 0
Bursari 0 0 0 0 0 0 0 0
Buruku 0 0 0 0 0 0 0 0
Burutu 0 0 0 0 0 0 0 0
Bwari 0 0 0 0 0 0 0 0
Calabar-Municipal 0 0 0 0 0 0 0 0
Calabar South 0 0 0 0 0 0 0 0
Chanchaga 0 0 0 0 0 0 0 0
Charanchi 0 0 0 0 0 0 0 0
Chibok 0 0 0 0 0 0 0 0
Chikun 0 0 0 0 0 0 0 0
Dala 0 0 0 0 0 0 0 0
Damaturu 0 0 0 0 0 0 0 0
Damban 0 0 0 0 0 0 0 0
Dambatta 0 0 0 0 0 0 0 0
Damboa 0 0 0 0 0 0 0 0
Dan Musa 0 0 0 0 0 0 0 0
Dandi 0 0 0 0 0 0 0 0
Dandume 0 0 0 0 0 0 0 0
Dange-Shuni 0 0 0 0 0 0 0 0
Danja 0 0 0 0 0 0 0 0
Darazo 0 0 0 0 0 0 0 0
Dass 0 0 0 0 0 0 0 0
Daura 0 0 0 0 0 0 0 0
Dawakin Kudu 0 0 0 0 0 0 0 0
Dawakin Tofa 0 0 0 0 0 0 0 0
Degema 0 0 0 0 0 0 0 0
Dekina 0 0 0 0 0 0 0 0
Demsa 0 0 0 0 0 0 0 0
Dikwa 0 0 0 0 0 0 0 0
Doguwa 0 0 0 0 0 0 0 0
Doma 0 0 0 0 0 0 0 0
Donga 0 0 0 0 0 0 0 0
Dukku 0 0 0 0 0 0 0 0
Dunukofia 0 0 0 0 0 0 0 0
Dutse 0 0 0 0 0 0 0 0
Dutsi 0 0 0 0 0 0 0 0
Dutsin-Ma 0 0 0 0 0 0 0 0
Eastern Obolo 0 0 0 0 0 0 0 0
Ebonyi 0 0 0 0 0 0 0 0
Edati 0 0 0 0 0 0 0 0
Ede North 0 0 0 0 0 0 0 0
Ede South 0 0 0 0 0 0 0 0
Edu 0 0 0 0 0 0 0 0
Efon 0 0 0 0 0 0 0 0
Egbado North 0 0 0 0 0 0 0 0
Egbado South 0 0 0 0 0 0 0 0
Egbeda 0 0 0 0 0 0 0 0
Egbedore 0 0 0 0 0 0 0 0
Egor 0 0 0 0 0 0 0 0
Ehime-Mbano 0 0 0 0 1 0 0 0
Ejigbo 0 0 0 0 0 0 0 0
Ekeremor 0 0 0 0 0 0 0 0
Eket 0 0 0 0 0 0 0 0
Ekiti 0 0 0 0 0 0 0 0
Ekiti East 0 0 0 0 0 0 0 0
Ekiti South West 0 0 0 0 0 0 0 0
Ekiti West 0 0 0 0 0 0 0 0
Ekwusigo 0 0 0 0 0 0 0 0
Eleme 0 0 0 0 0 0 0 0
Emohua 0 0 0 0 0 0 0 0
Emure 0 0 0 0 0 0 0 0
Enugu East 0 0 0 0 0 0 0 0
Enugu North 0 0 0 0 0 0 0 0
Enugu South 0 0 0 0 0 0 0 0
Epe 0 0 0 0 0 0 0 0
Esan Central 0 0 0 0 0 0 0 0
Esan North East 0 0 0 0 0 0 0 0
Esan South East 0 0 0 0 0 0 0 0
Esan West 0 0 0 0 0 0 0 0
Ese-Odo 0 0 0 0 0 0 0 0
Esit - Eket 0 0 0 0 0 0 0 0
Essien Udim 0 0 0 0 0 0 0 0
Etche 0 0 0 0 0 0 0 0
Ethiope East 0 0 0 0 0 0 0 0
Ethiope West 0 0 0 0 0 0 0 0
Eti-Osa 0 0 0 0 0 0 0 0
Etim Ekpo 0 0 0 0 0 0 0 0
Etinan 0 0 0 0 0 0 0 0
Etsako Central 0 0 0 0 0 0 0 0
Etsako East 0 0 0 0 0 0 0 0
Etsako West 0 0 0 0 0 0 0 0
Etung 0 0 0 0 0 0 0 0
Ewekoro 0 0 0 0 0 0 0 0
Ezeagu 0 0 0 0 0 0 0 0
Ezinihitte 0 0 0 1 0 0 0 0
Ezza North 0 0 0 0 0 0 0 0
Ezza South 0 0 0 0 0 0 0 0
Fagge 0 0 0 0 0 0 0 0
Fakai 0 0 0 0 0 0 0 0
Faskari 0 0 0 0 0 0 0 0
Fika 0 0 0 0 0 0 0 0
Fufore 0 0 0 0 0 0 0 0
Funakaye 0 0 0 0 0 0 0 0
Fune 0 0 0 0 0 0 0 0
Funtua 0 0 0 0 0 0 0 0
Gabasawa 0 0 0 0 0 0 0 0
Gada 0 0 0 0 0 0 0 0
Gagarawa 0 0 0 0 0 0 0 0
Gamawa 0 0 0 0 0 0 0 0
Ganjuwa 0 0 0 0 0 0 0 0
Ganye 0 0 0 0 0 0 0 0
Garki 0 0 0 0 0 0 0 0
Garko 0 0 0 0 0 0 0 0
Garum Mallam 0 0 0 0 0 0 0 0
Gashaka 0 0 0 0 0 0 0 0
Gassol 0 0 0 0 0 0 0 0
Gaya 0 0 0 0 0 0 0 0
Gbako 0 0 0 0 0 0 0 0
Gboko 0 0 0 0 0 0 0 0
Geidam 0 0 0 0 0 0 0 0
Gezawa 0 0 0 0 0 0 0 0
Giade 0 0 0 0 0 0 0 0
Girei 0 0 0 0 0 0 0 0
Giwa 0 0 0 0 0 0 0 0
Gokana 0 0 0 0 0 0 0 0
Gombe 0 0 0 0 0 0 0 0
Gombi 0 0 0 0 0 0 0 0
Goronyo 0 0 0 0 0 0 0 0
Gubio 0 0 0 0 0 0 0 0
Gudu 0 0 0 0 0 0 0 0
Gujba 0 0 0 0 0 0 0 0
Gulani 0 0 0 0 0 0 0 0
Guma 0 0 0 0 0 0 0 0
Gumel 0 0 0 0 0 0 0 0
Gummi 0 0 0 0 0 0 0 0
Gurara 0 0 0 0 0 0 0 0
Guri 0 0 0 0 0 0 0 0
Gusau 0 0 0 0 0 0 0 0
Guyuk 0 0 0 0 0 0 0 0
Guzamala 0 0 0 0 0 0 0 0
Gwadabawa 0 0 0 0 0 0 0 0
Gwagwalada 0 0 0 0 0 0 0 0
Gwale 0 0 0 0 0 0 0 0
Gwandu 0 0 0 0 0 0 0 0
Gwaram 0 0 0 0 0 0 0 0
Gwarzo 0 0 0 0 0 0 0 0
Gwer East 0 0 0 0 0 0 0 0
Gwer West 0 0 0 0 0 0 0 0
Gwiwa 0 0 0 0 0 0 0 0
Gwoza 0 0 0 0 0 0 0 0
Hadejia 0 0 0 0 0 0 0 0
Hawul 0 0 0 0 0 0 0 0
Hong 0 0 0 0 0 0 0 0
Ibadan North 0 0 0 0 0 0 0 0
Ibadan North East 0 0 0 0 0 0 0 0
Ibadan North West 0 0 0 0 0 0 0 0
Ibadan South East 0 0 0 0 0 0 0 0
Ibadan South West 0 0 0 0 0 0 0 0
Ibaji 0 0 0 0 0 0 0 0
Ibarapa Central 0 0 0 0 0 0 0 0
Ibarapa East 0 0 0 0 0 0 0 0
Ibarapa North 0 0 0 0 0 0 0 0
Ibeju/Lekki 0 0 0 0 0 0 0 0
Ibeno 0 0 0 0 0 0 0 0
Ibesikpo Asutan 0 0 0 0 0 0 0 0
Ibi 0 0 0 0 0 0 0 0
Ibiono Ibom 0 0 0 0 0 0 0 0
Idah 0 0 0 0 0 0 0 0
Idanre 0 0 0 0 0 0 0 0
Ideato North 0 0 0 0 0 0 0 0
Ideato South 1 0 0 0 0 0 0 0
Idemili North 0 0 0 0 0 0 0 0
Idemili South 0 0 0 0 0 0 0 0
Ido 0 0 0 0 0 0 0 0
Ido-Osi 0 0 0 0 0 0 0 0
Ifako-Ijaye 0 0 0 0 0 0 0 0
Ife Central 0 0 0 0 0 0 0 0
Ife East 0 0 0 0 0 0 0 0
Ife North 0 0 0 0 0 0 0 0
Ife South 0 0 0 0 0 0 0 0
Ifedayo 0 0 0 0 0 0 0 0
Ifedore 0 0 0 0 0 0 0 0
Ifelodun 0 0 0 0 0 0 0 0
Ifo 0 0 0 0 0 0 0 0
Igabi 0 0 0 0 0 0 0 0
Igalamela-Odolu 0 0 0 0 0 0 0 0
Igbo-Etiti 0 0 0 0 0 0 0 0
Igbo-Eze North 0 0 0 0 0 0 0 0
Igbo-Eze South 0 0 0 0 0 0 0 0
Igueben 0 0 0 0 0 0 0 0
Ihiala 0 0 0 0 0 0 0 0
Ihitte/Uboma 0 0 0 0 1 0 0 0
Ijebu East 0 0 0 0 0 0 0 0
Ijebu North 0 0 0 0 0 0 0 0
Ijebu North East 0 0 0 0 0 0 0 0
Ijebu Ode 0 0 0 0 0 0 0 0
Ijero 0 0 0 0 0 0 0 0
Ijumu 0 0 0 0 0 0 0 0
Ika 0 0 0 0 0 0 0 0
Ika North East 0 0 0 0 0 0 0 0
Ika South 0 0 0 0 0 0 0 0
Ikara 0 0 0 0 0 0 0 0
Ikeduru 1 0 0 0 0 0 0 0
Ikeja 0 0 0 0 0 0 0 0
Ikenne 0 0 0 0 0 0 0 0
Ikere 0 0 0 0 0 0 0 0
Ikole 0 0 0 0 0 0 0 0
Ikom 0 0 0 0 0 0 0 0
Ikono 0 0 0 0 0 0 0 0
Ikorodu 0 0 0 0 0 0 0 0
Ikot Abasi 0 0 0 0 0 0 0 0
Ikot Ekpene 0 0 0 0 0 0 0 0
Ikpoba-Okha 0 0 0 0 0 0 0 0
Ikwerre 0 0 0 0 0 0 0 0
Ikwo 0 0 0 0 0 0 0 0
Ikwuano 0 1 0 0 0 0 0 0
Ila 0 0 0 0 0 0 0 0
Ilaje 0 0 0 0 0 0 0 0
Ile-Oluji-Okeigbo 0 0 0 0 0 0 0 0
Ilejemeji 0 0 0 0 0 0 0 0
Ilesha East 0 0 0 0 0 0 0 0
Ilesha West 0 0 0 0 0 0 0 0
Illela 0 0 0 0 0 0 0 0
Ilorin East 0 0 0 0 0 0 0 0
Ilorin South 0 0 0 0 0 0 0 0
Ilorin West 0 0 0 0 0 0 0 0
Imeko-Afon 0 0 0 0 0 0 0 0
Ingawa 0 0 0 0 0 0 0 0
Ini 0 0 0 0 0 0 0 0
Ipokia 0 0 0 0 0 0 0 0
Irele 0 0 0 0 0 0 0 0
Irepo 0 0 0 0 0 0 0 0
Irepodun 0 0 0 0 0 0 0 0
Irepodun/Ifelodun 0 0 0 0 0 0 0 0
Irewole 0 0 0 0 0 0 0 0
Isa 0 0 0 0 0 0 0 0
Ise/Orun 0 0 0 0 0 0 0 0
Iseyin 0 0 0 0 0 0 0 0
Ishielu 0 0 0 0 0 0 0 0
Isi-Uzo 0 0 0 0 0 0 0 0
Isiala-Ngwa North 0 0 1 0 0 0 0 0
Isiala-Ngwa South 0 0 1 0 0 0 0 0
Isiala Mbano 0 0 0 0 0 1 0 0
Isin 0 0 0 0 0 0 0 0
Isiukwuato 0 0 1 0 0 0 0 0
Isokan 0 0 0 0 0 0 0 0
Isoko North 0 0 0 0 0 0 0 0
Isoko South 0 0 0 0 0 0 0 0
Isu 0 1 0 0 0 0 0 0
Itas/Gadau 0 0 0 0 0 0 0 0
Itesiwaju 0 0 0 0 0 0 0 0
Itu 0 0 0 0 0 0 0 0
Ivo 0 0 0 0 0 0 0 0
Iwajowa 0 0 0 0 0 0 0 0
Iwo 0 0 0 0 0 0 0 0
Izzi 0 0 0 0 0 0 0 0
Jaba 0 0 0 0 0 0 0 0
Jada 0 0 0 0 0 0 0 0
Jahun 0 0 0 0 0 0 0 0
Jakusko 0 0 0 0 0 0 0 0
Jalingo 0 0 0 0 0 0 0 0
Jama'are 0 0 0 0 0 0 0 0
Jega 0 0 0 0 0 0 0 0
Jema'a 0 0 0 0 0 0 0 0
Jere 0 0 0 0 0 0 0 0
Jibia 0 0 0 0 0 0 0 0
Jos East 0 0 0 0 0 0 0 0
Jos North 0 0 0 0 0 0 0 0
Jos South 0 0 0 0 0 0 0 0
Kabba/Bunu 0 0 0 0 0 0 0 0
Kabo 0 0 0 0 0 0 0 0
Kachia 0 0 0 0 0 0 0 0
Kaduna North 0 0 0 0 0 0 0 0
Kaduna South 0 0 0 0 0 0 0 0
Kafin Hausa 0 0 0 0 0 0 0 0
Kafur 0 0 0 0 0 0 0 0
Kaga 0 0 0 0 0 0 0 0
Kagarko 0 0 0 0 0 0 0 0
Kaiama 0 0 0 0 0 0 0 0
Kaita 0 0 0 0 0 0 0 0
Kajola 0 0 0 0 0 0 0 0
Kajuru 0 0 0 0 0 0 0 0
Kala/Balge 0 0 0 0 0 0 0 0
Kalgo 0 0 0 0 0 0 0 0
Kaltungo 0 0 0 0 0 0 0 0
Kanam 0 0 0 0 0 0 0 0
Kankara 0 0 0 0 0 0 0 0
Kanke 0 0 0 0 0 0 0 0
Kankia 0 0 0 0 0 0 0 0
Kano Municipal 0 0 0 0 0 0 0 0
Karasuwa 0 0 0 0 0 0 0 0
Karaye 0 0 0 0 0 0 0 0
Karim-Lamido 0 0 0 0 0 0 0 0
Karu 0 0 0 0 0 0 0 0
Katagum 0 0 0 0 0 0 0 0
Katcha 0 0 0 0 0 0 0 0
Katsina 0 0 0 0 0 0 0 0
Katsina-Ala 0 0 0 0 0 0 0 0
Kaugama 0 0 0 0 0 0 0 0
Kaura 0 0 0 0 0 0 0 0
Kaura Namoda 0 0 0 0 0 0 0 0
Kauru 0 0 0 0 0 0 0 0
Kazaure 0 0 0 0 0 0 0 0
Keana 0 0 0 0 0 0 0 0
Kebbe 0 0 0 0 0 0 0 0
Keffi 0 0 0 0 0 0 0 0
Khana 0 0 0 0 0 0 0 0
Kibiya 0 0 0 0 0 0 0 0
Kirfi 0 0 0 0 0 0 0 0
Kiri Kasamma 0 0 0 0 0 0 0 0
Kiru 0 0 0 0 0 0 0 0
Kiyawa 0 0 0 0 0 0 0 0
Kogi 0 0 0 0 0 0 0 0
Koko/Besse 0 0 0 0 0 0 0 0
Kokona 0 0 0 0 0 0 0 0
Kolokuma/Opokuma 0 0 0 0 0 0 0 0
Konduga 0 0 0 0 0 0 0 0
Konshisha 0 0 0 0 0 0 0 0
Kontagora 0 0 0 0 0 0 0 0
Kosofe 0 0 0 0 0 0 0 0
Kubau 0 0 0 0 0 0 0 0
Kudan 0 0 0 0 0 0 0 0
Kuje 0 0 0 0 0 0 0 0
Kukawa 0 0 0 0 0 0 0 0
Kumbotso 0 0 0 0 0 0 0 0
Kunchi 0 0 0 0 0 0 0 0
Kura 0 0 0 0 0 0 0 0
Kurfi 0 0 0 0 0 0 0 0
Kurmi 0 0 0 0 0 0 0 0
Kusada 0 0 0 0 0 0 0 0
Kwali 0 0 0 0 0 0 0 0
Kwami 0 0 0 0 0 0 0 0
Kwande 0 0 0 0 0 0 0 0
Kware 0 0 0 0 0 0 0 0
Kwaya Kusar 0 0 0 0 0 0 0 0
Lafia 0 0 0 0 0 0 0 0
Lagelu 0 0 0 0 0 0 0 0
Lagos Island 0 0 0 0 0 0 0 0
Lagos Mainland 0 0 0 0 0 0 0 0
Lamurde 0 0 0 0 0 0 0 0
Langtang North 0 0 0 0 0 0 0 0
Langtang South 0 0 0 0 0 0 0 0
Lapai 0 0 0 0 0 0 0 0
Lau 0 0 0 0 0 0 0 0
Lavun 0 0 0 0 0 0 0 0
Lere 0 0 0 0 0 0 0 0
Logo 0 0 0 0 0 0 0 0
Lokoja 0 0 0 0 0 0 0 0
Machina 0 0 0 0 0 0 0 0
Madagali 0 0 0 0 0 0 0 0
Madobi 0 0 0 0 0 0 0 0
Mafa 0 0 0 0 0 0 0 0
Magama 0 0 0 0 0 0 0 0
Magumeri 0 0 0 0 0 0 0 0
Mai'adua 0 0 0 0 0 0 0 0
Maiduguri 0 0 0 0 0 0 0 0
Maigatari 0 0 0 0 0 0 0 0
Maiha 0 0 0 0 0 0 0 0
Maiyama 0 0 0 0 0 0 0 0
Makoda 0 0 0 0 0 0 0 0
Makurdi 0 0 0 0 0 0 0 0
Malam Madori 0 0 0 0 0 0 0 0
Malumfashi 0 0 0 0 0 0 0 0
Mangu 0 0 0 0 0 0 0 0
Mani 0 0 0 0 0 0 0 0
Maradun 0 0 0 0 0 0 0 0
Mariga 0 0 0 0 0 0 0 0
Markafi 0 0 0 0 0 0 0 0
Marte 0 0 0 0 0 0 0 0
Maru 0 0 0 0 0 0 0 0
Mashegu 0 0 0 0 0 0 0 0
Mashi 0 0 0 0 0 0 0 0
Matazu 0 0 0 0 0 0 0 0
Mayo-Belwa 0 0 0 0 0 0 0 0
Mbaitoli 0 0 1 0 0 0 0 0
Mbo 0 0 0 0 0 0 0 0
Michika 0 0 0 0 0 0 0 0
Miga 0 0 0 0 0 0 0 0
Mikang 0 0 0 0 0 0 0 0
Minjibir 0 0 0 0 0 0 0 0
Misau 0 0 0 0 0 0 0 0
Mkpat Enin 0 0 0 0 0 0 0 0
Moba 0 0 0 0 0 0 0 0
Mobbar 0 0 0 0 0 0 0 0
Mokwa 0 0 0 0 0 0 0 0
Monguno 0 0 0 0 0 0 0 0
Mopa-Muro 0 0 0 0 0 0 0 0
Moro 0 0 0 0 0 0 0 0
Mubi North 0 0 0 0 0 0 0 0
Mubi South 0 0 0 0 0 0 0 0
Musawa 0 0 0 0 0 0 0 0
Mushin 0 0 0 0 0 0 0 0
Muya 0 0 0 0 0 0 0 0
Nafada 0 0 0 0 0 0 0 0
Nangere 0 0 0 0 0 0 0 0
Nasarawa 0 0 0 0 0 0 0 0
Nasarawa-Eggon 0 0 0 0 0 0 0 0
Ndokwa East 0 0 0 0 0 0 0 0
Ndokwa West 0 0 0 0 0 0 0 0
Nembe 0 0 0 0 0 0 0 0
Ngala 0 0 0 0 0 0 0 0
Nganzai 0 0 0 0 0 0 0 0
Ngaski 0 0 0 0 0 0 0 0
Ngor-Okpala 0 1 0 0 0 0 0 0
Nguru 0 0 0 0 0 0 0 0
Ningi 0 0 0 0 0 0 0 0
Njaba 0 0 0 0 0 0 0 0
Njikoka 0 0 0 0 0 0 0 0
Nkanu East 0 0 0 0 0 0 0 0
Nkanu West 0 0 0 0 0 0 0 0
Nkwerre 1 0 0 0 0 0 0 0
Nnewi North 0 0 0 0 0 0 0 0
Nnewi South 0 1 0 0 0 0 0 0
Nsit Atai 0 0 0 0 0 0 0 0
Nsit Ibom 0 0 0 0 0 0 0 0
Nsit Ubium 0 0 0 0 0 0 0 0
Nsukka 0 0 0 0 0 0 0 0
Numan 0 0 0 0 0 0 0 0
Nwangele 0 0 0 1 0 0 0 0
Obafemi-Owode 0 0 0 0 0 0 0 0
Obanliku 0 0 0 0 0 0 0 0
Obi 0 0 0 0 0 0 0 0
Obi Ngwa 0 1 0 0 0 0 0 0
Obia/Akpor 0 0 0 0 0 0 0 0
Obokun 0 0 0 0 0 0 0 0
Obot Akara 0 0 0 0 0 0 0 0
Obowo 0 0 0 0 0 1 0 0
Obubra 0 0 0 0 0 0 0 0
Obudu 0 0 0 0 0 0 0 0
Odeda 0 0 0 0 0 0 0 0
Odigbo 0 0 0 0 0 0 0 0
Odo-Otin 0 0 0 0 0 0 0 0
Odogbolu 0 0 0 0 0 0 0 0
Odukpani 0 0 0 0 0 0 0 0
Offa 0 0 0 0 0 0 0 0
Ofu 0 0 0 0 0 0 0 0
Ogba/Egbema/Ndoni 0 0 0 0 0 0 0 0
Ogbadibo 0 0 0 0 0 0 0 0
Ogbaru 0 0 0 0 0 0 0 0
Ogbia 0 0 0 0 0 0 0 0
Ogbomosho North 0 0 0 0 0 0 0 0
Ogbomosho South 0 0 0 0 0 0 0 0
Ogo Oluwa 0 0 0 0 0 0 0 0
Ogoja 0 0 0 0 0 0 0 0
Ogori/Magongo 0 0 0 0 0 0 0 0
Ogu/Bolo 0 0 0 0 0 0 0 0
Ogun waterside 0 0 0 0 0 0 0 0
Oguta 0 0 0 0 0 0 0 0
Ohafia 0 0 0 0 0 0 0 0
Ohaji/Egbema 0 0 0 0 0 0 0 0
Ohaozara 0 0 0 0 0 0 0 0
Ohaukwu 0 0 0 0 0 0 0 0
Ohimini 0 0 0 0 0 0 0 0
Oji-River 1 0 0 0 0 0 0 0
Ojo 0 0 0 0 0 0 0 0
Oju 0 0 0 0 0 0 0 0
Oke-Ero 0 0 0 0 0 0 0 0
Okehi 0 0 0 0 0 0 0 0
Okene 0 0 0 0 0 0 0 0
Okigwe 0 0 0 0 0 0 0 1
Okitipupa 0 0 0 0 0 0 0 0
Okobo 0 0 0 0 0 0 0 0
Okpe 0 0 0 0 0 0 0 0
Okpokwu 0 0 0 0 0 0 0 0
Okrika 0 0 0 0 0 0 0 0
Ola-oluwa 0 0 0 0 0 0 0 0
Olamabolo 0 0 0 0 0 0 0 0
Olorunda 0 0 0 0 0 0 0 0
Olorunsogo 0 0 0 0 0 0 0 0
Oluyole 0 0 0 0 0 0 0 0
Omala 0 0 0 0 0 0 0 0
Omumma 0 0 0 0 0 0 0 0
Ona-Ara 0 0 0 0 0 0 0 0
Ondo East 0 0 0 0 0 0 0 0
Ondo West 0 0 0 0 0 0 0 0
Onicha 0 0 0 0 0 0 0 0
Onitsha North 0 0 0 0 0 0 0 0
Onitsha South 0 0 0 0 0 0 0 0
Onna 0 0 0 0 0 0 0 0
Opobo/Nkoro 0 0 0 0 0 0 0 0
Oredo 0 0 0 0 0 0 0 0
Orelope 0 0 0 0 0 0 0 0
Orhionmwon 0 0 0 0 0 0 0 0
Ori Ire 0 0 0 0 0 0 0 0
Oriade 0 0 0 0 0 0 0 0
Orlu 0 1 0 0 0 0 0 0
Orolu 0 0 0 0 0 0 0 0
Oron 0 0 0 0 0 0 0 0
Orsu 0 0 0 0 0 0 0 0
Oru East 0 0 0 0 0 0 0 0
Oru West 0 0 0 0 0 0 0 0
Oruk Anam 0 0 0 0 0 0 0 0
Orumba North 0 1 0 0 0 0 0 0
Orumba South 0 0 0 0 1 0 0 0
Ose 0 0 0 0 0 0 0 0
Oshimili North 0 0 0 0 0 0 0 0
Oshimili South 0 0 0 0 0 0 0 0
Oshodi-Isolo 0 0 0 0 0 0 0 0
Osisioma Ngwa 0 0 1 0 0 0 0 0
Osogbo 0 0 0 0 0 0 0 0
Oturkpo 0 0 0 0 0 0 0 0
Ovia North East 0 0 0 0 0 0 0 0
Ovia South West 0 0 0 0 0 0 0 0
Owan East 0 0 0 0 0 0 0 0
Owan West 0 0 0 0 0 0 0 0
Owerri-Municipal 0 0 0 0 0 0 0 0
Owerri North 0 0 0 0 0 0 0 0
Owerri West 0 0 0 0 0 0 0 0
Owo 0 0 0 0 0 0 0 0
Oye 0 0 0 0 0 0 0 0
Oyi 0 0 0 0 0 0 0 0
Oyigbo 0 0 0 0 0 0 0 0
Oyo East 0 0 0 0 0 0 0 0
Oyo West 0 0 0 0 0 0 0 0
Oyun 0 0 0 0 0 0 0 0
Paikoro 0 0 0 0 0 0 0 0
Pankshin 0 0 0 0 0 0 0 0
Patani 0 0 0 0 0 0 0 0
Pategi 0 0 0 0 0 0 0 0
Port-Harcourt 0 0 0 0 0 0 0 0
Potiskum 0 0 0 0 0 0 0 0
Qua'an Pan 0 0 0 0 0 0 0 0
Rabah 0 0 0 0 0 0 0 0
Rafi 0 0 0 0 0 0 0 0
Rano 0 0 0 0 0 0 0 0
Remo North 0 0 0 0 0 0 0 0
Rijau 0 0 0 0 0 0 0 0
Rimi 0 0 0 0 0 0 0 0
Rimin Gado 0 0 0 0 0 0 0 0
Ringim 0 0 0 0 0 0 0 0
Riyom 0 0 0 0 0 0 0 0
Rogo 0 0 0 0 0 0 0 0
Roni 0 0 0 0 0 0 0 0
Sabon-Gari 0 0 0 0 0 0 0 0
Sabon Birni 0 0 0 0 0 0 0 0
Sabuwa 0 0 0 0 0 0 0 0
Safana 0 0 0 0 0 0 0 0
Sagbama 0 0 0 0 0 0 0 0
Sakaba 0 0 0 0 0 0 0 0
Saki East 0 0 0 0 0 0 0 0
Saki West 0 0 0 0 0 0 0 0
Sandamu 0 0 0 0 0 0 0 0
Sanga 0 0 0 0 0 0 0 0
Sapele 0 0 0 0 0 0 0 0
Sardauna 0 0 0 0 0 0 0 0
Shagamu 0 0 0 0 0 0 0 0
Shagari 0 0 0 0 0 0 0 0
Shanga 0 0 0 0 0 0 0 0
Shani 0 0 0 0 0 0 0 0
Shanono 0 0 0 0 0 0 0 0
Shelleng 0 0 0 0 0 0 0 0
Shendam 0 0 0 0 0 0 0 0
Shinkafi 0 0 0 0 0 0 0 0
Shira 0 0 0 0 0 0 0 0
Shiroro 0 0 0 0 0 0 0 0
Shomgom 0 0 0 0 0 0 0 0
Shomolu 0 0 0 0 0 0 0 0
Silame 0 0 0 0 0 0 0 0
Soba 0 0 0 0 0 0 0 0
Sokoto North 0 0 0 0 0 0 0 0
Sokoto South 0 0 0 0 0 0 0 0
Song 0 0 0 0 0 0 0 0
Southern Ijaw 0 0 0 0 0 0 0 0
Sule-Tankarkar 0 0 0 0 0 0 0 0
Suleja 0 0 0 0 0 0 0 0
Sumaila 0 0 0 0 0 0 0 0
Suru 0 0 0 0 0 0 0 0
Surulere 0 0 0 0 0 0 0 0
Tafa 0 0 0 0 0 0 0 0
Tafawa-Balewa 0 0 0 0 0 0 0 0
Tai 0 0 0 0 0 0 0 0
Takai 0 0 0 0 0 0 0 0
Takum 0 0 0 0 0 0 0 0
Talata Mafara 0 0 0 0 0 0 0 0
Tambuwal 0 0 0 0 0 0 0 0
Tangaza 0 0 0 0 0 0 0 0
Tarauni 0 0 0 0 0 0 0 0
Tarka 0 0 0 0 0 0 0 0
Tarmua 0 0 0 0 0 0 0 0
Taura 0 0 0 0 0 0 0 0
Tofa 0 0 0 0 0 0 0 0
Toro 0 0 0 0 0 0 0 0
Toto 0 0 0 0 0 0 0 0
Toungo 0 0 0 0 0 0 0 0
Tsafe 0 0 0 0 0 0 0 0
Tsanyawa 0 0 0 0 0 0 0 0
Tudun Wada 0 0 0 0 0 0 0 0
Tureta 0 0 0 0 0 0 0 0
Udenu 0 0 0 0 0 0 0 0
Udi 0 0 0 0 0 0 0 0
Udu 0 0 0 0 0 0 0 0
Udung Uko 0 0 0 0 0 0 0 0
Ughelli North 0 0 0 0 0 0 0 0
Ughelli South 0 0 0 0 0 0 0 0
Ugwunagbo 0 0 0 0 0 0 0 0
Uhunmwonde 0 0 0 0 0 0 0 0
Ukanafun 0 0 0 0 0 0 0 0
Ukum 0 0 0 0 0 0 0 0
Ukwa East 0 0 0 0 0 0 0 0
Ukwa West 0 0 0 0 0 0 0 0
Ukwuani 0 0 0 0 0 0 0 0
Umu-Nneochi 0 0 0 0 0 1 0 0
Umuahia North 0 0 0 1 0 0 0 0
Umuahia South 0 0 1 0 0 0 0 0
Ungogo 0 0 0 0 0 0 0 0
Unuimo 0 0 0 0 1 0 0 0
Uruan 0 0 0 0 0 0 0 0
Urue-Offong/Oruko 0 0 0 0 0 0 0 0
Ushongo 0 0 0 0 0 0 0 0
Ussa 0 0 0 0 0 0 0 0
Uvwie 0 0 0 0 0 0 0 0
Uyo 0 0 0 0 0 0 0 0
Uzo-Uwani 0 0 0 0 0 0 0 0
Vandeikya 0 0 0 0 0 0 0 0
Wamako 0 0 0 0 0 0 0 0
Wamba 0 0 0 0 0 0 0 0
Warawa 0 0 0 0 0 0 0 0
Warji 0 0 0 0 0 0 0 0
Warri North 0 0 0 0 0 0 0 0
Warri South 0 0 0 0 0 0 0 0
Warri South West 0 0 0 0 0 0 0 0
Wasagu/Danko 0 0 0 0 0 0 0 0
Wase 0 0 0 0 0 0 0 0
Wudil 0 0 0 0 0 0 0 0
Wukari 0 0 0 0 0 0 0 0
Wurno 0 0 0 0 0 0 0 0
Wushishi 0 0 0 0 0 0 0 0
Yabo 0 0 0 0 0 0 0 0
Yagba East 0 0 0 0 0 0 0 0
Yagba West 0 0 0 0 0 0 0 0
Yakurr 0 0 0 0 0 0 0 0
Yala 0 0 0 0 0 0 0 0
Yamaltu/Deba 0 0 0 0 0 0 0 0
Yankwashi 0 0 0 0 0 0 0 0
Yauri 0 0 0 0 0 0 0 0
Yenegoa 0 0 0 0 0 0 0 0
Yola North 0 0 0 0 0 0 0 0
Yola South 0 0 0 0 0 0 0 0
Yorro 0 0 0 0 0 0 0 0
Yunusari 0 0 0 0 0 0 0 0
Yusufari 0 0 0 0 0 0 0 0
Zaki 0 0 0 0 0 0 0 0
Zango 0 0 0 0 0 0 0 0
Zango-Kataf 0 0 0 0 0 0 0 0
Zaria 0 0 0 0 0 0 0 0
Zing 0 0 0 0 0 0 0 0
Zurmi 0 0 0 0 0 0 0 0
Zuru 0 0 0 0 0 0 0 07.4.3 Plotting fixed distance weight matrix.
We will plot the distance weight matrix by using the code chunk below.
plot(nga_wp$geometry, border="lightgrey")
plot(wm_d72, coords, add=TRUE)
plot(k1, coords, add=TRUE, col="blue", length=0.08)
The blue lines show the links of 1st nearest neighbors and the black lines show the links of neighbors within the cut-off distance of 72km.
Alternatively, we can plot both of them next to each other by using the code chunk below.
par(mfrow=c(1,2))
plot(nga_wp$geometry, border="lightgrey")
plot(k1, coords, add=TRUE, col="red", length=0.04, main="1st nearest neighbours")
plot(nga_wp$geometry, border="lightgrey")
plot(wm_d72, coords, add=TRUE, pch = 19, cex = 0.2, main="Distance link")
7.4.4 Computing adaptive distance weight matrix.
One of the characteristics of fixed distance weight matrix is that more densely settled areas (usually the urban areas) tend to have more neighbors and the less densely settled areas (usually the rural counties) tend to have lesser neighbors. Having many neighbors smoothes the neighbor relationship across more neighbors.
It is possible to control the numbers of neighbors directly using k-nearest neighbors, either accepting asymmetric neighbors or imposing symmetry as shown in the code chunk below.
knn6 <- knn2nb(knearneigh(coords, k=6))
knn6Neighbour list object:
Number of regions: 774
Number of nonzero links: 4644
Percentage nonzero weights: 0.7751938
Average number of links: 6
Non-symmetric neighbours listSimilarly, we can display the content of the matrix by using str().
str(knn6)List of 774
$ : int [1:6] 2 364 548 597 624 721
$ : int [1:6] 1 548 597 624 721 725
$ : int [1:6] 250 261 447 507 509 526
$ : int [1:6] 20 263 446 454 466 690
$ : int [1:6] 203 208 331 334 539 738
$ : int [1:6] 170 217 218 337 379 553
$ : int [1:6] 8 176 214 281 544 555
$ : int [1:6] 7 214 281 306 544 555
$ : int [1:6] 18 19 218 337 576 757
$ : int [1:6] 25 216 325 528 552 632
$ : int [1:6] 26 27 68 191 565 762
$ : int [1:6] 135 263 417 446 690 695
$ : int [1:6] 31 37 393 570 583 584
$ : int [1:6] 170 363 546 577 581 589
$ : int [1:6] 22 49 177 297 306 580
$ : int [1:6] 30 187 296 328 357 360
$ : int [1:6] 35 295 378 460 638 639
$ : int [1:6] 9 19 218 574 576 601
$ : int [1:6] 9 18 103 376 574 576
$ : int [1:6] 4 106 239 419 454 466
$ : int [1:6] 60 61 162 269 520 596
$ : int [1:6] 49 297 326 443 515 623
$ : int [1:6] 54 291 292 537 618 619
$ : int [1:6] 123 476 527 652 673 761
$ : int [1:6] 10 181 216 314 325 552
$ : int [1:6] 11 27 191 336 562 762
$ : int [1:6] 11 26 191 439 663 762
$ : int [1:6] 29 178 299 300 358 369
$ : int [1:6] 173 178 358 378 460 591
$ : int [1:6] 16 39 41 186 192 360
$ : int [1:6] 13 211 289 570 583 584
$ : int [1:6] 51 62 461 462 515 693
$ : int [1:6] 166 227 238 655 743 750
$ : int [1:6] 42 104 136 213 559 757
$ : int [1:6] 17 275 276 277 295 460
$ : int [1:6] 107 247 408 455 681 759
$ : int [1:6] 38 40 570 583 584 629
$ : int [1:6] 39 40 41 186 320 570
$ : int [1:6] 30 38 40 41 186 320
$ : int [1:6] 37 38 39 41 186 570
$ : int [1:6] 30 38 39 40 192 634
$ : int [1:6] 86 136 137 499 613 718
$ : int [1:6] 11 68 157 524 590 645
$ : int [1:6] 45 192 303 328 360 634
$ : int [1:6] 44 290 303 328 360 599
$ : int [1:6] 387 429 438 521 668 742
$ : int [1:6] 33 166 234 238 698 750
$ : int [1:6] 65 113 265 386 482 701
$ : int [1:6] 22 297 326 515 623 693
$ : int [1:6] 36 98 107 409 432 681
$ : int [1:6] 32 62 461 462 623 693
$ : int [1:6] 78 165 293 532 602 636
$ : int [1:6] 52 78 80 165 621 636
$ : int [1:6] 23 79 293 294 532 536
$ : int [1:6] 122 246 333 430 571 605
$ : int [1:6] 77 376 533 576 601 728
$ : int [1:6] 58 199 312 322 621 622
$ : int [1:6] 57 322 323 603 621 622
$ : int [1:6] 88 128 129 493 700 748
$ : int [1:6] 61 563 578 592 596 626
$ : int [1:6] 21 60 269 578 596 626
$ : int [1:6] 32 51 461 462 515 693
$ : int [1:6] 90 384 416 467 765 772
$ : int [1:6] 48 65 74 113 131 407
$ : int [1:6] 48 64 74 113 265 683
$ : int [1:6] 103 104 331 338 351 574
$ : int [1:6] 347 348 566 609 640 694
$ : int [1:6] 43 157 191 549 590 645
$ : int [1:6] 140 146 274 473 500 512
$ : int [1:6] 71 299 341 343 344 610
$ : int [1:6] 173 298 299 343 344 625
$ : int [1:6] 566 567 568 609 638 639
$ : int [1:6] 361 374 377 404 665 666
$ : int [1:6] 65 109 265 683 741 754
$ : int [1:6] 272 398 422 433 485 501
$ : int [1:6] 254 427 470 547 647 677
$ : int [1:6] 56 195 533 534 579 728
$ : int [1:6] 52 79 165 215 532 636
$ : int [1:6] 54 78 165 532 618 636
$ : int [1:6] 52 53 78 165 215 739
$ : int [1:6] 99 145 233 426 689 760
$ : int [1:6] 15 49 51 177 352 580
$ : int [1:6] 132 258 383 414 529 767
$ : int [1:6] 24 148 437 482 673 692
$ : int [1:6] 105 394 654 675 707 712
$ : int [1:6] 42 136 137 499 613 718
$ : int [1:6] 149 151 221 226 399 486
$ : int [1:6] 59 150 489 648 700 714
$ : int [1:6] 260 408 463 542 674 676
$ : int [1:6] 63 163 236 237 384 710
$ : int [1:6] 160 271 406 475 492 525
$ : int [1:6] 119 390 391 392 487 656
$ : int [1:6] 354 402 594 607 665 666
$ : int [1:6] 31 158 436 561 596 709
$ : int [1:6] 390 391 392 405 469 656
$ : int [1:6] 139 389 403 420 451 653
$ : int [1:6] 96 389 420 451 662 773
$ : int [1:6] 50 117 153 231 432 696
$ : int [1:6] 81 145 426 667 760 769
[list output truncated]
- attr(*, "region.id")= chr [1:774] "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"7.4.5 Plotting distance based neighbors.
We can plot the weight matrix using the code chunk below.
plot(nga_wp$geometry, border="lightgrey")
plot(knn6, coords, pch = 19, cex = 0.35, add = TRUE, col = "red")
8. Weights based on IDW.
Let us derive a spatial weight matrix based on Inversed Distance method.
First, we will compute the distances between areas by using nbdists() of spdep.
dist <- nbdists(wm_q, coords, longlat = TRUE)
ids <- lapply(dist, function(x) 1/(x))
ids[[1]]
[1] 0.25000205 0.09046782 0.10747703 0.09375983
[[2]]
[1] 0.25000205 0.08862914 0.14105584
[[3]]
[1] 0.01590084 0.01662956 0.01260998
[[4]]
[1] 0.01717590 0.03004788 0.01330541 0.02168731 0.05975734 0.04459112 0.01352838
[[5]]
[1] 0.05994237 0.05404009 0.04762801 0.03801382 0.06150355
[[6]]
[1] 0.03031931 0.03665471 0.03519739 0.05108370 0.03848687 0.02871387 0.01681994
[[7]]
[1] 0.04527349 0.03137554 0.03388681 0.03673492 0.01842930 0.02695780
[[8]]
[1] 0.04527349 0.03503427 0.04431895 0.04430146
[[9]]
[1] 0.08750537 0.02607067 0.03586348 0.03155024 0.05007085
[[10]]
[1] 0.06896664 0.10626681 0.05472395 0.04404016 0.05163643 0.06002816 0.05631991
[[11]]
[1] 0.03879630 0.03946088 0.02732362 0.01997084 0.03310675 0.02602152 0.03739768
[[12]]
[1] 0.02720192 0.02289252 0.02247421 0.02706160 0.01242536 0.02652922 0.02553668
[8] 0.01294330
[[13]]
[1] 0.04094495 0.02052331 0.01706481 0.06507531 0.03911223
[[14]]
[1] 0.03235821 0.02090215 0.03178682 0.02512533 0.03394803 0.02319313 0.03101831
[8] 0.01726529
[[15]]
[1] 0.05083319 0.03678314 0.04204379 0.03978005 0.03826900 0.03140347 0.04926498
[[16]]
[1] 0.03889753 0.03707198 0.06700044 0.08544715 0.04163595
[[17]]
[1] 0.05668031 0.05718452 0.05552066
[[18]]
[1] 0.08750537 0.07690648 0.02466231 0.04790108 0.03693977
[[19]]
[1] 0.07690648 0.03359907 0.02323195 0.04421283 0.05314287 0.05121231
[[20]]
[1] 0.033322003 0.029962683 0.012257596 0.008440251 0.013361445
[[21]]
[1] 0.03979094 0.01916518 0.02167794 0.02025683 0.02524210
[[22]]
[1] 0.1191950 0.2281036 0.2259755
[[23]]
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[1] 0.07016392 0.09308583 0.08951160 0.10229067 0.12122539 0.06814209
[[612]]
[1] 0.07496946 0.16135399 0.09978322 0.04366176
[[613]]
[1] 0.08070621 0.02468073 0.08933372 0.13834180 0.07871410
[[614]]
[1] 0.05397304 0.08103015 0.09066259 0.12122539 0.06437357
[[615]]
[1] 0.05651731 0.06391852 0.13856017 0.06198069 0.06814209 0.06437357 0.14732444
[[616]]
[1] 0.08514149 0.07124721 0.14732444
[[617]]
[1] 0.03801382 0.05416686 0.04342909 0.03445259 0.05778145 0.08687921 0.02802757
[[618]]
[1] 0.06270603 0.06292571 0.07632951 0.06567575 0.07050600
[[619]]
[1] 0.06410599 0.06379147 0.05045025 0.04720249 0.07050600 0.04934280
[[620]]
[1] 0.01798776 0.02189213 0.02545143 0.01505494 0.02837889 0.04434645 0.04577961
[[621]]
[1] 0.03668693 0.05278166 0.03735590 0.02797107 0.04050463
[[622]]
[1] 0.02126611 0.04571184 0.02052097 0.04191871 0.06398045 0.08629659 0.04050463
[[623]]
[1] 0.12870992 0.09023184 0.11679116 0.24561429 0.18072812
[[624]]
[1] 0.10747703 0.08862914 0.06796578 0.04299314 0.06093624 0.06641410 0.05506681
[8] 0.03674312
[[625]]
[1] 0.05264151 0.06085707 0.08987096 0.06266141 0.06109664 0.04523361 0.08153168
[[626]]
[1] 0.01726529 0.02053367 0.02420868 0.02323762 0.02570046 0.02434443 0.07380191
[8] 0.02751729
[[627]]
[1] 0.06668233 0.01525681 0.02670274 0.03447104 0.01505494 0.03438118
[7] 0.01592308 0.01359828 0.02410400 0.01393624
[[628]]
[1] 0.02047997 0.01820825 0.03151162 0.01582309 0.03438118 0.01564073
[[629]]
[1] 0.02759107 0.02324158 0.03500985 0.02837889 0.04505857
[[630]]
[1] 0.02560674 0.04434645 0.01592308 0.04505857 0.02169023
[[631]]
[1] 0.1158354 0.1445798
[[632]]
[1] 0.05631991 0.07055413 0.05671628 0.05812468 0.11583536 0.07562327
[[633]]
[1] 0.05285481 0.04095048 0.03258350 0.06009976 0.14457977 0.07562327
[[634]]
[1] 0.03204285 0.03270055 0.03269744 0.02202012 0.02718328 0.04577961 0.01359828
[[635]]
[1] 0.04032139 0.05634624 0.05449651 0.06816241 0.04363713
[[636]]
[1] 0.09869446 0.13016827 0.09497697 0.08939128 0.07959753
[[637]]
[1] 0.04951487 0.02645916 0.04632219 0.02928124 0.09243965 0.03358142 0.06572631
[8] 0.06920682
[[638]]
[1] 0.05718452 0.03580025 0.02028357 0.03493608 0.04813748 0.03912300 0.04606218
[8] 0.01619242 0.03838839
[[639]]
[1] 0.05552066 0.02846843 0.02466654 0.02121563 0.02356453 0.03821596 0.03838839
[[640]]
[1] 0.02447958 0.01663473 0.03159095 0.03417386 0.05735847 0.32201066 0.03263987
[[641]]
[1] 0.01336145 0.01977920 0.04069500 0.01039202 0.01197010 0.01343419 0.01193971
[8] 0.02255484 0.01384608
[[642]]
[1] 0.04166843 0.02120218 0.03281214 0.02845509 0.01521650 0.01550045 0.01638611
[[643]]
[1] 0.03737868 0.03733093 0.07126334 0.02649695 0.03487178
[[644]]
[1] 0.01363847 0.01324682 0.01141640 0.01345378 0.01973378 0.02214730
[[645]]
[1] 0.04059587 0.07587817 0.10028358 0.07007458
[[646]]
[1] 0.02658440 0.03185728 0.04135467
[[647]]
[1] 0.01765566 0.01724228 0.01988439 0.01392324 0.01521650 0.02477448
[[648]]
[1] 0.02225867 0.02578496 0.02392085 0.01441955 0.02030860 0.01573627 0.02754147
[[649]]
[1] 0.011349574 0.013508772 0.008631404 0.021280640 0.015560348
[[650]]
[1] 0.04229004 0.04328632 0.04072432 0.05741288 0.03988009
[[651]]
[1] 0.03564507 0.07980607 0.04023288 0.03727049 0.03624094
[[652]]
[1] 0.01817249 0.01248580 0.01148464 0.01435068 0.02117629 0.01769430 0.02269733
[[653]]
[1] 0.06325087 0.04176846 0.03586228 0.02801538 0.04363107
[[654]]
[1] 0.04027948 0.07821365 0.03913936 0.05817683 0.09655362
[[655]]
[1] 0.04350333 0.02089573 0.03696134 0.03847944 0.01983950 0.03200044
[[656]]
[1] 0.03874391 0.01725904 0.01967430 0.03762704 0.01074501 0.03816962 0.02277718
[[657]]
[1] 0.02992324 0.02136774 0.04627524 0.03142120 0.02962521 0.03529064 0.03894476
[[658]]
[1] 0.05245219 0.04092659 0.04538006 0.05892014 0.03902902
[[659]]
[1] 0.02880574 0.07076309 0.02402644 0.06256805
[[660]]
[1] 0.01511517 0.01870342 0.03185070
[[661]]
[1] 0.01452678 0.06116533 0.02552962 0.02598894
[[662]]
[1] 0.02880721 0.02626352 0.03054527 0.03228218 0.03078980 0.01612347
[[663]]
[1] 0.02468090 0.01651931 0.03545101 0.14673387 0.01430469 0.01810139 0.07126334
[8] 0.01667175 0.05559580
[[664]]
[1] 0.01711825 0.02117629 0.02829454 0.02249535
[[665]]
[1] 0.01696201 0.01889746 0.01404429 0.04193499 0.01918330
[[666]]
[1] 0.03644634 0.01304945 0.01918330
[[667]]
[1] 0.07803239 0.04065234 0.05224473 0.03332213 0.04371325 0.05226033
[[668]]
[1] 0.03745569 0.01623713 0.01969315 0.03577469 0.01313648 0.01433933 0.02277718
[8] 0.02592401
[[669]]
[1] 0.06807023 0.04630667 0.02182232 0.04333379
[[670]]
[1] 0.01395454 0.01312865
[[671]]
[1] 0.02022652 0.02877766 0.05692517 0.05412720 0.03778093 0.03790551
[[672]]
[1] 0.03121240 0.01761368 0.02743144 0.02295725 0.03041560
[[673]]
[1] 0.022593905 0.012933910 0.009665636 0.019263404 0.024451501 0.015414423
[7] 0.017694296 0.024868639
[[674]]
[1] 0.01944413 0.02577614 0.01760253 0.02591125 0.02259671 0.03230934 0.02663432
[8] 0.01826916
[[675]]
[1] 0.05065460 0.05175102 0.03167990 0.03619591 0.04334979
[[676]]
[1] 0.01911058 0.01791271 0.04701049 0.01818524 0.01785338 0.02663432 0.01747369
[[677]]
[1] 0.01647049 0.01983235 0.01893425 0.02973068 0.02846120 0.01550045 0.02477448
[[678]]
[1] 0.04161150 0.02524807 0.02547173
[[679]]
[1] 0.02916435 0.03827330 0.03235333 0.03953426 0.02416195
[[680]]
[1] 0.01179537 0.01625653 0.01511842 0.02007293 0.01384608 0.02128064 0.01083294
[[681]]
[1] 0.01234874 0.01788682 0.04820784 0.02971458 0.01626565
[[682]]
[1] 0.1232737 0.1482538 0.2282139 0.2639095
[[683]]
[1] 0.03918519 0.04537605 0.03094559 0.03748245
[[684]]
[1] 0.01840385 0.02378432 0.01259785 0.02393055 0.02349307 0.02568916 0.02402644
[8] 0.02962107
[[685]]
[1] 0.08611498 0.18020876 0.08042286
[[686]]
[1] 0.03392375 0.08086434 0.18020876 0.06043909
[[687]]
[1] 0.01242755 0.01456946 0.02278304 0.02356435 0.01590895 0.01671413 0.01747369
[[688]]
[1] 0.02279569 0.02505174 0.01687953 0.01884467 0.02186148 0.01667175 0.01622825
[[689]]
[1] 0.02600153 0.01994100 0.02644235 0.03402221 0.04900249 0.02494094
[[690]]
[1] 0.02652922 0.04625193 0.04442885 0.07427227
[[691]]
[1] 0.03606554 0.02206053 0.03056962 0.02718877
[[692]]
[1] 0.01821149 0.02199048 0.03125932 0.01888956 0.03275157
[[693]]
[1] 0.2250900 0.1123334 0.1810640 0.2359142 0.2350839 0.1807281
[[694]]
[1] 0.02505669 0.03090109 0.03333675 0.03081442 0.05898052 0.05206785
[7] 0.03707926 0.03197783 0.02476876 0.04366176 0.03263987
[[695]]
[1] 0.02553668 0.04193752 0.03527036 0.02247681 0.07427227
[[696]]
[1] 0.01026662 0.01664613 0.04941254 0.04179340 0.01876549 0.01550406
[7] 0.02085185 0.01556941 0.01638611 0.01242482
[[697]]
[1] 0.07799420 0.08537373 0.04548818 0.06718510 0.09243965
[[698]]
[1] 0.06319874 0.02797403 0.02699455 0.02924409 0.01982795 0.03056962
[[699]]
[1] 0.01451338 0.02716959 0.01699765 0.05666958
[[700]]
[1] 0.02114148 0.02886550 0.02128527 0.01038105
[[701]]
[1] 0.02421383 0.01794248 0.01546498 0.01934877 0.03848794 0.01916305 0.01937253
[8] 0.02743144 0.02052983
[[702]]
[1] 0.02619842 0.01925892 0.02461834 0.02234517 0.01852026 0.02054212
[[703]]
[1] 0.1162309 0.2152225 0.1609639 0.1861313
[[704]]
[1] 0.02224164 0.03659501 0.02792073 0.01885638
[[705]]
[1] 0.01482419 0.01990584 0.01620843 0.01741192 0.01114976
[[706]]
[1] 0.03465796 0.02831023 0.03245624 0.02097756 0.02901991 0.03200044
[[707]]
[1] 0.04229352 0.06151989 0.04402999 0.09655362 0.04898986
[[708]]
[1] 0.01589597 0.01252591 0.01767875 0.01324074 0.01093374 0.01601826
[7] 0.01533275 0.01372045 0.01485702 0.01242482
[[709]]
[1] 0.01352838 0.01294330 0.01772558 0.02438963 0.02328804 0.01666230
[[710]]
[1] 0.01077237 0.01824209 0.01400005
[[711]]
[1] 0.02224728 0.03504814 0.06507621 0.01974629
[[712]]
[1] 0.04237301 0.03543245 0.02552442 0.03282596 0.04994633 0.02580855 0.04334979
[[713]]
[1] 0.02435890 0.02155270 0.02281463 0.02385345 0.03377051 0.01641924 0.01349879
[8] 0.03988009 0.02718877
[[714]]
[1] 0.01274705 0.02479553 0.01964808 0.01673213 0.02466955 0.01571040 0.02295725
[[715]]
[1] 0.05092351 0.04683519 0.04292551 0.05231386 0.05164364
[[716]]
[1] 0.02512541 0.03857821 0.04389462 0.04484671 0.06655916 0.04222523 0.03506534
[8] 0.02754336 0.02903820
[[717]]
[1] 0.05842356 0.08218976 0.03380348 0.02120283
[[718]]
[1] 0.08795888 0.13834180 0.08787733
[[719]]
[1] 0.04828061 0.04883417 0.04250581 0.02204515 0.02784991 0.01622584
[7] 0.02649695 0.03983371 0.02332743 0.03200733
[[720]]
[1] 0.06303444 0.02726528 0.03487178 0.05842356 0.03983371 0.03433268 0.01637149
[[721]]
[1] 0.09375983 0.14105584 0.05259738 0.05506681 0.08108103 0.07152944
[[722]]
[1] 0.03267640 0.02706644 0.02132873 0.02007995 0.02410400 0.02169023
[[723]]
[1] 0.09284037 0.03678273 0.08687921 0.03358142 0.05201656
[[724]]
[1] 0.01442841 0.02418293 0.02404382 0.02360152
[[725]]
[1] 0.04535204 0.06327485 0.04091200 0.06572631 0.08108103 0.05201656 0.05262315
[[726]]
[1] 0.04233409 0.06692828 0.03674312 0.06920682 0.07152944 0.05262315
[[727]]
[1] 0.02927951 0.08833582 0.02722936 0.02332743
[[728]]
[1] 0.04203531 0.04741976 0.05118852 0.04055731 0.03888815 0.05713023 0.04934280
[[729]]
[1] 0.06294305 0.07443655 0.04432937 0.04228192 0.06948901 0.03079300 0.09123385
[[730]]
[1] 0.06869842 0.05732947 0.08432369 0.07751373 0.09123385
[[731]]
[1] 0.23343373 0.05960775 0.37559789 0.04232994 0.11773651 0.06621344 0.05242834
[8] 0.11097720 0.04898986
[[732]]
[1] 0.07050714 0.06953899 0.09436231 0.07828010 0.07416036 0.07303446 0.08186823
[[733]]
[1] 0.06512923 0.05752275 0.04815531 0.05027095 0.04404427 0.06543436
[[734]]
[1] 0.07574564 0.08083277 0.08305264 0.07871410 0.08787733
[[735]]
[1] 0.03114032 0.02451643 0.01660108 0.02151820 0.01919778 0.03995683
[[736]]
[1] 0.01486522 0.01494772 0.05666958
[[737]]
[1] 0.07975391 0.08218976 0.03200733 0.03433268 0.04937969
[[738]]
[1] 0.06150355 0.04391627 0.08986141 0.04478465 0.03705669 0.05611563
[7] 0.02552658 0.07892048 0.02802757 0.06543436
[[739]]
[1] 0.02233121 0.03932310 0.02681878 0.02410878 0.02634218 0.03203595 0.03308590
[8] 0.02903820
[[740]]
[1] 0.04118300 0.02033933 0.02201355 0.03132704 0.03995683
[[741]]
[1] 0.02801712 0.03293699 0.02289309 0.04350084 0.03094559 0.08042286 0.06043909
[8] 0.02054212 0.02825862
[[742]]
[1] 0.02565494 0.02744083 0.02639082 0.02641007 0.02592401
[[743]]
[1] 0.02898622 0.06672812 0.03439425 0.06423976 0.03786080 0.04451413 0.04540491
[[744]]
[1] 0.02224485 0.01877823 0.03816312 0.01765833
[[745]]
[1] 0.02284483 0.01775545 0.01866221 0.01399552 0.01393624 0.01564073 0.02182232
[8] 0.01994000 0.02491164
[[746]]
[1] 0.04913316 0.04333379 0.03380348 0.04937969 0.01994000 0.04443275
[[747]]
[1] 0.02306033 0.02120283 0.01637149 0.02491164 0.04443275
[[748]]
[1] 0.01818614 0.01198318 0.01185572 0.01145364 0.01356361 0.02829454 0.02599665
[[749]]
[1] 0.01088636 0.02012537 0.01383199 0.02256269 0.01461842
[[750]]
[1] 0.03080902 0.03520382 0.03090653 0.04252325 0.04725483 0.05054051 0.04540491
[[751]]
[1] 0.014058050 0.016198054 0.010731694 0.008468845 0.025635904 0.013739429
[7] 0.023601521
[[752]]
[1] 0.03196922 0.03558222 0.03845935 0.02754147
[[753]]
[1] 0.01921030 0.02337382 0.02093427 0.01248131 0.01174434 0.01556035 0.01083294
[[754]]
[1] 0.02441654 0.03841227 0.03041560 0.03748245 0.02052983 0.02825862
[[755]]
[1] 0.01997679 0.02350928 0.02350019 0.01972384 0.06186406 0.01973378 0.03295296
[[756]]
[1] 0.01331920 0.03159086 0.01684760 0.02138877 0.03131401 0.02214730 0.03295296
[[757]]
[1] 0.05007085 0.01725714 0.03216998 0.03154985 0.03401151
[[758]]
[1] 0.01681994 0.02755745 0.01561211 0.02578081 0.02194882 0.01290787 0.04489254
[8] 0.03203162
[[759]]
[1] 0.01912638 0.01921669 0.03576139 0.01299533 0.02119822 0.02405341 0.01826916
[[760]]
[1] 0.03509855 0.03980297 0.08656493 0.04371325 0.04669436
[[761]]
[1] 0.04226673 0.03981480 0.02486864
[[762]]
[1] 0.05206725 0.08900568 0.01954195 0.03392995 0.05559580 0.01622825
[[763]]
[1] 0.04557970 0.08808149
[[764]]
[1] 0.02666081 0.02930741 0.03799950 0.01988767 0.08808149
[[765]]
[1] 0.023912214 0.009662212 0.041841694 0.028512550 0.020869454 0.044784601
[[766]]
[1] 0.01739769 0.01715659 0.01010186 0.01037576
[[767]]
[1] 0.01299732 0.03907677 0.01043680 0.01696819 0.01037576
[[768]]
[1] 0.02267659 0.03020608 0.02208175 0.01765217 0.01622307 0.02331492 0.02502735
[[769]]
[1] 0.03255181 0.02453020 0.05226033 0.04669436
[[770]]
[1] 0.02181910 0.02016180 0.01564940 0.01986300 0.02878944 0.01616484
[[771]]
[1] 0.02716015 0.02815537 0.06256805 0.02962107
[[772]]
[1] 0.03644564 0.04478460
[[773]]
[1] 0.01964310 0.02315608 0.01602168 0.01550717 0.02484935 0.01783186 0.01612347
[8] 0.02547173
[[774]]
[1] 0.02206573 0.02269733 0.02249535 0.025996658.1 Row-standardized weights matrix.
Next, 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/(#ofneighbors) 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 stick with the style=“W” option for simplicity’s sake but note that other more robust options are available, notably style=“B”.
rswm_q <- nb2listw(wm_q,
style="W",
zero.policy = TRUE)
set.ZeroPolicyOption(TRUE)[1] FALSErswm_qCharacteristics of weights list object:
Neighbour list object:
Number of regions: 774
Number of nonzero links: 4440
Percentage nonzero weights: 0.7411414
Average number of links: 5.736434
1 region with no links:
86
Weights style: W
Weights constants summary:
n nn S0 S1 S2
W 773 597529 773 285.0658 3198.414The 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 of FALSE would return an error.
To see the weight of the first polygon’s eight neighbors type:
rswm_q$weights[10][[1]]
[1] 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571Each neighbor is assigned a 0.1428 of the total weight.
Using the same method, we can also derive a row standardised distance weight matrix by using the code chunk below.
rswm_ids <- nb2listw(wm_q, glist=ids, style="B", zero.policy=TRUE)Warning in nb2listw(wm_q, glist = ids, style = "B", zero.policy = TRUE): zero
sum general weightsrswm_idsCharacteristics of weights list object:
Neighbour list object:
Number of regions: 774
Number of nonzero links: 4440
Percentage nonzero weights: 0.7411414
Average number of links: 5.736434
1 region with no links:
86
Weights style: B
Weights constants summary:
n nn S0 S1 S2
B 773 597529 182.3683 26.1191 252.344rswm_ids$weights[1][[1]]
[1] 0.25000205 0.09046782 0.10747703 0.09375983summary(unlist(rswm_ids$weights)) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.005368 0.020523 0.031591 0.041074 0.048311 0.375598 9. Compute Global and Local Measure of Spatial Autocorrelation (GLSA) by using spdep package.
9.1 Distribution of functional and non-functional water points.
Now, we are going to prepare a basemap and a choropleth map showing the distribution of functional and non-functional water points by using qtm() of tmap package.
equal <- tm_shape(nga_wp) +
tm_fill("wpt functional",
n = 5,
style = "equal") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Equal interval classification")
quantile <- tm_shape(nga_wp) +
tm_fill("wpt functional",
n = 5,
style = "quantile") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Equal quantile classification")
tmap_arrange(equal,
quantile,
asp=1,
ncol=2)
equal <- tm_shape(nga_wp) +
tm_fill("wpt non-functional",
n = 5,
style = "equal") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Equal interval classification")
quantile <- tm_shape(nga_wp) +
tm_fill("wpt non-functional",
n = 5,
style = "quantile") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Equal quantile classification")
tmap_arrange(equal,
quantile,
asp=1,
ncol=2)
9.2 Global Spatial Autocorrelation.
Let us compute global spatial autocorrelation statistics and to perform spatial complete randomness test for global spatial autocorrelation.
9.2.1 Computing Contiguity Spatial Weights.
Before we can compute the global spatial autocorrelation statistics, we need to construct a spatial weights of the study area. The spatial weights are used to define the neighbourhood relationships between the geographical units (i.e. county) in the study area.
In the code chunk below, poly2nb() of spdep package is used to compute contiguity weight matrices for the study area. This function builds a neighbours list based on regions with contiguous boundaries. If you look at the documentation you will see that you can pass a “queen” argument that takes TRUE or FALSE as options. If you do not specify this argument the default is set to TRUE, that is, if you don’t specify queen = FALSE this function will return a list of first order neighbours using the Queen criteria.
More specifically, the code chunk below is used to compute Queen contiguity weight matrix.
wm_q <- poly2nb(nga_wp,
queen=TRUE)
summary(wm_q)Neighbour list object:
Number of regions: 774
Number of nonzero links: 4440
Percentage nonzero weights: 0.7411414
Average number of links: 5.736434
1 region with no links:
86
Link number distribution:
0 1 2 3 4 5 6 7 8 9 10 11 12 14
1 2 14 57 125 182 140 122 72 41 12 4 1 1
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 links9.3 Global Spatial Autocorrelation: Moran’s I
Let us perform Moran’s I statistics testing by using moran.test() of spdep.
moran.test(nga_wp$'wpt functional',
listw=rswm_q,
zero.policy = TRUE,
na.action=na.omit)
Moran I test under randomisation
data: nga_wp$"wpt functional"
weights: rswm_q n reduced by no-neighbour observations
Moran I statistic standard deviate = 25.665, p-value < 2.2e-16
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.5512247163 -0.0012953368 0.0004634494 9.4 Global Spatial Autocorrelation: Geary’s
The code chunk below performs Geary’s C test for spatial autocorrelation by using geary.test() of spdep.
geary.test(nga_wp$'wpt functional', listw=rswm_q)
Geary C test under randomisation
data: nga_wp$"wpt functional"
weights: rswm_q
Geary C statistic standard deviate = 15.042, p-value < 2.2e-16
alternative hypothesis: Expectation greater than statistic
sample estimates:
Geary C statistic Expectation Variance
0.462472090 1.000000000 0.001277003 geary.test(nga_wp$'wpt non-functional', listw=rswm_q)
Geary C test under randomisation
data: nga_wp$"wpt non-functional"
weights: rswm_q
Geary C statistic standard deviate = 14.457, p-value < 2.2e-16
alternative hypothesis: Expectation greater than statistic
sample estimates:
Geary C statistic Expectation Variance
0.6170907765 1.0000000000 0.0007014859 9.5 Spatial Correlogram
Spatial correlograms are great to examine patterns of spatial autocorrelation in your data or model residuals. They show how correlated are pairs of spatial observations when you increase the distance (lag) between them - they are plots of some index of autocorrelation (Moran’s I or Geary’s c) against distance.Although correlograms are not as fundamental as variograms (a keystone concept of geostatistics), they are very useful as an exploratory and descriptive tool. For this purpose they actually provide richer information than variograms.
In the code chunk below, sp.correlogram() of spdep package is used to compute a 6-lag spatial correlogram of GDPPC. The global spatial autocorrelation used in Moran’s I. The plot() of base Graph is then used to plot the output.
MI_corr <- sp.correlogram(wm_q,
nga_wp$'wpt functional',
order=6,
method="I",
style="W")
plot(MI_corr)
MI_corr <- sp.correlogram(wm_q,
nga_wp$'wpt non-functional',
order=6,
method="I",
style="W")
plot(MI_corr)
By plotting the output might not allow us to provide complete interpretation. This is because not all autocorrelation values are statistically significant. Hence, it is important for us to examine the full analysis report by printing out the analysis results as in the code chunk below.
print(MI_corr)Spatial correlogram for nga_wp$"wpt non-functional"
method: Moran's I
estimate expectation variance standard deviate Pr(I) two sided
1 (773) 4.3393e-01 -1.2953e-03 4.7152e-04 20.0433 < 2.2e-16
2 (773) 2.6647e-01 -1.2953e-03 2.0206e-04 18.8374 < 2.2e-16
3 (773) 1.9507e-01 -1.2953e-03 1.2189e-04 17.7863 < 2.2e-16
4 (773) 1.4019e-01 -1.2953e-03 8.7589e-05 15.1181 < 2.2e-16
5 (773) 6.3735e-02 -1.2953e-03 6.8779e-05 7.8413 4.459e-15
6 (773) 2.1698e-02 -1.2953e-03 5.7380e-05 3.0354 0.002402
1 (773) ***
2 (773) ***
3 (773) ***
4 (773) ***
5 (773) ***
6 (773) **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 19.6 Compute Geary’s C correlogram and plot.
In the code chunk below, sp.correlogram() of spdep package is used to compute a 6-lag spatial correlogram of GDPPC. The global spatial autocorrelation used in Geary’s C. The plot() of base Graph is then used to plot the output.
GC_corr <- sp.correlogram(wm_q,
nga_wp$'wpt non-functional',
order=6,
method="C",
style="W")
plot(GC_corr)
GC_corr <- sp.correlogram(wm_q,
nga_wp$'wpt functional',
order=6,
method="C",
style="W")
plot(GC_corr)
Similar to the previous step, we will print out the analysis report by using the code chunk below.
print(GC_corr)Spatial correlogram for nga_wp$"wpt functional"
method: Geary's C
estimate expectation variance standard deviate Pr(I) two sided
1 (773) 0.46247209 1.00000000 0.00127700 -15.0420 < 2.2e-16 ***
2 (773) 0.55716070 1.00000000 0.00084660 -15.2197 < 2.2e-16 ***
3 (773) 0.62832603 1.00000000 0.00065344 -14.5399 < 2.2e-16 ***
4 (773) 0.70783718 1.00000000 0.00063934 -11.5547 < 2.2e-16 ***
5 (773) 0.79087031 1.00000000 0.00067363 -8.0576 7.781e-16 ***
6 (773) 0.85959605 1.00000000 0.00077929 -5.0296 4.916e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 19.7 Cluster and Outlier Analysis.
Local Indicators of Spatial Association or LISA are statistics that evaluate the existence of clusters in the spatial arrangement of a given variable.
Let us apply appropriate Local Indicators for Spatial Association (LISA), especially local Moran’I to detect cluster and/or outlier from Nigeria data.
To compute local Moran’s I, the localmoran() function of spdep will be used. It computes Ii values, given a set of zi values and a listw object providing neighbour weighting information for the polygon associated with the zi values.
The code chunks below are used to compute local Moran’s I of nga_wp at the region level for functional water point
fips <- order(nga_wp$'wpt functional')
localMI <- localmoran(nga_wp$'wpt functional', rswm_q)
head(localMI) Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
1 0.43849688 -7.191834e-04 0.138521511 1.1801022 0.2379596
2 0.34159457 -2.904635e-04 0.074723729 1.2506933 0.2110464
3 0.69235062 -8.956670e-04 0.230277001 1.4446488 0.1485566
4 0.02080384 -3.884365e-04 0.042599612 0.1026774 0.9182190
5 0.30973779 -3.884365e-04 0.059795173 1.2682517 0.2047081
6 0.05824105 -4.231402e-05 0.004642162 0.8554300 0.3923132The code chunks below are used to compute local Moran’s I of nga_wp at the region level for non-functional water point.
fips_nf <- order(nga_wp$'wpt non-functional')
localMI_nf <- localmoran(nga_wp$'wpt non-functional', rswm_q)
head(localMI_nf) Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
1 -0.32365786 -9.995243e-04 1.924638e-01 -0.73547576 0.46204980
2 0.07000542 -4.092463e-05 1.053077e-02 0.68258288 0.49487045
3 1.25819985 -1.627684e-03 4.181728e-01 1.94819847 0.05139122
4 -0.03537489 -5.427505e-05 5.954304e-03 -0.45773361 0.64714384
5 0.01201533 -2.590965e-04 3.988998e-02 0.06145673 0.95099547
6 0.00768085 -1.538445e-07 1.687859e-05 1.86960486 0.06153871localmoran() function returns a matrix of values whose columns are:
Ii: the local Moran’s I statistics
E.Ii: the expectation of local moran statistic under the randomisation hypothesis
Var.Ii: the variance of local moran statistic under the randomisation hypothesis
Z.Ii:the standard deviate of local moran statistic
Pr(): the p-value of local moran statistic
9.8 Creating a LISA Cluster Map
nci <- moran.plot(nga_wp$'wpt functional', rswm_q,
labels=as.character(nga_wp$shapeName),
xlab="Functional Water Point",
ylab="Spatially Lag Water Point")
nci <- moran.plot(nga_wp$'wpt non-functional', rswm_q,
labels=as.character(nga_wp$shapeName),
xlab="Non Functional Water Point",
ylab="Spatially Lag Water Point")
9.9 Mapping the local Moran’s I
Before mapping the local Moran’s I map, it is wise to append the local Moran’s I dataframe (i.e. localMI) onto nga_wp SpatialPolygonDataFrame. The code chunks below can be used to perform the task. The out SpatialPolygonDataFrame is called nga_wp.localMI.
nga_wp.localMI <- cbind(nga_wp,localMI) %>%
rename(Pr.Ii = Pr.z....E.Ii..)For non-functional.
nga_wp.localMI_nf <- cbind(nga_wp,localMI_nf) %>%
rename(Pr.Ii = Pr.z....E.Ii..)9.10 Mapping local Moran’s I values.
Using choropleth mapping functions of tmap package, we can plot the local Moran’s I values by using the code chinks below.
tm_shape(nga_wp.localMI) +
tm_fill(col = "Ii",
style = "pretty",
palette = "RdBu",
title = "local moran statistics") +
tm_borders(alpha = 0.5)Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
Plotting for non-functional
tm_shape(nga_wp.localMI_nf) +
tm_fill(col = "Ii",
style = "pretty",
palette = "RdBu",
title = "local moran statistics") +
tm_borders(alpha = 0.5)Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
9.11 Mapping both local Moran’s I values and p-values
For effective interpretation, it is better to plot both the local Moran’s I values map and its corresponding p-values map next to each other.
The code chunk below will be used to create such visualisation.
localMI_nf.map <- tm_shape(nga_wp.localMI_nf) +
tm_fill(col = "Ii",
style = "pretty",
title = "local moran statistics") +
tm_borders(alpha = 0.5)
pvalue.map <- tm_shape(nga_wp.localMI_nf) +
tm_fill(col = "Pr.Ii",
breaks=c(-Inf, 0.001, 0.01, 0.05, 0.1, Inf),
palette="-Blues",
title = "local Moran's I p-values") +
tm_borders(alpha = 0.5)
tmap_arrange(localMI_nf.map, pvalue.map, asp=1, ncol=2)Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
9.12 Creating a LISA Cluster Map.
The LISA Cluster Map shows the significant locations color coded by type of spatial autocorrelation. The first step before we can generate the LISA cluster map is to plot the Moran scatterplot.
The Moran scatterplot is an illustration of the relationship between the values of the chosen attribute at each location and the average value of the same attribute at neighboring locations.
The code chunk below plots the Moran scatterplot of non-functional water point by using moran.plot() of spdep.
nci <- moran.plot(nga_wp$'wpt non-functional', rswm_q,
labels=as.character(nga_wp$shapeName),
xlab="Non Functional Water Point",
ylab="Spatially Lag Water Point")
Notice that the plot is split in 4 quadrants
9.13 Preparing LISA map classes.
The code chunks below show the steps to prepare a LISA cluster map for non-functional water points.
quadrant <- vector(mode="numeric",length=nrow(localMI_nf))Next, derives the spatially lagged variable of interest (i.e. water points) and centers the spatially lagged variable around its mean.
nga_wp$lag_waterpoints <- lag.listw(rswm_q, nga_wp$'wpt non-functional')
DV <- nga_wp$lag_waterpoints - mean(nga_wp$lag_waterpoints) This is follow by centering the local Moran’s around the mean.
LM_I <- localMI_nf[,1] - mean(localMI_nf[,1]) Next, we will set a statistical significance level for the local Moran.
signif <- 0.05 These four command lines define the low-low (1), low-high (2), high-low (3) and high-high (4) categories.
quadrant[DV <0 & LM_I>0] <- 1
quadrant[DV >0 & LM_I<0] <- 2
quadrant[DV <0 & LM_I<0] <- 3
quadrant[DV >0 & LM_I>0] <- 4 Lastly, places non-significant Moran in the category 0.
quadrant[localMI_nf[,5]>signif] <- 0- Plotting LISA map.
Now, we can build the LISA map by using the code chunks below.
nga_wp.localMI_nf$quadrant <- quadrant
colors <- c("#ffffff", "#2c7bb6", "#abd9e9", "#fdae61", "#d7191c")
clusters <- c("insignificant", "low-low", "low-high", "high-low", "high-high")
tm_shape(nga_wp.localMI_nf) +
tm_fill(col = "quadrant",
style = "cat",
palette = colors[c(sort(unique(quadrant)))+1],
labels = clusters[c(sort(unique(quadrant)))+1],
popup.vars = c("")) +
tm_view(set.zoom.limits = c(11,17)) +
tm_borders(alpha=0.5)
For effective interpretation, it is better to plot both the local Moran’s I values map and its corresponding p-values map next to each other.
The code chunk below will be used to create such visualization.
wpt_non_functional <- qtm(nga_wp, "wpt non-functional")
nga_wp.localMI_nf$quadrant <- quadrant
colors <- c("#ffffff", "#2c7bb6", "#abd9e9", "#fdae61", "#d7191c")
clusters <- c("insignificant", "low-low", "low-high", "high-low", "high-high")
LISAmap <- tm_shape(nga_wp.localMI_nf) +
tm_fill(col = "quadrant",
style = "cat",
palette = colors[c(sort(unique(quadrant)))+1],
labels = clusters[c(sort(unique(quadrant)))+1],
popup.vars = c("")) +
tm_view(set.zoom.limits = c(11,17)) +
tm_borders(alpha=0.5)
tmap_arrange(wpt_non_functional, LISAmap,
asp=1, ncol=2)
11. Hot Spot and Cold Spot Area Analysis.
Beside detecting cluster and outliers, localised spatial statistics can be also used to detect hot spot and/or cold spot areas.
The term ‘hot spot’ has been used generically across disciplines to describe a region or value that is higher relative to its surroundings (Lepers et al 2005, Aben et al 2012, Isobe et al 2015).
11.1 Getis and Ord’s G-Statistics.
An alternative spatial statistics to detect spatial anomalies is the Getis and Ord’s G-statistics (Getis and Ord, 1972; Ord and Getis, 1995). It looks at neighbors within a defined proximity to identify where either high or low values clutser spatially. Here, statistically significant hot-spots are recognised as areas of high values where other areas within a neighborhood range also share high values too.
The analysis consists of three steps:
Deriving spatial weight matrix
Computing Gi statistics
Mapping Gi statistic
11.2 Deriving distance-based weight matrix.
First, we need to define a new set of neighbours. Whist the spatial autocorrelation considered units which shared borders, for Getis-Ord we are defining neighbours based on distance.
There are two type of distance-based proximity matrix, they are:
fixed distance weight matrix; and
adaptive distance weight matrix.
11.2.1 Deriving the centroid.
We will need points to associate with each polygon before we can make our connectivity graph. It will be a little more complicated than just running st_centroid() on the sf object: us.bound. We need the coordinates in a separate data frame for this to work. To do this we will use a mapping function. The mapping function applies a given function to each element of a vector and returns a vector of the same length. Our input vector will be the geometry column of us.bound. Our function will be st_centroid(). We will be using map_dbl variation of map from the purrr package. For more documentation, check out map documentation
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(nga_wp$geometry, ~st_centroid(.x)[[1]])We do the same for latitude with one key difference. We access the second value per each centroid with [[2]].
latitude <- map_dbl(nga_wp$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)11.3 Determine the cut-off distance.
Firstly, we need to determine the upper limit for distance band by using the steps below:
Return a matrix with the indices of points belonging to the set of the k nearest neighbours of each other by using knearneigh() of spdep.
Convert the knn object returned by knearneigh() into a neighbours list of class nb with a list of integer vectors containing neighbour region number ids by using knn2nb().
Return the length of neighbour relationship edges by using nbdists() of spdep. The function returns in the units of the coordinates if the coordinates are projected, in km otherwise.
Remove the list structure of the returned object by using unlist().
#coords <- coordinates(hunan)
k1 <- knn2nb(knearneigh(coords))
k1dists <- unlist(nbdists(k1, coords, longlat = TRUE))
summary(k1dists) Min. 1st Qu. Median Mean 3rd Qu. Max.
2.662 12.815 20.242 22.031 27.706 71.661 The summary report shows that the largest first nearest neighbour distance is 71.661 km, so using this as the upper threshold gives certainty that all units will have at least one neighbor.
11.3.1 Computing fixed distance weight matrix.
Now, we will compute the distance weight matrix by using dnearneigh() as shown in the code chunk below.
wm_d72 <- dnearneigh(coords, 0, 72, longlat = TRUE)
wm_d72Neighbour list object:
Number of regions: 774
Number of nonzero links: 18112
Percentage nonzero weights: 3.023323
Average number of links: 23.40052 Next, nb2listw() is used to convert the nb object into spatial weights object.
wm72_lw <- nb2listw(wm_d72, style = 'B')
summary(wm72_lw)Characteristics of weights list object:
Neighbour list object:
Number of regions: 774
Number of nonzero links: 18112
Percentage nonzero weights: 3.023323
Average number of links: 23.40052
Link number distribution:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
5 8 12 21 32 35 33 35 28 36 25 21 19 23 16 14 10 13 15 17 16 11 13 10 6 12
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
12 5 16 13 12 7 9 9 12 7 12 15 13 9 10 4 5 4 7 8 8 8 6 5 3 2
53 54 55 56 57 58 59 60 61 62 63 64 65 67 68 70
3 4 5 3 6 5 2 6 4 8 8 4 4 3 1 1
5 least connected regions:
90 112 123 237 670 with 1 link
1 most connected region:
585 with 70 links
Weights style: B
Weights constants summary:
n nn S0 S1 S2
B 774 599076 18112 36224 261407211.4 Computing adaptive distance weight matrix.
One of the characteristics of fixed distance weight matrix is that more densely settled areas (usually the urban areas) tend to have more neighbours and the less densely settled areas (usually the 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 as shown in the code chunk below.
knn <- knn2nb(knearneigh(coords, k=8))
knnNeighbour list object:
Number of regions: 774
Number of nonzero links: 6192
Percentage nonzero weights: 1.033592
Average number of links: 8
Non-symmetric neighbours listNext, nb2listw() is used to convert the nb object into spatial weights object.
knn_lw <- nb2listw(knn, style = 'B')
summary(knn_lw)Characteristics of weights list object:
Neighbour list object:
Number of regions: 774
Number of nonzero links: 6192
Percentage nonzero weights: 1.033592
Average number of links: 8
Non-symmetric neighbours list
Link number distribution:
8
774
774 least connected regions:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 with 8 links
774 most connected regions:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 with 8 links
Weights style: B
Weights constants summary:
n nn S0 S1 S2
B 774 599076 6192 11152 20194212 Computing Gi statistics.
12.1 Computing Gi statistics
fips <- order(nga_wp$shapeName)
gi.fixed <- localG(nga_wp$'wpt non-functional', wm72_lw)
gi.fixed [1] -3.4117746649 -3.4325327295 -1.5896654726 0.1024035837 -1.4372020951
[6] 2.8392431096 -1.2451608781 -2.0881598529 0.6943166939 -4.0181176937
[11] -3.5543643584 0.7028327021 1.0557642518 -1.2648020444 -3.1100102991
[16] 4.4236791649 0.7253370351 -0.1170313444 -0.8468860707 0.1418378466
[21] -0.4444525907 -2.5787439152 -5.9408386919 0.0490221299 -4.6250101263
[26] -3.4526479798 -3.2842327545 2.6307586789 3.1531206419 3.9017827701
[31] -0.3901172116 -2.0709442943 -0.1348087246 2.5315321685 0.5142842691
[36] 2.8157847314 2.0572944746 2.3429395384 2.1827306253 1.9238345799
[41] 2.1897325824 -0.3645157327 -3.8639919963 4.5399631849 4.3061500016
[46] 2.9608766578 -0.2864102817 1.1746065746 -2.8181694612 3.0318488026
[51] -2.3715234853 -5.9628191057 -4.8665465676 -5.9978486948 -2.1521995859
[56] -2.6990377972 -4.0738422433 -4.2582561763 0.1434597247 -1.4480552039
[61] -0.3664736626 -2.0660238126 1.3560050446 1.1941723552 0.6520255991
[66] -0.7061126715 5.3395987192 -3.9654078235 -2.8742418968 4.4607142525
[71] 6.0841484326 0.2440050847 -0.0717557313 -0.2146512615 1.2805963197
[76] 2.5430378175 -4.3209911455 -6.1864779959 -6.3091804665 -5.2074212405
[81] 1.5724065864 -3.0134148246 -1.6605766843 2.1372905711 -1.9370094219
[86] 0.5351722462 0.7781339095 -0.0991617191 -0.2983694288 1.6845537280
[91] -1.9481984678 2.4106784274 -0.1209884988 -0.0294216102 1.2014584129
[96] 0.5487724545 0.5230691396 -1.0049421880 2.2086577453 0.6860104564
[101] -0.8358802808 3.3934885075 -2.5915246147 0.7361865356 -1.3919152488
[106] 1.6857908883 3.3349825302 1.4948688424 -0.6649376028 0.1049808942
[111] 0.1797402897 -0.2296312591 1.5924606930 -0.4220300892 -2.4843821512
[116] -0.5403453732 2.4425124231 3.1901956500 3.7026209747 5.7708188247
[121] -2.9992528883 -2.8867680713 0.1450845287 5.6210067488 0.1300463875
[126] -1.4642795114 -0.1821964578 0.2019905157 -0.3933126058 -1.0687092481
[131] 1.1862683742 -1.2897330306 2.0774178384 -2.7261489805 1.1595913792
[136] 0.0590529868 0.0415312576 -0.1479027888 0.2286220369 -1.8068739793
[141] 1.8128875616 -1.8465165657 -1.9263347704 -0.1217435299 0.1562862363
[146] -2.0483697898 -0.1728823598 1.1206769217 2.2828808379 -1.2897776976
[151] 2.4167160591 0.3088772578 -0.1569586843 1.5067812784 -1.4232058416
[156] -1.3192224285 -3.7789524190 -0.8757699225 -1.6603243945 -2.5183861649
[161] 0.0497131757 0.5395983509 2.4584177542 1.7817721289 -5.8940988934
[166] 0.5616363675 1.6014174947 -0.7737526146 -1.8608470741 1.2807175799
[171] 1.8546118600 5.6507070121 4.9899574161 3.8565952505 5.4648235612
[176] -0.6763153032 -2.1820247405 1.1201091686 5.1137113602 -1.0998628514
[181] -4.9652045029 3.5387758960 -2.3886751887 -0.4653196107 4.1348276862
[186] 3.5932572298 3.8543059497 5.2228382133 -5.6296206205 -2.9973115744
[191] -3.9852174640 3.2727422969 -2.4310631930 -3.0070376123 -3.2384084218
[196] -3.0486056593 -2.0595597139 -2.5844113063 -3.5641507085 -1.7577329410
[201] 0.3786055236 -0.5079560968 -1.9294510731 -3.7986404726 -2.7848792901
[206] -2.5102897931 -1.9939528637 -2.3691669213 -0.9886885003 -2.0151263289
[211] -0.9581884502 -0.5086561528 3.7754185180 -2.4231288260 -5.6010575750
[216] -4.0749852290 0.3576511087 0.2927426140 -1.6443111430 0.8302229411
[221] -0.5062515700 -0.1547202287 -2.4843821512 1.0086589118 -2.1770380403
[226] 2.4626146208 0.0440638219 0.1920286012 1.3983932449 -0.8496076016
[231] -0.6691384516 -1.2911170263 1.5345894073 -0.5529326952 -1.5457781106
[236] -1.0324483227 1.1200463362 -0.0620637069 0.4247713032 2.0054718680
[241] -1.8702382558 -1.1521974135 -0.2921554734 -2.4950665627 3.5976949011
[246] -2.7962939270 4.8434048492 -2.5148770948 -1.1267981706 -2.4941996452
[251] -0.2630217751 -2.1970004876 -0.7730147072 0.6798537235 1.2492399224
[256] -0.6888953395 0.8358603199 1.6654804315 -0.4827650151 -0.5433051006
[261] -2.7605516416 -0.8707537202 0.0947302301 -2.0368588754 0.0696307201
[266] -0.6203604939 -2.3237912877 0.3598097208 0.3526946680 1.4430576168
[271] -1.8702382558 -0.1272890978 -2.2962397403 -2.7219828700 0.2521141177
[276] 0.2110907859 -0.0140816269 -0.4035735646 -0.1936101468 -3.4726936093
[281] -1.1266733840 -1.0891489463 -0.8054781266 -3.1021011147 -0.2258676440
[286] -0.4264717884 1.5869426397 -0.6349762230 -1.8844344179 3.0598056534
[291] -5.6754076087 -5.4830137576 -5.5819971667 -5.6384828208 -0.5824687042
[296] 5.3088751125 -2.6487517374 5.7673863636 4.9440575555 3.7543412120
[301] 4.2490060765 6.6608670387 3.7340891879 4.3840930783 4.6799452358
[306] -2.6462032630 4.3397784922 -2.4054408070 -3.4238843779 -2.5120530221
[311] -2.9450547146 -2.6317957384 -5.5485595954 -4.2448418644 1.3083580350
[316] -0.0008175288 -0.5453801285 -2.0157446995 4.7484630417 2.6229111706
[321] -2.5261266494 -3.2713201180 -2.9903547011 1.6380043965 -4.6631468209
[326] -2.3492073446 -3.3004858380 4.2761022253 4.0780178934 3.5616720390
[331] -1.2136320131 -2.7979941928 -2.0118940444 -1.5111729044 -2.2358982231
[336] -3.9541762690 2.0820588544 -2.6099148033 6.7003704884 1.0903162691
[341] 4.4835629508 6.0629547434 6.0427221000 6.1036461610 -1.0477720725
[346] 6.6228782572 4.9953238917 5.1302151080 0.5368152260 1.0651968009
[351] -0.8283153141 -2.4311349096 0.8470591553 0.3745724752 5.5209564889
[356] 5.4927540484 5.2676551986 2.0959129929 -0.6275540802 4.1792561383
[361] -0.0590747566 -1.4318034953 -1.8389648967 -3.6800688787 -3.7817839487
[366] -5.4245454867 5.8696037200 -4.6411506929 1.6117911279 -2.6522843897
[371] -2.9391365455 -5.4894734274 0.8980569535 0.1738054683 -0.1017873640
[376] -2.9644127980 -0.3483319452 2.5906642146 2.5757689632 2.8049352195
[381] -1.8542483216 0.4788736337 0.3119912756 0.9113252618 -1.0636046195
[386] 1.2128128576 2.3166975797 -2.4409676368 1.2514290700 1.2361156601
[391] 2.1848914329 1.9356540658 1.1829505160 -2.1398569553 1.5986911707
[396] 3.5405748932 4.2315968938 1.4478008085 0.7997941089 -2.4579198656
[401] 1.7729143861 -0.2688161324 1.1759997435 -0.4785272245 0.0824185174
[406] -1.9481984678 1.8474148195 2.0216768147 0.6370561918 -1.0801487314
[411] 1.8849519770 -0.4439225392 -2.1627301474 -0.9568239420 -2.1208906249
[416] 1.9959594097 1.8550481979 0.0768132901 0.6124793401 1.3560776058
[421] 2.3411474563 1.5216483269 2.8604474525 -0.5765991732 1.1856817116
[426] 0.8921599476 0.9184331234 1.0249162410 1.3154778076 -2.2722342410
[431] -0.7116331447 2.1453405077 0.6730570517 -1.1800027342 -0.5199578683
[436] 0.4808652570 -0.0235257009 0.8165479237 -3.0529318612 -2.0225625108
[441] 2.9455769746 0.2363832344 -2.2768267875 0.3466223084 3.2255102937
[446] 0.1411952313 -1.9481984678 -2.1661859263 -0.4676066168 -1.6782098794
[451] -0.0777842289 1.4868205720 -0.3505589498 -0.1726887164 3.5929560597
[456] 2.7346136617 -0.9465537461 -0.2014424404 1.3012749110 1.5028765507
[461] -2.2932985630 -2.2505626380 0.7936258264 2.4032040546 2.3557037349
[466] 0.1826814641 0.1072325498 1.2844732698 -0.8760327358 1.7752129147
[471] 0.2516184985 1.2342409373 -2.0753935530 -1.8558057677 -2.5640880571
[476] -0.2895493962 -2.0222513579 1.9291991379 -2.0461307940 1.7044807663
[481] -2.1018239835 2.2216843179 -0.0429522457 0.4370541700 1.2654796893
[486] -0.8407609199 2.7630963689 1.3973854719 -1.0153143497 0.3214749983
[491] 2.0826454414 -2.2510480140 -0.9777552530 -0.2462903412 1.4346392906
[496] -1.1545484236 -1.5476742545 -5.2094566914 0.0549402737 -2.6381224548
[501] 1.2314091065 3.0942547654 -0.1945298933 -0.3139665937 -1.4312402705
[506] 6.0035680834 -1.5896654726 1.7438033643 -2.7605516416 3.6248484270
[511] 3.2842322630 -2.5504783218 -2.2884473891 -1.2742029215 -2.1794770390
[516] -0.9452868662 0.9226508131 -0.2808815183 -1.3017627523 0.1028146689
[521] 0.7903470822 -4.7773019724 -4.0599839099 -2.8929329559 -2.2510480140
[526] -2.5436200487 0.5572826624 -4.0077436082 0.5743933705 0.7561771796
[531] -5.4711387856 -5.9881769115 -1.7097095914 -3.2145613317 -5.4921528592
[536] -5.6065224752 -5.8927691095 -0.5158206706 -0.6169568186 -0.3646687228
[541] -3.1236269572 -0.6335776619 -5.7248166602 -2.7991201431 3.1510854408
[546] 0.8119571110 1.5362711705 -3.3968533218 -3.8153771055 5.9423980975
[551] -1.9815928102 -3.8748698800 2.7449840241 3.5912450649 -1.5471567880
[556] 1.1545459720 4.7634173376 -3.1812422270 -0.1539058338 5.4349953766
[561] -1.4215986299 -4.1250618121 -2.1372786039 -5.4053629216 -3.3734475553
[566] 3.1128103289 3.3148834211 2.8896814604 3.3780876342 2.1415338074
[571] -2.6827643843 1.3599575208 -5.4155248133 -0.3658278560 -4.8168371931
[576] -1.0570272459 -0.2384946211 -1.1607522581 -5.6452073745 -2.2576555531
[581] 1.7975786222 4.6952651933 1.4302665280 1.3989314523 -5.3349149523
[586] 1.4675795928 -0.2318308926 -2.7751843183 -1.2798894874 -3.2243185582
[591] 3.7238463041 -1.9040602801 5.3597510246 0.3377758326 -0.6590453290
[596] 0.5564706605 -3.6996922635 0.2560104627 4.2395964721 3.9198676182
[601] -0.7367636763 -5.4524005156 -5.3185082518 -0.8488602190 -1.9028377268
[606] -1.0064673530 -0.3045753085 -2.8225903216 2.5443505078 4.8232865803
[611] -5.7372577835 5.3162941702 0.0533854848 -5.5653175208 -5.4554505787
[616] -5.3342170944 -2.0355539983 -6.2184435395 -5.6475542692 1.4165917345
[621] -4.8095950608 -5.3585676554 -2.4123005609 -3.7802073197 5.5704091882
[626] -0.4207639553 -0.0820196887 0.4913428569 1.9668518061 1.0669220785
[631] -4.3830103212 -4.0535150724 -4.2297431678 2.9774246125 4.2206187107
[636] -5.8592562511 -2.6995188099 1.2724261692 -0.0111640361 4.9598342782
[641] -0.8975285513 3.2695447281 -3.3804769565 1.0233101789 -3.3797348722
[646] 0.6090977147 2.9539283043 -1.1786140304 -0.0727091531 -0.8216517560
[651] -2.6810951026 0.2470088329 1.2742462639 -1.9488365342 1.0167687803
[656] 2.4696394873 0.9420722071 0.4885664676 3.1287725372 -0.9497319744
[661] 2.4425891604 -0.2105136963 -3.5070892894 0.8476063553 -0.4188028499
[666] -0.1801619339 2.1839721672 2.9349648477 -2.3517158696 0.6303115465
[671] -2.9726649671 -0.3251973156 0.5816313363 -0.3216700089 -1.8966498040
[676] -2.6167225944 2.1462680474 -0.7007776964 -0.2901563432 -0.6294507710
[681] 3.1848222332 -2.2628381729 -0.1928257328 3.7543250494 -0.6898963477
[686] -0.4579363450 -2.8263574815 -1.9107113505 1.9767736485 1.3356841113
[691] -0.1992862573 2.1555107134 -2.1759945372 3.8196059687 1.3667153170
[696] 1.5606747950 -2.8180979983 0.1258362477 1.6362250633 -1.1755606793
[701] 0.0556083826 -0.6254612030 -1.8245553490 1.7673690255 -1.7099082238
[706] 0.3279568070 -1.8322536644 -0.4720914115 1.2302888089 -0.2537342611
[711] -1.0194530184 -1.4775673577 -0.0896050089 -1.1801300237 -2.3509568161
[716] -4.6307512034 -2.7407326147 -0.0025370071 -3.0988099215 -3.1167461569
[721] -3.4662013761 -1.4743456301 -1.9718471619 2.1607674145 -2.9497681287
[726] -3.3579719453 -3.6200569828 -4.8364896839 -3.6643604569 -3.4079170777
[731] -1.2277114161 -5.4597614286 0.0712972970 -0.1507192365 2.3528477525
[736] 1.8167556955 -2.5958296857 -0.4791719568 -3.9373952476 4.0454927846
[741] -0.8497222111 4.1914248594 -1.3905853881 -1.1087182179 -1.5160448019
[746] -2.6117513854 -2.7085894788 0.0867810375 1.8316046394 -1.3189997474
[751] 0.6456167365 -0.8342620760 -0.2755550651 -0.1319774555 3.8329382654
[756] 3.9433436976 1.4629084755 2.5181371643 2.4671867473 1.4737976377
[761] 1.0618293805 -3.2385810885 -2.9516603752 -3.1601387394 0.2150638765
[766] -1.2461555271 -0.1547825213 0.7644963108 2.3956916991 3.1096694186
[771] 3.4527292514 -0.9854012267 -1.0503779238 0.1911170626
attr(,"cluster")
[1] Low Low Low Low Low High Low Low High Low Low High Low Low High
[16] High Low High Low High High Low Low High Low Low Low High High High
[31] Low Low Low High High High Low High High High High High Low Low Low
[46] High High High High Low Low Low Low Low Low Low Low Low Low Low
[61] Low Low High Low High Low High Low Low High High Low High Low High
[76] Low Low Low Low Low High High High Low High Low Low Low High High
[91] Low High High High High High High Low High Low Low High Low High High
[106] Low High High High High Low High Low Low Low High Low High High High
[121] Low Low Low High Low Low Low Low Low Low High Low High Low High
[136] Low High Low High Low Low High Low High High Low Low High High Low
[151] Low High Low High High Low Low High Low Low High Low High High Low
[166] Low Low High Low Low High High Low High Low Low Low Low High Low
[181] Low High Low Low High Low High High Low Low Low Low Low Low Low
[196] High Low Low Low Low High High Low Low Low Low Low Low Low Low
[211] Low Low High Low Low Low High High Low Low High Low Low High Low
[226] Low Low Low Low High Low Low High Low High High High Low High High
[241] Low High Low Low High Low Low Low Low Low Low Low Low High High
[256] Low Low Low Low Low Low High High Low High Low Low High Low High
[271] Low High Low Low Low High Low Low Low Low High Low High High Low
[286] Low Low Low Low High Low Low Low Low Low Low Low High High High
[301] High Low High High High Low High Low Low Low Low Low Low Low Low
[316] Low Low Low High High Low Low Low Low Low Low Low High High High
[331] Low High Low Low Low Low High High High Low High Low High High High
[346] Low Low High Low Low Low Low High Low High High Low High Low Low
[361] Low High High Low Low Low High Low High Low Low Low Low Low High
[376] Low High High Low High Low Low Low High Low High High Low Low High
[391] Low High Low Low High High Low Low Low Low High High High High Low
[406] Low High High High Low High Low High Low High High High Low Low High
[421] High Low High Low High High Low Low Low Low Low Low High High High
[436] High High High Low Low High Low Low Low High High Low Low Low High
[451] High High Low High High High High Low High High Low Low Low High Low
[466] Low High Low High High Low Low Low Low Low High Low High Low High
[481] Low High Low Low Low Low High High Low High Low Low High Low High
[496] High Low Low Low Low Low High Low Low High Low Low High Low High
[511] High Low Low Low Low High High Low High High High High Low Low Low
[526] Low High Low Low Low Low Low Low Low Low Low Low High Low High
[541] Low Low Low Low High High High High High High High Low High High Low
[556] High High Low High High Low Low Low Low Low Low High High High Low
[571] Low Low High Low High High High Low Low Low Low High High High Low
[586] High High Low Low Low Low Low High Low Low Low Low High High High
[601] High Low Low Low Low Low Low High Low High Low High Low Low Low
[616] Low High Low Low High Low Low Low Low High Low Low Low Low Low
[631] Low Low Low High High Low Low Low Low High Low High Low High Low
[646] Low High Low Low Low Low High Low Low Low High High High High High
[661] Low Low High High Low High High High Low Low High Low High Low Low
[676] Low High Low High Low High Low Low High Low Low Low Low High Low
[691] High High Low High Low Low Low High High High High Low Low High Low
[706] High Low Low High Low High High High High Low Low Low High Low Low
[721] High Low Low High High High Low Low Low Low Low Low Low Low High
[736] High Low High Low High Low High High Low Low Low Low High High Low
[751] High Low High High High High High High High Low Low Low Low Low High
[766] Low Low High Low Low High High High Low
Levels: Low High
attr(,"gstari")
[1] FALSE
attr(,"call")
localG(x = nga_wp$"wpt non-functional", listw = wm72_lw)
attr(,"class")
[1] "localG"The output of localG() is a vector of G or Gstar values, with attributes “gstari” set to TRUE or FALSE, “call” set to the function call, and class “localG”.
The Gi statistics is represented as a Z-score. Greater values represent a greater intensity of clustering and the direction (positive or negative) indicates high or low clusters.
Next, we will join the Gi values to their corresponding nga_wp sf data frame by using the code chunk below.
nga_wp.gi <- cbind(nga_wp, as.matrix(gi.fixed)) %>%
rename(gstat_fixed = as.matrix.gi.fixed.)In fact, the code chunk above performs three tasks. First, it convert the output vector (i.e. gi.fixed) into r matrix object by using as.matrix(). Next, cbind() is used to join hunan@data and gi.fixed matrix to produce a new SpatialPolygonDataFrame called hunan.gi. Lastly, the field name of the gi values is renamed to gstat_fixed by using rename().
12.2 Mapping Gi values with fixed distance weights.
The code chunk below shows the functions used to map the Gi values derived using fixed distance weight matrix.
wpt_non_func <- qtm(nga_wp, "wpt non-functional")
Gimap <-tm_shape(nga_wp.gi) +
tm_fill(col = "gstat_fixed",
style = "pretty",
palette="-RdBu",
title = "local Gi") +
tm_borders(alpha = 0.5)
tmap_arrange(wpt_non_func, Gimap, asp=1, ncol=2)Variable(s) "gstat_fixed" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
12.3 Gi statistics using adaptive distance.
The code chunk below are used to compute the Gi values for GDPPC2012 by using an adaptive distance weight matrix (i.e knb_lw).
fips <- order(nga_wp$shapeName)
gi.adaptive <- localG(nga_wp$'wpt non-functional', knn_lw)
hunan.gi <- cbind(nga_wp, as.matrix(gi.adaptive)) %>%
rename(gstat_adaptive = as.matrix.gi.adaptive.)