pacman::p_load(sf, tidyverse, tmap, spdep, funModeling, DT)Take-home Exercise 1: Geospatial Analytics for Social Good
The Task
The specific tasks of this take-home exercise are as follows:
Using appropriate sf method, import the shapefile into R and save it in a simple feature data frame format. Note that there are three Projected Coordinate Systems of Nigeria, they are: EPSG: 26391, 26392, and 26303. We can use any one of them.
Using appropriate tidyr and dplyr methods, derive the proportion of functional and non-functional water point at LGA level.
Combining the geospatial and aspatial data frame into simple feature data frame.
Performing outliers/clusters analysis by using appropriate local measures of spatial association methods.
Performing hotspot areas analysis by using appropriate local measures of spatial association methods.
Thematic Mapping
- Plot maps to show the spatial distribution of functional and non-functional water point rate at Local Government Area (LGA) level by using appropriate thematic mapping technique provided by tmap package.
Analytical Mapping
- Plot hotspot areas and outliers/clusters maps of functional and non-functional water point rate at LGA level by using appropriate thematic mapping technique provided by tmap package.
Overview
Geospatial analytics hold tremendous potential to address complex problems faced by society. In this study, we are tasked to apply appropriate global and local measures of spatial association techniques to reveals the spatial patterns of non-functional water points. For the purpose of this study, Nigeria will be used as the study country.
Installing & Loading R Packages
In the code chunk below, p_load() of pacman package is used to install and load the following R packages into R environment:
sf
tidyverse
tmap
spdep
funModeling, to be used for rapid Exploratory Data Analysis
The Data
Aspatial data
For the purpose of this exercise, data from WPdx Global Data Repositories will be used. There are two versions of the data. They are: WPdx-Basic and WPdx+. We are required to use WPdx+ data set.
Geospatial data
Nigeria Level-2 Administrative Boundary (also known as Local Government Area) polygon features GIS data will be used in this exercise. The data can be downloaded either from The Humanitarian Data Exchange portal or geoBoundaries.
Importing Geospatial Data
Two geospatial data sets used are:
geo_exportnga_admbnda_adm2_osgof_20190417
Importing water point geospatial data
First, we are going to import the water point geospatial data (i.e. geo_export) by using the code chunk below.
(Since we have previously used this data set in the in-class exercise, we will use the data directly from there.)
wp <- st_read(dsn = "C:/Jacobche/ISSS624/In-class_Ex/rawdata",
layer = "geo_export",
crs = 4326) %>%
filter(clean_coun == "Nigeria")Things to learn from the code chunk above:
st_read() of sf package is used to import
geo_exportshapefile into R environment and save the imported geospatial data into simple feature data table.filter() of dplyr package is used to extract water point records of Nigeria only.
Note: Avoid performing transformation if you plan to use st_intersects() of sf package in the later stage of the geoprocessing. This is because st_intersects() only works correctly if the geospatial data are in geographic coordinate system (i.e wgs84).
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 wp_nga.rds and it is saved in rawdata sub-folder, which will not be uploaded to Git.
wp_nga <- write_rds(wp,
"C:/Jacobche/ISSS624/In-class_Ex/rawdata/wp_nga.rds")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:/Jacobche/ISSS624/In-class_Ex/data",
layer = "nga_admbnda_adm2_osgof_20190417",
crs = 4326)Reading layer `nga_admbnda_adm2_osgof_20190417' from data source
`C:\Jacobche\ISSS624\In-class_Ex\data' using driver `ESRI Shapefile'
Simple feature collection with 774 features and 16 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84Thing to learn from the code chunk above.
- st_read() of sf package is used to import
nga_admbnda_adm2_osgof_20190417shapefile into R environment and save the imported geospatial data into simple feature data table.
Data Wrangling
Recoding NA values into string
In the code chunk below, replace_na() is used to recode all the NA values in status_cle field into Unknown.
wp_nga <- read_rds("C:/Jacobche/ISSS624/In-class_Ex/rawdata/wp_nga.rds") %>%
mutate(status_cle = replace_na(status_cle, "Unknown"))Exploratory Data Analysis
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')
status_cle frequency percentage cumulative_perc
1 Functional 45883 48.29 48.29
2 Non-Functional 29385 30.93 79.22
3 Unknown 10656 11.22 90.44
4 Functional but needs repair 4579 4.82 95.26
5 Non-Functional due to dry season 2403 2.53 97.79
6 Functional but not in use 1686 1.77 99.56
7 Abandoned/Decommissioned 234 0.25 99.81
8 Abandoned 175 0.18 99.99
9 Non functional due to dry season 7 0.01 100.00Extracting Water Point Data
In this section, we will extract the water point records by using classes in status_cle field.
Extracting functional water point
In the code chunk below, filter() of dplyr is used to select functional water points.
wpt_functional <- wp_nga %>%
filter(status_cle %in%
c("Functional",
"Functional but not in use",
"Functional but needs repair"))Exploratory Data Analysis (functional)
In the code chunk below, freq() of funModeling package is used to display the distribution of status_cle field in wpt_functional.
freq(data=wpt_functional,
input = 'status_cle')
status_cle frequency percentage cumulative_perc
1 Functional 45883 87.99 87.99
2 Functional but needs repair 4579 8.78 96.77
3 Functional but not in use 1686 3.23 100.00Extracting non-functional 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"))Exploratory Data Analysis (non-functional)
In the code chunk below, freq() of funModeling package is used to display the distribution of status_cle field in wpt_nonfunctional.
freq(data=wpt_nonfunctional,
input = 'status_cle')
status_cle frequency percentage cumulative_perc
1 Non-Functional 29385 91.25 91.25
2 Non-Functional due to dry season 2403 7.46 98.71
3 Abandoned/Decommissioned 234 0.73 99.44
4 Abandoned 175 0.54 99.98
5 Non functional due to dry season 7 0.02 100.00Extracting 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")Performing Point-in-Polygon Count
The code chunk below performs two operations at one go. Firstly, identify water points located inside each LGA by using st_intersects(). Next, length() of Base R is used to calculate numbers of water points that fall inside each LGA.
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)))Saving the Analytical Data Table
The code chunk below computes the proportion of functional and non-functional water point at LGA level.
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_functionalandpct_non-functional.to keep the file size small, select() of dplyr is used to retain only fields 3, 4, 9, 10, 18, 19, 20, 21, 22 and 23.
Now, we have the tidy sf data table for subsequent analysis. We will save the sf data table into rds format.
write_rds(nga_wp, "C:/Jacobche/ISSS624/In-class_Ex/data/nga_wp.rds")Visualising the spatial distribution of water points
The code below uses qtm() of tmap package to plot side-by-side choropleth maps showing the spatial water points distribution by LGA levels in Nigeria.
nga_wp <- read_rds("C:/Jacobche/ISSS624/In-class_Ex/data/nga_wp.rds")
total <- qtm(nga_wp, "total wpt") +
tm_layout(scale = 0.7)
wp_functional <- qtm(nga_wp, "wpt functional")+
tm_layout(scale = 0.7)
wp_nonfunctional <- qtm(nga_wp, "wpt non-functional")+
tm_layout(scale = 0.6)
unknown <- qtm(nga_wp, "wpt unknown")+
tm_layout(scale = 0.7)
tmap_arrange(total, wp_functional, wp_nonfunctional, unknown, nrow=2, ncol=2)
Next we will create an interactive choropleth map for non-functional water points which would allow us to zoom in for a closer look.
tmap_mode("view")
tm_shape(nga_wp) +
tm_polygons("wpt non-functional",
breaks = c(0, 71, 141, 211, 280),
palette = "Reds") +
tm_scale_bar()tmap_mode("plot")From the map, we can see that the distribution of non-functional water points is not even with LGAs like Ifelodun and Igabi having a higher concentration than others. Nevertheless, there seem to be areas where they are clustered - i.e. around the Central and Western region of Nigeria.
Global Spatial Autocorrelation
In order to confirm our observation of signs of spatial clustering, we will make use of global autocorrection technique. We will compute the global spatial autocorrelation statistics and perform spatial complete randomness test for global spatial autocorrelation.
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 is used to define the neighbourhood relationships between the geographical units (i.e. LGA) in the study area.
The code chunk below uses poly2nb() of spdep package to compute the Queen contiguity weight matrix for Nigeria.
wm_q <- poly2nb(nga_wp,
queen=TRUE)
set.ZeroPolicyOption(TRUE)[1] FALSEsummary(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 LGAs in Nigeria. The most connected LGA has 14 neighbours. There are two LGAs with only one neighbours.
Row-standardised 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”).
rswm_q <- nb2listw(wm_q,
style="W",
zero.policy = TRUE)
rswm_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 3198.414Global Spatial Autocorrelation: Moran’s I
Maron’s I test
The code chunk below performs Moran’s I statistical testing using moran.test() of spdep.
moran.test(nga_wp$`wpt non-functional`,
listw=rswm_q,
zero.policy = TRUE,
na.action=na.omit)
Moran I test under randomisation
data: nga_wp$`wpt non-functional`
weights: rswm_q n reduced by no-neighbour observations
Moran I statistic standard deviate = 20.043, p-value < 2.2e-16
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.433932927 -0.001295337 0.000471516 From the Moran’s I test since the p-value < 2.2e-16 which is approximately 0, we can reject the null hypothesis at 99% confidence interval and can conclude that the distribution of non-functional water points in the LGAs are not randomly distributed. As the Moran I statistic = 0.433932927 > 0, we can infer that there is sign of “clustered” spatial pattern.
Computing Monte Carlo Moran’s I
The code chunk below performs permutation test for Moran’s I statistic by using moran.mc() of spdep. A total of 1000 simulation will be performed.
set.seed(1234)
bperm= moran.mc(nga_wp$`wpt non-functional`,
listw=rswm_q,
nsim=999,
zero.policy = TRUE,
na.action=na.omit)
bperm
Monte-Carlo simulation of Moran I
data: nga_wp$`wpt non-functional`
weights: rswm_q
number of simulations + 1: 1000
statistic = 0.43393, observed rank = 1000, p-value = 0.001
alternative hypothesis: greaterFrom the Monte Carlo simulation since the p-value = 0.001, we can reject the null hypothesis at 99% confidence interval and can conclude that the distribution of non-functional water points in the LGAs are not randomly distributed. As the Moran I statistic = 0.43393 > 0, we can infer that there is sign of “clustered” spatial pattern.
Global Spatial Autocorrelation: Geary’s
Geary’s C test
The code chunk below performs Geary’s C test for spatial autocorrelation by using geary.test() of spdep.
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 From the Geary’s C test since the p-value < 2.2e-16 which is approximately 0, we can reject the null hypothesis at 99% confidence interval and can conclude that the distribution of non-functional water points in the LGAs are not randomly distributed. As the Geary C statistic = 0.6170907765 < 1, we can again infer that there is sign of “clustered” spatial pattern.
Computing Monte Carlo Geary’s C
The code chunk below performs permutation test for Geary’s C statistic by using geary.mc() of spdep.
set.seed(1234)
bperm=geary.mc(nga_wp$`wpt non-functional`,
listw=rswm_q,
nsim=999)
bperm
Monte-Carlo simulation of Geary C
data: nga_wp$`wpt non-functional`
weights: rswm_q
number of simulations + 1: 1000
statistic = 0.61709, observed rank = 1, p-value = 0.001
alternative hypothesis: greaterFrom the Monte Carlo simulation since the p-value = 0.001, we can reject the null hypothesis at 99% confidence interval and can conclude that the distribution of non-functional water points in the LGAs are not randomly distributed. As the Geary C statistic = 0.61709 < 1, we can again infer that there is sign of “clustered” spatial pattern.
Cluster and Outlier Analysis
Local Indicators of Spatial Association (LISA) are statistics that evaluate the existence of clusters in the spatial arrangement of a given variable. We will apply appropriate LISA, especially the local Moran’s I to detect cluster and/or outlier from non-functional water points of Nigeria.
Computing local Moran’s I
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 non-functional water points at the LGA level.
fips <- order(nga_wp$ADM2_EN)
localMI <- localmoran(nga_wp$`wpt non-functional`, rswm_q)
head(localMI) 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 0.06153871The code chunk below lists the content of the local Moran matrix derived by using printCoefmat(). Additionally, paste() of base R is used since ADM2_EN contains duplicated name but from different ADM2_PCODE.
temp <- data.frame(localMI[fips,],
row.names = paste(nga_wp$ADM2_PCODE,nga_wp$ADM2_EN)[fips]) %>%
rename(Pr.Ii = Pr.z....E.Ii..)
printCoefmat(temp, digits = 4,
check.names=FALSE) Ii E.Ii Var.Ii Z.Ii Pr.Ii
NG001001 Aba North -3.237e-01 -9.995e-04 1.925e-01 -7.355e-01 0.462
NG001002 Aba South 7.001e-02 -4.092e-05 1.053e-02 6.826e-01 0.495
NG008001 Abadam 1.258e+00 -1.628e-03 4.182e-01 1.948e+00 0.051
NG015001 Abaji -3.537e-02 -5.427e-05 5.954e-03 -4.577e-01 0.647
NG003001 Abak 1.202e-02 -2.591e-04 3.989e-02 6.146e-02 0.951
NG011001 Abakaliki 7.681e-03 -1.538e-07 1.688e-05 1.870e+00 0.062
NG028001 Abeokuta North 2.372e-01 -6.654e-04 8.523e-02 8.146e-01 0.415
NG028002 Abeokuta South 1.350e-01 -6.951e-05 1.340e-02 1.167e+00 0.243
NG009001 Abi 5.847e-01 -3.917e-04 6.029e-02 2.383e+00 0.017
NG017001 Aboh-Mbaise 1.915e-01 -2.288e-04 2.510e-02 1.210e+00 0.226
NG033001 Abua/Odual 6.748e-01 -7.693e-04 8.433e-02 2.327e+00 0.020
NG015002 Abuja Municipal 1.348e-01 -9.278e-04 8.887e-02 4.554e-01 0.649
NG023001 Adavi 1.629e-02 -4.092e-05 6.302e-03 2.057e-01 0.837
NG007001 Ado -3.166e-02 -2.946e-05 2.824e-03 -5.951e-01 0.552
NG028003 Ado-Odo/Ota -3.164e-01 -1.025e-02 1.114e+00 -2.901e-01 0.772
NG013001 Ado Ekiti 6.861e-02 -9.278e-04 1.427e-01 1.841e-01 0.854
NG031001 Afijio 2.427e-02 -1.987e-05 5.113e-03 3.397e-01 0.734
NG011002 Afikpo North 5.871e-01 -1.179e-03 1.814e-01 1.381e+00 0.167
NG011003 Afikpo South -8.637e-03 -8.662e-05 1.110e-02 -8.115e-02 0.935
NG027001 Agaie -2.112e-02 -1.825e-05 2.810e-03 -3.981e-01 0.691
NG007002 Agatu -6.130e-02 -1.448e-04 2.229e-02 -4.096e-01 0.682
NG025001 Agege 2.897e-01 -1.330e-03 3.417e-01 4.978e-01 0.619
NG004001 Aguata 8.386e-01 -1.192e-03 1.833e-01 1.961e+00 0.050
NG027002 Agwara 1.589e-03 -2.748e-05 5.296e-03 2.222e-02 0.982
NG017002 Ahiazu-Mbaise 1.318e-01 -8.662e-05 9.502e-03 1.352e+00 0.176
NG033002 Ahoada East 8.006e-01 -9.391e-04 1.808e-01 1.885e+00 0.059
NG033003 Ahoada West 8.408e-01 -1.628e-03 2.503e-01 1.684e+00 0.092
NG030001 Aiyedade -1.994e-03 -1.210e-02 1.311e+00 8.824e-03 0.993
NG030002 Aiyedire 3.086e-01 -2.232e-04 2.449e-02 1.973e+00 0.048
NG013002 Aiyekire (Gbonyin) 2.547e-01 -3.187e-04 3.054e-02 1.459e+00 0.145
NG023002 Ajaokuta 9.304e-03 -6.338e-06 8.123e-04 3.267e-01 0.744
NG025002 Ajeromi-Ifelodun 1.090e+00 -1.550e-03 3.983e-01 1.730e+00 0.084
NG020001 Ajingi 8.053e-02 -1.987e-05 2.546e-03 1.596e+00 0.110
NG009002 Akamkpa 2.262e-01 -2.748e-05 3.014e-03 4.120e+00 0.000
NG031002 Akinyele -1.737e-01 -3.542e-04 3.885e-02 -8.795e-01 0.379
NG016001 Akko 3.622e+00 -1.725e-02 1.625e+00 2.854e+00 0.004
NG012001 Akoko-Edo -9.799e-02 -6.951e-05 4.827e-03 -1.409e+00 0.159
NG029001 Akoko North East 2.613e+00 -5.783e-03 6.308e-01 3.298e+00 0.001
NG029002 Akoko North West 1.873e+00 -1.575e-02 2.989e+00 1.093e+00 0.275
NG029003 Akoko South East 2.970e+00 -3.902e-03 7.492e-01 3.435e+00 0.001
NG029004 Akoko South West 4.081e+00 -1.364e-02 2.072e+00 2.845e+00 0.004
NG009003 Akpabuyo 7.746e-01 -8.207e-03 1.569e+00 6.250e-01 0.532
NG033004 Akuku Toru 7.545e-01 -1.330e-03 3.417e-01 1.293e+00 0.196
NG029005 Akure North -9.451e-02 -2.004e-04 2.568e-02 -5.885e-01 0.556
NG029006 Akure South -4.089e-01 -1.062e-03 2.729e-01 -7.807e-01 0.435
NG026001 Akwanga 1.726e-02 -1.856e-06 3.576e-04 9.130e-01 0.361
NG020002 Albasu -6.067e-01 -4.402e-03 6.749e-01 -7.331e-01 0.464
NG022001 Aleiro 3.814e-02 -1.090e-05 2.102e-03 8.321e-01 0.405
NG025003 Alimosho -2.648e-01 -9.278e-04 1.017e-01 -8.275e-01 0.408
NG005001 Alkaleri -3.649e-01 -1.739e-04 1.667e-02 -2.825e+00 0.005
NG025004 Amuwo-Odofin 6.406e-01 -1.062e-03 1.017e-01 2.012e+00 0.044
NG004002 Anambra East 5.229e-01 -6.163e-04 7.894e-02 1.863e+00 0.062
NG004003 Anambra West 5.182e-01 -5.691e-04 5.453e-02 2.221e+00 0.026
NG004004 Anaocha 6.885e-01 -7.164e-04 7.854e-02 2.459e+00 0.014
NG033005 Andoni 7.475e-01 -8.806e-04 2.264e-01 1.573e+00 0.116
NG014001 Aninri 5.867e-01 -1.628e-03 2.503e-01 1.176e+00 0.240
NG010001 Aniocha North 9.140e-01 -1.260e-03 2.425e-01 1.859e+00 0.063
NG010002 Aniocha South 7.193e-01 -1.330e-03 1.457e-01 1.888e+00 0.059
NG037001 Anka -3.096e-02 -5.238e-04 8.062e-02 -1.072e-01 0.915
NG023003 Ankpa 3.290e-03 -3.337e-07 3.661e-05 5.437e-01 0.587
NG007003 Apa 7.160e-02 -2.913e-04 4.484e-02 3.395e-01 0.734
NG025005 Apapa 1.117e+00 -1.628e-03 2.083e-01 2.451e+00 0.014
NG035001 Ardo-Kola 5.956e-01 -6.071e-04 7.777e-02 2.138e+00 0.033
NG022002 Arewa-Dandi -7.031e-02 -2.946e-05 2.824e-03 -1.323e+00 0.186
NG022003 Argungu 4.021e-02 -3.187e-04 4.906e-02 1.830e-01 0.855
NG001003 Arochukwu 9.046e-02 -1.126e-03 1.441e-01 2.412e-01 0.809
NG024001 Asa 6.636e-02 -1.856e-06 2.036e-04 4.651e+00 0.000
NG033006 Asari-Toru 9.961e-01 -1.126e-03 4.347e-01 1.513e+00 0.130
NG008002 Askira/Uba 9.514e-01 -1.126e-03 9.572e-02 3.079e+00 0.002
NG030003 Atakumosa East 1.576e+00 -2.390e-03 3.672e-01 2.605e+00 0.009
NG030004 Atakumosa West 1.368e+00 -3.547e-03 3.008e-01 2.500e+00 0.012
NG031003 Atiba 9.247e-02 -1.492e-04 1.431e-02 7.743e-01 0.439
NG031004 Atigbo -3.713e-02 -8.320e-05 1.066e-02 -3.588e-01 0.720
NG022004 Augie -3.152e-02 -1.265e-04 1.621e-02 -2.466e-01 0.805
NG018001 Auyo -9.232e-03 -1.952e-04 2.141e-02 -6.176e-02 0.951
NG026002 Awe -3.803e-02 -2.395e-06 2.296e-04 -2.510e+00 0.012
NG014002 Awgu 7.149e-01 -1.062e-03 1.359e-01 1.942e+00 0.052
NG004005 Awka North 8.434e-01 -1.628e-03 1.783e-01 2.001e+00 0.045
NG004006 Awka South 7.252e-01 -9.995e-04 1.538e-01 1.852e+00 0.064
NG004007 Ayamelum 5.389e-01 -4.803e-04 7.393e-02 1.984e+00 0.047
NG018002 Babura 3.490e+00 -9.484e-03 1.447e+00 2.910e+00 0.004
NG025006 Badagry 1.126e+00 -3.320e-03 8.515e-01 1.224e+00 0.221
NG036001 Bade -4.132e-01 -3.917e-04 7.546e-02 -1.503e+00 0.133
NG022005 Bagudo -1.679e-01 -5.427e-05 8.358e-03 -1.836e+00 0.066
NG020003 Bagwai -1.046e-01 -3.859e-05 4.233e-03 -1.607e+00 0.108
NG009005 Bakassi 0.000e+00 0.000e+00 0.000e+00 NaN NaN
NG021001 Bakori 9.705e-02 -1.492e-04 1.913e-02 7.028e-01 0.482
NG037002 Bakura 1.324e-01 -1.126e-03 1.441e-01 3.517e-01 0.725
NG016002 Balanga 8.682e-01 -5.349e-03 5.837e-01 1.143e+00 0.253
NG035002 Bali 2.832e-01 -1.613e-03 1.370e-01 7.693e-01 0.442
NG008003 Bama 1.258e+00 -1.628e-03 3.132e-01 2.251e+00 0.024
NG032001 Barikin Ladi 1.567e+00 -3.209e-03 4.100e-01 2.453e+00 0.014
NG024002 Baruten 1.534e-02 -2.748e-05 3.521e-03 2.590e-01 0.796
NG023004 Bassa 6.584e-03 -9.878e-04 9.461e-02 2.462e-02 0.980
NG032002 Bassa 7.683e-02 -6.645e-05 8.516e-03 8.333e-01 0.405
NG021002 Batagarawa 1.034e-01 -8.320e-05 9.128e-03 1.083e+00 0.279
NG021003 Batsari 1.365e-02 -1.018e-04 1.568e-02 1.098e-01 0.913
NG005002 Bauchi 1.144e-01 -8.662e-05 1.334e-02 9.914e-01 0.322
NG021004 Baure 1.751e+00 -5.069e-03 7.767e-01 1.993e+00 0.046
NG008004 Bayo 2.396e-01 -3.990e-04 6.143e-02 9.682e-01 0.333
NG020004 Bebeji -3.329e-02 -9.995e-04 1.538e-01 -8.234e-02 0.934
NG009006 Bekwara 1.316e+00 -1.536e-03 2.361e-01 2.712e+00 0.007
NG001004 Bende 4.032e-01 -6.654e-04 6.375e-02 1.599e+00 0.110
NG009007 Biase 1.882e-01 -2.532e-04 2.427e-02 1.210e+00 0.226
NG020005 Bichi -1.445e-01 -1.018e-04 1.305e-02 -1.264e+00 0.206
NG027003 Bida -2.775e-01 -1.265e-04 4.888e-02 -1.254e+00 0.210
NG016003 Billiri 2.501e+00 -3.320e-03 6.378e-01 3.136e+00 0.002
NG021005 Bindawa -6.576e-02 -1.224e-04 1.568e-02 -5.242e-01 0.600
NG034001 Binji -1.566e-01 -3.542e-04 4.538e-02 -7.333e-01 0.463
NG018003 Biriniwa -4.071e-01 -7.520e-03 9.565e-01 -4.086e-01 0.683
NG018004 Birni Kudu -5.257e-04 -5.691e-04 5.453e-02 1.858e-04 1.000
NG019001 Birnin-Gwari 1.437e+00 -5.349e-03 4.070e-01 2.261e+00 0.024
NG022006 Birnin Kebbi -3.891e-01 -6.163e-04 7.894e-02 -1.383e+00 0.167
NG037003 Birnin Magaji 2.835e-01 -3.990e-04 5.112e-02 1.256e+00 0.209
NG008005 Biu 1.107e+00 -1.628e-03 1.558e-01 2.809e+00 0.005
NG034002 Bodinga -3.913e-03 -2.748e-05 4.231e-03 -5.973e-02 0.952
NG005003 Bogoro -2.276e-01 -2.288e-04 5.887e-02 -9.372e-01 0.349
NG009008 Boki 2.652e+00 -7.023e-03 1.074e+00 2.566e+00 0.010
NG032003 Bokkos 5.068e-01 -2.232e-04 2.860e-02 2.998e+00 0.003
NG030005 Boluwaduro 3.191e-01 -8.320e-05 1.604e-02 2.520e+00 0.012
NG010003 Bomadi 6.117e-01 -1.401e-03 2.155e-01 1.321e+00 0.187
NG033007 Bonny 1.087e+00 -1.628e-03 2.083e-01 2.385e+00 0.017
NG027004 Borgu -1.604e-01 -2.591e-04 2.205e-02 -1.079e+00 0.281
NG030006 Boripe 1.112e+00 -1.772e-03 2.267e-01 2.339e+00 0.019
NG027005 Bosso -2.050e-02 -1.492e-04 1.913e-02 -1.471e-01 0.883
NG006001 Brass 2.996e-01 -6.163e-04 2.381e-01 6.153e-01 0.538
NG018005 Buji 4.503e-03 -3.337e-07 5.139e-05 6.281e-01 0.530
NG037004 Bukkuyum -3.143e-02 -2.591e-04 3.989e-02 -1.561e-01 0.876
NG037005 Bungudu 2.024e-01 -8.806e-04 1.128e-01 6.053e-01 0.545
NG020006 Bunkure 3.634e-02 -1.265e-04 1.388e-02 3.096e-01 0.757
NG022007 Bunza 8.843e-01 -2.584e-03 3.968e-01 1.408e+00 0.159
NG036002 Bursari 4.799e-01 -5.238e-04 5.019e-02 2.145e+00 0.032
NG007004 Buruku 5.041e-01 -7.590e-04 9.720e-02 1.619e+00 0.105
NG010004 Burutu 8.026e-01 -1.401e-03 2.697e-01 1.548e+00 0.122
NG015003 Bwari 3.210e-01 -2.532e-04 4.878e-02 1.454e+00 0.146
NG009010 Calabar-Municipal -5.641e-01 -2.288e-04 5.887e-02 -2.324e+00 0.020
NG009009 Calabar South 1.090e-01 -3.859e-05 7.437e-03 1.264e+00 0.206
NG027006 Chanchaga 1.462e-02 -2.395e-06 1.854e-03 3.395e-01 0.734
NG021006 Charanchi 5.512e-02 -5.158e-05 6.610e-03 6.787e-01 0.497
NG008006 Chibok 9.592e-01 -1.062e-03 2.729e-01 1.838e+00 0.066
NG019002 Chikun -9.543e-01 -1.330e-03 1.273e-01 -2.671e+00 0.008
NG020007 Dala -1.521e-01 -1.224e-04 2.358e-02 -9.900e-01 0.322
NG036003 Damaturu 1.022e+00 -1.401e-03 2.155e-01 2.203e+00 0.028
NG005004 Damban 1.731e-02 -1.825e-05 2.339e-03 3.583e-01 0.720
NG020008 Dambatta 5.882e-01 -8.697e-04 1.338e-01 1.610e+00 0.107
NG008007 Damboa 1.201e+00 -1.628e-03 1.558e-01 3.048e+00 0.002
NG021007 Dan Musa 1.983e-01 -1.126e-03 1.234e-01 5.677e-01 0.570
NG022008 Dandi 6.035e-01 -5.208e-03 9.986e-01 6.092e-01 0.542
NG021008 Dandume 2.569e-02 -1.856e-06 3.576e-04 1.359e+00 0.174
NG034003 Dange-Shuni 8.694e-02 -1.265e-04 1.388e-02 7.391e-01 0.460
NG021009 Danja -2.504e-01 -2.913e-04 3.732e-02 -1.295e+00 0.195
NG005005 Darazo 5.245e-03 -1.090e-05 1.196e-03 1.520e-01 0.879
NG005006 Dass 2.436e-01 -3.612e-04 1.396e-01 6.531e-01 0.514
NG021010 Daura 4.007e-01 -8.134e-04 2.091e-01 8.779e-01 0.380
NG020009 Dawakin Kudu -1.390e-01 -4.310e-04 5.522e-02 -5.896e-01 0.555
NG020010 Dawakin Tofa 5.357e-01 -1.062e-03 1.359e-01 1.456e+00 0.145
NG033008 Degema 8.954e-01 -1.475e-03 1.412e-01 2.387e+00 0.017
NG023005 Dekina 1.934e-02 -8.134e-04 1.567e-01 5.092e-02 0.959
NG002001 Demsa 7.665e-01 -1.401e-03 1.535e-01 1.960e+00 0.050
NG008008 Dikwa 1.258e+00 -1.628e-03 2.083e-01 2.761e+00 0.006
NG020011 Doguwa -8.489e-03 -1.090e-05 1.397e-03 -2.268e-01 0.821
NG026003 Doma -6.361e-02 -5.427e-05 4.619e-03 -9.351e-01 0.350
NG035003 Donga 1.198e+00 -1.613e-03 1.544e-01 3.053e+00 0.002
NG016004 Dukku 2.070e+00 -5.208e-03 5.684e-01 2.753e+00 0.006
NG004008 Dunukofia 5.546e-01 -6.163e-04 9.485e-02 1.803e+00 0.071
NG018006 Dutse -2.240e-01 -1.475e-03 1.412e-01 -5.922e-01 0.554
NG021011 Dutsi -2.014e-01 -2.004e-04 2.198e-02 -1.357e+00 0.175
NG021012 Dutsin-Ma -6.027e-02 -6.071e-04 7.777e-02 -2.139e-01 0.831
NG003002 Eastern Obolo 1.377e-01 -3.990e-04 6.143e-02 5.573e-01 0.577
NG011004 Ebonyi -8.398e-03 -2.395e-06 3.070e-04 -4.792e-01 0.632
NG027007 Edati 3.979e-01 -1.692e-03 6.527e-01 4.946e-01 0.621
NG030007 Ede North 5.951e-02 -6.645e-05 1.710e-02 4.556e-01 0.649
NG030008 Ede South -8.193e-02 -6.338e-06 5.394e-04 -3.527e+00 0.000
NG024003 Edu 5.195e+00 -7.352e-03 1.124e+00 4.907e+00 0.000
NG013003 Efon -6.005e-01 -3.253e-04 6.268e-02 -2.397e+00 0.017
NG028004 Egbado North 4.137e-01 -3.253e-04 5.008e-02 1.850e+00 0.064
NG028005 Egbado South -5.196e-02 -1.475e-03 2.268e-01 -1.060e-01 0.916
NG031005 Egbeda -5.084e-02 -5.427e-05 8.358e-03 -5.555e-01 0.579
NG030009 Egbedore 1.496e-01 -5.158e-05 4.945e-03 2.128e+00 0.033
NG012002 Egor 8.770e-02 -2.004e-04 5.156e-02 3.871e-01 0.699
NG017003 Ehime-Mbano 2.677e-01 -3.253e-04 5.008e-02 1.198e+00 0.231
NG030010 Ejigbo 6.346e-01 -7.860e-03 8.555e-01 6.946e-01 0.487
NG006002 Ekeremor 2.115e-01 -1.492e-04 2.876e-02 1.248e+00 0.212
NG003003 Eket 2.193e-02 -1.056e-04 2.035e-02 1.544e-01 0.877
NG024004 Ekiti 1.400e+00 -5.931e-03 7.557e-01 1.617e+00 0.106
NG013004 Ekiti East -1.066e+00 -1.330e-03 2.045e-01 -2.355e+00 0.019
NG013005 Ekiti South West 6.196e-01 -1.247e-03 1.366e-01 1.680e+00 0.093
NG013006 Ekiti West 6.656e-01 -1.536e-03 1.470e-01 1.740e+00 0.082
NG004009 Ekwusigo 8.475e-01 -1.401e-03 2.155e-01 1.829e+00 0.067
NG033009 Eleme 9.456e-01 -1.628e-03 2.083e-01 2.076e+00 0.038
NG033010 Emohua 6.902e-01 -1.475e-03 1.412e-01 1.841e+00 0.066
NG013007 Emure -7.791e-01 -5.238e-04 8.062e-02 -2.742e+00 0.006
NG014003 Enugu East 7.709e-01 -1.330e-03 2.045e-01 1.708e+00 0.088
NG014004 Enugu North 9.209e-01 -1.550e-03 2.384e-01 1.889e+00 0.059
NG014005 Enugu South 7.655e-01 -1.550e-03 3.983e-01 1.215e+00 0.224
NG025007 Epe -3.699e-01 -1.772e-03 1.696e-01 -8.940e-01 0.371
NG012003 Esan Central 3.077e-01 -4.387e-04 8.453e-02 1.060e+00 0.289
NG012004 Esan North East 2.795e-01 -4.387e-04 6.754e-02 1.077e+00 0.281
NG012005 Esan South East 3.077e-01 -4.387e-04 3.732e-02 1.595e+00 0.111
NG012006 Esan West 2.533e-01 -5.238e-04 6.709e-02 9.799e-01 0.327
NG029007 Ese-Odo -1.197e-01 -8.134e-04 1.252e-01 -3.359e-01 0.737
NG003004 Esit - Eket -6.642e-02 -2.232e-04 2.860e-02 -3.914e-01 0.696
NG003005 Essien Udim -9.931e-03 -6.338e-06 6.954e-04 -3.764e-01 0.707
NG033011 Etche 3.595e-01 -9.995e-04 1.096e-01 1.089e+00 0.276
NG010005 Ethiope East 1.775e-01 -7.164e-04 1.380e-01 4.798e-01 0.631
NG010006 Ethiope West 2.396e-01 -5.691e-04 8.759e-02 8.115e-01 0.417
NG025008 Eti-Osa 3.568e-01 -1.628e-03 2.503e-01 7.165e-01 0.474
NG003006 Etim Ekpo 5.976e-04 -6.951e-05 8.908e-03 7.068e-03 0.994
NG003007 Etinan -5.312e-02 -2.004e-04 3.086e-02 -3.012e-01 0.763
NG012007 Etsako Central 1.758e-01 -1.492e-04 2.298e-02 1.161e+00 0.246
NG012008 Etsako East 1.523e-01 -3.253e-04 2.768e-02 9.174e-01 0.359
NG012009 Etsako West 2.426e-01 -3.612e-04 3.962e-02 1.221e+00 0.222
NG009011 Etung 1.400e+00 -1.692e-03 4.346e-01 2.126e+00 0.033
NG028006 Ewekoro 5.253e-01 -7.693e-04 9.852e-02 1.676e+00 0.094
NG014006 Ezeagu 7.581e-01 -9.391e-04 1.445e-01 1.997e+00 0.046
NG017004 Ezinihitte 1.642e-01 -4.803e-04 7.393e-02 6.058e-01 0.545
NG011005 Ezza North 5.875e-01 -8.697e-04 1.114e-01 1.763e+00 0.078
NG011006 Ezza South 3.641e-01 -3.187e-04 4.906e-02 1.645e+00 0.100
NG020012 Fagge 1.858e-01 -1.330e-03 2.045e-01 4.138e-01 0.679
NG022009 Fakai -3.277e-01 -9.391e-04 1.029e-01 -1.018e+00 0.308
NG021013 Faskari 2.460e-02 -1.316e-03 1.260e-01 7.300e-02 0.942
NG036004 Fika 1.742e-01 -1.330e-03 1.130e-01 5.223e-01 0.601
NG002002 Fufore 1.127e+00 -1.628e-03 2.083e-01 2.473e+00 0.013
NG016005 Funakaye 9.087e-01 -4.532e-03 6.947e-01 1.096e+00 0.273
NG036005 Fune 5.808e-01 -4.803e-04 4.603e-02 2.709e+00 0.007
NG021014 Funtua -3.757e-01 -5.691e-04 8.759e-02 -1.267e+00 0.205
NG020013 Gabasawa -1.250e-01 -1.330e-03 1.702e-01 -2.997e-01 0.764
NG034004 Gada -1.334e-01 -3.990e-04 7.688e-02 -4.795e-01 0.632
NG018007 Gagarawa -2.026e-01 -6.951e-05 8.908e-03 -2.145e+00 0.032
NG005007 Gamawa -7.611e-01 -1.045e-02 1.326e+00 -6.519e-01 0.514
NG005008 Ganjuwa 2.716e-02 -8.806e-04 8.435e-02 9.654e-02 0.923
NG002003 Ganye 5.082e-01 -1.260e-03 2.425e-01 1.034e+00 0.301
NG018008 Garki 2.651e-01 -1.939e-03 1.856e-01 6.199e-01 0.535
NG020014 Garko -5.334e-01 -8.806e-04 9.653e-02 -1.714e+00 0.087
NG020015 Garum Mallam -1.634e-02 -1.856e-06 2.857e-04 -9.663e-01 0.334
NG035004 Gashaka 1.175e-01 -5.153e-04 9.927e-02 3.746e-01 0.708
NG035005 Gassol 1.312e+00 -3.664e-03 4.678e-01 1.923e+00 0.054
NG020016 Gaya -1.377e-01 -1.401e-03 2.697e-01 -2.623e-01 0.793
NG027008 Gbako 4.252e-02 -9.878e-04 1.520e-01 1.116e-01 0.911
NG007005 Gboko 7.784e-01 -8.697e-04 1.338e-01 2.130e+00 0.033
NG036006 Geidam 1.132e+00 -1.628e-03 2.083e-01 2.484e+00 0.013
NG020017 Gezawa -6.303e-01 -2.390e-03 3.672e-01 -1.036e+00 0.300
NG005009 Giade 4.951e-02 -8.662e-05 1.669e-02 3.839e-01 0.701
NG002005 Girei 1.083e+00 -1.475e-03 2.268e-01 2.276e+00 0.023
NG019003 Giwa 3.822e+00 -8.743e-03 7.376e-01 4.460e+00 0.000
NG033012 Gokana 1.073e+00 -1.550e-03 2.984e-01 1.967e+00 0.049
NG016006 Gombe -1.810e-01 -6.338e-06 2.450e-03 -3.657e+00 0.000
NG002004 Gombi 7.060e-01 -6.163e-04 7.894e-02 2.515e+00 0.012
NG034006 Goronyo 1.638e-01 -2.004e-04 2.568e-02 1.023e+00 0.306
NG008009 Gubio 1.246e+00 -1.628e-03 2.503e-01 2.494e+00 0.013
NG034007 Gudu -5.956e-02 -3.253e-04 8.369e-02 -2.048e-01 0.838
NG036007 Gujba 1.142e+00 -1.628e-03 1.783e-01 2.707e+00 0.007
NG036008 Gulani -2.903e-01 -1.550e-03 1.698e-01 -7.006e-01 0.484
NG007006 Guma 2.048e-01 -3.542e-04 3.014e-02 1.182e+00 0.237
NG018009 Gumel 3.813e-01 -1.006e-02 2.562e+00 2.445e-01 0.807
NG037006 Gummi 6.027e-02 -3.990e-04 4.376e-02 2.900e-01 0.772
NG027009 Gurara -1.899e-02 -3.253e-04 3.568e-02 -9.882e-02 0.921
NG018010 Guri -2.602e-01 -4.803e-04 5.267e-02 -1.131e+00 0.258
NG037007 Gusau -1.503e-02 -6.951e-05 6.663e-03 -1.833e-01 0.855
NG002006 Guyuk 2.524e-01 -8.806e-04 1.696e-01 6.151e-01 0.538
NG008010 Guzamala 1.258e+00 -1.628e-03 2.083e-01 2.761e+00 0.006
NG034005 Gwadabawa -1.661e-02 -8.320e-05 1.066e-02 -1.601e-01 0.873
NG015004 Gwagwalada 4.494e-02 -8.320e-05 1.066e-02 4.360e-01 0.663
NG020018 Gwale 1.298e-01 -3.253e-04 6.268e-02 5.196e-01 0.603
NG022010 Gwandu 3.315e-02 -2.390e-03 4.596e-01 5.242e-02 0.958
NG018011 Gwaram 1.715e-01 -8.240e-04 7.893e-02 6.135e-01 0.540
NG020019 Gwarzo 4.447e-01 -9.995e-04 1.280e-01 1.246e+00 0.213
NG007007 Gwer East 1.901e-01 -1.247e-03 1.060e-01 5.879e-01 0.557
NG007008 Gwer West -3.554e-02 -5.238e-04 6.709e-02 -1.352e-01 0.892
NG018012 Gwiwa 1.569e-01 -3.917e-04 5.018e-02 7.020e-01 0.483
NG008011 Gwoza 1.258e+00 -1.628e-03 3.132e-01 2.251e+00 0.024
NG018013 Hadejia 4.853e-01 -1.692e-03 4.346e-01 7.387e-01 0.460
NG008012 Hawul 1.059e+00 -1.475e-03 2.268e-01 2.228e+00 0.026
NG002007 Hong 1.025e+00 -1.475e-03 1.888e-01 2.363e+00 0.018
NG031006 Ibadan North -1.652e-02 -1.330e-03 2.045e-01 -3.360e-02 0.973
NG031007 Ibadan North East -2.912e-02 -1.090e-05 1.397e-03 -7.787e-01 0.436
NG031008 Ibadan North West 2.389e-01 -2.591e-04 3.989e-02 1.197e+00 0.231
NG031009 Ibadan South East 1.914e-01 -1.192e-03 1.833e-01 4.499e-01 0.653
NG031010 Ibadan South West 2.697e-01 -2.591e-04 4.993e-02 1.208e+00 0.227
NG023006 Ibaji 2.277e-01 -2.004e-04 2.198e-02 1.537e+00 0.124
NG031011 Ibarapa Central -2.271e-01 -7.590e-04 1.168e-01 -6.624e-01 0.508
NG031012 Ibarapa East 1.884e-02 -4.092e-05 5.245e-03 2.608e-01 0.794
NG031013 Ibarapa North -3.897e-02 -5.603e-04 8.623e-02 -1.308e-01 0.896
NG025009 Ibeju/Lekki 2.239e-03 -1.090e-05 4.214e-03 3.466e-02 0.972
NG003008 Ibeno 1.290e-01 -8.240e-04 1.268e-01 3.647e-01 0.715
NG003009 Ibesikpo Asutan -9.200e-03 -1.492e-04 2.298e-02 -5.970e-02 0.952
NG035006 Ibi -4.000e-01 -2.913e-04 3.195e-02 -2.236e+00 0.025
NG003010 Ibiono Ibom 1.358e-02 -1.056e-04 1.353e-02 1.176e-01 0.906
NG023007 Idah 6.772e-02 -1.987e-05 5.113e-03 9.473e-01 0.343
NG029008 Idanre 2.563e-01 -1.690e-04 1.438e-02 2.138e+00 0.032
NG017005 Ideato North 8.912e-01 -1.330e-03 1.273e-01 2.501e+00 0.012
NG017006 Ideato South 8.308e-01 -1.192e-03 2.295e-01 1.737e+00 0.082
NG004010 Idemili North 8.357e-01 -1.475e-03 1.616e-01 2.083e+00 0.037
NG004011 Idemili South 9.413e-01 -1.330e-03 1.702e-01 2.285e+00 0.022
NG031014 Ido 1.337e-01 -1.492e-04 1.431e-02 1.119e+00 0.263
NG013008 Ido-Osi -2.640e-01 -1.401e-03 2.155e-01 -5.656e-01 0.572
NG025010 Ifako-Ijaye -8.552e-02 -4.803e-04 7.393e-02 -3.127e-01 0.754
NG030011 Ife Central 1.270e+00 -2.390e-03 4.596e-01 1.877e+00 0.060
NG030012 Ife East 1.804e+00 -3.547e-03 6.813e-01 2.190e+00 0.029
NG030013 Ife North 5.331e-01 -3.187e-04 3.495e-02 2.853e+00 0.004
NG030014 Ife South 1.163e+00 -9.278e-04 1.188e-01 3.375e+00 0.001
NG030015 Ifedayo -2.434e-01 -4.092e-05 5.245e-03 -3.360e+00 0.001
NG029009 Ifedore 6.292e-01 -2.486e-03 2.721e-01 1.211e+00 0.226
NG024005 Ifelodun 7.931e+00 -5.258e-02 3.460e+00 4.292e+00 0.000
NG030016 Ifelodun 1.203e+00 -1.114e-03 2.862e-01 2.250e+00 0.024
NG028007 Ifo 1.209e-01 -9.391e-04 7.985e-02 4.312e-01 0.666
NG019004 Igabi 4.716e+00 -2.861e-02 2.366e+00 3.085e+00 0.002
NG023008 Igalamela-Odolu 5.181e-01 -7.164e-04 6.093e-02 2.102e+00 0.036
NG014007 Igbo-Etiti 6.025e-01 -8.240e-04 1.268e-01 1.694e+00 0.090
NG014008 Igbo-Eze North 7.857e-01 -1.401e-03 2.155e-01 1.696e+00 0.090
NG014009 Igbo-Eze South 9.710e-01 -1.401e-03 2.697e-01 1.872e+00 0.061
NG012010 Igueben 1.262e-01 -1.739e-04 2.228e-02 8.464e-01 0.397
NG004012 Ihiala 2.475e-01 -1.492e-04 1.637e-02 1.936e+00 0.053
NG017007 Ihitte/Uboma 3.111e-01 -5.691e-04 8.759e-02 1.053e+00 0.292
NG028008 Ijebu East -9.107e-01 -1.475e-03 1.253e-01 -2.568e+00 0.010
NG028009 Ijebu North -2.157e-02 -5.427e-05 5.203e-03 -2.982e-01 0.766
NG028010 Ijebu North East 1.000e-02 -3.337e-07 6.433e-05 1.247e+00 0.212
NG028011 Ijebu Ode 7.623e-02 -6.163e-04 1.187e-01 2.230e-01 0.824
NG013009 Ijero -2.131e-01 -2.993e-03 4.595e-01 -3.100e-01 0.757
NG023009 Ijumu 3.473e-01 -3.917e-04 3.754e-02 1.794e+00 0.073
NG003011 Ika -8.729e-03 -3.337e-07 6.433e-05 -1.088e+00 0.276
NG010007 Ika North East 6.109e-01 -1.628e-03 2.503e-01 1.224e+00 0.221
NG010008 Ika South 3.748e-01 -8.806e-04 1.696e-01 9.123e-01 0.362
NG019005 Ikara -3.343e-01 -5.691e-04 6.240e-02 -1.336e+00 0.182
NG017008 Ikeduru 2.823e-01 -6.654e-04 1.024e-01 8.842e-01 0.377
NG025011 Ikeja 7.851e-01 -1.550e-03 1.484e-01 2.042e+00 0.041
NG028012 Ikenne 2.955e-01 -1.062e-03 2.044e-01 6.559e-01 0.512
NG013010 Ikere 1.155e-01 -1.448e-04 2.229e-02 7.746e-01 0.439
NG013011 Ikole 3.177e-01 -6.559e-04 7.191e-02 1.187e+00 0.235
NG009012 Ikom 1.751e+00 -1.855e-03 2.373e-01 3.599e+00 0.000
NG003012 Ikono 2.825e-02 -1.987e-05 2.180e-03 6.055e-01 0.545
NG025012 Ikorodu -7.153e-02 -4.310e-04 6.635e-02 -2.760e-01 0.783
NG003013 Ikot Abasi 4.748e-02 -6.951e-05 1.070e-02 4.596e-01 0.646
NG003014 Ikot Ekpene 6.482e-03 -8.662e-05 2.229e-02 4.400e-02 0.965
NG012011 Ikpoba-Okha 3.159e-02 -1.987e-05 2.180e-03 6.771e-01 0.498
NG033013 Ikwerre 4.347e-01 -1.475e-03 2.839e-01 8.186e-01 0.413
NG011007 Ikwo 1.660e-01 -2.748e-05 3.521e-03 2.798e+00 0.005
NG001005 Ikwuano -1.406e-02 -1.856e-06 2.378e-04 -9.113e-01 0.362
NG030017 Ila 1.054e+00 -5.603e-04 6.144e-02 4.256e+00 0.000
NG029010 Ilaje -4.650e-02 -6.654e-04 1.282e-01 -1.280e-01 0.898
NG029011 Ile-Oluji-Okeigbo 1.797e+00 -2.296e-03 3.528e-01 3.028e+00 0.002
NG013012 Ilejemeji -2.083e-01 -3.253e-04 6.268e-02 -8.307e-01 0.406
NG030018 Ilesha East 4.547e-01 -1.952e-04 3.762e-02 2.345e+00 0.019
NG030019 Ilesha West 1.093e-01 -1.090e-05 2.102e-03 2.385e+00 0.017
NG034008 Illela -9.544e-02 -1.050e-03 2.698e-01 -1.817e-01 0.856
NG024006 Ilorin East -3.293e-01 -4.092e-05 7.888e-03 -3.707e+00 0.000
NG024007 Ilorin South -1.776e-01 -1.216e-05 2.344e-03 -3.667e+00 0.000
NG024008 Ilorin West 5.691e-01 -2.232e-04 3.437e-02 3.071e+00 0.002
NG028013 Imeko-Afon -2.784e-01 -1.628e-03 2.503e-01 -5.533e-01 0.580
NG021015 Ingawa -2.270e-02 -3.990e-04 3.395e-02 -1.210e-01 0.904
NG003015 Ini 2.360e-01 -7.164e-04 9.175e-02 7.816e-01 0.434
NG028014 Ipokia -3.187e-01 -2.591e-04 4.993e-02 -1.425e+00 0.154
NG029012 Irele 3.434e-01 -2.232e-04 4.302e-02 1.657e+00 0.098
NG031015 Irepo -2.132e-02 -1.216e-05 2.344e-03 -4.401e-01 0.660
NG024009 Irepodun 6.695e+00 -1.189e-02 1.505e+00 5.466e+00 0.000
NG030020 Irepodun 5.608e-03 -1.538e-07 3.959e-05 8.914e-01 0.373
NG013013 Irepodun/Ifelodun -2.121e-01 -1.265e-04 1.213e-02 -1.925e+00 0.054
NG030021 Irewole 1.687e-01 -6.645e-05 1.023e-02 1.668e+00 0.095
NG034009 Isa 2.235e-01 -9.995e-04 1.280e-01 6.276e-01 0.530
NG013014 Ise/Orun -3.662e-01 -1.550e-03 1.984e-01 -8.187e-01 0.413
NG031016 Iseyin -6.013e-03 -8.240e-04 7.893e-02 -1.847e-02 0.985
NG011008 Ishielu 4.574e-01 -1.692e-03 1.853e-01 1.067e+00 0.286
NG014010 Isi-Uzo -1.820e-02 -1.856e-06 1.579e-04 -1.448e+00 0.148
NG001006 Isiala-Ngwa North 6.130e-03 -1.056e-04 1.626e-02 4.890e-02 0.961
NG001007 Isiala-Ngwa South -2.275e-02 -5.427e-05 5.954e-03 -2.941e-01 0.769
NG017009 Isiala Mbano 1.359e-01 -5.427e-05 6.956e-03 1.630e+00 0.103
NG024010 Isin 1.603e+00 -2.232e-04 5.743e-02 6.689e+00 0.000
NG001008 Isiukwuato 4.328e-01 -5.691e-04 8.759e-02 1.464e+00 0.143
NG030022 Isokan 1.336e-01 -5.158e-05 7.942e-03 1.499e+00 0.134
NG010009 Isoko North 1.804e-01 -2.913e-04 5.612e-02 7.626e-01 0.446
NG010010 Isoko South 2.299e-01 -5.238e-04 8.062e-02 8.114e-01 0.417
NG017010 Isu 8.018e-01 -1.475e-03 2.839e-01 1.508e+00 0.132
NG005010 Itas/Gadau -1.338e-02 -2.395e-06 2.296e-04 -8.827e-01 0.377
NG031017 Itesiwaju -1.989e-01 -1.126e-03 2.168e-01 -4.247e-01 0.671
NG003016 Itu 2.514e-02 -1.090e-05 2.102e-03 5.486e-01 0.583
NG011009 Ivo 2.998e-01 -5.691e-04 7.290e-02 1.113e+00 0.266
NG031018 Iwajowa -2.726e-01 -1.388e-03 1.776e-01 -6.437e-01 0.520
NG030023 Iwo 2.954e-04 -1.538e-07 2.369e-05 6.071e-02 0.952
NG011010 Izzi -2.382e-01 -1.492e-04 2.298e-02 -1.570e+00 0.116
NG019006 Jaba 1.802e+00 -2.486e-03 3.819e-01 2.920e+00 0.004
NG002008 Jada 1.093e+00 -1.401e-03 3.601e-01 1.823e+00 0.068
NG018014 Jahun 4.154e-02 -1.987e-05 2.546e-03 8.235e-01 0.410
NG036009 Jakusko -1.399e-01 -1.330e-03 1.457e-01 -3.632e-01 0.716
NG035007 Jalingo 2.712e-01 -2.532e-04 6.513e-02 1.064e+00 0.287
NG005011 Jama'are -2.187e-03 -1.987e-05 3.830e-03 -3.502e-02 0.972
NG022011 Jega 5.141e-02 -8.134e-04 1.042e-01 1.618e-01 0.871
NG019007 Jema'a 2.266e+00 -1.231e-02 1.559e+00 1.825e+00 0.068
NG008013 Jere 1.357e-01 -1.987e-05 3.830e-03 2.193e+00 0.028
NG021016 Jibia -1.382e-01 -8.662e-05 1.334e-02 -1.196e+00 0.232
NG032004 Jos East 1.526e-01 -1.224e-04 1.568e-02 1.220e+00 0.222
NG032005 Jos North -1.317e-01 -2.004e-04 3.862e-02 -6.689e-01 0.504
NG032006 Jos South 7.829e-01 -1.772e-03 2.725e-01 1.503e+00 0.133
NG023010 Kabba/Bunu -4.159e-02 -1.216e-05 1.559e-03 -1.053e+00 0.292
NG020020 Kabo 3.375e-01 -1.401e-03 1.535e-01 8.649e-01 0.387
NG019008 Kachia 3.677e-01 -1.461e-03 1.600e-01 9.229e-01 0.356
NG019009 Kaduna North 7.038e-01 -7.590e-04 1.952e-01 1.595e+00 0.111
NG019010 Kaduna South -1.385e+00 -1.126e-03 2.894e-01 -2.573e+00 0.010
NG018015 Kafin Hausa -1.399e-01 -1.550e-03 1.984e-01 -3.105e-01 0.756
NG021017 Kafur 1.037e-01 -2.591e-04 3.320e-02 5.708e-01 0.568
NG008014 Kaga 1.228e+00 -1.628e-03 2.503e-01 2.458e+00 0.014
NG019011 Kagarko 6.986e-01 -7.023e-03 7.651e-01 8.067e-01 0.420
NG024011 Kaiama 1.686e-01 -1.536e-03 1.965e-01 3.837e-01 0.701
NG021018 Kaita 3.216e-01 -3.209e-03 4.100e-01 5.072e-01 0.612
NG031019 Kajola -8.379e-02 -1.690e-04 4.349e-02 -4.010e-01 0.688
NG019012 Kajuru -9.590e-01 -9.391e-04 7.985e-02 -3.390e+00 0.001
NG008015 Kala/Balge 1.258e+00 -1.628e-03 4.182e-01 1.948e+00 0.051
NG022012 Kalgo 2.695e-01 -8.134e-04 1.252e-01 7.641e-01 0.445
NG016007 Kaltungo 2.436e+00 -1.461e-03 2.246e-01 5.143e+00 0.000
NG032007 Kanam 3.048e-01 -1.050e-03 1.150e-01 9.017e-01 0.367
NG021019 Kankara 1.118e-01 -2.913e-04 3.195e-02 6.272e-01 0.531
NG032008 Kanke 7.379e-01 -2.114e-03 3.249e-01 1.298e+00 0.194
NG021020 Kankia 4.749e-03 -2.946e-05 3.232e-03 8.406e-02 0.933
NG020021 Kano Municipal -1.874e-01 -1.224e-04 1.884e-02 -1.364e+00 0.172
NG036010 Karasuwa 2.608e-01 -7.164e-04 1.102e-01 7.876e-01 0.431
NG020022 Karaye -4.562e-02 -3.859e-05 5.942e-03 -5.913e-01 0.554
NG035008 Karim-Lamido 5.263e-01 -8.697e-04 6.647e-02 2.045e+00 0.041
NG026004 Karu 6.347e-01 -5.153e-04 4.383e-02 3.034e+00 0.002
NG005012 Katagum -3.692e-02 -1.216e-05 1.334e-03 -1.010e+00 0.312
NG027010 Katcha -2.123e-01 -6.163e-04 9.485e-02 -6.872e-01 0.492
NG021021 Katsina 2.597e-01 -3.187e-04 8.197e-02 9.082e-01 0.364
NG007009 Katsina-Ala 2.770e-01 -1.224e-04 1.342e-02 2.392e+00 0.017
NG018016 Kaugama -2.623e-02 -1.056e-04 1.012e-02 -2.596e-01 0.795
NG019013 Kaura 3.816e-01 -2.850e-04 5.491e-02 1.630e+00 0.103
NG037008 Kaura Namoda 2.522e-01 -4.803e-04 9.253e-02 8.305e-01 0.406
NG019014 Kauru 2.201e-01 -1.448e-04 2.229e-02 1.476e+00 0.140
NG018017 Kazaure 1.489e+00 -4.148e-03 4.532e-01 2.218e+00 0.027
NG026005 Keana -9.462e-03 -2.395e-06 4.617e-04 -4.403e-01 0.660
NG034010 Kebbe -8.309e-02 -1.492e-04 1.913e-02 -5.997e-01 0.549
NG026006 Keffi -2.308e-01 -5.691e-04 2.198e-01 -4.911e-01 0.623
NG033014 Khana 4.334e-01 -4.803e-04 4.086e-02 2.146e+00 0.032
NG020023 Kibiya 2.965e-03 -3.337e-07 6.433e-05 3.697e-01 0.712
NG005013 Kirfi -8.159e-02 -1.216e-05 1.873e-03 -1.885e+00 0.059
NG018018 Kiri Kasamma 7.675e-01 -3.209e-03 4.100e-01 1.204e+00 0.229
NG020024 Kiru -4.413e-02 -8.320e-05 9.128e-03 -4.610e-01 0.645
NG018019 Kiyawa -8.136e-01 -2.390e-03 3.672e-01 -1.339e+00 0.181
NG023011 Kogi -6.457e-04 -1.538e-07 2.369e-05 -1.326e-01 0.894
NG022013 Koko/Besse -1.791e-02 -3.100e-03 3.961e-01 -2.353e-02 0.981
NG026007 Kokona 2.890e-02 -5.439e-06 5.214e-04 1.266e+00 0.206
NG006003 Kolokuma/Opokuma 4.112e-01 -1.550e-03 3.983e-01 6.540e-01 0.513
NG008016 Konduga 1.128e+00 -1.628e-03 1.243e-01 3.204e+00 0.001
NG007010 Konshisha 1.094e+00 -1.939e-03 2.124e-01 2.377e+00 0.017
NG027011 Kontagora -3.907e-02 -1.056e-04 2.718e-02 -2.364e-01 0.813
NG025013 Kosofe 5.784e-01 -1.192e-03 2.295e-01 1.210e+00 0.226
NG019015 Kubau -3.123e-01 -4.803e-04 7.393e-02 -1.147e+00 0.252
NG019016 Kudan 1.769e+00 -1.939e-03 2.481e-01 3.555e+00 0.000
NG015005 Kuje 3.253e-01 -9.878e-04 1.520e-01 8.370e-01 0.403
NG008017 Kukawa 1.258e+00 -1.628e-03 4.182e-01 1.948e+00 0.051
NG020025 Kumbotso 2.986e-01 -9.995e-04 7.638e-02 1.084e+00 0.278
NG020026 Kunchi 6.714e-02 -7.164e-04 1.380e-01 1.827e-01 0.855
NG020027 Kura -1.885e-03 -1.856e-06 3.576e-04 -9.957e-02 0.921
NG021022 Kurfi 1.268e-01 -6.559e-04 1.009e-01 4.012e-01 0.688
NG035009 Kurmi 7.582e-01 -1.536e-03 2.361e-01 1.563e+00 0.118
NG021023 Kusada 1.009e-01 -1.401e-03 2.697e-01 1.970e-01 0.844
NG015006 Kwali 2.604e-01 -9.278e-04 2.385e-01 5.352e-01 0.593
NG016008 Kwami 2.191e+00 -2.993e-03 3.824e-01 3.547e+00 0.000
NG007011 Kwande 3.447e-01 -1.952e-04 2.501e-02 2.181e+00 0.029
NG034011 Kware -9.205e-02 -5.158e-05 4.945e-03 -1.308e+00 0.191
NG008018 Kwaya Kusar 1.058e+00 -1.550e-03 2.984e-01 1.940e+00 0.052
NG026008 Lafia 6.204e-01 -2.584e-03 2.470e-01 1.253e+00 0.210
NG031020 Lagelu 1.789e-03 -8.320e-05 9.128e-03 1.959e-02 0.984
NG025014 Lagos Island 8.698e-01 -8.806e-04 1.696e-01 2.114e+00 0.034
NG025015 Lagos Mainland 1.054e+00 -1.475e-03 2.268e-01 2.216e+00 0.027
NG002009 Lamurde -1.584e-01 -1.628e-03 2.083e-01 -3.436e-01 0.731
NG032009 Langtang North 1.420e+00 -2.486e-03 2.721e-01 2.726e+00 0.006
NG032010 Langtang South -1.187e-02 -3.337e-07 6.433e-05 -1.480e+00 0.139
NG027012 Lapai 8.416e-02 -2.004e-04 2.568e-02 5.264e-01 0.599
NG035010 Lau -2.599e-01 -1.179e-03 1.129e-01 -7.700e-01 0.441
NG027013 Lavun -1.320e-01 -5.427e-05 1.046e-02 -1.290e+00 0.197
NG019017 Lere -2.557e-01 -9.278e-04 1.427e-01 -6.743e-01 0.500
NG007012 Logo 4.036e-01 -3.542e-04 5.453e-02 1.730e+00 0.084
NG023012 Lokoja -2.365e-01 -6.163e-04 4.712e-02 -1.087e+00 0.277
NG036011 Machina -2.014e-01 -1.126e-03 1.732e-01 -4.812e-01 0.630
NG002010 Madagali 1.167e+00 -1.628e-03 3.132e-01 2.089e+00 0.037
NG020028 Madobi 1.805e-01 -3.253e-04 3.118e-02 1.024e+00 0.306
NG008019 Mafa 1.072e+00 -1.628e-03 2.083e-01 2.352e+00 0.019
NG027014 Magama 1.003e-02 -5.439e-06 6.970e-04 3.802e-01 0.704
NG008020 Magumeri 1.165e+00 -1.475e-03 1.616e-01 2.901e+00 0.004
NG021024 Mai'adua 1.314e-01 -4.532e-03 6.947e-01 1.631e-01 0.870
NG008021 Maiduguri 6.314e-01 -1.330e-03 5.132e-01 8.832e-01 0.377
NG018020 Maigatari 4.295e-03 -1.538e-07 2.369e-05 8.824e-01 0.378
NG002011 Maiha 1.069e+00 -1.330e-03 2.560e-01 2.115e+00 0.034
NG022014 Maiyama 2.427e-01 -1.448e-04 1.855e-02 1.783e+00 0.075
NG020029 Makoda -1.762e-01 -1.475e-03 1.616e-01 -4.346e-01 0.664
NG007013 Makurdi -2.057e-02 -1.987e-05 3.830e-03 -3.320e-01 0.740
NG018021 Malam Madori -7.223e-01 -6.163e-04 9.485e-02 -2.343e+00 0.019
NG021025 Malumfashi 3.576e-01 -5.691e-04 8.759e-02 1.210e+00 0.226
NG032011 Mangu 1.325e+00 -3.100e-03 3.961e-01 2.110e+00 0.035
NG021026 Mani 8.668e-02 -2.232e-04 2.860e-02 5.138e-01 0.607
NG037009 Maradun 1.245e-01 -1.492e-04 1.270e-02 1.106e+00 0.269
NG027015 Mariga 2.300e-01 -3.187e-04 2.711e-02 1.399e+00 0.162
NG019018 Markafi -1.169e+00 -1.401e-03 2.697e-01 -2.248e+00 0.025
NG008022 Marte 1.258e+00 -1.628e-03 3.132e-01 2.251e+00 0.024
NG037010 Maru 3.209e-02 -5.158e-05 4.389e-03 4.852e-01 0.628
NG027016 Mashegu -2.250e-02 -2.946e-05 2.824e-03 -4.228e-01 0.672
NG021027 Mashi 4.070e-01 -2.850e-04 5.491e-02 1.738e+00 0.082
NG021028 Matazu -4.120e-02 -3.859e-05 7.437e-03 -4.773e-01 0.633
NG002012 Mayo-Belwa 2.600e-01 -1.401e-03 1.191e-01 7.573e-01 0.449
NG017011 Mbaitoli 4.167e-01 -7.693e-04 6.542e-02 1.632e+00 0.103
NG003017 Mbo 2.404e-02 -6.951e-05 1.340e-02 2.083e-01 0.835
NG002013 Michika 1.036e+00 -1.260e-03 3.238e-01 1.823e+00 0.068
NG018022 Miga 5.301e-02 -1.550e-03 2.384e-01 1.117e-01 0.911
NG032012 Mikang 2.208e+00 -2.486e-03 6.381e-01 2.767e+00 0.006
NG020030 Minjibir 5.417e-02 -1.475e-03 1.616e-01 1.384e-01 0.890
NG005014 Misau 1.829e-02 -1.216e-05 1.873e-03 4.229e-01 0.672
NG003018 Mkpat Enin -1.608e-01 -1.114e-03 1.426e-01 -4.231e-01 0.672
NG013015 Moba -1.773e-01 -8.662e-05 1.110e-02 -1.682e+00 0.093
NG008023 Mobbar 1.258e+00 -1.628e-03 2.503e-01 2.518e+00 0.012
NG027017 Mokwa 4.141e-01 -1.050e-03 5.700e-02 1.739e+00 0.082
NG008024 Monguno 1.258e+00 -1.628e-03 2.503e-01 2.518e+00 0.012
NG023013 Mopa-Muro 6.845e-02 -2.532e-04 6.513e-02 2.692e-01 0.788
NG024012 Moro 8.420e-01 -6.559e-04 5.578e-02 3.568e+00 0.000
NG002014 Mubi North 1.117e+00 -1.550e-03 2.984e-01 2.048e+00 0.041
NG002015 Mubi South 1.159e+00 -1.550e-03 3.983e-01 1.839e+00 0.066
NG021029 Musawa 2.373e-01 -3.990e-04 3.824e-02 1.216e+00 0.224
NG025016 Mushin 1.043e+00 -1.260e-03 1.938e-01 2.372e+00 0.018
NG027018 Muya -1.131e-03 -1.538e-07 2.965e-05 -2.076e-01 0.836
NG016009 Nafada 7.454e-01 -1.316e-03 2.534e-01 1.484e+00 0.138
NG036012 Nangere 9.027e-02 -9.391e-04 1.203e-01 2.630e-01 0.793
NG020031 Nasarawa -6.423e-02 -5.158e-05 6.610e-03 -7.894e-01 0.430
NG026009 Nasarawa 1.980e-01 -5.603e-04 5.369e-02 8.568e-01 0.392
NG026010 Nasarawa-Eggon 3.134e-01 -8.134e-04 1.567e-01 7.938e-01 0.427
NG010011 Ndokwa East -7.157e-02 -1.825e-05 1.750e-03 -1.711e+00 0.087
NG010012 Ndokwa West 1.146e-01 -1.739e-04 1.907e-02 8.313e-01 0.406
NG006004 Nembe 2.802e-01 -2.004e-04 3.086e-02 1.596e+00 0.110
NG008025 Ngala 1.258e+00 -1.628e-03 4.182e-01 1.948e+00 0.051
NG008026 Nganzai 1.090e+00 -1.628e-03 1.783e-01 2.585e+00 0.010
NG022015 Ngaski 5.137e-02 -1.952e-04 2.501e-02 3.261e-01 0.744
NG017012 Ngor-Okpala 3.814e-01 -8.240e-04 7.893e-02 1.360e+00 0.174
NG036013 Nguru 1.207e-02 -6.338e-06 9.760e-04 3.866e-01 0.699
NG005015 Ningi -9.890e-02 -5.691e-04 5.453e-02 -4.211e-01 0.674
NG017013 Njaba 7.647e-01 -1.126e-03 1.732e-01 1.840e+00 0.066
NG004013 Njikoka 6.258e-01 -7.693e-04 9.852e-02 1.996e+00 0.046
NG014011 Nkanu East 9.303e-02 -1.550e-03 1.317e-01 2.606e-01 0.794
NG014012 Nkanu West 1.522e-01 -2.946e-05 4.536e-03 2.260e+00 0.024
NG017014 Nkwerre 5.765e-01 -5.238e-04 6.709e-02 2.228e+00 0.026
NG004014 Nnewi North 8.868e-01 -9.391e-04 2.414e-01 1.807e+00 0.071
NG004015 Nnewi South 9.206e-01 -1.475e-03 1.412e-01 2.454e+00 0.014
NG003019 Nsit Atai 1.168e-03 -1.018e-04 1.963e-02 9.065e-03 0.993
NG003020 Nsit Ibom 4.361e-02 -2.288e-04 4.409e-02 2.088e-01 0.835
NG003021 Nsit Ubium -2.045e-02 -6.645e-05 6.370e-03 -2.554e-01 0.798
NG014013 Nsukka 6.723e-01 -9.391e-04 1.203e-01 1.942e+00 0.052
NG002016 Numan 6.230e-01 -1.628e-03 3.132e-01 1.116e+00 0.264
NG017015 Nwangele 4.972e-01 -5.691e-04 8.759e-02 1.682e+00 0.093
NG028015 Obafemi-Owode 3.234e-01 -6.654e-04 6.375e-02 1.283e+00 0.199
NG009013 Obanliku 9.312e-01 -6.071e-04 1.561e-01 2.358e+00 0.018
NG007014 Obi 1.469e-02 -3.859e-05 7.437e-03 1.708e-01 0.864
NG026011 Obi 1.695e-01 -4.722e-04 9.098e-02 5.636e-01 0.573
NG001009 Obi Ngwa -2.420e-01 -4.932e-03 4.177e-01 -3.668e-01 0.714
NG033015 Obia/Akpor -6.524e-02 -5.439e-06 5.967e-04 -2.670e+00 0.008
NG030024 Obokun 8.451e-01 -1.692e-03 1.437e-01 2.234e+00 0.026
NG003022 Obot Akara 9.989e-03 -1.825e-05 2.002e-03 2.236e-01 0.823
NG017016 Obowo 1.979e-01 -2.591e-04 3.989e-02 9.920e-01 0.321
NG009014 Obubra 1.498e+00 -5.208e-03 5.684e-01 1.994e+00 0.046
NG009015 Obudu 1.383e+00 -2.390e-03 3.056e-01 2.507e+00 0.012
NG028016 Odeda 3.684e-02 -1.987e-05 2.180e-03 7.895e-01 0.430
NG029013 Odigbo 4.067e-01 -7.860e-03 8.555e-01 4.482e-01 0.654
NG030025 Odo-Otin 2.602e+00 -5.492e-03 5.992e-01 3.369e+00 0.001
NG028017 Odogbolu 8.487e-03 -3.612e-04 3.462e-02 4.756e-02 0.962
NG009016 Odukpani 1.158e-01 -2.296e-03 1.591e-01 2.960e-01 0.767
NG024013 Offa 6.623e+00 -3.902e-03 3.008e+00 3.820e+00 0.000
NG023014 Ofu 2.737e-02 -3.253e-04 3.568e-02 1.466e-01 0.883
NG033016 Ogba/Egbema/Ndoni 3.213e-01 -8.240e-04 7.007e-02 1.217e+00 0.224
NG007015 Ogbadibo 1.103e-01 -4.092e-05 6.302e-03 1.390e+00 0.165
NG004016 Ogbaru 5.272e-01 -8.240e-04 6.298e-02 2.104e+00 0.035
NG006005 Ogbia 4.239e-01 -4.803e-04 7.393e-02 1.561e+00 0.119
NG031021 Ogbomosho North -2.050e-01 -1.739e-04 4.474e-02 -9.685e-01 0.333
NG031022 Ogbomosho South 2.017e-01 -2.532e-04 4.878e-02 9.142e-01 0.361
NG031023 Ogo Oluwa 7.313e-01 -1.179e-03 1.814e-01 1.720e+00 0.085
NG009017 Ogoja 2.042e+00 -2.204e-03 3.387e-01 3.512e+00 0.000
NG023015 Ogori/Magongo 7.188e-03 -2.591e-04 1.001e-01 2.354e-02 0.981
NG033017 Ogu/Bolo 1.234e+00 -1.628e-03 2.503e-01 2.470e+00 0.014
NG028018 Ogun waterside -1.466e-01 -1.492e-04 2.298e-02 -9.660e-01 0.334
NG017017 Oguta -2.974e-01 -3.187e-04 3.054e-02 -1.700e+00 0.089
NG001010 Ohafia 2.234e-01 -4.803e-04 9.253e-02 7.360e-01 0.462
NG017018 Ohaji/Egbema -1.702e-01 -8.320e-05 9.128e-03 -1.780e+00 0.075
NG011011 Ohaozara -8.401e-02 -8.134e-04 1.042e-01 -2.578e-01 0.797
NG011012 Ohaukwu 5.059e-01 -1.692e-03 3.255e-01 8.896e-01 0.374
NG007016 Ohimini 2.901e-02 -1.216e-05 2.344e-03 5.995e-01 0.549
NG014014 Oji-River 6.648e-01 -7.693e-04 7.369e-02 2.452e+00 0.014
NG025017 Ojo -2.819e-01 -8.662e-05 1.669e-02 -2.181e+00 0.029
NG007017 Oju -1.648e-01 -1.265e-04 1.388e-02 -1.398e+00 0.162
NG024014 Oke-Ero 3.874e+00 -6.082e-03 6.632e-01 4.765e+00 0.000
NG023016 Okehi -4.326e-02 -1.448e-04 2.229e-02 -2.888e-01 0.773
NG023017 Okene -5.705e-02 -5.158e-05 7.942e-03 -6.396e-01 0.522
NG017019 Okigwe 3.643e-01 -2.913e-04 2.792e-02 2.182e+00 0.029
NG029014 Okitipupa 1.609e-01 -1.224e-04 1.884e-02 1.173e+00 0.241
NG003023 Okobo 3.116e-02 -3.859e-05 4.233e-03 4.795e-01 0.632
NG010013 Okpe 4.887e-01 -1.192e-03 1.306e-01 1.356e+00 0.175
NG007018 Okpokwu 6.109e-02 -1.056e-04 1.353e-02 5.260e-01 0.599
NG033018 Okrika 1.139e+00 -1.550e-03 2.384e-01 2.337e+00 0.019
NG030026 Ola-oluwa -4.797e-01 -7.164e-04 1.380e-01 -1.289e+00 0.197
NG023018 Olamabolo 2.933e-01 -6.654e-04 7.296e-02 1.088e+00 0.276
NG030027 Olorunda 5.817e-01 -5.603e-04 5.369e-02 2.513e+00 0.012
NG031024 Olorunsogo -4.935e-02 -1.056e-04 1.159e-02 -4.575e-01 0.647
NG031025 Oluyole 2.432e-01 -5.691e-04 5.453e-02 1.044e+00 0.297
NG023019 Omala -9.354e-02 -8.662e-05 1.110e-02 -8.869e-01 0.375
NG033019 Omumma 3.843e-01 -1.192e-03 2.295e-01 8.047e-01 0.421
NG031026 Ona-Ara -1.362e-01 -2.850e-04 3.651e-02 -7.114e-01 0.477
NG029015 Ondo East 1.565e+00 -1.939e-03 2.981e-01 2.870e+00 0.004
NG029016 Ondo West 1.404e+00 -3.902e-03 4.264e-01 2.156e+00 0.031
NG011013 Onicha 7.370e-01 -3.100e-03 2.963e-01 1.360e+00 0.174
NG004017 Onitsha North 8.526e-01 -1.550e-03 1.698e-01 2.073e+00 0.038
NG004018 Onitsha South 9.578e-01 -1.475e-03 3.790e-01 1.558e+00 0.119
NG003024 Onna 2.209e-02 -2.946e-05 3.775e-03 3.600e-01 0.719
NG033020 Opobo/Nkoro 5.481e-01 -1.260e-03 2.425e-01 1.115e+00 0.265
NG012012 Oredo 7.810e-02 -1.265e-04 3.255e-02 4.336e-01 0.665
NG031027 Orelope 8.674e-03 -6.338e-06 9.760e-04 2.779e-01 0.781
NG012013 Orhionmwon -3.882e-01 -6.071e-04 3.858e-02 -1.973e+00 0.048
NG031028 Ori Ire -2.307e-02 -1.987e-05 1.905e-03 -5.282e-01 0.597
NG030028 Oriade 1.338e+00 -3.209e-03 2.447e-01 2.711e+00 0.007
NG017020 Orlu 6.641e-01 -7.164e-04 9.175e-02 2.195e+00 0.028
NG030029 Orolu 3.816e-01 -6.071e-04 1.170e-01 1.118e+00 0.264
NG003025 Oron -2.982e-01 -1.062e-03 1.633e-01 -7.351e-01 0.462
NG017021 Orsu 7.546e-01 -1.126e-03 1.732e-01 1.816e+00 0.069
NG017022 Oru East 5.153e-01 -9.995e-04 1.096e-01 1.560e+00 0.119
NG017023 Oru West 2.116e-01 -9.995e-04 2.570e-01 4.195e-01 0.675
NG003026 Oruk Anam -2.013e-02 -5.158e-05 5.658e-03 -2.669e-01 0.790
NG004019 Orumba North 5.995e-01 -6.654e-04 1.024e-01 1.875e+00 0.061
NG004020 Orumba South 6.436e-01 -8.806e-04 1.128e-01 1.919e+00 0.055
NG029017 Ose 1.303e+00 -3.782e-03 4.134e-01 2.033e+00 0.042
NG010014 Oshimili North 7.983e-01 -1.260e-03 1.938e-01 1.816e+00 0.069
NG010015 Oshimili South 6.277e-01 -8.240e-04 9.033e-02 2.091e+00 0.036
NG025018 Oshodi-Isolo 6.956e-01 -1.628e-03 2.503e-01 1.394e+00 0.163
NG001011 Osisioma Ngwa 8.075e-03 -5.238e-04 5.019e-02 3.838e-02 0.969
NG030030 Osogbo 4.686e-01 -5.603e-04 6.144e-02 1.893e+00 0.058
NG007019 Oturkpo 1.125e-02 -4.092e-05 3.923e-03 1.803e-01 0.857
NG012014 Ovia North East -2.941e-02 -6.951e-05 5.317e-03 -4.023e-01 0.687
NG012015 Ovia South West -1.424e-01 -8.662e-05 1.110e-02 -1.351e+00 0.177
NG012016 Owan East -7.368e-03 -1.492e-04 2.298e-02 -4.762e-02 0.962
NG012017 Owan West -5.548e-03 -5.427e-05 8.358e-03 -6.009e-02 0.952
NG017026 Owerri-Municipal 4.779e-01 -1.475e-03 5.692e-01 6.353e-01 0.525
NG017024 Owerri North 2.271e-01 -1.265e-04 1.621e-02 1.785e+00 0.074
NG017025 Owerri West 2.135e-01 -4.387e-04 5.621e-02 9.024e-01 0.367
NG029018 Owo 5.751e-01 -2.390e-03 2.616e-01 1.129e+00 0.259
NG013016 Oye 1.637e-01 -8.697e-04 1.338e-01 4.499e-01 0.653
NG004021 Oyi 3.163e-01 -1.739e-04 2.677e-02 1.934e+00 0.053
NG033021 Oyigbo 3.447e-01 -1.475e-03 1.412e-01 9.212e-01 0.357
NG031029 Oyo East -6.337e-02 -1.265e-04 1.076e-02 -6.096e-01 0.542
NG031030 Oyo West 1.788e-01 -8.240e-04 9.033e-02 5.978e-01 0.550
NG024015 Oyun 8.312e+00 -1.881e-02 2.025e+00 5.854e+00 0.000
NG027019 Paikoro -2.052e-03 -3.337e-07 2.840e-05 -3.850e-01 0.700
NG032013 Pankshin 1.953e+00 -3.209e-03 3.509e-01 3.302e+00 0.001
NG010016 Patani 5.261e-01 -9.995e-04 1.538e-01 1.344e+00 0.179
NG024016 Pategi 1.120e+00 -4.797e-03 6.118e-01 1.438e+00 0.150
NG033022 Port-Harcourt 6.206e-01 -7.693e-04 1.482e-01 1.614e+00 0.106
NG036014 Potiskum 7.920e-01 -1.192e-03 3.064e-01 1.433e+00 0.152
NG032014 Qua'an Pan 2.038e+00 -4.274e-03 5.455e-01 2.765e+00 0.006
NG034012 Rabah 4.493e-01 -1.260e-03 1.381e-01 1.213e+00 0.225
NG027020 Rafi -8.405e-03 -3.337e-07 5.139e-05 -1.172e+00 0.241
NG020032 Rano 2.699e-02 -3.253e-04 5.008e-02 1.220e-01 0.903
NG028019 Remo North 3.680e-01 -4.803e-04 7.393e-02 1.355e+00 0.175
NG027021 Rijau 1.710e-01 -1.114e-03 1.220e-01 4.926e-01 0.622
NG021030 Rimi -8.303e-02 -2.946e-05 4.536e-03 -1.232e+00 0.218
NG020033 Rimin Gado 6.599e-01 -1.260e-03 1.938e-01 1.502e+00 0.133
NG018023 Ringim 3.803e-02 -1.260e-03 1.613e-01 9.783e-02 0.922
NG032015 Riyom 1.530e+00 -3.433e-03 3.753e-01 2.503e+00 0.012
NG020034 Rogo -1.105e-01 -8.134e-04 8.917e-02 -3.673e-01 0.713
NG018024 Roni -5.178e-03 -1.247e-03 1.918e-01 -8.978e-03 0.993
NG019019 Sabon-Gari 9.049e+00 -1.725e-02 3.267e+00 5.016e+00 0.000
NG034013 Sabon Birni -1.217e-01 -5.158e-05 1.327e-02 -1.056e+00 0.291
NG021031 Sabuwa -3.291e-01 -6.951e-05 1.340e-02 -2.843e+00 0.004
NG021032 Safana -2.239e-02 -3.612e-04 4.628e-02 -1.024e-01 0.918
NG006006 Sagbama -4.223e-02 -5.439e-06 4.629e-04 -1.963e+00 0.050
NG022016 Sakaba 3.120e-01 -7.065e-04 1.361e-01 8.476e-01 0.397
NG031031 Saki East -1.630e-03 -2.395e-06 3.688e-04 -8.472e-02 0.932
NG031032 Saki West 2.372e-02 -5.158e-05 1.327e-02 2.063e-01 0.837
NG021033 Sandamu 1.335e-02 -1.856e-06 2.378e-04 8.658e-01 0.387
NG019020 Sanga 8.449e-01 -1.050e-03 1.005e-01 2.668e+00 0.008
NG010017 Sapele 5.704e-01 -1.062e-03 2.044e-01 1.264e+00 0.206
NG035011 Sardauna -3.154e-01 -1.739e-04 6.720e-02 -1.216e+00 0.224
NG028020 Shagamu -1.152e-01 -3.917e-04 5.018e-02 -5.124e-01 0.608
NG034014 Shagari -9.666e-02 -5.691e-04 8.759e-02 -3.247e-01 0.745
NG022017 Shanga 8.060e-02 -9.278e-04 8.887e-02 2.735e-01 0.784
NG008027 Shani 1.314e-01 -1.628e-03 1.558e-01 3.371e-01 0.736
NG020035 Shanono 2.298e-01 -3.253e-04 5.008e-02 1.028e+00 0.304
NG002017 Shelleng 9.927e-01 -1.330e-03 1.457e-01 2.604e+00 0.009
NG032016 Shendam 1.462e+00 -4.274e-03 4.669e-01 2.146e+00 0.032
NG037011 Shinkafi 2.692e-02 -6.338e-06 1.631e-03 6.667e-01 0.505
NG005016 Shira -5.865e-02 -6.645e-05 1.023e-02 -5.791e-01 0.563
NG027022 Shiroro -8.591e-02 -2.591e-04 2.842e-02 -5.081e-01 0.611
NG016010 Shomgom 1.956e+00 -4.663e-03 7.148e-01 2.319e+00 0.020
NG025019 Shomolu 1.069e+00 -1.401e-03 2.697e-01 2.061e+00 0.039
NG034015 Silame -6.584e-02 -6.654e-04 1.282e-01 -1.821e-01 0.856
NG019021 Soba 3.674e+00 -1.275e-02 1.207e+00 3.357e+00 0.001
NG034016 Sokoto North 2.974e-01 -1.401e-03 3.601e-01 4.979e-01 0.619
NG034017 Sokoto South 3.002e-01 -1.192e-03 2.295e-01 6.291e-01 0.529
NG002018 Song 9.350e-01 -1.126e-03 1.234e-01 2.665e+00 0.008
NG006007 Southern Ijaw 2.562e-01 -2.913e-04 3.195e-02 1.435e+00 0.151
NG018025 Sule-Tankarkar 8.919e-01 -5.153e-04 6.601e-02 3.473e+00 0.001
NG027023 Suleja 6.124e-02 -8.240e-04 1.587e-01 1.558e-01 0.876
NG020036 Sumaila 1.692e-01 -8.697e-04 1.675e-01 4.157e-01 0.678
NG022018 Suru 1.486e-01 -2.748e-05 4.231e-03 2.284e+00 0.022
NG025020 Surulere 1.008e+00 -1.192e-03 1.526e-01 2.583e+00 0.010
NG031033 Surulere 1.338e+00 -2.390e-03 1.656e-01 3.293e+00 0.001
NG027024 Tafa -4.203e-01 -1.062e-03 1.633e-01 -1.037e+00 0.300
NG005017 Tafawa-Balewa -2.038e-01 -3.990e-04 3.051e-02 -1.164e+00 0.244
NG033023 Tai 1.071e+00 -1.475e-03 2.268e-01 2.252e+00 0.024
NG020037 Takai -1.307e-01 -2.584e-03 3.303e-01 -2.229e-01 0.824
NG035012 Takum 7.988e-01 -1.692e-03 3.255e-01 1.403e+00 0.161
NG037012 Talata Mafara -3.158e-01 -7.065e-04 1.361e-01 -8.541e-01 0.393
NG034018 Tambuwal 2.491e-02 -5.158e-05 4.389e-03 3.768e-01 0.706
NG034019 Tangaza -3.424e-03 -3.337e-07 4.277e-05 -5.234e-01 0.601
NG020038 Tarauni 3.325e-01 -1.192e-03 2.295e-01 6.966e-01 0.486
NG007020 Tarka 1.960e-01 -8.320e-05 1.604e-02 1.548e+00 0.122
NG036015 Tarmua 8.834e-01 -1.260e-03 1.938e-01 2.010e+00 0.044
NG018026 Taura -2.183e-01 -9.878e-04 1.265e-01 -6.109e-01 0.541
NG020039 Tofa 7.379e-01 -1.550e-03 2.384e-01 1.515e+00 0.130
NG005018 Toro 6.422e-02 -1.739e-04 1.330e-02 5.584e-01 0.577
NG026012 Toto 6.055e-02 -1.825e-05 2.339e-03 1.252e+00 0.210
NG002019 Toungo -2.776e-01 -1.550e-03 3.983e-01 -4.374e-01 0.662
NG037013 Tsafe -8.472e-03 -5.439e-06 1.048e-03 -2.615e-01 0.794
NG020040 Tsanyawa -3.662e-03 -1.538e-07 1.688e-05 -8.913e-01 0.373
NG020041 Tudun Wada -2.385e-01 -1.050e-03 8.925e-02 -7.948e-01 0.427
NG034020 Tureta -3.330e-01 -6.071e-04 6.657e-02 -1.288e+00 0.198
NG014015 Udenu 6.730e-01 -1.550e-03 2.384e-01 1.381e+00 0.167
NG014016 Udi 7.362e-01 -9.391e-04 7.985e-02 2.608e+00 0.009
NG010018 Udu 7.613e-01 -1.330e-03 2.560e-01 1.507e+00 0.132
NG003027 Udung Uko -4.292e-03 -1.538e-07 3.959e-05 -6.821e-01 0.495
NG010019 Ughelli North 3.225e-01 -5.238e-04 4.005e-02 1.614e+00 0.106
NG010020 Ughelli South 6.885e-01 -7.164e-04 7.854e-02 2.459e+00 0.014
NG001012 Ugwunagbo 8.333e-02 -1.448e-04 1.855e-02 6.129e-01 0.540
NG012018 Uhunmwonde 5.367e-02 -1.739e-04 2.228e-02 3.607e-01 0.718
NG003028 Ukanafun 4.858e-02 -5.427e-05 8.358e-03 5.320e-01 0.595
NG007021 Ukum 7.115e-01 -9.278e-04 1.787e-01 1.685e+00 0.092
NG001013 Ukwa East 1.105e-01 -3.542e-04 3.885e-02 5.626e-01 0.574
NG001014 Ukwa West -3.187e-01 -6.559e-04 8.401e-02 -1.097e+00 0.273
NG010021 Ukwuani 1.142e-02 -2.395e-06 4.617e-04 5.318e-01 0.595
NG001017 Umu-Nneochi 5.767e-01 -7.164e-04 7.854e-02 2.060e+00 0.039
NG001015 Umuahia North 2.789e-01 -5.691e-04 6.240e-02 1.119e+00 0.263
NG001016 Umuahia South 6.062e-03 -3.337e-07 5.139e-05 8.456e-01 0.398
NG020042 Ungogo 3.081e-01 -7.693e-04 6.542e-02 1.208e+00 0.227
NG017027 Unuimo 6.794e-01 -1.475e-03 1.616e-01 1.694e+00 0.090
NG003029 Uruan -1.169e-02 -2.395e-06 3.070e-04 -6.668e-01 0.505
NG003030 Urue-Offong/Oruko 4.636e-03 -2.395e-06 3.688e-04 2.415e-01 0.809
NG007022 Ushongo 9.668e-01 -2.993e-03 3.824e-01 1.568e+00 0.117
NG035013 Ussa 1.096e+00 -1.247e-03 3.204e-01 1.939e+00 0.053
NG010022 Uvwie 6.791e-01 -1.062e-03 1.633e-01 1.683e+00 0.092
NG003031 Uyo -7.664e-03 -1.090e-05 8.341e-04 -2.650e-01 0.791
NG014017 Uzo-Uwani 7.888e-01 -1.550e-03 1.484e-01 2.052e+00 0.040
NG007023 Vandeikya 3.434e-01 -1.224e-04 1.884e-02 2.503e+00 0.012
NG034021 Wamako 2.073e-02 -1.216e-05 1.035e-03 6.448e-01 0.519
NG026013 Wamba 4.617e-02 -5.439e-06 8.375e-04 1.595e+00 0.111
NG020043 Warawa -2.181e-04 -1.448e-04 1.588e-02 -5.821e-04 1.000
NG005019 Warji 1.312e-01 -4.092e-05 7.888e-03 1.477e+00 0.140
NG010023 Warri North 1.929e-01 -3.253e-04 2.768e-02 1.161e+00 0.246
NG010024 Warri South 3.442e-01 -2.004e-04 2.568e-02 2.149e+00 0.032
NG010025 Warri South West 7.091e-01 -1.192e-03 1.833e-01 1.659e+00 0.097
NG022019 Wasagu/Danko -7.649e-02 -5.603e-04 6.144e-02 -3.063e-01 0.759
NG032017 Wase 1.186e-01 -6.645e-05 1.023e-02 1.173e+00 0.241
NG020044 Wudil -8.035e-03 -1.987e-05 2.180e-03 -1.717e-01 0.864
NG035014 Wukari 8.429e-01 -2.584e-03 2.827e-01 1.590e+00 0.112
NG034022 Wurno 1.225e-01 -3.612e-04 6.960e-02 4.656e-01 0.641
NG027025 Wushishi 6.025e-02 -6.071e-04 6.657e-02 2.359e-01 0.814
NG034023 Yabo -6.106e-02 -8.320e-05 1.066e-02 -5.906e-01 0.555
NG023020 Yagba East 5.454e-03 -1.538e-07 1.688e-05 1.328e+00 0.184
NG023021 Yagba West 2.204e+00 -1.247e-03 1.366e-01 5.967e+00 0.000
NG009018 Yakurr 1.462e+00 -6.388e-03 9.775e-01 1.486e+00 0.137
NG009019 Yala 1.425e+00 -4.402e-03 4.202e-01 2.206e+00 0.027
NG016011 Yamaltu/Deba 1.787e+00 -6.702e-03 7.304e-01 2.099e+00 0.036
NG018027 Yankwashi -1.148e-01 -2.946e-05 4.536e-03 -1.704e+00 0.088
NG022020 Yauri -3.426e-01 -7.164e-04 1.842e-01 -7.965e-01 0.426
NG006008 Yenegoa 4.821e-01 -6.654e-04 8.523e-02 1.654e+00 0.098
NG002020 Yola North 1.041e+00 -1.401e-03 5.409e-01 1.418e+00 0.156
NG002021 Yola South 9.911e-01 -1.126e-03 1.732e-01 2.384e+00 0.017
NG035015 Yorro 9.192e-02 -5.158e-05 6.610e-03 1.131e+00 0.258
NG036016 Yunusari 1.084e+00 -1.628e-03 3.132e-01 1.940e+00 0.052
NG036017 Yusufari 6.918e-01 -1.260e-03 1.938e-01 1.574e+00 0.115
NG005020 Zaki 8.196e-02 -1.018e-04 1.117e-02 7.764e-01 0.437
NG021034 Zango -5.190e-01 -3.990e-04 7.688e-02 -1.870e+00 0.061
NG019022 Zango-Kataf -5.571e-01 -4.803e-04 6.153e-02 -2.244e+00 0.025
NG019023 Zaria 1.039e+01 -1.126e-02 2.146e+00 7.104e+00 0.000
NG035016 Zing -2.541e-01 -4.722e-04 1.824e-01 -5.938e-01 0.553
NG037014 Zurmi -2.399e-02 -5.439e-06 5.214e-04 -1.050e+00 0.294
NG022021 Zuru -1.449e-01 -2.004e-04 3.862e-02 -7.364e-01 0.461Mapping 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..)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's Statistics") +
tm_borders(alpha = 0.5)
Mapping local Moran’s I p-values
The choropleth shows there is evidence for both positive and negative Ii values. However, it is useful to consider the p-values for each of these values, as considered above. The code chunks below produce a choropleth map of Moran’s I p-values by using functions of tmap package.
tm_shape(nga_wp.localMI) +
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)
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.
localMI.map <- tm_shape(nga_wp.localMI) +
tm_fill(col = "Ii",
style = "pretty",
title = "Local Moran's Statistics") +
tm_borders(alpha = 0.5) +
tm_layout(scale = 0.7)
pvalue.map <- tm_shape(nga_wp.localMI) +
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) +
tm_layout(scale = 0.7)
tmap_arrange(localMI.map, pvalue.map, asp=1, ncol=2)
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.
Plotting 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 points by using moran.plot() of spdep.
nci <- moran.plot(nga_wp$`wpt non-functional`, rswm_q,
labels=as.character(nga_wp$ADM2_EN),
xlab="Non-functional Water Points",
ylab="Spatially Lag Non-functional Water Points")
Notice that the plot is split into 4 quadrants. The top right corner belongs to areas that have high numbers of non-functional water points and are surrounded by other areas that have the average numbers of non-functional water points. These are the high-high locations.
Plotting Moran scatterplot with standardised variable
The code below is used to plot the Moran scatterplot after applying scale() to centers and scales the variable wpt non-functional.
nga_wp$Z.NFWP <- scale(nga_wp$`wpt non-functional`) %>% as.vector
nci2 <- moran.plot(nga_wp$Z.NFWP, rswm_q,
labels=as.character(nga_wp$ADM2_EN),
xlab="z-Non-functional Water Points",
ylab="Spatially Lag z-Non-functional Water Points")
Preparing LISA map classes
The code chunks below shows the steps to prepare a LISA cluster map which involves the following:
To derive the spatially lagged variable of interest (i.e.
wpt non-functional) and center the spatially lagged variable around its meanTo center the local Moran’s around the mean
To set a statistical significance level for the local Moran
To define the high-high, low-low, low-high and high-low categories
To places non-significant Moran in the category 0
quadrant <- vector(mode="numeric",length=nrow(localMI))
nga_wp$lag_NFWP <- lag.listw(rswm_q, nga_wp$`wpt non-functional`)
DV <- nga_wp$lag_NFWP - mean(nga_wp$lag_NFWP)
LM_I <- localMI[,1]
signif <- 0.05
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
quadrant[localMI[,5]>signif] <- 0Plotting LISA map
The code chunk below is used to build the LISA map.
nga_wp.localMI$quadrant <- quadrant
colors <- c("#ffffff", "#2c7bb6", "#abd9e9", "#fdae61", "#d7191c")
clusters <- c("insignificant", "low-low", "low-high", "high-low", "high-high")
tm_shape(nga_wp.localMI) +
tm_fill(col = "quadrant",
style = "cat",
palette = colors[c(sort(unique(quadrant)))+1],
labels = clusters[c(sort(unique(quadrant)))+1],
popup.vars = c("")) +
tm_view(set.zoom.limits = c(11,17)) +
tm_borders(alpha=0.5)
For effective interpretation, it is better to plot the choropleth map showing the non-functional water points distribution and the LISA map next to each other.
nfwpc <- qtm(nga_wp, "wpt non-functional") +
tm_layout(scale = 0.7)
nga_wp.localMI$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) +
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) +
tm_layout(scale = 0.7)
tmap_arrange(nfwpc, LISAmap, asp=1, ncol=2)
We can see from the LISA map that there’s a high-high cluster indicating positive autocorrelation for the LGAs having adjacent high numbers of non-functional water points. There is also a low-high cluster indicating negative autocorrelation/outliers whereby those LGAs with low numbers of non-functional water points are surrounded by those with high numbers of non-functional water points. And clearly the eastern region is very much a low-low cluster having adjacent low numbers of non-functional water points.
Hot Spot and Cold Spot Area Analysis
Beside detecting cluster and outliers, localised spatial statistics can also be 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.
Getis and Ord’s G-Statistics
An alternative spatial statistics to detect spatial anomalies is the Getis and Ord’s G-statistics. It looks at neighbours 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 neighbourhood range also share high values too.
The analysis consists of three steps:
Deriving spatial weight matrix
Computing Gi statistics
Mapping Gi statistics
Deriving distance-based weight matrix
Firstly, we need to define a new set of neighbours. While the spatial autocorrelation considered units which shared borders, for Getis-Ord we are defining neighbours based on adaptive distance weight matrix here for the case of LGAs in NIgeria.
Deriving the centroid
The code chunk below uses cbind() to put longitude and latitude into the same object for the centroid.
longitude <- map_dbl(nga_wp$geometry, ~st_centroid(.x)[[1]])
latitude <- map_dbl(nga_wp$geometry, ~st_centroid(.x)[[2]])
coords <- cbind(longitude, latitude)Computing adaptive distance weight matrix
We can 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 201942Gi statistics using adaptive distance
The code chunk below is used to compute the Gi values for wpt non-functional by using an adaptive distance weight matrix (i.e knn_lw).
fips <- order(nga_wp$ADM2_EN)
gi.adaptive <- localG(nga_wp$`wpt non-functional`, knn_lw)
nga_wp.gi <- cbind(nga_wp, as.matrix(gi.adaptive)) %>%
rename(gstat_adaptive = as.matrix.gi.adaptive.)Mapping Gi values with adaptive distance weights
It is time for us to visualise the locations of hot spot and cold spot areas. The choropleth mapping functions of tmap package will be used to map the Gi values.
The code chunk below shows the functions used to map the Gi values derived using adaptive distance weight matrix.
nfwpc <- qtm(nga_wp, "wpt non-functional") +
tm_layout(scale = 0.7)
Gimap <- tm_shape(nga_wp.gi) +
tm_fill(col = "gstat_adaptive",
style = "pretty",
palette="-RdBu",
title = "Local Gi") +
tm_borders(alpha = 0.5) +
tm_layout(scale = 0.7)
tmap_arrange(nfwpc, Gimap, asp=1, ncol=2)
In the Gi map above, LGAs shaded in red are the hot spot areas and LGAs shaded in blue are the cold spot areas. The darkness of the colours representing the intensity of the Gi values.
The code below creates an interactive Gi map which would then tell us exactly which are the hot spot and cold spot areas.
tmap_mode("view")
tm_shape(nga_wp.gi) +
tm_fill(col = "gstat_adaptive",
style = "pretty",
palette="-RdBu",
title = "Local Gi") +
tm_borders(alpha = 0.5) +
tm_scale_bar()tmap_mode("plot")After zooming in, some of the hot spot LGAs are Ilorin South, Kaduna North and Kaduna South while some of the cold spot LGAs are Kala/Balge, Kukawa and Marte.
List of hot spot LGAs by rank
hotspot_rank <- nga_wp.gi %>%
select(ADM2_EN, gstat_adaptive) %>%
filter(gstat_adaptive > 4) %>%
arrange(desc(gstat_adaptive))
hotspot_rank <- hotspot_rank %>%
mutate(rank = 1:nrow(hotspot_rank)) %>%
select(rank, ADM2_EN, gstat_adaptive)DT::datatable(hotspot_rank,
rownames = FALSE,
colnames = c('Rank' = 'rank',
'LGAs' = 'ADM2_EN',
'Local Gi' = 'gstat_adaptive'),
filter = 'top',
class = 'display')As seen above, there are 15 hot spot LGAs with Ilorin South being the highest ranked LGA in Nigeria. It is no wonder residents in Ilorin groaned over lack of water.
List of cold spot LGAs by rank
coldspot_rank <- nga_wp.gi %>%
select(ADM2_EN, gstat_adaptive) %>%
filter(gstat_adaptive < -2) %>%
arrange(gstat_adaptive)
coldspot_rank <- coldspot_rank %>%
mutate(rank = 1:nrow(coldspot_rank)) %>%
select(rank, ADM2_EN, gstat_adaptive)DT::datatable(coldspot_rank,
rownames = FALSE,
colnames = c('Rank' = 'rank',
'LGAs' = 'ADM2_EN',
'Local Gi' = 'gstat_adaptive'),
filter = 'top',
class = 'display')As seen above, there are 136 cold spot LGAs with Kala/Balge being the highest ranked LGA in Nigeria.