









































Pa
ge

 
1



Pa
ge

 
27

American Journal of  
Geospatial Technology (AJGT)

Spatial Analysis of  Internally Displaced Persons’ Camps in Borno State, Nigeria
Tobenna Nnaemeka Nzelibe1*, Michael Ajide Oyinloye2, Olusola Olalekan Popoola2, Felix Aromo Ilesanmi3

Volume 3 Issue 1, Year 2024
ISSN: 2833-8006 (Online)

DOI: https://doi.org/10.54536/ajgt.v3i1.2614
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: March 23, 2024

Accepted: April 26, 2024

Published: April 29, 2024

The situation in Borno State, Nigeria has been exacerbated by the ongoing insurgency led by 
Boko Haram in northeast Nigeria, leading to widespread displacement and the establishment 
of  Internally Displaced Persons (IDP) camps. This study aims to identify the locations of  IDP 
camps in Borno State and analyze their spatial distribution, density, and clustering patterns. 
Furthermore, it seeks to investigate the geospatial implications of  these patterns. To achieve 
this, the study employs geospatial analysis techniques such as Kernel Density Estimation, 
Cluster and Outlier Analysis, Nearest Neighbour Analysis, and Spatial Autocorrelation 
Analysis. The data collected for this study includes location coordinates, satellite imagery, 
demographic information, and administrative maps. The findings reveal that the distribution 
of  IDP camps in Borno State is non-random and demonstrates varying camp densities 
across senatorial districts, with predominant high-high clusters and a deliberate, non-random 
arrangement. These results align with spatial analysis theories, emphasizing the importance 
of  recognizing hidden inequalities in IDP camps and informing targeted interventions 
and resource planning for a more effective and equitable humanitarian response in Borno 
State. The implications extend to policy planning and enhanced vulnerability assessment 
for effective humanitarian responses, stressing the need for tailored interventions based on 
identified hotspots that address root causes and implement both immediate and long-term 
solutions. Future research directions could involve more in-depth investigations into the 
influencing factors, socio-economic impacts, and temporal dynamics of  spatial patterns in 
displacement.

Keywords
Geographic Information Systems 
(GIS), Geospatial Analysis, 
Geostatistical Methods, 
Internally Displaced Persons 
(IDPs), Humanitarian Crisis

1 Department of  Urban and Regional Planning Nigerian Army University, Biu, Nigeria
2 Department of  Urban and Regional Planning, Federal University of  Technology, Akure, Nigeria
3 Department of  Urban and Regional Planning, Modibbo Adama University, Yola, Nigeria
* Corresponding author’s e-mail: tobennanzelibe@gmail.com

INTRODUCTION
Displacement is a complex issue that is driven by a range 
of  factors. Adams (2021) and Tesfaw (2022) identified 
conflict and climate-induced factors as the major drivers 
of  displacement. Northeast Nigeria has been grappling 
with a protracted humanitarian crisis primarily due to 
the activities of  the Boko Haram insurgency which 
has persisted for over a decade, resulting in widespread 
displacement, with Borno state as the worst-hit state 
(Internal Displacement Monitoring Center (IDMC), 2020; 
International Organization for Migration (IOM) Nigeria, 
2021; Omogunloye et al., 2023). The impact of  internal 
displacement in Borno State, Nigeria, is significant, with 
over 2 million people rendered homeless and compelled 
to seek refuge in Internally Displaced Persons’ (IDPs’) 
camps, resulting in a dire humanitarian crisis. (Internal 
Displacement Monitoring Center (IDMC), 2020; 
International Organization for Migration (IOM) Nigeria, 
2021).
This crisis has not only affected the socio-economic 
and political fabric of  Borno State, but has also drawn 
attention to the urgent need for humanitarian assistance 
and intervention for IDPs in the region (Musa et al., 
2019; Patrick & Terungwa, 2022). Various international 
organizations provide essential healthcare, education, 
shelter, security, food and non-food items, orientation, 
durable solutions, and psychosocial support for IDPs 
(Adeleye & Osadola, 2022). However, lack of  a clear 

national policy and institutional framework in addressing 
internal displacement in Nigeria has further intensified 
the plight of  the IDPs (Akujobi & Awhefeada, 2021; 
Gbigbiddje et al., 2020; Gwadabe et al., 2018). The 
humanitarian crisis in Borno State emphasises the 
need for a comprehensive understanding of  the spatial 
distribution and patterns of  these IDP camps to facilitate 
effective planning, management, and resource allocation. 
In the context of  Borno State, where the displacement 
crisis is exacerbated by security concerns, environmental 
conditions, and resource availability, geospatial analysis 
has become an indispensable tool (Anselin, 1995).
Research on the vulnerability and risk assessment of  
internal displacement (Mbaya et al., 2017; Mohammed, 
2017; Granville, 2020) commonly omits the spatial 
dimension of  displacement, creating an analytical gap. 
Goodchild and Janelle (2010) posited that geospatial 
analysis harnesses the power of  spatial data and 
technologies, providing valuable insights for a holistic 
perspective into the spatial intricacies of  the distribution, 
density, and clustering of  IDP Camps.
Several studies have successfully employed a range of  
geospatial techniques to analyse the distribution of  
IDP camps in contexts similar to internal displacement 
in Borno State. Tiede and Lang (2009) applied object-
based image analysis algorithms to extract dwellings and 
calculate value-added products such as dwelling density 
and camp structure. Bramante and Raju (2013) used 



Pa
ge

 
28

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 3(1) 27-34, 2024

logistic regression to predict the distribution of  IDP 
camps in Port-au-Prince, Haiti, based on factors such as 
the distance from the international airport, distance from 
the city centre, and elevation. Füreder et al. (2015) provided 
Earth observation-based information services to support 
humanitarian operations in refugee/IDP camps, including 
population monitoring and analysis of  camp structure 
and evolution. Dobryakova et al. (2023); Weigand et al. 
(2023)  highlight the use of  geospatial techniques to 
analyse the distribution pattern of  IDP camps. Weigand’s 
work focuses on the structural morphology of  these 
camps using satellite imagery and machine learning to 
create a global database of  settlement structures. This 
approach can be further enhanced by incorporating the 
findings of  Dobryakova, who emphasised the importance 
of  geoinformation mapping in understanding the spatial 
dynamics of  population distribution.
 Thus, this study seeks to examine the spatial patterns 
associated with the distribution, density, and clustering of  
IDP camps in Borno. This involved mapping IDP camps 
in Borno State, examining their spatial distribution, 
density, and clustering patterns, and analysing the 
geospatial implications of  the identified patterns. The 
outcomes of  this geospatial analysis are anticipated to 
guide targeted interventions, enhance the efficiency of  
resource allocation, and ultimately improve the overall 
management of  IDP camps in Borno. 

The Study Area
Borno State is located in north-eastern Nigeria, with 
Maiduguri as the capital city. The state’s absolute 
geographic location lies between latitudes 10 °01’ 37″ 
and 13 °74’ 49″ North of  the equator and longitudes 11 
°54’ 26″ and 14 °67’ 30″ East of  the Greenwich meridian, 
as illustrated in Figure 1. It shares borders with Niger 
to the North, Cameroon to the East, Adamawa State to 
the South, Gombe State to the Southwest, Yobe State to 
the West, and a greater part of  the Chad Basin to the 
Northeast. Borno State as at 2006 had a population of  
4,171,104, projected to be around 7,498,333 in 2021, 
spread across three Senatorial Districts comprised 
of  nine (9) Local Government Areas (LGAs) each 
aggregating to twenty-seven (27) LGAs (National 
Population Commission of  Nigeria (NPC), 2006). The 
state has a predominance of  Kanuri people and other 
ethnic groups, such as the Lapang, Babur, Bura, Mandara, 
Marghi, and Shuwa Arabs among others (Scheinfeldt et 
al., 2010). Like many states in the northeast of  Nigeria, 
their predominant occupation is agriculture. Borno state 
being the epicentre of  conflict-induced displacement and 
humanitarian crisis in Nigeria, it is therefore crucial to 
understand the spatial distribution and patterns of  IDP 
camps in Borno.

MATERIALS AND METHOD
Theoretical Framework of  the Research
To understand the spatial intricacies of  IDP camps 
in Borno State, our study adopted a multidimensional 
approach, drawing insights from human geography, 

critical spatial analysis, and spatial justice to ensure a 
comprehensive analysis. Guided by Harvey’s (2009) 
critical spatial analysis, our methodology explored the 
dialectic relationship between space and society. This 
approach explores the impact of  political decisions, 
conflict dynamics, and power relations on the distribution 
and management of  IDP camps, thereby revealing 
hidden inequalities and social injustices. Complementing 
this, Soja’s (2010) spatial justice concept has become 
pivotal, advocating for the fair distribution of  resources 
within a given space. This framework evaluates resource 
distribution patterns and addresses both physical 
structures and demographic characteristics (Harvey 2009; 
Soja 2010). Aligning with the quantitative geography 
theoretical framework, Tobler’s (1970) First Law of  
Geography justifies the application of  spatial analysis 
techniques to unveil patterns, clusters, and relationships 
within the IDP camp dataset. Thus, this study integrates 
geospatial analysis, including GIS and spatial statistics, 
using techniques such as Kernel Density Estimation 
(KDE), Cluster and Outlier Analysis, Nearest Neighbour 
Analysis, and Spatial Autocorrelation. 
Ahasan et al. (2022); Getis and Ord (1992) Goodchild 
(1992) emphasised the importance of  geospatial 
analysis in visualising distribution patterns. Anselin’s 
Local Moran’s I gained prominence in analysing spatial 
clustering and outliers, contributing to the identification 
of  High-High (HH) and Low-Low (LL) clusters of  IDPs’ 
camps (Anselin, 1995). The Nearest Neighbour Analysis, 
rooted in Clark and Evans (1954), assesses spatial 
patterns in point data. Spatial Autocorrelation Analysis, 
particularly Moran’s I statistic, which is widely used 
across disciplines, helps to examine spatial patterns (Cliff  
& Ord, 1970). This approach allows for the aggregation 
of  statistical data and enhances analytical capabilities 
through geospatial methods including data integration, 
visualisation, Exploratory Spatial Data Analysis (ESDA), 
Confirmatory Spatial Data Analysis (CSDA), and 
statistical modelling. The selection of  clustering as a 
primary analytical method was motivated by its ability 
to automatically aggregate displacement operation data, 
identify an appropriate scale of  analysis, and correct for 
both multiple testing and spatial dependence.
In exploring the spatial arrangement of  objects in this 
context, IDP camps and Point Pattern Analysis have 
emerged as the most appropriate methods to address 
the challenges surrounding IDP camp distribution and 
evaluate the effectiveness of  humanitarian responses. 
Scholars like Ahasan et. al. (2022); Dobryakova et. al. 
(2023) and Weigand et. al. (2023) have successfully 
employed these techniques to analyse the spatial 
distribution, density, clustering, and dispersion of  IDP 
camps, treating them as points in space.

Data Collection
The data collected for the research includes location 
coordinates of  the IDP camps, satellite imagery, demographic 
information, and administrative maps. Table 1 presents the 
details of  the data acquired in this study.



Pa
ge

 
29

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 3(1) 27-34, 2024

Method of  Data Processing and Analysis
Data preprocessing for the analysis involved importing 
the IDP camp location data into GIS software and 
converting them into a point feature layer. Cleaning and 
filtering of  the data were then performed to remove 
errors or duplicates. Thus, the GIS and demographic 
datasets were standardised to ensure consistency and 
accuracy in subsequent analyses. The geospatial analysis 
carried out in this study involves a workflow of  sequential 
application of  Kernel Density Estimation (KDE), 
Cluster and Outlier Analysis (COA), Nearest Neighbour 
Analysis (NNA), and Spatial Autocorrelation Analysis 
(SAA). The uniqueness of  each step contributes to a 
holistic understanding of  the spatial patterns, clusters, 
and relationships within IDP camps across Borno State. 
KDE was employed to perform point density analysis 
which aided in understanding the overall spatial patterns 
and variations. This technique enabled the visualisation 
of  the distribution and concentration of  IDP camps, 
identifying hotspots and coldspots, by creating a density 
surface that shows the number of  IDP camps per unit 
area across Borno State. NNA was used to determine if  
the IDP camps were randomly distributed, clustered, or 
dispersed. This analysis involves comparing the observed 
nearest neighbour distance with the expected distribution 
under randomness which implies measuring the distance 
between each IDP camp and its nearest neighbour. 
Clustered patterns have smaller distances between their 
nearest neighbours, whereas dispersed patterns have larger 
distances. Anselin Local Moran’s I was used to identify 
groups of  points that were closer to each other than to 
other points in the study area. This technique helped 
detect hotspots or outliers and uncover clusters of  high-
density camps (High-High), low-density camps (Low-
Low), and areas with unexpected patterns (High-Low and 
Low-High). The Anselin Local Moran’s I was required to 
weigh the COA. To understand the distribution of  the 
weighting, a summary statistic of  individuals in the IDPs 
camps in Borno State was calculated. Moran’s I statistic 
was employed for SAA to assess and measure the degree 
to which spatially adjacent observations tended to be 

more similar (positive spatial autocorrelation) or dissimilar 
(negative spatial autocorrelation) than would be expected 
by chance. This analysis helped to determine if  there was 
a significant spatial correlation between the IDP camp 
locations and other spatial variables such as elevation, 
land use, population density distance to resources, or 
vulnerability index. The methodology adopted was based 
on the global Moran’s I statistic calculated using Equation 
(1) (Chainey et al., 2008).

                 (1)

Table 1: Details of  the data collected for the study
Datasets Description Sources Uses in the study
Location 
coordinates

Northing Easting coordinates of  
the IDP camps in projected as UTM 
Zone 33N, WGS 84 datum

Field surveys, humanitarian 
organizations and 
government agencies reports

For determination of  the 
precise locations of  the 
existing IDP camps

Satellite 
imagery

High-resolution satellite images 
obtained from 

GeoEye, now part of  Maxar 
Technologies, 

served a powerful tool for 
the geospatial analysis

Demographic 
and aspatial 
attribute 

the number of  households and 
individuals in each IDP camp, 

International Organization 
for Migration (IOM) dataset.

Essential for understanding 
the population density and 
assessing the impact on the 
camps.

Administrative 
maps

Information on Senatorial district 
boundaries, Local Government 
Areas (LGA), wards, neighborhoods

Grid 3 Nigeria and Ministry 
of  lands 

This dataset forms 
the foundation for the 
geospatial analysis

Source: Author’s Computation, 2023

Where: 
N = number of  spatial units indexed by i and j; 
X = variable of  interest, u
    = mean of  X; and 
ωij = element of  a matrix of  spatial weights. with zeroes 
on the diagonal (i.e., ωij=0)
W = sum of  all ωij 
The value of  Moran’s I ranges from -1 (perfect negative 
spatial autocorrelation) to 1 (perfect positive spatial 
autocorrelation), with values close to 0 indicating no 
spatial autocorrelation.

RESULTS AND DISCUSSION
Presentation of  Results 
The geospatial analysis conducted in this study utilised 
a combination of  Kernel Density Estimation (KDE), 
Cluster and Outlier Analysis, Nearest Neighbour Analysis, 
and Spatial Autocorrelation Analysis techniques. This 
required mapping IDP camps location in Borno State 
(Figure 1). The results presented in the analysis were 
derived from a series of  geospatial techniques aimed 
at understanding the spatial patterns and distribution 
of  Internally Displaced Persons (IDP) camps in Borno 
State. Kernel Density Estimation (KDE) analysis was 
used to estimate the distribution of  IDP camps in Borno 
state. This analysis visually highlighted the varying camp 
densities (Figure 3a). Extending the exploration of  
spatial patterns, Weighted Cluster and Outlier Analysis 
(Anselin Local Moran’s I) was employed to understand 



Pa
ge

 
30

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 3(1) 27-34, 2024

spatial variations in the prevalence and concentration 
of  camps. The results categorised camps into different 
clusters (High-High, High-Low, Low-High, Low-Low) 
based on their counts and neighbouring camps (Figure 
3b). Prior to this, Summary Statistics were conducted to 
evaluate the spatial distribution of  weighting parameters, 
considering both the values and their spatial relationships 
spanning the study area, which covers approximately 
79823060373.942566 square meters. This revealed the 
distribution and variability in the number of  individuals 
across the camps (Figure 2a). To provide additional 
information, the spatial relationships between the values 
and their lags were evaluated using a Moran’s scatter plot 
(Figure 2b). Consequently, Nearest Neighbour Analysis 
(Figure 3c) was used to assess the spatial arrangement 
of  IDP camps by comparing the observed distances 
between them to the expected distances in a random 

distribution. Spatial Autocorrelation Analysis (Figure 
3d) employed the Global Moran’s Index to evaluate the 
degree of  similarity between neighbouring IDP camps 
in terms of  a specific variable. Statistically significant 
positive spatial autocorrelation revealed a non-random 
pattern in the distribution of  IDP camps based on the 
analysed variables.
The analysis used the “IDP_Camps” feature class, focusing 
on the “INDIVIDUAL” field. The conceptualization 
method employed was “INVERSE_DISTANCE,” 
with a distance method of  “EUCLIDEAN.” Row 
standardisation was applied (True), and a distance 
threshold of  337873.2495m was used to capture 
spatial dependencies or patterns. This specific context 
considered the scale of  the study, the nature of  the 
data, and characteristics of  the spatial processes being 
investigated (Getis, 2010).

Figure 1: Locations of  IDP camps by their respective senatorial districts in Borno State, Nigeria.
Source: Vectorised from Digital Global Satellite Imageryat@ 0.6-meter resolution (2021), paper map acquired from the Borno State Ministry 
(Borno State Ministry Physical Planning and Urban Development, 1988; International Organization for Migration (IOM) Nigeria, 2021).

Figure 2: (a) Dataset for weighting of  Cluster and Outlier Analysis of  IDPs Camps distribution in Borno state; (b) 
Moran’s Scatterplot of  Cluster and Outlier Analysis (Anselin Local Moran’s I) of  IDPs Camps distribution in Borno state.
Source: Author’s Computation, 2023.



Pa
ge

 
31

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 3(1) 27-34, 2024

Figure 3: (a) Spatial Patterns and Hotspot Analysis of  IDPs’ Camps in Borno State: A Kernel Density Estimation 
Approach (b) Spatial Distribution Patterns of  IDPs’ Camps in Borno State: A Cluster and Outlier Analysis (c) Spatial 
Analysis of  IDPs’ Camps Distribution in Borno State: Nearest Neighbour Analysis (d) Spatial Analysis of  IDPs’ 
Camps Distribution in Borno State: Spatial Autocorrelation Analysis
Source: Author’s Computation, 2023

DISCUSSION 
Integrated geospatial analysis involving Kernel Density 
Estimation (KDE), Cluster and Outlier Analysis, 
Nearest Neighbour Analysis and Spatial Autocorrelation 

Analysis was employed to understand the spatial patterns, 
distribution, and management of  IDP camps in the 
study area. This is rooted in the theoretical framework 
of  quantitative geography Tobler (1970) First Law 



Pa
ge

 
32

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 3(1) 27-34, 2024

of  Geography, stating that “Everything is related to 
everything else, but near things are more related than 
distant things” underpins the rationale for employing 
spatial analysis techniques to uncover patterns, clusters, 
and relationships within the IDP camps. This synthesis 
yielded a comprehensive and dynamic assessment of  the 
spatial patterns of  IDP camps in the region for effective 
planning and allocation of  resources.
The Kernel Density Estimation (KDE) analysis in 
Figure 3a reveals distinct spatial patterns of  IDP camps 
in Borno State. Visual representation indicates varying 
camp densities, with the Borno Central Senatorial 
District showing a significantly higher concentration, 
while camps in the Northern and Southern districts 
appear more dispersed. These patterns are indicative of  
the underlying dynamics of  conflict, accessibility, and 
resource availability in Borno State. This aligns with 
the literature on the subject (Bramante & Raju, 2013), 
confirming the vulnerability of  certain regions to hosting 
higher numbers of  IDP camps. 
Results from the summary statistics of  individuals in IDP 
camps in Borno State for the weighted Cluster and Outlier 
Analysis revealed a right-skewed distribution, as indicated 
by the significant difference between the mean (3643.69) 
and median (894). With a high standard deviation of  
8465.684, the data exhibited substantial variability in 
the number of  individuals across camps, ranging from a 
minimum of  55 to a maximum of  88652. The skewness 
value (5.893874) suggested a few camps with exceptionally 
high numbers, contributing to the rightward tail, whereas 
the kurtosis value (49.27554) indicated extremely heavy 
tails and the presence of  extreme outliers (Figure 2a). 
The right-skewed distribution and heavy-tailed nature 
of  the data imply that certain camps had significantly 
higher numbers of  individuals. Spatial analysis using 
Anselin Local Moran’s I identified clusters, outliers, and 
spatial patterns, contributing to the understanding of  
the distribution of  IDPs’ camps in Borno (Figure 2b). 
Moran’s Scatterplot from the weighting of  cluster and 
outlier analyses depicted a weak positive relationship 
between z-transformed values and their spatial lags. 
This suggests a tendency for locations with higher 
values to be surrounded by neighbouring locations with 
similarly elevated values. These insights provide valuable 
information for targeted intervention and resource 
allocation in this region.
The Cluster and Outlier Analysis (Anselin Local Moran’s 
I) conducted on the spatial distribution of  IDP camps 
in Borno State, as depicted in Figures 3b, reveals notable 
patterns. Of  the 245 assessed IDP camps, 4.49% (11 
camps) were identified as High-High (HH) clusters, 
indicating locations with a high number of  neighbouring 
camps with high counts. Additionally, 2.86% (seven 
camps) exhibited a High-Low (HL) pattern, signifying 
areas with a high number of  neighbouring camps but 
with low counts. A mere 0.82% (two camps) displayed 
a Low-High (LH) pattern, suggesting locations with 
a low count of  camps surrounded by areas with high 

counts. The majority, comprising 53.06% (130 camps), 
were categorised as Low-Low (LL), representing 
locations with a low number of  neighbouring camps 
and low counts. Furthermore, 38.78% (95 camps) were 
deemed insignificant. The identification of  distinct 
patterns, including High-High clusters and Low-Low 
locations, suggests spatial variations in the prevalence 
and concentration of  camps. These results build upon 
existing research on the spatial dynamics of  displacement, 
contributing to the broader discourse on the geographical 
aspects of  humanitarian crises (Dobryakova et al., 2023; 
Weigand et al., 2023). The observed patterns may indicate 
underlying socio-economic or environmental factors that 
influence the distribution of  IDP camps. 
The Nearest Neighbour Analysis of  IDP Camps 
distribution in Borno State, as depicted in Figure 3c, 
revealed a highly significant clustered pattern with a 
z-score of  -22.175359 and a corresponding p-value of  
0.000000. This result indicates a less than 1% likelihood 
that the observed clustered pattern could be attributed 
to random chance, suggesting a non-random deliberate 
arrangement of  IDP camps in the region. The Average 
Nearest Neighbour analysis further quantified the 
spatial distribution, with an observed mean distance 
of  2341.5215m, which is significantly lower than the 
expected mean distance of  9025.0819m. The Nearest 
Neighbour Ratio of  0.259446 supports the presence 
of  a clustered pattern, highlighting the non-uniform 
distribution of  the IDP camps. This significant 
clustering aligns with previous studies that emphasise the 
importance of  understanding the spatial organisation of  
IDP camps for effective humanitarian responses (Füreder 
et al., 2015). Deliberate clustering may be influenced by 
factors such as security concerns, resource accessibility, 
and governmental policies. 
The spatial autocorrelation analysis of  IDP camps in Borno 
State, as illustrated in Figure 3d, employed the Global 
Moran’s Index to reveal a statistically significant positive 
spatial autocorrelation (I = 0.099072, z = 4.704613, p < 
0.001). This result indicates a tendency for similar values 
of  the individual variables to cluster in space, suggesting 
a non-random pattern in the distribution of  IDP Camps 
based on the analysed variable. The observed variance 
of  0.000481 and strong positive spatial autocorrelation 
emphasise the presence of  a structured arrangement of  
IDP camps, reinforcing the importance of  understanding 
the spatial organisation of  displaced populations.
The findings from the integrated geospatial analysis 
of  IDPs’ Camps in Borno State imply that the spatial 
arrangement of  IDP camps in Borno State is not random, 
suggesting a localised and concentrated arrangement 
rather than a uniform distribution. This aligns with 
previous analyses, emphasising the need for spatial analysis 
in humanitarian studies to uncover patterns and inform 
effective intervention strategies (Ahasan et al., 2022; 
Dobryakova et al., 2023; Füreder et al., 2015; Weigand et 
al., 2023). Statistically significant positive autocorrelation 
suggests that areas with similar characteristics tend to 



Pa
ge

 
33

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 3(1) 27-34, 2024

host IDP camps in close proximity. The implications 
of  this non-random pattern extend to policy planning 
and resource allocation, emphasising the importance of  
tailoring interventions based on specific characteristics 
and spatial distribution of  displaced populations.

CONCLUSION
The geospatial analysis shed light on the intricate 
dynamics of  IDP camp placement in Borno State, as a 
consequence of  the enduring Boko Haram insurgency. 
Examining the spatial distribution, density, and clustering 
patterns facilitated a comprehensive understanding of  the 
dynamics of  IDP camps placement in Borno State. This 
was achieved by employing an array of  geospatial analysis 
techniques, including Kernel Density Estimation, Cluster 
and Outlier Analysis, Nearest Neighbour Analysis, and 
Spatial Autocorrelation Analysis, which enabled a holistic 
interpretation of  the data.
The uneven distribution of  displaced populations, 
particularly concentrated in areas like Maiduguri 
Metropolitan Council and Monguno LGA highlights the 
underlying dynamics of  conflict, accessibility, and resource 
availability. Cluster and Outlier Analysis provided further 
granularity for tailored interventions, emphasizing the 
need for intensified support in High-High clusters and 
proactive measures in Low-Low areas to prevent further 
vulnerability. Conversely, highly significant clustered 
patterns in the distribution of  IDP camps, coupled with 
the low nearest-neighbour ratio, confirmed a localised 
and concentrated distribution rather than a random 
one, echoed by the statistically significant positive spatial 
autocorrelation. 
This emphasises the need for distinct and localised 
interventions, considering the unique challenges faced by 
different regions. By recognizing hidden inequalities and 
social injustices in IDP camp arrangements, policymakers 
and humanitarian organizations can align resource 
allocation, aid distribution, and infrastructure planning 
more effectively. This approach, in line with critical spatial 
analysis theories, ensures that interventions address 
the unique needs of  different areas, promoting more 
equitable support for the well-being of  IDP populations.

Acknowledgments
Acknowledgements were extended to the participants, 
local authorities, and organisations that facilitated the 
research process. Their cooperation and support were 
integral to the successful completion of  this study.

REFERENCES
Adams, P. A. (2021). The Plights of  Female Internally 

Displaced Persons (IDPs) in Borno State: The 
Response Christian Association of  Nigeria (CAN). 
IGWEBUIKE: An African Journal of  Arts and 
Humanities, 7(1), 2021. https://doi.org/10.13140/
RG.2.2.19482.59846

Adeleye, O. A., & Osadola, O. S. (2022). International 
Aid in Managing IDPs: The Case of  United Nations 

in Nigeria. SIASAT, 7(3), 235–246. https://doi.
org/10.33258/siasat.v7i3.127

Ahasan, R., Alam, M. S., Chakraborty, T., Ali, S. M. A., 
Alam, T. B., Islam, T., & Hossain, M. M. (2022). 
Applications of  geospatial analyses in health research 
among homeless people: A systematic scoping review 
of  available evidence. Health Policy and Technology, 11(3), 
100647. https://doi.org/10.1016/j.hlpt.2022.100647

Akujobi, T. A., & Awhefeada, V. U. (2021). Migration 
and Displacement: Legal Constraints of  Internally 
Displaced Persons in Nigeria. International Journal of  
Law and Society, 4(3), 169. https://doi.org/10.11648/j.
ijls.20210403.13

Anselin, L. (1995). Local Indicators of  Spatial 
Association—LISA. Geographical Analysis, 27(2), 
93–115. https://doi.org/10.1111/j.1538-4632.1995.
tb00338.x

Borno State Ministry Physical Planning and Urban 
Development. (1988). Borno State Ministry Physical 
Planning and Urban Development 1988 Gazettes.

Bramante, J. F., & Raju, D. K. (2013). Predicting the 
distribution of  informal camps established by the 
displaced after a catastrophic disaster, Port-au-Prince, 
Haiti. Applied Geography, 40, 30–39. https://doi.
org/10.1016/j.apgeog.2013.02.001

Chainey, S., Tompson, L., & Uhlig, S. (2008). The Utility 
of  Hotspot Mapping for Predicting Spatial Patterns 
of  Crime. Security Journal, 21(1–2), 4–28. https://doi.
org/10.1057/palgrave.sj.8350066

Clark, P. J., & Evans, F. C. (1954). Distance to Nearest 
Neighbor as a Measure of  Spatial Relationships in 
Populations. Ecology, 35(4), 445–453. https://doi.
org/10.2307/1931034

Cliff, A. D., & Ord, K. (1970). Spatial Autocorrelation: 
A Review of  Existing and New Measures with 
Applications. Economic Geography, 46, 269. https://doi.
org/10.2307/143144

Dobryakova, V., Dobryakov, A., & Makarova, K. 
(2023). Geoinformation mapping of  population 
distribution for analysis of  its spatial dynamics. 
InterCarto. InterGIS, 29(2), 150–161. https://doi.
org/10.35595/2414-9179-2023-2-29-150-161

Füreder, P., Stefan, L., Michael, H., Dirk, T., Lorenz, 
W., & Edith, Rogenhofer. (2015). Earth observation 
and GIS to support humanitarian operations in 
refugee/IDP camps. https://www.researchgate.net/
publication/277581998

Gbigbiddje, L. D., Fredrick, O. T., & Onwordi, T. M. 
(2020). Forced Displacement and its Impact on 
Internally Displaced Persons (IDPs) in North-
Eastern Nigeria. www.theinterscholar.org/journals/
index.php/isjassr

Getis, A. (2010). Spatial Autocorrelation. In Handbook of  
Applied Spatial Analysis (pp. 255–278). Springer Berlin 
Heidelberg. https://doi.org/10.1007/978-3-642-
03647-7_14

Getis, A., & Ord, J. K. (1992). The Analysis of  
Spatial Association by Use of  Distance Statistics. 



Pa
ge

 
34

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 3(1) 27-34, 2024

Geographical Analysis, 24(3), 189–206. https://doi.
org/10.1111/j.1538-4632.1992.tb00261.x

Goodchild, M. F. (1992). Geographical information 
science. International Journal of  Geographical 
Information Systems, 6(1), 31–45. https://doi.
org/10.1080/02693799208901893

Goodchild, M. F., & Janelle, D. G. (2010). Toward critical 
spatial thinking in the social sciences and humanities. 
GeoJournal, 75(1), 3–13. https://doi.org/10.1007/
s10708-010-9340-3

Granville, C. K. (2020). The Impact of  Boko Haram 
Insurgency on the People of  Borno The Impact of  
Boko Haram Insurgency on the People of  Borno 
State State. https://scholarworks.waldenu.edu/
dissertations

Gwadabe, N. M., Salleh, M. A., Ahmad, A. A., & Jamil, 
S. (2018). Forced Displacement and the Plight of  
Internally Displaced Persons in Northeast Nigeria. 
Humanities and Social Science Research, 1(1), 46. https://
doi.org/10.30560/hssr.v1n1p46

Harvey, D. (2009). Social Justice and the City. University of  
Georgia Press. https://doi.org/10.1353/book13205

Internal Displacement Monitoring Center (IDMC). 
(2020). Global Report on Internal Displacement. 
https://www.internal-displacement.org/global-
report/grid2020/

International Organization for Migration (IOM) Nigeria. 
(2021). Dtm Nigeria Iom Nigeria Displacement 
Tracking Matrix (Dtm) Displacement Report 37.

Mbaya, A. S., Waksha, H. M., & Wakawa, M. W. (2017). 
Effects of  Insurgency on the Physical and Socio-
Economic Activities in Maiduguri. International Journal 
of  Scientific Research in Educational Studies & Social 
Development, 2(2).

Mohammed, F. K. (2017). The Causes and Consequences 
of  Internal Displacement in Nigeria and Related 
Governance Challenges Working Paper FG 8 April 
2017 SWP Berlin SWP-Berlin Causes, Dynamics, and 
Consequences of  Internal Displacement in Ethiopia. 

www.swp-berlin.org
National Population Commission of  Nigeria (NPC). 

(2006). Population Census, Archived at the Wayback 
Machine. Federal Republic of  Nigeria, National 
Bureau of  Statistics.

Omogunloye, O. G., Iyasele, N. S., Olunlade, O. A., 
Abiodun, O. E., Salami, T. J., & Alabi, A. O. (2023). 
Mapping of  human displacement by Boko Haram 
in Nigeria from 2009 to 2021. South African Journal 
of  Geomatics, 12(1), 73–85. https://doi.org/10.4314/
sajg.v12i1.5

Scheinfeldt, L. B., Soi, S., & Tishkoff, S. A. (2010). 
Working toward a synthesis of  archaeological, 
linguistic, and genetic data for inferring African 
population history. Proceedings of  the National Academy 
of  Sciences, 107(supplement_2), 8931–8938. https://
doi.org/10.1073/pnas.1002563107

Soja, E. W. (2010). Seeking Spatial Justice. University 
of  Minnesota Press. https://doi.org/10.5749/
minnesota/9780816666676.001.0001

Tesfaw, T. A. (2022). Internal Displacement in Ethiopia: 
A Scoping Review of  its Causes, Trends and 
Consequences. http://journalofinternaldisplacement.
com

Tiede, D., & Lang, S. (2009). IDP camp evolvement 
analysis in Darfur using VHSR optical satellite image 
time series and scientific visualization on virtual 
globes (H. Guo & C. Wang, Eds.; p. 78401E). https://
doi.org/10.1117/12.872849

Tobler, W. R. (1970). A Computer Movie Simulating 
Urban Growth in the Detroit Region. Economic 
Geography, 46, 234. https://doi.org/10.2307/143141

Weigand, M., Worbis, S., Sapena, M., & Taubenböck, 
H. (2023). A structural catalogue of  the settlement 
morphology in refugee and IDP camps. International 
Journal of  Geographical Information Science, 37(6), 1338–
1364. https://doi.org/10.1080/13658816.2023.2189
724


