









































Pa
ge

 
1



Pa
ge

 
1

American Journal of  
Geospatial Technology (AJGT)

Combination of  GIS, MCDA and AHP for the Selection of  Most Suitable Location for 
Primary Health Care Facilities

Aroge Sunday Kayode1*, Adewole Babatunde Emmanuel2, Amoo Nureni. Babatunde1, Adeleke Jacob Sola3

Volume 2 Issue 1, Year 2022
ISSN: 2833-8006 (Online)

DOI: https://doi.org/10.54536/ajgt.v2i1.1820
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: June 28, 2023

Accepted: July 23, 2023

Published: August 22, 2023

This study reveals the necessity of  offering a solution to the Primary Health Care (PHC) fa-
cility site location issue in the healthcare industry. PHC is a crucial component of  healthcare 
that serves as the first point of  contact for individuals, families, and communities. It allows 
for the delivery of  care as close as possible to people’s homes and places of  employment, 
but over time, a solution to this sector’s location issues has been neglected. The purpose of  
this study is to locate the optimum locations in the study area for new PHC facilities. After 
careful evaluation of  various literature, oversight organizations, and local government offi-
cials, the raw data needed were obtained. The effectiveness of  a Geographical Information 
System (GIS), Multi-Criteria Decision Analysis (MCDA), and the Analytical Hierarchy Pro-
cess (AHP) were integrated to provide a novel method for choosing acceptable locations for 
PHC facilities in the Ado Local Government Area. The specified criteria layers were overlaid 
using a weighted linear combination (WLC) approach, such as Map algebra. The results 
showed that within the land mass of  the research region; only 10% were deemed to be the 
most suitable areas, while 53% of  the places were suitable, and 37% of  the areas were unsuit-
able. As a result, the study suggests that the integration of  GIS, AHP, and MCDA methods 
is beneficial in addressing the issue of  site appropriateness assessments for PHC facilities.

Keywords
Geographical Information 
System, Analytical Hierarchy 
Process, Primary Health Care 
Facilities, Multi-Criteria 
Decision Analysis

1 Department of  Surveying and Geoinformatics, School of  Environmental Studies, Federal Polytechnic Ede, Ede, Nigeria
2 Department of  Surveying and Geoinformatics, School of  Environmental Technology, Federal University of  Technology Akure, 
  Akure, Nigeria
3 Department of  Building Technology, School of  Environmental Studies, Federal Polytechnic Ede, Ede, Nigeria
* Corresponding author’s e-mail: kayodearoge2000@gmail.com

INTRODUCTION
Primary health care (PHC) is a crucial component of  
healthcare that is based on techniques and technologies that 
are widely available to people, families, and communities 
through their active participation and at a reasonable price. 
PHC is a fundamental component and ideally, the major 
emphasis of  a nation’s health system (WHO and UNICEF, 
1978).  It enables health care to be offered as close to 
people’s homes and places of  employment as possible 
and is seen as the initial level of  contact for communities, 
families and individuals. Better health for all, as recognized 
in the statement of  the world conference on primary health 
care in Alma Ata, Russia in 1978, is the key objective of  
primary health care (Rasanathan, & Evans, 2020). This goal 
includes accessibility, equity, and affordability of  healthcare.
According to Hogan, et al. (2018) and World Health 
Organization. (2018), ensuring that everyone has access 
to quality healthcare is crucial for both sustainable 
development and international security. As a result, health 
care-related facilities should be widely available, acceptable, 
and adequate for everyone. Unfortunately, most often in 
underdeveloped nations worldwide, healthcare services fall 
short of  public expectations and requests. For instance, 
in Ekiti State of  Nigeria, Ado LGA has a total of  21 
government-owned PHC facilities as of  the year 2020, 
serving a population of  444,595 (i.e., a ratio of  1: 21,000). 
This is obviously insufficient because the National Primary 
Health Care Development Agency (NPHCDA) (2020) 
proposed a ratio of  1:10,000, which means that a PHC 
facility should provide care to at least 10,000 individuals 
within a political ward. 

Nasidi (2022),  affirmed that earlier studies on the 
distribution of  medical facilities in Nigeria tended to 
concentrate on first-order cities like Lagos, Ibadan, Kano, 
Kaduna, and Benin because these cities have specialized 
hospitals and a large number of  private clinics. Studies on 
health care facilities in secondary or medium-sized Nigerian 
cities are quite rare. These cities and towns also make up 
the majority of  the state capitals and local government 
area headquarters in Nigeria. Additionally, whereas past 
studies in Nigeria’s main cities and state capitals employed 
GIS and Multi-Criteria Decision Analysis (MCDA), there 
is no known work that investigates the state of  PHC in the 
Ado LGA of  Ekiti State. As a result, this represents a gap 
that will be filled by this study. Since the research region is 
one of  Nigeria’s LGAs that has been developing quickly 
over the past two decades, it is intended to use GIS and 
MCDA to examine the best locations for the establishment 
of  PHC.
The MCDA approach and other systems and 
methodologies, such as geographic information systems, 
are utilized in tandem. Combining these technologies 
has a synergistic impact that improves the accuracy 
and standard of  spatial analysis for choosing industrial 
sites (Lopes et al., (2021). Over the past several decades, 
MCDA has seen extensive use. With the help of  its various 
approaches, including the well-known Analytic Hierarchy 
Process (AHP), its importance in several application fields 
has substantially expanded. AHP has been defined as a 
measuring theory that depends on pairwise comparisons 
and expert judgment to derive (Rao & Pawar, 2018; Kuo, 
2021).



Pa
ge

 
2

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

Am. J. Geo Spat. Technol. 2(1) 1-6, 2023

The Study Area
Ekiti State’s capital, Ado-Ekiti LGA, is located in the 
southwest of  Nigeria. The LGA is situated between 
longitude 05° 06’ 18” and 05°24’ 00” East of  the Greenwich 
Meridian and latitude 07° 32’ 11” to 07°40’ 28” North of  
the Equator. The LGA spans roughly 293 km2 in size. Its 
largest east-west stretch is around 20 km long and its longest 
north-south extension is about 16 km (Obele, 2020). In 

order to facilitate administration and bring government 
closer to the people, the LGA is divided into 13 political 
wards. Ado LGA’s population has grown significantly in 
recent years due to the migration of  individuals looking for 
greener pastures over the previous 20 years and its status 
as the capital of  a new state. According to the National 
Population Commission (NPC), there will be 444,700 
people living there by 2050 (NPC, 2007).

Figure 1: Map Showing the Area of  Study
Source: Aroge et al (2023)

METHODOLOGY
In order to choose appropriate locations for the building 
of  PHC facilities in Ado LGA, a novel methodology was 
devised by combining the effectiveness of  GIS, MCDA, 
and AHP. This phase involves the collection of  data which 
are both primary and secondary data: primary data was 
collected from the field through questionnaire, while the 
secondary was downloaded from relevant agencies and 

organizations. The selected criteria layers were layered on 
top of  each other using the weighted linear combination 
(WLC) approach, which is similar to map algebra. The 
study uses seven influential factors to determine the 
preferred locations for new PHC facilities:  land use or 
land cover, a water body, a slope, a population, a road, the 
service area of  an existing PHC facility, and a residential 
area. These factors were divided into three (3) main 

Table 1: Ranking of  the Study's Sub-Criteria
S/N Selection Criteria Description of  the criteria Buffer zones Ranking
1. Population data The local population should be taken into account when 

deciding where to place PHC. The amount of  facilities 
that should conveniently service the population is 
determined using the demographic data.

<10,000
10,000- 20,000
>20,000

0
1

2
2. Nearness to 

residential areas
To ensure simple access and the safety of  patients and 
caregivers, health facilities should not be located too far 
from residential areas.

>5km
4km-5km
<4km

0
1
2

3. Existing PHC The effectiveness of  the service that any PHC facility 
currently in operation can provide in terms of  trip time 
coverage is determined by spatial allocation. When 
choosing a location for new PHC facilities, it is crucial 
to consider the area that the existing PHC facilities 
currently covers

>500m

>500m

0

2



Pa
ge

 
3

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

Am. J. Geo Spat. Technol. 2(1) 1-6, 2023

categories: technical, socioeconomic, and environmental. 
After careful evaluation of  various literature, oversight 
organizations, and local government officials, the raw 
data were obtained from various sources.

Identification of  Site Location Criteria for PhD Facilities 
A crucial phase in the site selection process is the 
identification of  criteria.  For the accurate identification 
of  suitable places, a large number of  factors have been 
examined from the literature. The peculiarities of  the 
study field, as well as the purpose and goals of  the 
research, must be taken into account when examining 
and evaluating the criteria. This study has carefully 
incorporated some standards that are used in the health 
sector generally as well as the PHC guidelines provided 
by various regulatory bodies. Not all criteria are equally 
relevant; depending on the goals of  the study, some 
criteria are very significant while others are less.

Preparation of  Thematic Maps
Many layers of  thematic maps were needed to be 

prepared before carrying out site selection study. For 
this study, thematic maps were created from different 
map sources such as satellite images (sentinel-2), ASTER 
GDEM and Open Street Maps. From these maps and 
imageries, other criteria map layers were been extracted 
and prepared accordingly. These includes water bodies, 
elevation, slope, residential areas, population, roads, and 
land use. All maps are limited to the extent of  the study 
area boundary and are projected using WGS 1984 UTM 
Zone 31N projection system.

Model for Decision Hierarchy
The decision hierarchy model for the location of  the 
facilities in this study is shown in Figure 2.1. The hierarchy 
of  PHC facility site was constructed. Seven criteria, divided 
into three primary groups were used in the computation 
process: land use or land cover, a water body, a slope, a 
population, a road, an existing PHC facility, and a residential. 
Environmental criteria are included in the first group, 
socioeconomic factors are included in the second group, 
and technical aspects are included in the third group. These 

4. Nearness to roads In order to protect patients from noise pollution, major 
roadways must be set back from health facilities, but 
these locations must still be close to roads

<45m
45-100m
>100m

0
1
2

5. Land use and Land 
cover

Land use indicates how humans use a piece of  land, so 
choosing the best location to build a public facility is 
important.

Forest 
Water body 
Residential  
Cultivated

0
0
1
2

6. Slope gradient Buildings that cater to the weakest people (patients) in 
particular need to take into account the topography of  
the land, so flat surfaces are generally encouraged

200-450
150-200
<150

0
1
2

7. Nearness to water 
bodies

PHC facilities cannot be built next to water bodies, such 
as ponds, dams, rivers, etc. To prevent an environmental 
catastrophe

<45m
45-100m
>100m

1
0
2

Figure 2: Showing Hierarchy Model

groups were adapted from the study of  Kmail et al., (2017). 

Calculation of  the Criteria Weight
Pair-wise comparison techniques, established by Saaty in 
1980, are widely employed as a step in the process of  

allocating weights to the criteria in the decision-making 
process. The computation of  the weights involves 
three stages. The First demands that the values in each 
matrix column be added together. Each component of  
the matrix was then divided by the sum of  its respective 



Pa
ge

 
4

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

Am. J. Geo Spat. Technol. 2(1) 1-6, 2023

column. Second, the normalize matrix’s elements’ 
averages for each row are calculated. Lastly, multiply the 
total of  each row’s normalized scores by the quantity of  
criteria. An estimation of  the relative weights of  the being 
compared criteria is given by the determined average. 
These calculations were carried out using an AHP-based 
template and a Microsoft Excel spreadsheet. After that, the 
matrix was normalized before the weight was obtained. As 
a result, normalization was accomplished by dividing each 
value by the sum of  the corresponding column. 
By computing the average value of  each row, that is, adding 
up all the values in each row, and dividing the result by 

Table 2:  Shows The Results of  Weighing the Criteria
Criteria Weight Percentage
Road 0.167 16.7%
Population 0.074 7.4%
Slope 0.123 12.3%
Residential 0.039 3.9%
Water Bodies 0.207 20.7%
Existing PHC 0.068 6.8%
Land Use 0.322 32.2%
Total 1.000 100.0%

the total number of  criteria utilized, the weights of  all the 
criteria were generated from the normalization matrix.

Consistency Ratio Calculation
After calculating the weight, it is crucial to quickly 
examine the uniformity of  the entire process. This is 
important because the values used to compute the weights 
were obtained from experts with different preferences, 
positions, and perspectives. As a result, there is a chance 
that these experts made mistakes in their judgment, which 
would undoubtedly have an impact on the matrix’s final 
stage of  computation (Saaty, 2005). Using the equation 
below, the consistency index (CI) was calculated.
CI = (λmax- n) / (n-1)     
For the weight consistency check, the CR is anticipated 
to be less than 0.10, which will show that the judgment 
is valid and suitable for analysis. To identify the causes 
of  discrepancies and reevaluate, the assessment of  the 
AHP matrix will need to be repeated if  the CR is larger 
than 0.10 (Saaty, 2008; Djokanovi et al., 2016). According 
to this study’s CR value of  0.09, which is less than 0.10, 
the AHP matrix is consistent, and the weight given to the 
criterion can be used for analysis.

Combination of  Linear Weights (WLC) 
Weight Linear Combination (WLC) is a method that 
requires standardizing the suitability maps, assigning 
weights of  relative importance to the suitability maps, 
and combining the weights and standardized suitability 
maps to obtain an overall suitability score, according 
to Marzuki, et al., (2022). Each criterion was given a 
weight depending on its significance and standardized 
to a common measuring scale. By dividing the ranking 

Figure 3: Site Selection Using Weighted Linear Combination

values of  the sub-criteria using the Raster calculator tool 
in ArcGIS, the best locations were determined from the 
sum of  the products.
Using ArcGIS 10.5, the weighted linear combination 
technique in map algebra was utilized to overlay the seven 
criteria, and a viable land location was created.

RESULTS AND DISCUSSION
The AHP method was used to generate important ranking 
of  the criteria to create the site suitability map, several 
raster layers were combined to produce maps which is 
suitable for PHC locations. The criteria were reclassified 
to make the result more accurate. Similarly, the weight 
of  criteria were used in weighted linear combination 
using Map algebra (raster calculator tool) in ArcGIS 10.5 
based on the feedback from the questionnaire. Thus, the 
suitable areas were selected.

Analysis of  the Site Suitability Area 
Once the various map layers were overlaid using the 
Map Algebra tool’s raster calculator operation, The PHC 
facility site selection for the research area was completed 
with the acquisition of  the site suitability map. The final 
probable sites map was produced using the analysis by 
superimposing the weights for each of  the seven criteria 
(land use/land cover, road, residential, water body, 
population, existing PHC and slope,) assigned to each 
map layer. The map of  probable places that might be 
appropriate is shown in figures 4 and the pie chart shown 
in 5 below.
The most appropriate locations belong to the class with 
the greatest value; in this study, all areas with a value of  
2 were regarded as the most appropriate sites. Then, the 
conditional expression for sites with a value of  2 (shown 
in red color) was extracted, and the remaining sites were 



Pa
ge

 
5

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

Am. J. Geo Spat. Technol. 2(1) 1-6, 2023

removed from the new layer. This extraction of  the 
optimal position was done using the con tool in spatial 
analysis. From the result in figure 5, it was determined 
that 53% of  the research area was suitable, 37% was 
unsuitable, and only 10% was determined to be the most 
favorable location.

CONCLUSION 
There are two main objectives for this study. The first 
step is to establish pertinent criteria for suitable locations 
for primary healthcare facilities in the study area. This is 
crucial because primary healthcare facilities are distinct 
from other types of  healthcare facilities and have 
characteristics that set them apart from them. Using six 
influential factors; a water body, a population, a land use 
or land cover, a slope, a road, a residential area, and the 
service area of  an existing PHC facility. These factors 
were divided into three main groups: socioeconomic, 
technical and environmental. The raw data were obtained 
from several sources.
The second and final goal of  this work is to locate 
potential sites for future PHC facilities in the research 
area, particularly in neglected political wards. In this 
study, the best probable locations for PHC facilities were 
determined utilizing the integration of  GIS and MCDA 
(using AHP). The application of  these techniques and 
instruments in this study has demonstrated their efficacy 

in the site selection for PHC facilities. Therefore, a model 
to find the best sites for new PHC facilities was proposed 
by the research. 
The study has also filled a gap in the body of  knowledge 
by demonstrating the value of  GIS, MCDA and AHP 
approaches in addressing the issue of  PHC facility site 
location in Ado LGA of  Ekiti State, Nigeria.

REFERENCES
Aroge, S. K., Ojo, R.S., Suru, W.P., Odeyemi, F. G., & 

Adewole, B. E. (2023) Geospatial Distribution Pattern 
of  Primary Schools in Ado Local Government Area 
of  Ekiti State. International Journal of  Advances in 
Engineering and Management (IJAEM), 5(1), 1110-1127 
www.ijaem.net 

Djokanović, S., Abolmasov, B., & Jevremović, D. (2016). 
GIS application for landfill site selection: a case study 
in Pančevo, Serbia. Bulletin of  Engineering Geology and the 
Environment, 75, 1273-1299. 

Faruk, B. U., & Akpu, B. (2022). Geospatial Analysis 
of  the Spatio -Temporal Growth of  Kwali Area 
Council, Federal Capital Territory, Abuja. American 
Journal of  Geospatial Technology, 1(1), 7–14. https://doi.
org/10.54536/ajgt.v1i1.359

Hogan, D. R., Stevens, G. A., Hosseinpoor, A. R., & Boerma, 
T. (2018). Monitoring universal health coverage within 
the Sustainable Development Goals: development and 
baseline data for an index of  essential health services. 
The Lancet Global Health, 6(2), e152-e168.

Kmail, A., Jubran, J., Sabbah, W., & Jenin, P. (2017). 
Coupling GIS-based MCA and AHP techniques for 
Hospital Site Selection. International Journal of  Computer 
Science and Information Security (IJCSIS), 15(12). 

Kuo, T. (2021). An Ordinal Consistency Indicator for 
Pairwise Comparison Matrix. Symmetry, 13(11), 2183.

Lopes, D. F., Marques, J. L., & Castro, E. A. (2021, 
September). A MCDA/GIS-based approach 
for evaluating accessibility to health facilities. In 
International Conference on Computational Science and 
Its Applications (pp. 311-322). Cham: Springer 
International Publishing. 

Marzuki, A., Bagheri, M., Ahmad, A., Masron, T., & Akhir, 
M. F. Establishing a GIS-SMCDA model of  sustainable 
eco-tourism development in Pahang, Malaysia. Episodes 
Journal of  International Geoscience. 

Nasidi, N. A. (2022). Urbanism and the Conservation 
of  The Natural Environment for Sustainable 
Development: A Case Study of  Kano State, Nigeria, 
1989-2020 (Doctoral dissertation, IFRA-Nigeria).

NPHCDA, (2020). Minimum Standards for Primary 
Health Care in Nigeria. Retrieved from https://www.
dataphyte.com/latest-reports/economy/what-is-the-
minimum-healthcare-requirements-for-nigerias-200-
million-people/

Obele, T. E. (2020). Household Cooking Energy Use in 
Nigeria: A Case of  Ado Ekiti Local Government Area 
of  Ekiti State. Anchor University Journal of  Humanities 
Management and Social Sciences, 1(1).

Figure 4: Map of  Suitable locations

Figure 5: Pie Chart of  Suitability analysis



Pa
ge

 
6

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

Am. J. Geo Spat. Technol. 2(1) 1-6, 2023

Rao, M. S., & Pawar, P. J. (2018). Application of  AHP 
for process parameter selection and consistency 
verification in secondary steel manufacturing. Materials 
today: proceedings, 5(13), 27166-27170.

Rasanathan, K., & Evans, T. G. (2020). Primary health 
care, the Declaration of  Astana and COVID-19. 
Bulletin of  the World Health Organization, 98(11), 801.

Saaty, T. L. (1980).The Analytic Hierarchy Process. Mathematical 
model for Decision support, 109-121

Saaty, T. L. (2005). Theory and Applications of  the Analytic 
Network Process: Decision Making with Benefits, 
Opportunities, Costs, and Risks. Retrieved from 

https://www.amazon.com/Theory-Applications-
Analytic-Network-rocess/dp/1888603062

World Health Organization. (2018). A vision for primary 
health care in the 21st century: towards universal health coverage 
and the Sustainable Development Goals (No. WHO/HIS/
SDS/2018.15). World Health Organization.

Yau, A. S. ., Yusuf, Y. Y. ., Yusuf, M. ., & Ahmed Suleiman, 
M. (2022). Geospatial Distribution of  Health Centers 
in Dass Local Government Area of  Bauchi State, 
Using Geographic Information System (GIS) Tools. 
American Journal of  Geospatial Technology, 1(1), 1–6. 
https://doi.org/10.54536/ajgt.v1i1.272


