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Assessment of quality of life using GIS

“The quality of life of a person is what he/she perceives it to be” (Zaid and Popoola, 2010). Quality of Life (QoL) has been studied in geography, criminology, urban planning, and sociology as a multidisciplinary subject (Michalos and Zumbo 2000). As the concept of QoL itself is multifaceted and loosely-defined, literature studies have revealed that no universal framework for assessing and describing QoL and human well-being currently exists (Leidelmijer et al. 2002). The definition of QoL by Foo (2000) who defines urban Quality of life as individuals overall satisfaction with life is adopted for the purpose of this research.

QoL is often measured using either subjective or objective indicators. Foo (2000), subjective indicators have lower data reliability and higher validity. This research uses subjective indicators and the purpose is to assess QoL using GIS approach for poverty alleviation decision-making. The research uses twelve indictors under various domains of life in order to assess QoL, and reveal areas that consist of people with low QoL as this will aid poverty alleviation decision making process since policy makers are interested to know the most effective means of improving individuals” life. The outcomes of this study is expected to help city planners to understand and prioritize the problems that the community face.

Research method
The study area is Egor LGA, the LGA is the lowest level of government in Nigeria’s governmental structure and therefore government at the LGA level represents government of the grass-root. Egor LGA is one of the Local Government Areas that make up Benin City which is the administrative capital of Edo State, which is one of the states that make up south-south Nigeria.

Generally, the people of Egor LGA rely on the government to improve their QoL, as the government is in possession of their common wealth. Unfortunately, previous government has failed in terms of delivery development to the people Egor LGA. With the aim of assessing QoL using GIS approach, a model was adopted from Pearl, 2011. From the literature and local experience, the following indicators were used in assessing QoL of Egor LGA as shown in the table below;

Responses to the indicators with the aid of a questionnaire were used in measuring the assessment of these indicators by respondents. The questions are constructed in a five-point Likert scale (Tesfazghi, 2009) to measure their QoL assessment from extremely poor to excellent. To ensure adequate representation of the entire population, stratified, random, purposive and systematic sampling techniques was used in the administering of the questionnaire. Firstly, the Egor LGA consists of ten wards, and ten major streets were picked from the different wards. A pool was made up of the ten wards, from which five streets were randomly selected. Questionnaire was then administered in the various randomly selected five streets of each ward; an average of 5 questionnaire was administered per street. On each street, a die of 1 and 2 was casted to determine the first house visited, and this is added to 2 to determine the next house visited and it follows in that systematic order. This method was adapted from Olayiwola & Adeleye (2006) and is shown in the equation below;

Y = R + 2
Where R is the first die picked
Y is the next house to be sampled

A total of 243 questionnaire was administered which represent 0.5% of the total number of households in the study area. GPS readings was taken for all the houses sampled as this served as the points to which field measurement was stored in the GIS environment. The map below shows the survey points and streets of the study area.

The socio-economic characteristics of the respondents are given below;

The chart below gives a representation of the responses of the respondents to the different indicators.

In line with the model adopted for the research, spatial auto-correlation check was performed on the prepared datasets using the moran’s I test, this was necessary in order to determine the method used in the mapping of the indicators. Nature of roads and security had positive correlation and was mapped using the inverse distance weighting method, while other indicators had negative correlation and was mapped using voronoi polygons.

The various mapped indicators are combined under the various domains, while the various mapped domains are combined to generate the overall QoL map using the normalised weights as given by the respondents. The method of multi-criteria decision analysis used is the weighted sum method. The weights of the different indicators are given diagrammatically below;

Figure showing the weights of the different indicators

Various domains of life maps were combined to generate the overall QoL map below;

The generated QoL map produced that shows that, 17.9% of the geographic space displays extremely poor, 67.6% displays below average while 14.5% displays average. Wards 3, 5, 6, 8 and 10 shows relatively lower level of QoL. The classes above average and excellent were not over the geographic space.

The generated QoL was cross-validated using the leave-one-out method. This is a type of cross-validation method which uses each sampling point to determine the accuracy of the prediction. Each sampling point is evaluated against the whole data set. From the cross-validation conducted, the highest under-prediction is -1.491, while the highest over-prediction is 0.927 with a mean of 0.072909. If the cross-validation residuals are small, and the mean is close to zero (Brivand et al., 2008), then it means the prediction is relatively accurate. This therefore means that the QoL prediction map produced is relatively of sufficient quality.

Summary, implications and conclusions
This paper has been able to assess QoL using GIS approach in order to aid poverty alleviation decision-making process. Most QoL researches are carried out at the national level for purpose of international comparison and this covers details of variability at lower scales. Also most developing countries exhibit low ranking in international QoL rankings, whereas most QoL empirical theories and models are developed from the western society, there is therefore a great mismatch in this regard.

The study revealed that very few people of Egor LGA exhibits average QoL, while others exhibit below average or extremely poor QoL. The research has shown that governance, power and water provision, income, and housing quality have relatively more impact on the QoL of the people of Egor LGA. The implication is such that, there is urgent need for proper intervention in order to improve the QoL, the study has also shown the relevance of participatory planning, as those being planned for can best determine priority areas. The intervention must utilize scientific studies such as this research in order to understand the problem and tackle priority areas. Conclusively, there is eminent need for intervention in order to improve the QoL of the people of Egor LGA.


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