DIFFERENTIATING SOCIO-ECONOMIC DEVELOPMENT UNITS USING LINEAR DISCRIMINANT ANALYSIS IN THE RURAL SPA

Despite significant advances in economic growth, inequality continues to characterise the socio-economic development pattern, particularly in rural areas. Using linear discriminant analysis, this study looked into the socio-economic development levels of rural areas in Mkpat Enin LGA. To do so, researchers used a questionnaire and field observation to collect data on 53 socioeconomic development indicators from 87 communities in the study area. For the objective of establishing characteristics of rural socio-economic development, K-Mean Cluster analysis was used to classify all 87 communities into various development regions based on their levels of performance on six extracted parameters. The findings revealed a greater discrepancy in socioeconomic development among the study area's areas. The communities were divided into development areas (clusters/groups) based on the socioeconomic development characteristics previously determined, generating a total of five (5) categories. This study demonstrated that regional differences existed even at the rural level. This meant that some localities performed poorly in terms of socioeconomic development features. To go even further, Multiple Linear Discriminant Analysis (MLDA) was used to analyse the optimality of earlier community groupings in the research area, as well as to find the characteristics that separated the previously derived groups. MLDA properly identified 87.4% of rural settlements, according to the findings. 54.5 percent of Group 1 communities were correctly identified, however 45.5 percent of Group 1 rural communities were incorrectly classified as Group 2 rural areas. It accurately classified 50.0 percent of Group 2 while incorrectly classifying the remaining 50.0 percent as Group 5. It successfully identified all rural communities in Groups 3, 4, and 5, with no misclassifications. Furthermore, the most important indicators that discriminated the five groups of communities of the study area earlier derived from the cluster analysis solution were identified as Co-operative Societies and Medium Scale Industries, Modern Socio-economic and Infrastructural Facilities, Neighbourhood Religious/Health and Infrastructural Factor, and Modern Agricultural Facilities, highlighting their role in improving the socio-economic development level in the study area.

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