Scholarlink

<p>Spatial disparities in socio-economic conditions are increasingly recognized as a significant challenge for policymakers. Even minor discrepancies can exacerbate societal inequalities, posing risks to both economic and social stability. In terms of literature, there is hardly any study that includes spatial disparity in Meghalaya with respect to socio-economic aspects. Thereby, to address this lacuna in previous studies, this research focuses on the pronounced spatial disparities in the state of Meghalaya, using Principal Component Analysis (PCA), Moran’s I, Local Indicators of Spatial Association (LISA) and Prin score, through which it aims to identify clusters, spatial autocorrelation and rank districts based on socio-economic indicators and identify underlying factors contributing to these disparities. Additionally, Z-scores have been employed to assess the severity of backwardness among different districts. Spatial analysis presents the factors in PC2 (i.e. educational) are clustered at local level (LISA = high-high, East Khasi Hills, &lt; 0.01; Ri-Bhoi, &lt; 0.05) with moderate spatial autocorrelation (Moran’s I = 0.299, p-value = 0.154). Results also portray notable spatial disparity exists in Meghalaya, with districts like West Garo Hills (1st rank, Q1) and East Khasi Hills (2nd rank, Q1) are 13.8 and 14 times more developed in terms of cropped area and number of industries than East Jaintia Hills (11th rank, Q4) and South West Khasi Hills (10th rank, Q4), respectively, which remains less developed, reflecting Matthew's effect and core-periphery pattern. The paper concludes by proposing balanced regional development through potential mitigation strategies, including enhanced community participation, expansion of welfare schemes (e.g., Meghalaya State Rural Livelihood Mission and Border Area Development Programme) in underdeveloped districts and equitable resource allocation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Unveiling socio-economic inequalities and spatial disparities across districts in Meghalaya: insights from PCA, Moran’s I and LISA

  • 期刊论文

  • 发表日期：

    2026年1月7日

查看全文
  • Sanjib Majumder,
  • Laitpharlang Cajee

摘要

Spatial disparities in socio-economic conditions are increasingly recognized as a significant challenge for policymakers. Even minor discrepancies can exacerbate societal inequalities, posing risks to both economic and social stability. In terms of literature, there is hardly any study that includes spatial disparity in Meghalaya with respect to socio-economic aspects. Thereby, to address this lacuna in previous studies, this research focuses on the pronounced spatial disparities in the state of Meghalaya, using Principal Component Analysis (PCA), Moran’s I, Local Indicators of Spatial Association (LISA) and Prin score, through which it aims to identify clusters, spatial autocorrelation and rank districts based on socio-economic indicators and identify underlying factors contributing to these disparities. Additionally, Z-scores have been employed to assess the severity of backwardness among different districts. Spatial analysis presents the factors in PC2 (i.e. educational) are clustered at local level (LISA = high-high, East Khasi Hills, < 0.01; Ri-Bhoi, < 0.05) with moderate spatial autocorrelation (Moran’s I = 0.299, p-value = 0.154). Results also portray notable spatial disparity exists in Meghalaya, with districts like West Garo Hills (1st rank, Q1) and East Khasi Hills (2nd rank, Q1) are 13.8 and 14 times more developed in terms of cropped area and number of industries than East Jaintia Hills (11th rank, Q4) and South West Khasi Hills (10th rank, Q4), respectively, which remains less developed, reflecting Matthew's effect and core-periphery pattern. The paper concludes by proposing balanced regional development through potential mitigation strategies, including enhanced community participation, expansion of welfare schemes (e.g., Meghalaya State Rural Livelihood Mission and Border Area Development Programme) in underdeveloped districts and equitable resource allocation.