Spatial distribution and opportunity mapping: Applicability of evidence-based policy implications in Punjab using remote sensing and global products. (October 2019)

• Record Type: Journal Article
• Authors: Imran, M.; Sumra, K.; Abbas, N.; Majeed, I.
• Is Part Of: Sustainable cities and society. Volume 50(2019)
• Publication Date: 2019-10
• Subjects: Urban sustainability -- Social exclusion -- Composite opportunity index -- NOAA NTL -- Spatial mapping -- Geographical weighted regression; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2019.101652
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: We model the spatial distribution of facilities using global data and remote sensing. We develop opportunity indicator to model inequality patterns in household surveys. We link the public facilities and the indicator using local regression. Interpreting local coefficients validates regional inequality at fine resolutions. Remote sensing is useful for cost-effective inequality mapping at large scales. Abstract: Evidence-based policy-making generally applies scientifically rigorous methods to arrive at location-based decisions for public service facilities. The provision of equal opportunities is becoming increasingly difficult due to urbanization. Most studies examined the inequality in context with social indicators and few empirical studies focused on the distribution of opportunities in the form of opportunity indices. This study aims to examine the spatial distribution of public facilities in 36 districts of Punjab province, Pakistan. We develop a composite opportunity index from various social indicators retrieved from household surveys. Using global and local Moran's indices, we analyzed the social indicators along three lines, i.e., health, education, and civic facilities. We investigated inequalities in opportunities through its various factors derived from NOAA nighttime light data and other global data sets. Results reveal that the allocation of facilities is clustered in north and north-eastern regions of Punjab while southern areas are having large inequality of opportunities in accessing basic services. The inequalities are driven particularly in terms of poverty, road accessibility, and urban infrastructure depicted through nighttime data, while it is not driven by population. The evidence-based method in this research will help implicate policies through prioritizing resource allocation in terms of space and equal opportunities.