Appraisal of infrastructural amenities to analyze spatial backwardness of Murshidabad district using WSM and GIS-based kernel estimation

F Parvin, S Najmul Islam Hashmi, SA Ali - GeoJournal, 2021 - Springer
F Parvin, S Najmul Islam Hashmi, SA Ali
GeoJournal, 2021Springer
Backwardness is the result of different factors that exist in a society. The present study
focused on infrastructural facilities and basic amenities to estimate the spatial distribution of
backward areas in the Murshidabad district of West Bengal, India. An adequate supply of
infrastructure has long been essential for economic development for both academicians and
policymakers. Improved infrastructure has an aggregate impact on income and economic
development. From this point of view, the present study aimed to appraise the infrastructural …
Abstract
Backwardness is the result of different factors that exist in a society. The present study focused on infrastructural facilities and basic amenities to estimate the spatial distribution of backward areas in the Murshidabad district of West Bengal, India. An adequate supply of infrastructure has long been essential for economic development for both academicians and policymakers. Improved infrastructure has an aggregate impact on income and economic development. From this point of view, the present study aimed to appraise the infrastructural facilities and basic amenities to analyze the spatial extension of backwardness. For the same, 17 decision criteria under four parameters including physical infrastructure, medical and health service, educational amenities and recreational facilities were selected and geospatial technique i.e. kernel density estimation was used for spatial mapping of those decision criteria to show the spatial density of available services. Concurrently, weight sum model as a multi-criteria decision approach was applied for map overlaying and displaying backward areas by considering reverse scale factor from 1 to 5. 5 indicate the high spatial density of infrastructural facilities and low backward areas and 1 indicates the low density of infrastructural facilities and high areas of backwardness. Using the above rating scale, the spatial extension of backwardness was estimated. Unlike using the traditional method to estimate backwardness, the present study applied a geospatial technique which is quite new in this type of study. The present study also measured the accuracy of the result using prediction accuracy. The result revealed that the overall prediction accuracy signifies 82% (Pa = 0.82) which validates the weight sum model and kernel density applied in spatial analysis of backwardness. The present study evidences the efficiency of geospatial technique which may also helpful for applying in different fields of research.
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