Geospatial Nexus of Land Use Land Cover dynamics and Rapid Population Growth with Emphasis on Trend Prediction of Built-Up Areas in District Hangu, Pakistan

Farishta Khan, Atta-Ur Rahman, Uzma Manglore, Shazia Dilbar ()
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Farishta Khan, Atta-Ur Rahman, Uzma Manglore, Shazia Dilbar: Department of Geography and Geomatics, University of Peshawar, Pakistan

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 9, 157-173

Abstract: This study aims to analyze the land use land cover (LULC) and predict the patterns in the built-up areas of District Hangu and examine how population growth affects land use and land cover. Rapid population increase remains a continuous threat to the district’s land resources. Projections show that the population will grow from 518,811 in 2017 to 833,964 by 2051. Along with this growth, there is an ongoing expansion of underground utilities and infrastructure, driven by demographic pressures and urban development. The Logistic Regression (LR) model was used to forecast an expansion in the district’s built-up area. Through this model, potential zones for future development are identified, and anticipated changes in planned Land Use/Land Cover (LU/LC) are evaluated. All variables were transformed into raster format and standardized to a 0–1 range using a raster calculator, ensuring uniformity in statistical comparisons. Factor standardization played a central role in the multivariate analysis, where the Variance Inflation Factor (VIF) method in SPSS was applied to resolve multicollinearity issues. Predictors with VIF values exceeding 10 were substituted with alternatives falling below this limit. Land use and land cover data were obtained from Landsat images for the years 1991, 2001, 2011, and 2021, each at a 30-meter resolution. Results indicate that the proportion of built-up areas increased from 8% in 1991 to 11% in 2021, while vegetation cover decreased from 43% to 45%. During the same period, barren land reduced from 47% to 40%, and water bodies expanded from 3% to 4%. Future projections of built-up areas identify the most suitable zones for urban growth. The LR model integrates multiple variables—such as railways, primary roads, tracks, commercial zones, educational and health facilities, and economic hubs—using tools including SPSS, IDRISI, and ArcMap. IDRISI Selva is applied for future land use modeling, estimating that built-up areas will cover 161.22 km² by 2050. The prediction results indicate that population growth will continue to be a significant driver of built-up expansion in Hangu District.

Keywords: Land Use Land Cover; Population Growth; Built-up Areas Logistic Regression Model; IRISI Salva; SPSS (search for similar items in EconPapers)
Date: 2025
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