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

Authors

Farishta khan Department of Geography and Geomatics, University of Peshawar, Pakistan
Atta ur Rahman Department of Geography and Geomatics, University of Peshawar, Pakistan
Uzma Manglore Department of Geography and Geomatics, University of Peshawar, Pakistan
Shazia Dilbar Department of Geography and Geomatics, University of Peshawar, Pakistan

Keywords:

Land Use Land Cover, Population Growth, Built-up Areas Logistic Regression Model, IRISI Salva, SPSS

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.

References

M. S. A. Muhammad Arslan Ashraf, Iqbal Hussain, Rizwan Rasheed, Muhammad Iqbal, Muhammad Riaz, “Advances in microbe-assisted reclamation of heavy metal contaminated soils over the last decade: A review,” J. Environ. Manage., vol. 198, no. 1, pp. 132–143, 2017, doi: https://doi.org/10.1016/j.jenvman.2017.04.060.

M. Brauer et al., “Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution,” Environ. Sci. Technol., vol. 46, no. 2, pp. 652–660, Jan. 2012, doi: 10.1021/ES2025752/SUPPL_FILE/ES2025752_SI_005.ZIP.

G. B. Chaolin Tan, Fei Weng Sui, Shang Chew, Youxiang, “Progress and perspectives in laser additive manufacturing of key aeroengine materials,” Int. J. Mach. Tools Manuf., vol. 170, p. 103804, 2021, doi: https://doi.org/10.1016/j.ijmachtools.2021.103804.

S. Covaleda et al., “Land-use effects on the distribution of soil organic carbon within particle-size fractions of volcanic soils in the Transmexican Volcanic Belt (Mexico),” Soil Use Manag., vol. 27, no. 2, pp. 186–194, Jun. 2011, doi: 10.1111/J.1475-2743.2011.00341.X.

D. R. F. Gerald R. Fowkes, “Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis,” Lancet, vol. 382, no. 9901, pp. 1329–1340, 2013, [Online]. Available: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(13)61249-0/fulltext

A. Geddes, “Half a Century of Population Trends in India: A Regional Study of Net Change and Variability, 1881-1931,” Geogr. J., vol. 98, no. 5/6, p. 228, Nov. 1941, doi: 10.2307/1787456.

“World Population Data Sheet.” Accessed: Jun. 08, 2024. [Online]. Available: https://www.prb.org/wp-content/uploads/2023/12/2023-World-Population-Data-Sheet-Booklet.pdf

D. W. Huang et al., “Extracting biological meaning from large gene lists with DAVID,” Curr. Protoc. Bioinforma., vol. Chapter 13, no. SUPPL. 27, 2009, doi: 10.1002/0471250953.BI1311S27,.

A. Khan, S. Khan, M. A. Khan, Z. Qamar, and M. Waqas, “The uptake and bioaccumulation of heavy metals by food plants, their effects on plants nutrients, and associated health risk: a review,” Environ. Sci. Pollut. Res. 2015 2218, vol. 22, no. 18, pp. 13772–13799, Jul. 2015, doi: 10.1007/S11356-015-4881-0.

B. M. I. N. Abel Kadeba, “Land cover change and plants diversity in the Sahel: A case study from northern Burkina Faso,” Ann. For. Res., vol. 58, no. 1, 2015, [Online]. Available: https://www.afrjournal.org/index.php/afr/article/view/350

J. M. E.F. Lambin, H.K. Gibbs, L. Ferreira, R. Grau, P. Mayaux, P. Meyfroidt, D.C. Morton, T.K. Rudel, I. Gasparri, “Estimating the world’s potentially available cropland using a bottom-up approach,” Glob. Environ. Chang., vol. 23, no. 5, pp. 892–901, 2013, doi: https://doi.org/10.1016/j.gloenvcha.2013.05.005.

X. H. Yanghao Li, Naiyan Wang, Jiaying Liu, “Demystifying Neural Style Transfer,” arXiv:1701.01036, 2017, doi: https://doi.org/10.48550/arXiv.1701.01036.

M. Livi‐Bacci, “A Concise History of World Population,” Feb. 2017, doi: 10.1002/9781119406822.

M. S. Loudermilk, “Estimation of Fractional Dependent Variables in Dynamic Panel Data Models With an Application to Firm Dividend Policy,” J. Bus. Econ. Stat., vol. 25, no. 4, pp. 462–472, Oct. 2007, doi: 10.1198/073500107000000098.

M. S. Abdoul Salam Ouedraogo, “High prevalence of extended-spectrum ß-lactamase producing enterobacteriaceae among clinical isolates in Burkina Faso,” BMC Infect. Dis., vol. 16, 2016, doi: 10.1186/s12879-016-1655-3.

L. Roser, M., Ritchie, H., Ortiz-Ospina, E., & Rodés-Guirao, “World population growth,” Our world data, 2013.

Y. Samiullah, “Prediction of the Environmental Fate of Chemicals,” Book, 1990, doi: 10.1007/978-94-009-2211-2.

S. G. Setegn, R. Srinivasan, B. Dargahi, and A. M. Melesse, “Spatial delineation of soil erosion vulnerability in the Lake Tana Basin, Ethiopia,” Hydrol. Process., vol. 23, no. 26, pp. 3738–3750, Dec. 2009, doi: 10.1002/HYP.7476;JOURNAL:JOURNAL:10991085.

M. Shafiq-Ul-hassan et al., “Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels,” Med. Phys., vol. 44, no. 3, pp. 1050–1062, Mar. 2017, doi: 10.1002/MP.12123;WEBSITE:WEBSITE:AAPM;PAGE:STRING:ARTICLE/CHAPTER.

M. Shirazi, R. Zane, and D. Maksimovic, “An autotuning digital controller for DC-DC power converters based on online frequency-response measurement,” IEEE Trans. Power Electron., vol. 24, no. 11, pp. 2578–2588, 2009, doi: 10.1109/TPEL.2009.2029691.

Arch G. Woodside, “Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory,” J. Bus. Res., vol. 66, no. 4, pp. 463–472, 2013, doi: https://doi.org/10.1016/j.jbusres.2012.12.021.

C. Wrigley and K. Straker, “Designing innovative business models with a framework that promotes experimentation,” Strateg. Leadersh., vol. 44, no. 1, pp. 11–19, Jan. 2016, doi: 10.1108/SL-06-2015-0048/FULL/XML.

X.-P. Z. Xiao-Lin Xie, Yiu-Wing Mai, “Dispersion and alignment of carbon nanotubes in polymer matrix: A review,” Mater. Sci. Eng. R Reports, vol. 49, no. 4, pp. 89–112, 2005, doi: https://doi.org/10.1016/j.mser.2005.04.002.

X. Z. Chaodong Tan, Song Wang, Hanwen Deng, Guoqing Han, Guanghao Du, Wenrong Song, “The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning,” Geofluids, 2021, doi: https://doi.org/10.1155/2021/6641395.

E. G. Stein Emil Vollset, “Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study,” Lancet, vol. 396, no. 10258, pp. 1285–1306, 2020, doi: 10.1016/S0140-6736(20)30677-2.

L. F. The MINERvA collaboration, “Measurement of Muon Antineutrino Quasi-Elastic Scattering on a Hydrocarbon Target at E_ν ~ 3.5 GeV,” Phys. Rev. Lett., 2024, doi: https://doi.org/10.1103/PhysRevLett.111.022501.