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Geographically weighted regression to measure the role of intra-urban drivers for urban growth modelling in Kathmandu, Central Himalayas

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Abstract

The present study analyzes spatio-temporal urban growth in Kathmandu Urban Agglomeration (KUA) and its impact on ecological infrastructures between 2000 and 2020. A geographically weighted regression (GWR) approach was used to investigate intra-urban drivers of urban growth between 2000–2010 (epoch 1) and 2010–2020 (epoch 2), and to predict built-up growth for the year 2030. The study makes crucial contributions to the urban growth modelling literature by (a) investigating six different neighborhood sizes (3 × 3 to 13 × 13) for computing proportion of built-up cells and (b) eliminating/selecting covariates using correlation check and global lasso regression. The superiority of GWR model over global regression model has been evaluated using the Akaike information criterion score and the stationarity test. The chronological urban growth analysis of KUA highlights rapid growth in mountainous landscape from 54.90 to 166 km2 during 2000–2020 and is projected to increase to 224.22 km2 during 2000–2030. This has significantly altered the forest-agriculture landscape during 2000–2020 (111.2 km2) and is expected to affect a large segment of ecological (blue-green) infrastructure (138 km2) by 2030. The findings of this study may be used for policy formulation, appropriate land use planning, and sustainable urban development.

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Data availability

The data and scripts used in the study are available at https://github.com/PratyushTripathy/Kathmandu_urban_growth_gwr.

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Acknowledgements

The authors would like to acknowledge the United States Geological Survey (USGS) for providing LANDSAT data series, Alaska Science Facility (ASF) for providing ALOS PALSAR DEM data, and OpenStreetMap (OSM) for providing street network and waterbody that has been used in the study.

Funding

This work was supported by the Department of Biotechnology, Government of India, under the R&D project (No. BT/PR12899/NBD/39/506/2015).

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Authors and Affiliations

  1. Department of Geoinformatics, Central University of Jharkhand, Ranchi, 835205, India

    Diksha & Amit Kumar

  2. Department of Geography, University of California, Santa Barbara, 93106, USA

    Pratyush Tripathy

  3. MapSolve AI Pvt. Ltd., Bengaluru, India

    Pratyush Tripathy

  4. International Union for the Conservation of Nature (IUCN), Commission on Ecosystem Management (CEM), Geneva, Switzerland

    Amit Kumar

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Diksha, Amit Kumar and Pratyush Tripathy designed the conceptual framework of the study. Diksha drafted the main manuscript text with inputs from Pratyush Tripathy. All the authors reviewed and edited the manuscript. Amit Kumar finalized the manuscript.

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Correspondence to Amit Kumar.

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Diksha, Kumar, A. & Tripathy, P. Geographically weighted regression to measure the role of intra-urban drivers for urban growth modelling in Kathmandu, Central Himalayas. Environ Monit Assess 195, 627 (2023). https://doi.org/10.1007/s10661-023-11164-2

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