Role of GIS Technology, Spatial Data Analysis, and Remote Sensing in Urban Planning

Abstract

This study examines the application of Geographic Information Systems (GIS), spatial data analysis, and remote sensing in urban planning, using Faisalabad, Pakistan, as a case study. The research aims to analyze urban land-use changes and spatial expansion from 2000 to 2023, assess relationships between population density, land-use patterns, and infrastructure, and highlight the role of geospatial frameworks in sustainable urban development. Secondary data were sourced from NASA Landsat and European Space Agency Sentinel programs, complemented by population and transportation datasets. GIS processing and spatial analysis were conducted using QGIS and Python libraries, including GeoPandas, Rasterio, and PySAL. Land-use classification was performed using the Maximum Likelihood method, while NDBI and NDVI indices were used for time-series urbanization analysis. Results indicate that approximately 38.4% of urban fringe agricultural land was converted to built-up areas, with 74.2% of development occurring within 1.5 km of major roads, reflecting corridor-based growth. Spatial clustering identified seven significant high-density population zones. The findings demonstrate that GIS and remote sensing provide effective tools for monitoring urban growth and support evidence-based, sustainable planning in rapidly expanding cities.