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<p>The transportation system depends on existing road networks that connect destinations, forming the backbone of urban mobility. This study focuses on the creation and analysis of a road network dataset (ND) designed for spatial network analysis, with an emphasis on route optimization within urban areas and support for sustainable urban planning and smart infrastructure. The methodology includes data acquisition and digitization using geographic information system (GIS) technologies, specifically OpenStreetMap and Google MyMaps, followed by CAD-to-GIS format conversion to ensure precise spatial alignment. This process yields a comprehensive ND that enables shortest path analysis and optimized route planning. Validation showed distance discrepancies under ± 0.52% between ArcMap and QGIS tools, with GPS error within ± 2.09%, confirming high spatial accuracy. Well-connected nodes contribute to a robust geospatial network foundation. The final dataset supports transportation planning in academic environments and demonstrates potential for broader urban deployment. It enhances traffic flow, connectivity, and environmental outcomes through improved route strategies. The study offers a transferable framework applicable across cities, addressing infrastructure challenges and advancing smart, resilient, and sustainable urban growth. These findings contribute to spatial information science through practical methods that improve data reliability and decision-making.</p>

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Harnessing GIS and CAD in network dataset and spatial network analysis

  • 期刊论文

  • 发表日期：

    2026年1月7日

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  • R. Anand,
  • D. Sivakumar

摘要

The transportation system depends on existing road networks that connect destinations, forming the backbone of urban mobility. This study focuses on the creation and analysis of a road network dataset (ND) designed for spatial network analysis, with an emphasis on route optimization within urban areas and support for sustainable urban planning and smart infrastructure. The methodology includes data acquisition and digitization using geographic information system (GIS) technologies, specifically OpenStreetMap and Google MyMaps, followed by CAD-to-GIS format conversion to ensure precise spatial alignment. This process yields a comprehensive ND that enables shortest path analysis and optimized route planning. Validation showed distance discrepancies under ± 0.52% between ArcMap and QGIS tools, with GPS error within ± 2.09%, confirming high spatial accuracy. Well-connected nodes contribute to a robust geospatial network foundation. The final dataset supports transportation planning in academic environments and demonstrates potential for broader urban deployment. It enhances traffic flow, connectivity, and environmental outcomes through improved route strategies. The study offers a transferable framework applicable across cities, addressing infrastructure challenges and advancing smart, resilient, and sustainable urban growth. These findings contribute to spatial information science through practical methods that improve data reliability and decision-making.