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<p>This study develops and evaluates an integrated geoinformation framework for modeling urban surface thermal energy deprivation (USTED) and its spatial association with land surface temperature (LST) in Tabriz, Iran, from 2015 to 2024. The framework integrated Landsat- and MODIS-derived LST, municipal building and land use/land cover (LULC) datasets, demographic information, and terrain variables within a GIS based multiple decision analysis workflow. For this goal, after selecting the relevant criteria, the Analytic Network Process (ANP) was integrated with Monte Carlo simulation (MCS) and global sensitivity analysis (GSA) to quantify weight-related uncertainty and improve model robustness. Bivariate spatial association and geovisualization techniques were then used to examine daytime and nighttime USTED–LST relationships, and the resulting susceptibility patterns were evaluated against winter energy-consumption records. Spatiotemporal tracking indicated an eastward shift of high-high clusters and the persistence of energy-stress zones in dense residential areas. Detailed results shows that nighttime high-high clusters expanded within urban centers (<i>p</i> &lt; 0.01), consistent with thermal retention and urban heat-island behavior. On the other hand, daytime high-low clusters were concentrated in compact built-up zones, reflecting relative cooling compared with barren surroundings. Overall, the proposed framework supports uncertainty-aware mapping of urban thermal-energy susceptibility and provides a decision-support basis for targeting energy-efficiency and heat-mitigation interventions in semi-arid cities.</p>

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An integrated Spatial Association and Geovisualization Framework for Urban Surface Thermal Energy Deprivation Modeling and Decision Support

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Bakhtiar Feizizadeh,
  • Atefeh Nasrollahi Aghdam,
  • Sajjad Moshiri,
  • Amin Naboureh,
  • Mohammad Ali Koushesh Vatan,
  • Firouz Aghazadeh,
  • Hui Lin,
  • Murat Yakar

摘要

This study develops and evaluates an integrated geoinformation framework for modeling urban surface thermal energy deprivation (USTED) and its spatial association with land surface temperature (LST) in Tabriz, Iran, from 2015 to 2024. The framework integrated Landsat- and MODIS-derived LST, municipal building and land use/land cover (LULC) datasets, demographic information, and terrain variables within a GIS based multiple decision analysis workflow. For this goal, after selecting the relevant criteria, the Analytic Network Process (ANP) was integrated with Monte Carlo simulation (MCS) and global sensitivity analysis (GSA) to quantify weight-related uncertainty and improve model robustness. Bivariate spatial association and geovisualization techniques were then used to examine daytime and nighttime USTED–LST relationships, and the resulting susceptibility patterns were evaluated against winter energy-consumption records. Spatiotemporal tracking indicated an eastward shift of high-high clusters and the persistence of energy-stress zones in dense residential areas. Detailed results shows that nighttime high-high clusters expanded within urban centers (p < 0.01), consistent with thermal retention and urban heat-island behavior. On the other hand, daytime high-low clusters were concentrated in compact built-up zones, reflecting relative cooling compared with barren surroundings. Overall, the proposed framework supports uncertainty-aware mapping of urban thermal-energy susceptibility and provides a decision-support basis for targeting energy-efficiency and heat-mitigation interventions in semi-arid cities.