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<p>Thermal stress, driven by the combined influence of high temperature and humidity, has become a critical public health concern in rapidly urbanizing Mediterranean cities. This study introduces the QGIS Summer Simmer Index (QSSI) Calculator, an open source plugin developed in Python and Qt Designer to provide fast and reproducible mapping of summer thermal stress. The tool integrates the Summer Simmer Index (SSI) directly into the QGIS environment, enabling users to generate spatially explicit bioclimatic stress maps without relying on external modeling platforms. Adana was selected as the pilot area due to its prolonged hot and humid summer conditions. The results indicate that more than 80% of the urban population is exposed to Hot or Very Hot SSI classes during the summer season, highlighting significant risks for nearly 1.8&#xa0;million residents. These findings demonstrate the plugin’s value as a practical spatial decision support tool for climate adaptation and urban heat management. Future versions will incorporate additional environmental variables to improve model detail and broaden applicability.</p>

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QSSI (QGIS summer simmer index) calculator plugin: an open-source tool for thermal comfort analysis in gis applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月24日

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  • Fatih Adiguzel,
  • Mansur Bestas,
  • Enes Karadeniz,
  • Asir Yuksel Kaya,
  • Rukiye Gizem Oztas Karli

摘要

Thermal stress, driven by the combined influence of high temperature and humidity, has become a critical public health concern in rapidly urbanizing Mediterranean cities. This study introduces the QGIS Summer Simmer Index (QSSI) Calculator, an open source plugin developed in Python and Qt Designer to provide fast and reproducible mapping of summer thermal stress. The tool integrates the Summer Simmer Index (SSI) directly into the QGIS environment, enabling users to generate spatially explicit bioclimatic stress maps without relying on external modeling platforms. Adana was selected as the pilot area due to its prolonged hot and humid summer conditions. The results indicate that more than 80% of the urban population is exposed to Hot or Very Hot SSI classes during the summer season, highlighting significant risks for nearly 1.8 million residents. These findings demonstrate the plugin’s value as a practical spatial decision support tool for climate adaptation and urban heat management. Future versions will incorporate additional environmental variables to improve model detail and broaden applicability.