Automated mapping process of frontal area and thermal potential indexes: GIS algorithm development and implementation

• Published in: Urban climate Vol. 53; p. 101799
• Main Authors: Favretto, Ana Paula Oliveira, de Souza, Léa Cristina Lucas, Rodrigues, Daniel Souto
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2024
• Subjects: Frontal Area Index; GIS; Urban Heat Island; Urban thermal comfort; Wind route; Urban Heat Island; GIS; Frontal Area Index; Wind route; Urban thermal comfort
• ISSN: 2212-0955; 2212-0955
• DOI: 10.1016/j.uclim.2023.101799

The impacts of urban areas on microclimate conditions are well-known and highlight the importance of climatological guided urban planning. The growth and evolution of Geographic Information Systems (GIS) have set a backdrop for implementing strategies to apply urban climatological knowledge in planning daily practice. This research brings algorithms to automate the Frontal Area Index (FAI) and Thermal Potential Index (TPI) mapping process. This type of algorithms type facilitates the extraction of detailed spatial information for the decision-making process, making it highly relevant for urban planning and management. Their calculation method is implemented as ESRI © ArcGIS Pro embedded Python Stand Alone Script Tools, using the Python ArcPy library to access the ArcGIS geoprocessing functions. The algorithms are described in detail, allowing their implementation in other GIS platforms. The output maps allow the urban thermal conditions and morphology assessment. The findings from this research may substantially contribute both to the advance in the urban climatology scientific field and to guide urban planners' and managers' practical decision making. •The provided algorithms automatize the Frontal Area Index (FAI) and Thermal Potential Index (TPI) calculation within a GIS.•The algorithms are implemented and made available as ArcGIS Script Tools.•The TPI mapping algorithm enables users to assign weights to diverse land cover materials.•Maps created with the FAI mapping algorithm may be used coupled with Least Cost Path Analysis to predict urban wind routes.•The implemented algorithms act as simple models with simplified input data.