# Project-25010 Understanding Urban Heat Island Effects through Geospatial Explainable AI and Visual Analytics **Project Title** Understanding Urban Heat Island Effects through Geospatial Explainable AI and Visual Analytics **Chuan Chen**, PHD Candidate, Chair of Cartography and Visual Analytics, TUM **Project Description** The GeoVisX framework is an innovative integration of geospatial analysis, visual analytics, and explainable artificial intelligence (XAI), aiming to enhance the transparency and interpretability of spatial modeling. This project proposes the application of GeoVisX to analyze the Urban Heat Island (UHI) phenomenon in major metropolitan areas. UHI is a significant urban environmental issue characterized by elevated temperatures in densely built-up areas relative to surrounding regions. It has direct implications for urban sustainability, public health, and climate adaptation planning. The GeoVisX framework allows for fine-grained spatial attribution of contributing factors (e.g., land surface characteristics, population density, NDVI, building patterns), visual decomposition of spatial model predictions, and interactive dashboards for stakeholder engagement. **Tasks and Responsibilities** Key Requirements and Workflow: 1\. Familiarization with GeoVisX Components Understand the three pillars: a) Geospatial Analysis (e.g., spatial regression, GWR, spatial autocorrelation) b) Explainable AI (e.g., SHAP, LIME applied to XGBoost or deep learning models) c) Visual Analytics (e.g., interactive heatmaps, geospatial dashboards) 2\. Data Preparation Acquire and preprocess urban-scale geospatial datasets: Remote sensing imagery for LST and NDVI Urban built-up and land cover maps Socioeconomic and demographic data Project datasets into a unified coordinate system Generate grids or neighborhood units for analysis 3\. Modeling and Explainable AI Train machine learning models (e.g., XGBoost, Random Forest, or deep CNNs) to predict LST or UHI intensity Use XAI to quantify feature contributions Integrate spatial location as a feature for GeoSHAP analysis Compare results with classic spatial statistics 4\. Visual Analytics & GeoVisX Implementation Visualize SHAP values spatially via heatmaps and spatial cluster detection Implement interactive dashboards to explore variable contributions Build temporal comparison modules to assess seasonal UHI variation Highlight interpretable insights 5\. Policy-Relevant Interpretation and Reporting Identify priority zones for UHI mitigation based on explainable model results Recommend urban interventions (e.g., greening, reflective surfaces) Provide reproducible codebase and analytic pipeline using Python **Minimum Qualifications** Proficient in Python, ArcGIS and various geospatial analysis methods **Terms of the Project** 6 months **Key Words** GeoVisX, Urban Heat Island (UHI), Explainable AI (XAI), Geospatial Analysis, Visual Analytics Research Topics Urban Climate, GeoAI, Spatial Explainability, Visual Analytics Deliverables Utilize the GeoVisX framework to identify, explain, and visualize the spatial drivers of urban heat island effects across selected cities using remote sensing and socio-environmental data, thereby informing policy interventions and adaptive planning.