Regional search and visualization methodologies for multi-criteria geographic retrieval

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Geo-related information is essential for citizens to locate important places and understand local infrastructure. Geographic Information Retrieval (GIR) systems and local search services currently provide a means for users to access relevant spatial information through map interfaces, allowing for easy searching and visualization of geo-entities. However, existing GIR systems fall short in supporting decision-making scenarios where users need to analyze and compare geographic regions based on multiple criteria. This limitation necessitates more complex search types and analytical support for a comprehensive view of the underlying data, whether for tourism, relocation planning, or general city exploration. This research investigates methodologies for search and visualization that help users uncover knowledge within multi-dimensional geographic regions, aiding their decision-making processes. We propose extracting spatially relevant information from publicly available web data sources, such as OpenStreetMap and social media. Additionally, we introduce novel query methods that enable users to characterize their regions of interest beyond popular place names. Our proposed geovisualization methods facilitate easy access and interaction with spatial databases, allowing users to search, explore, and compare urban areas across multiple spatial dimensions. The search experience is enhanced through efficient regional ranking and optimiz