What is spatial data science? Explore real-world examples

Spatial data science is a specialized field of data science that focuses on analyzing and visualizing data with a geographic or spatial component, integrating location directly into the analysis process. Unlike traditional data science, spatial data science employs geographic information systems (GIS) and machine learning to explore spatial relationships such as distance and proximity, providing insights into patterns across space. It is applied across various industries, aiding in tasks like monitoring environmental changes, managing public services, and optimizing logistics. The field is closely related to geospatial data science, which emphasizes mapping technologies and geographic data pipelines, and spatial analysis, which involves techniques to study spatial patterns and relationships. Tools commonly used include GIS platforms, spatial databases, and geospatial libraries, while Felt AI offers a platform for exploring spatial data with features like natural language to SQL translation.