A topic in the Open Knowledge Graph — a free, open map of 15,290 topics and the order to learn them in.
A Geographic Information System (GIS) is a framework for capturing, storing, analyzing, and visualizing geographically referenced data. Unlike a simple map, a GIS links spatial features (points, lines, polygons, rasters) to attribute databases, enabling queries that combine location and characteristics: "show all parcels within 500 meters of a river that are zoned residential and have slope less than 15%." GIS integrates data from many sources (remote sensing, surveys, GPS, census, administrative records) into a common spatial framework, making it possible to ask spatial questions, detect patterns, and model scenarios that would be impossible with tabular data alone.
GIS emerged in the 1960s from the realization that geographic data stored digitally could be analyzed in ways impossible with paper maps. The core innovation was linking spatial features (where things are) with attributes (what they are), enabling queries that combine spatial and thematic criteria.
A GIS organizes data in layers (or themes), each representing a different type of geographic feature: roads, buildings, land parcels, elevation, land cover, soil types, population density. Each layer shares a common coordinate system, allowing them to be overlaid and combined. Vector layers represent discrete features as points (wells, stations), lines (roads, rivers), or polygons (parcels, lakes) with associated attribute tables. Raster layers represent continuous phenomena (elevation, temperature, satellite imagery) as regular grids of cells.
The analytical power of GIS comes from spatial operations. Buffering creates zones at specified distances from features. Overlay operations (intersection, union, difference) combine layers to identify areas meeting multiple criteria. Network analysis finds shortest paths, service areas, and optimal routes. Terrain analysis derives slope, aspect, and watersheds from elevation data. Spatial statistics identify clusters, hotspots, and spatial autocorrelation. Each operation leverages the explicit spatial relationships that distinguish geographic data from ordinary tabular data.
Modern GIS has evolved from expensive desktop software (ArcGIS, QGIS) into a broad ecosystem including web-based platforms (ArcGIS Online, Google Earth Engine), spatial databases (PostGIS), programming libraries (GeoPandas, sf in R), and cloud-native geospatial formats. The fundamental principles remain: geographic data integration, spatial analysis, and evidence-based spatial decision-making.