GIS

GIS
original image: Geo arbo vectorization: Own work · CC BY-SA 3.0 · source
Name	Geographic Information Systems
Caption	A thematic map overlaid on a satellite image
Type	Geospatial technology
Invented	1960s
Developers	Roger Tomlinson; Esri; USGS; Ordnance Survey
Major users	National Aeronautics and Space Administration; European Space Agency; United Nations; World Bank; National Oceanic and Atmospheric Administration

GIS A geographic information system is a technology for capturing, storing, analyzing, managing, and visualizing spatially referenced information. It integrates maps, remote sensing, surveying, and database management to support decision-making in contexts ranging from urban planning to environmental conservation. Practitioners often combine satellite imagery, cadastral records, demographic censuses, and field survey datasets to produce thematic maps, spatial statistics, and predictive models.

Overview

A system for handling spatially referenced datasets ties together cartographic maps, remote sensing imagery, Global Positioning System observations, and tabular databases to represent real-world features. Organizations such as the United States Geological Survey, Ordnance Survey, National Aeronautics and Space Administration, European Space Agency, and Esri promote standards, tools, and data for spatial analysis. Applications cross municipal agencies like New York City Department of City Planning and international programs like the United Nations's humanitarian mapping initiatives. The integration of raster imagery, vector topologies, and attribute databases enables workflows used by institutions including World Bank, National Oceanic and Atmospheric Administration, and United Nations Environment Programme.

History

Early automated cartography projects in the 1950s and 1960s led to operational systems in the 1960s developed by pioneers such as Roger Tomlinson and organizations like Canada Land Inventory. Subsequent decades saw advances driven by defense research in institutions such as the Defense Advanced Research Projects Agency and civil agencies such as the United States Geological Survey. The rise of satellite programs—Landsat, SPOT, and missions by the European Space Agency—expanded remote sensing inputs, while standards bodies like the Open Geospatial Consortium and national mapping agencies including Ordnance Survey shaped data interoperability. Commercialization occurred as companies such as Esri and software projects like GRASS GIS and QGIS broadened access, paralleled by academic centers at universities like University of Redlands and Massachusetts Institute of Technology that advanced geospatial science.

Components and Technology

Core components include data capture hardware (satellite sensors from Landsat and Sentinel missions, airborne LiDAR platforms manufactured by firms such as Leica Geosystems), positioning systems like Global Positioning System and GLONASS, and software stacks produced by vendors such as Esri and open-source projects including QGIS and GRASS GIS. Database engines including PostgreSQL with PostGIS extensions and spatial services conforming to Open Geospatial Consortium specifications support storage and dissemination. Visualization leverages map projections formalized by institutions like the International Hydrographic Organization and symbology standards used by agencies such as the United States Geological Survey. Interoperability protocols from the Open Geospatial Consortium enable web mapping services implemented by cloud providers like Amazon Web Services and Microsoft Azure.

Data Types and Data Models

Spatial information is commonly represented as vector geometries—points, lines, polygons—using formats like those championed by ESRI shapefiles and standards from the Open Geospatial Consortium; and as raster grids from satellite sensors of Landsat and Sentinel missions. Attribute data often derive from national censuses by agencies such as the United States Census Bureau and land registries like HM Land Registry. Elevation models come from LiDAR campaigns coordinated by organizations like the National Aeronautics and Space Administration and mapping authorities including Ordnance Survey. Temporal datasets used in epidemiology and disaster response link to reports from World Health Organization and United Nations Office for the Coordination of Humanitarian Affairs for change detection and time-series analysis.

Analysis and Applications

Spatial analysis methods—overlay operations, network routing, spatial interpolation, and hotspot detection—support tasks in urban planning executed by city agencies such as New York City Department of City Planning, environmental monitoring by United Nations Environment Programme, and agricultural mapping used by Food and Agriculture Organization. Emergency management leverages situational awareness from agencies like Federal Emergency Management Agency and humanitarian responders coordinated by United Nations Office for the Coordination of Humanitarian Affairs. Public health studies combine spatial statistics with data from institutions like the Centers for Disease Control and Prevention and World Health Organization. Transportation modeling integrates inputs from authorities such as Federal Highway Administration and transit agencies including Transport for London.

Implementation and Software

Implementations range from enterprise deployments by companies such as Esri and national services run by United States Geological Survey to open-source ecosystems built on PostgreSQL/PostGIS, GDAL, QGIS, and GRASS GIS. Web mapping stacks use protocols from the Open Geospatial Consortium and technologies provided by cloud platforms like Amazon Web Services and Google Cloud Platform. Mobile data collection employs devices compatible with Global Positioning System and applications developed by projects such as OpenStreetMap and commercial vendors. Academic curricula at institutions like Massachusetts Institute of Technology and professional certifications from bodies such as the GIS Certification Institute support workforce development.

Challenges and Future Directions

Key challenges include interoperability gaps addressed by standards bodies like the Open Geospatial Consortium, data privacy concerns highlighted by regulators such as the European Commission, and ethical issues raised in cases involving surveillance by agencies such as National Security Agency. Emerging directions involve integration with machine learning models from research hubs like Carnegie Mellon University and Stanford University, real-time processing enabled by cloud providers like Amazon Web Services and Google Cloud Platform, and increased use of high-resolution sensors from missions by European Space Agency and companies such as Planet Labs. Cross-sector collaborations among institutions like the United Nations, World Bank, and national mapping agencies aim to improve resilience, sustainability, and equitable access to geospatial intelligence.

Category:Geospatial technology