Published: 20 November 2023
Contributor: Alexandra Jonker
Geographic information systems (GIS) are computer systems that produce connected visualizations of geospatial data—that is, data spatially referenced to Earth. Beyond creating visualizations, GIS is capable of capturing, storing, analyzing and managing geospatial data.
With GIS, users can create interactive queries, analyze spatial information, edit data, integrate maps and present the results of these tasks. GIS is part of Geographic Information Science, which is the overarching field concerned with all aspects of GIS—such as hardware and software, programming languages, geospatial data and how they all work together.
GIS connect and overlay what are often considered disparate data sets to help people, businesses and governments better understand our world, identifying patterns and relationships previously untapped. Through GIS mapping and analysis, organizations can improve the decision making and optimization of resource management, asset management, environmental impact assessments, marketing, supply chain management and many other activities.
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Many of today’s most pressing challenges can benefit from GIS tools and location-based information, such as climate change and natural disasters. For example, GIS makes it possible for facility managers to easily assess impacts on facility assets in the event of a natural disaster. Or GIS maps can help us understand exact geographic locations of pollution sites in relation to bodies of water and wetlands to identify at-risk water supplies.
Real-world uses of GIS technology include applications by these agencies:
- The National Oceanic and Atmospheric Administration (NOAA) uses GIS to map the spread of oil spills, better understand sea level fluctuations and plot hurricane paths.
- The Centers for Disease Control and Prevention (CDC) uses GIS technology to address public health issues, such as analyzing the impact of location on disease.
- NASA and the University of Maryland’s Global Ecosystem Dynamics Investigation (GEDI) space borne LiDAR instrument aboard the International Space Station (ISS) aids in the effort to better understand Earth’s forests and topography.
These use cases are the tip of the iceberg when it comes to the power of GIS. Geospatial analysis, GIS projects and still-developing GIS tools help people, businesses and government bodies around the world make better decisions—from predicting weather to urban planning.
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One of the first instances of spatial analysis happened in 1854, when British physician John Snow mapped London’s cholera outbreak locations and other geographic data. He discovered that cholera cases occurred along water lines.1
But it was not until computers and computational geography emerged in the 1960s that the field of GIS found its stride. During this time, Esri—an industry leader in GIS software development—was also founded. Esri went on to develop many of the GIS methods and technologies used today, such as ArcGIS.
In the 1970s, faster, cheaper and more advanced computers enabled the commercialization of GIS software. This, along with the rise of satellites and remote sensing technology, encouraged governments, businesses and academic institutions to adopt GIS.
Today’s GIS has become ubiquitous. Open-source GIS data from the United States Geological Survey (USGS) and other agencies, and GIS applications (such as QGIS) and their tutorials are easily accessible. Outside of government and academic settings, GIS is used to track packages, route trips and hail rideshares.
Geospatial data describes objects, events or other geographic features with a location on or near the Earth’s surface. Geospatial data combines location data (such as coordinates) and attribute data (the characteristics of the object, event or other geographical features) with temporal data (the time or life span at which the location and attributes exist).
Geospatial data contains large data sets from diverse sources including census and demographic data, satellite imagery (including remote sensing data), real estate data, weather data, cell phone data, drawn images and social media data.
GIS apps can ingest many types of data formats: cartographic files, spreadsheets, images and more. GIS tools then overlay any combination of this data to produce visualizations and digital maps. These geospatial data layers provide insights well beyond the capacity of paper maps and traditional cartography.
Organizations may find geospatial data most useful when it can be discovered, shared, analyzed and used with traditional business data. When properly used, geospatial data can provide organizations with advance warnings of incoming changes, a deeper understanding of analytics solutions and heightened efficiency of overall operations. These are key to building the workplaces of the future.
GIS uses two main geospatial data file formats: raster data and vector data.
Raster data consists of grids or cells of pixels with spatial information associated with each cell, such as elevation, temperature or even land use. Raster data is used to create complex, high-resolution imagery, such as photographs and satellite images. For example, a satellite image represented by a data matrix that contains a city’s weather information allows citizens to check the radar for rain.
Vector data is the representation of a geospatial element through its x and y coordinates. The most basic form of vector data is a point. Two or more points form a line, and three or more lines form a polygon. For example, Google Maps—a common web map and visual representation that uses vector data—defines the location of a city using points; roads using lines; and buildings or boundaries using polygons.
Remote sensing collects geospatial data and makes measurements about the surface of the Earth from above. The process uses remote sensors on satellites, balloons, drones and airplanes, which sense and record reflected or released energy. This remotely sensed information can be integrated with GIS programs to help users make data-informed decisions about the Earth with a global perspective.
There are two types of remote sensing: active and passive.
Active remote sensing uses sensors that emit their own energy or light source and then detect the radiation reflected. An example of this is LiDAR (light detection and ranging), which uses laser beams to measure distances and movement in real time.
LiDAR is used to create topographic maps as well as the precise, 3D models that guide autonomous vehicles through streets. Active remote sensing is also used to assess natural disasters like lava flows, landslides and floods.
Passive remote sensing does not emit its own energy. Rather, it collects naturally emitted and reflected radiation, that is, from the sun. Common examples of passive remote sensors include radiometers (which measure electromagnetic radiation) and accelerometers (which measure acceleration).
The volume of remote sensing data has increased significantly in recent years, due primarily to the increase of satellites and improvements in sensing technology. This has made remote sensing data management increasingly difficult as well. AI foundation models are being introduced to help analyze the growing volume of remote sensing data, making it easier for organizations and government agencies to perform analyses and answer specific questions.
Geospatial analysis identifies patterns and makes predictions using geospatial data. Organizations can employ geospatial analysis by using GIS hardware and software to produce visualizations that display spatial relationships, that is, how different geospatial elements relate to each other. These visualizations can include maps, graphs, statistics and cartograms.
Without GIS technology and GIS analysis, large geospatial data sets and the insights within are easily overlooked due to their complexity. The GIS visualizations mentioned earlier display this data in digestible formats with recognizable patterns.
The easy-to-understand additional context of geospatial analysis brings new perspectives to the business and enables more informed decision making. For example, a utilities company can use geospatial analysis to analyze the performance of hundreds of thousands of miles of power lines to help predict service disruptions from extreme weather, see which areas are most at risk and optimize maintenance schedules.
Geospatial analysis effectively conveys the shape and the energy of a changing situation. And as an organization gathers more spatial data around a scenario, it becomes even easier to spot nuances and make better decisions around them.
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Footnotes
1 International Journal of Epidemiology (link resides outside ibm.com), Volume 42, Issue 6, December 2013