Geospatial - A Complete Introduction

What is Geospatial?

Geospatial definition: Any data that is indicated by or related to a geographic location. Geospatial technology collects and analyzes the geospatial data.

Image depicts example of geospatial visualization.

What is Geospatial Data?

Geospatial data, also known as geodata, has locational information connected to a dataset such as address, city or ZIP code. Geospatial data can also come from Global Positioning System (GPS) data, geospatial satellite imagery, telematics devices, IoT and geotagging.

What are the Types of Geospatial Data?

The two main types of geospatial data are vector data and raster data.

Image from Seunghee Park

Vector Data:

Uses geometric shapes to show the location and shape of geographic features. Points, lines and polygons can represent things like cities, roads and waterways. Vector data is scalable, has small file sizes and ideal for depicting boundaries.

Raster Data:

Represents data through a digital image such a scanned map or photograph. It also includes aerial and satellite imagery. Raster data uses a cell-based format called stairsteping to record data as pixels or grids with an image. Spatial analysis depends heavily on raster datasets.

What is Geospatial Technology?

Geospatial technology is used to collect, analyze and store geographic information. It uses software to map geographic locations while analyzing the impact of human activity. Geographic Information System (GIS) uses digital software to combine maps and datasets about environmental events and socioeconomic trends. GIS creates layered maps to better analyze complex data. The layering is possible because each data point is connected to a precise location on Earth. Other forms of geospatial technology include GPS, remote sensing, and geofencing.

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What is Geospatial Intelligence?

Geospatial intelligence is also known as GEOINT, an intelligence discipline that analyzes geospatial data to better understand and visually depict human activity based on a specific geographic location. Data sources for geospatial intelligence imagery include mapping data from satellites, aircraft, GPS and census information. While GEOINT was originally the domain of the U.S. government and military, it has more widespread commercial use. Other countries have also adopted these surveillance practices and founded their own geospatial intelligence agency.

Image from Textron Systems

A Brief History of Geospatial

One of the biggest moments in geospatial history was the launch of Google Maps in 2005. It made mapping technology available to a mass audience.

But the seeds for what we know of geospatial technology today were first planted in 1832. During a cholera outbreak in Paris that year, French cartographer Charles Picquet created one of the first heat maps to show where the incidents of illness were concentrated.

When cholera struck London in 1854, physician John Snow built upon the Paris example. In addition to making a map that depicts the location of cholera deaths, he used spatial analysis of the data to show the connection between contaminated water sources and cholera.

By the early 1900s, photozincography was invented. It was a form of map printing with separate layers. Each layer could visually represent data on the map.

In the 1960s, Roger Tomlinson pioneered the concept of a Geographic Information System (GIS) that took traditional cartography to a new level. The advent of satellites focused on national security, scientific, and commercial ventures provided images of the Earth’s surface and human activity for the first time, opening up more ways to visualize data. A GIS combines a base map with the capture, manipulation and management of data. A GIS map can contain unlimited amounts of data. This geospatial data not only lets users visualize and analyze the data, it helps users better understand trends, relationships and patterns.

GIS was further refined until the early 2000s when Google Maps produced a user-friendly version for the masses in 2005.

Since then, geospatial technology has evolved from the desktop to a cloud-based system. Geospatial databases used to be proprietary but open source software has changed that, making access to this type of information more widespread.

Future of Geospatial Technology

The future of geospatial technology largely involves the further integration of machine learning and AI. Mapping as a service, drones and autonomous vehicles are growth areas driving geospatial technology adoption.

Geospatial AI

  • Places a geographic component on machine learning. App users can provide real-time information about traffic or other conditions in their surroundings. Myriads of contributors improve the accuracy of geospatial data, allowing for better predictions when managing things like traffic flow. Also known as Geo AI.

Autonomous Vehicles

  • The eye of an autonomous vehicle is LiDAR (Light Detection and Ranging), which is a sensing method that uses pulsed laser light to measure variable distances. Geospatial technology make LiDAR possible and mapping companies will take it to the next level to make autonomous driving safer and more accessible.

Mapping as a Service

  • Not every map found online is high-resolution. But geospatial mapping technology can make on-demand maps of any location desired. The maps can be created based on customer needs and used for any number of industries, including construction. Geospatial imagery and the Mapping as a Service market could reach $8 billion by 2025.


  • Drones used for aerial mapping will become more common. Combined with GIS and high precision sensors, the drone-capture images can fly over acres of land to gather data to influence everything from farming to urban planning.

Learn about the role of geospatial technology in the future of data science and the future of telecom industry.

What are Geospatial Technologies?

Geospatial technologies provide data for a number of industries that include the military, utility companies, urban planners and industrial engineers. The application of geospatial data is useful for biodiversity conservation, forest fire suppression, agricultural monitoring, humanitarian relief and any field that could benefit from better visualization and analysis of geographic data.

Diagram depicts various geospatial technologies and tools involved.
Image from Geospatial World

Some of the most common geospatial technologies include the following:

  • Remote Sensing
  • Space or airborne camera and sensor platforms provide imagery and data at great detail. Images that can zoom into less than one meter are available on some commercial satellites.
  • Geographic Information System (GIS)
  • Offers software that can map a specific geographic location anywhere on Earth and analyze geospatial data. GIS geospatial can also detect patterns in the data.
  • Global Positioning System (GPS)
  • Provides coordinate locations for military and civilian use through a network of U.S. Department of Defense satellites. A European version is called Galileo.
  • Internet Mapping Technologies
  • Google Earth and Microsoft Virtual Earth are examples of geospatial software and geospatial mapping tools that makes it easier for people to view and share geospatial data.

What is Geospatial Visualization?

Geospatial visualization or geovisualization (also referred to as geographic visualization) is the use of geospatial visualization tools and techniques for the analysis of geospatial data. Geospatial data visualization is a constructive practice that integrates interactive visualization into traditional maps, allowing the ability to explore different layers of a map, zoom in and out, change the visual appearance of the map, and relate a variety of factors to the geographic area. Geospatial modeling uses advanced cartographic technologies allowing analysts to visualize geospatial data, spanning industries such as urban planning, transportation, and investment management.

Geospatial visualization tools like HeavyImmerse cross-filter geospatial data and other big data sets to instantly derive insights and guide business decisions. Geospatial visualization software takes advantage of GPU-acceleration to render changes to a map in real time, creating geospatial visual analytics that communicate location intelligence and big data in ways never seen before.

What is a Geospatial Analyst?

A geospatial analyst develops ways to retrieve data from geographical information that can be used for understanding the world. Geospatial analysts and geospatial engineers use technologies like GPS, geospatial satellite imagery and laser mapping to create layered maps of geographic information that is natural or built by humans. This visualization provides insights into the impact of human activity on Earth which can help to combat climate change.

Geospatial engineers work in the private and government sector. Their geospatial jobs utilize technologies like GPS, LiDAR, unmanned aerial vehicles and high-resolution mobile laser scanning.

Image depicts a geospatial analyst analyzing geospatial data with OmniSci's visualization technology.

How Geospatial Analysis is Used

  • Telco data analytics - Enables the correction of issues before customers notice. Geospatial analysis helps companies quickly find and resolve network anomalies by visualizing where the problems occur.
  • Military analytics - Provides situational awareness for military logistics. Geospatial predictive analytics can help the military optimize placement of resources, anticipate maintenance needs and meet deadlines.
  • GIS Technologies for Disaster Response - Visualizes extreme weather like blizzards, wildfires and hurricanes to enable enough advance warning for evacuation alerts, and effective response after disaster strikes. Geospatial data science also helps airlines with routing and gives insurance companies a better way to assess property risk.
  • Urban Planning/Development - Helps planners visualize big data public sector analytics to determine how growing populations affect energy, transportation and housing resources. It can also show how crime, public health and education outcomes vary by location.
  • Natural Resource Exploration - Informs exploration and production of oil and gas from mapping to drilling. Geologists and project managers can use the visualized data to make decisions that reduce costs, minimize risks and improve output.

Geospatial Software Solutions

Muthukumar Kumar, managing editor of the Geoawesomeness blog about geospatial technologies, created a list of the ”Top 100 Geospatial Companies and Startups in 2019” that offer the best database for geospatial queries. The geospatial solutions include:


  • Web-based data visualization interface, leveraging the GPU-accelerated speed and rendering capabilities of HeavyDB and HeavyRender for unparalleled visual interaction.

Orbital Insight

  • Remote sensing, photogrammetry, drones, computer vision, augmented reality. Palo Alto, CA. Founded 2013.


  • Remote sensing, photogrammetry, computer vision, augmented reality. San Francisco. Founded 2016.

Development Seed

  • Location data analytics, location intelligence, remote sensing, photogrammetry, mapping, GIS. Washington, D.C. Founded 2003.


  • Part of Analytical Graphics, Inc. Navigation, mapping, GIS. Exton, PA. Founded 1989.


  • Location data analytics, location intelligence, mapping, GIS. Redlands, CA. Founded 1969.

Spatial AI

  • Location data analytics, location intelligence. Cincinnati, OH. Founded 2016.


  • Remote sensing, photogrammetry. Vancouver. Founded 2010.


  • Organization with the goal of protecting, promoting and growing the GIS profession. URISA supports GIS professionals at all stages of their careers with essential training and resources.

Geospatial Resources

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