Geospatial Analytics Definition

Geospatial analytics gathers, manipulates and displays geographic information system (GIS) data and imagery including GPS and satellite photographs. Geospatial data analytics rely on geographic coordinates and specific identifiers such as street address and zip code. They are used to create geographic models and data visualizations for more accurate modeling and predictions of trends.

Animation depicting geospatial analytics from geographic information system (GIS) data and imagery including GPS and satellite photographs.

 

FAQs

What is Geospatial Analytics?

Geospatial analytics uses data from all kinds of technology — GPS, location sensors, social media, mobile devices, satellite imagery — to build data visualizations for understanding phenomena and finding trends in complex relationships between people and places. This geo-referenced data can be applied to nearly any happening on earth. The visualizations can include maps, graphs, statistics and cartograms that show historical changes and current shifts. This can make predictions easier and more accurate.

Geospatial analytics adds timing and location to traditional types of data and this additional context allows for a more complete picture of events. Insights that might have been lost in a massive spreadsheet are revealed in easy-to-recognize visual patterns and images.

Geospatial analytics companies are able to instantly process huge amounts of geographic and geometric data. This gives users the ability to interact with billions of mapped points while looking at real-time geospatial visualizations. Users can explore data across time and space to instantly see how something has changed from days to years.

 

Benefits of Using Geospatial Data in Analytics

Geospatial data analytics began in the 1960s when Canada used the first geographic information systems (GIS) to catalog natural resources. Today, geospatial analysis is used for data capture to understand anything from weather modeling, population forecasting to sales trends.

Geospatial big data analytics breaks data out of the endless rows and columns of a traditional spreadsheet and organizes it visually by time and space. It is easier for the human brain to absorb information this way. Geospatial data analytics lets the eye recognize patterns like distance, proximity, contiguity and affiliation that are hidden in massive datasets. The visualization of spatial data also makes it easier to see how things are changing over time and where the change is most pronounced.

Benefits of geospatial analytics include:

  • Engaging insights — Seeing data in the context of a visual map makes it easier to understand how events are unfolding and how to react to those events.
  • Better foresight — Seeing how spatial conditions are changing in real time can help an organization better prepare for change and determine future action.
  • Targeted solutions — Seeing location-based data helps organizations understand why some locations and countries, such as the United States, are more successful for business than others.

 

What is Geospatial Imagery Analytics?

Geospatial imagery analytics provides video and image data of the earth. Companies in many sectors use the data to determine future risk and contingency plans.

Geospatial imagery analytics uses data collected from satellite images. The geo-referenced images are then presented as raster and vector images. Raster images are individual pixels of color, also known as bitmaps. Vector images are graphics comprised of mathematical formulas and can be infinitely scaled.

Geospatial imagery analytics is most often used for examining climate conditions, urban planning and disaster management.

There are five markets in geospatial imagery analytics:

  • Imaging — segmented into video and image.
  • Technology — segmented into GPS, geographical information systems (GIS), remote sensing (RS) and unmanned aerial vehicles (UAVS).
  • Analysis — segmented into surface analysis, network analysis and geovisualization.
  • Application — segmented into agriculture, defense/security, energy, engineering/construction, environment-monitoring, government, healthcare, insurance, mining/manufacturing, natural resources. 
  • Geography — segmented into Asia-Pacific, Europe, North America and Rest of the World (RoW). RoW includes Africa, Middle East and South America.

Geospatial imagery analytics companies are building their own satellites to gather the best data. The geospatial imagery analytics market is expected to have a revenue of $9 billion by 2026, driven by demands from the mining/manufacturing and engineering/construction industries.

 

Geospatial Analytics Use Cases

Telecommunications — Quickly visualize call detail records and network logs so network operations centers can fix issues before customers notice. Since network signal strength fluctuates by location over time, geospatial analytics helps telecommunications companies understand where anomalies occur and then resolve them.

 

Military — Logistics for military operations that provide a true view of situational awareness. Geospatial predictive analytics helps the military optimize placement of resources while using predictive analytics to assess infrastructure, anticipate maintenance needs and meet deadlines.

 

Weather — Rapid response to extreme weather by visualizing blizzards, wildfires and hurricanes fast enough for effective evacuation alerts. Geospatial data analytics also helps airlines with routing and gives insurance companies a better way to assess property risk.

 

Urban Planning/Development — Determine how growing populations affect energy, transportation and housing resources. Geospatial big data analytics helps planners visualize large datasets at the speed and scale. It also allows for compiling and cross-filtering data from many sources to see how crime, public health, education and housing/real estate outcomes vary by location.


Natural Resource Exploration — Improve efficiencies in exploration and field operations for oil and gas industries. Geospatial analytics helps inform every phase of upstream exploration and production from mapping to drilling. Geologists and project managers can use the visualized data to make decisions that reduce costs, minimize risks and improve output.

 

Which Enterprises Benefit by Using Geospatial Analytics?

Nearly 80 percent of enterprises possess location data, according to CIO. They benefit from geospatial analytics for business intelligence by being able to locate customers on a map. With address or zip code data, businesses can see where competitors are in relation to customers and decide where to locate a store. In retail, customers who download an app on a mobile phone can be tracked in the store and receive offers in real time

 

Geospatial analytics benefits transportation and manufacturing sectors when it comes to logistics and supply chain management. Enterprises can visualize the most efficient routing scenarios and business processes

 

Government and energy organizations that depend on geographic boundaries benefit from geospatial analytics by knowing instantly and accurately where municipal lines are drawn and the location of underground pipes, power poles and their relation to populated areas. 

 

Here are two examples of companies using geospatial analytics to benefit from real-time situational awareness and decision-making:

 

  • Skyhook — The mobile positioning and location provider uses geospatial analytics to run up to 10 billion transactions daily and map billions of data points in real time
  • Simulmedia — Uses geospatial analytics to process more than 300 million viewing events per day from 20 different sources to show national advertisers the effectiveness of ad campaigns.

 

Does OmiSci Offer Geospatial Analytics?

Yes. OmniSci makes geospatial capabilities a top feature of our GPU-accelerated geospatial analytics platform. Geospatial analysts can use OmniSci to interactively explore up to millions of polygons and billions of mapped points, and business analysts can easily incorporate spatio-temporal analysis in their regular big data analytics workflows.