Geospatial data is information that describes objects, events or other features with a location on or near the surface of the earth. Geospatial data typically combines location information (usually coordinates on the earth) and attribute information (the characteristics of the object, event or phenomena concerned) with temporal information (the time or life span at which the location and attributes exist). The location provided may be static in the short term (for example, the location of a piece of equipment, an earthquake event, children living in poverty) or dynamic (for example, a moving vehicle or pedestrian, the spread of an infectious disease).
Geospatial data typically involves large sets of spatial data gleaned from many diverse sources in varying formats and can include information such as census data, satellite imagery, weather data, cell phone data, drawn images and social media data. Geospatial data is most useful when it can be discovered, shared, analyzed and used in combination with traditional business data.
Geospatial analytics is used to add timing and location to traditional types of data and to build data visualizations. These visualizations can include maps, graphs, statistics and cartograms that show historical changes and current shifts. This additional context allows for a more complete picture of events. Insights that might be overlooked in a massive spreadsheet are revealed in easy-to-recognize visual patterns and images. This can make predictions faster, easier and more accurate.
Geospatial information systems (GIS) relate specifically to the physical mapping of data within a visual representation. For example, when a hurricane map (which shows location and time) is overlaid with another layer showing potential areas for lightning strikes, you’re seeing GIS in action.
Types of geospatial data
Geospatial data is information recorded in conjunction with a geographic indicator of some type. There are two primary forms of geospatial data: vector data and raster data.
Vector data is data in which points, lines and polygons represent features such as properties, cities, roads, mountains and bodies of water. For example, a visual representation using vector data might include houses represented by points, roads represented by lines and entire towns represented by polygons.
Raster data is pixelated or gridded cells which are identified according to row and column. Raster data creates imagery that’s substantially more complex, such as photographs and satellite images.
Examples of geospatial data
Examples of geospatial data include:
Geospatial technology refers to all the technology required for the collecting, storing and organizing of geographic information. It includes the satellite technology which allowed for the geographic mapping and analysis of Earth. Geospatial technology can be found in several related technologies, such as Geographic Information Systems (GIS), Global Positioning Systems (GPS), geofencing and remote sensing.
Geospatial technology and Python
The popular programming language Python is well suited to working with geospatial data and is capable of accommodating both vector data and raster data, the two ways in which geospatial data are typically represented. Vector data can be worked with by using programs such as Fiona and GeoPandas. Raster data can be worked with by using a program such as xarray.
Dealing with large geospatial data sets presents many challenges. For this reason, many organizations struggle to take full advantage of geospatial data.
First, there is the sheer volume of geospatial data. For example, it is estimated that 100 TB of weather-related data is generated daily. This alone presents considerable storage and access problems for most organizations. Geospatial data is also stored across many different files, which makes it difficult to find the files that contain the data needed to solve your specific problem.
In addition, geospatial data is stored in many different formats and calibrated by different standards. Any effort to compare, combine or map data first requires a significant amount of data scrubbing and reformatting.
Finally, working with raw geospatial data requires specialized knowledge and the application of advanced mathematics to conduct necessary tasks, such as geospatial alignment of data layers. Unless analysts are proficient and experienced at this work, they will not get value from the data or make progress toward their organization’s business goals.
Geospatial data collection
Because the sheer volume of geospatial data routinely required by enterprises is prohibitively large, many organizations look to using a service to obtain curated geospatial data.
Regardless of where you source your geospatial data, data quality must always be maintained. Poor data results in models of little or limited use. (The cautionary phrase “Bad data in — bad insights out” proves brutally true.) It seems self-evident that organizations can benefit significantly from having a solution in place that curates and checks data, so any “garbage” data gets properly accounted for.
Geospatial data management
With so much data now in abundance, managing it takes on considerable importance. Many organizations are finding themselves overrun with data and are turning to their in-house data scientists to help them manage it. It has been estimated that as much as 90% of data scientists’ time is spent on data-curation activities, including organizing, “cleaning” and reformatting data. That leaves those data scientists with only 10% of their workday to devote to analyzing data trends and using those insights to help shape business policy.
When a company turns over data collection and management to a solution such as IBM Environmental Intelligence Suite, both data collection and data management activities can be executed more efficiently. The solution is scalable, cloud-based and able to accommodate different file formats. By using a curated database of optimized information, data scientists can have more time to concentrate on how to use analytic insights and convert them into organizational progress and business impact.
Through data anomalies, geospatial data can give organizations a heads-up regarding incoming changes set to affect their enterprise.
Using geospatial data can provide organizations with evidence of why and how some analytics solutions work well while others don’t.
Organizations can use the numerical precision provided by geospatial data to improve the overall efficiency of company operations.
Although geospatial analysis, as empowered by GIS, was originally used in connection with life sciences such as geology, ecology and epidemiology, its use has since become manifest throughout most industries. Its applications now touch industries as diverse as defense and social sciences. And the insights that geospatial analysis generates affect matters as critically important as natural resource management and national intelligence.
Geospatial analysis lends itself to the study of many things at once, monitoring hundreds or even thousands of events and collecting pertinent data from them. This provides enterprises of all sizes the chance to leverage data to make more informed business decisions:
Efforts to analyze massive amounts of data have become more challenging in recent years due to a relative explosion within the Internet of Things (IoT). Objects and devices of all types and purposes are now being engineered to be able to transmit data relevant to that device’s performance or protocols. That’s good news for geospatial analysis, which involves a profusion of data in order to glean valuable insights.
When the collection of data achieved through geospatial analysis is combined with a heightened visual approach that maximizes the data’s impact by organizing it according to time and space, that is geospatial analytics.
When data is made visual in this way, it makes it easier for those studying it to derive indications about trends that might be at work. Geospatial analytics is able to effectively convey the shape and the energy of a changing situation. And as increasing amounts of data are gathered about that scenario, it becomes easier to spot even more subtle nuances within that situation.
The geospatial analytics market is presently experiencing considerable and steady growth; in fact, the market is expected to grow in value to USD 96.3 billion by 2025, achieving a 12.9% annual sales growth during the 5-year period under review.¹
Geospatial applications by industry
Here’s how different industries are using geospatial analytics:
COVID-19 mapping is performed using geospatial analytic models, based on population data, livestreaming video, maps and weather.
Through user defined functions (UDFs), geospatial analytics enables those involved in vegetation management to assess water and moisture levels.
User defined functions are also useful at helping meteorologists work with incoming data to chart the path of tornadoes that could be moving through an area.
Having relevant data — such as satellite imagery, census data and wind forecasts — in one platform lets incident commanders chart wildfire growth and movement.
Most experts expect geospatial technology to become increasingly sophisticated, especially as that technology comes into closer contact with machine learning and AI.
In fact, it is expected that geospatial AI will also come into its own, bringing a geographic element to machine learning. Experts also forecast the arrival of mapping as a service, in which custom maps of remarkably high resolution can be produced for hire, based on consumer or industrial need.
Also in development are new types of vehicles that rely expressly on geospatial technology. They will be used in greater frequency — whether they traverse the sky carrying packages (drones) or drive themselves down streets (autonomous vehicles). New applications for these technologies will also be found, such as using drones for aerial-mapping purposes.
Meet the platform engineered for geospatial-temporal data, complete with over 6 PB of datasets representing a huge array of categories.
See what happens when you apply powerful analytics to the management of all vegetation within a service territory.
Power location-based apps and capably handle advanced geospatial queries while utilizing storage optimized for spatial data.
Master fundamentals of geospatial data for Python, including the difference between vector and raster data, working with coordinate systems, and geospatial data samples.
Learn more about environmental and weather related effects on business operations in articles on the IBM Business Operations Blog.