By 2025, the geospatial analytics market is estimated to be valued at $96.3 billion. That’s a 12.9% CAGR over the forecast period beginning in 2020. Esri co-founder and CEO, Jack Dangermond, believes the future of the geospatial industry lies in the proliferation of location data and the ability to connect vast quantities of data for deeper insights about a physical place.
Given these forecasts, the industry can expect to see a rapid increase in organizations incorporating geospatial data into their workflows and architectures as they become more data mature. This will vary by use case, with some aspects of a location being more important to one industry than to another. For example, a retailer may rely more on points of interest (POI) data to evaluate the competitive landscape, while an insurer may require detailed environmental data to underwrite policies. Whatever the industry, everything has a location, and geospatial data makes location actionable.
With the multitudes of location data existing today and the rapid growth expected in years to come, what does the geospatial data ecosystem look like? How do datasets relate to one another, and how do organizations determine how to leverage location to make better decisions?
SafeGraph is committed to democratizing access to data. To help organizations navigate the changing data landscape, we’ve mapped out a geospatial data ecosystem.
Methodology: SafeGraph conducted market research to compile a list of geospatial data categories. Categories are thematically defined, and align with how users search for data and apply it in an analysis. In most cases, this means datasets within a category are typically the same type (raster, point, line, or polygon), as is the case with Boundaries data (predominantly made up of polygonal data). However, other categories are comprised of multiple types of datasets, such as Environment data, which can contain lines for rivers or rasters for vegetation.
Each category is defined as the following for this ecosystem map:
While we include anonymized mobility data, we have chosen not to include individual-level consumer data or other personally identifiable information (PII). Other categories of geospatial data exist, such as those that include PII. This ecosystem maps the most common geospatial data categories that do not include PII.
Every day, more data is created with a location component. The creation of unique identifiers, such as Placekey, makes it easier than ever to link data together for deeper insights and more efficient analysis. While it would be impossible to fully capture the multitudes of location data within one graphic, we hope this ecosystem map provides context for the geospatial data industry and sparks a healthy debate about what constitutes the given categories. As a US-based company, we recognize there are many international geospatial data providers out there we might have missed. Did we leave something out? Let us know.
Important note: The scope of this market research did not extend to open source data providers. It should be noted that government entities, such as the U.S. Census Bureau and other organizations, do provide geospatial data across the various categories, but they are not listed here. This list also does not include geospatial software providers, although some companies listed do provide both software and data. For purposes of this study, SafeGraph is strictly defining ecosystem as the network of datasets that can be used in a geospatial analysis.
We were strategic in our placement of each category in the ecosystem map:
The geospatial analytics industry’s rapid development is bound to have an effect on data ecosystems in years to come. New categories of data could develop, new companies will inevitably join the market, and how these datasets operate together will likely change. The SafeGraph team is thrilled to be part of the growing geospatial industry.
As the geospatial market continues to grow, so will this map. Did we leave something out? Let us know.