Geospatial data is any data related to an object’s position on earth. The object may be a natural landform (mountain, lake, river) or man-made (road, car, building). They object may be static (such as a mountain or building) or dynamic position (ship, storm). The data is generally recorded as coordinates and graphed topologically.
Geospatial data surrounds us. In the last decade or so, it has become so prominent that it has become a feature of technology which once had no geographic component, such as geo-tagged tweets, images on Facebook or Instagram, and so on.
This data category includes many sub-categories of data, such as traffic data (road, marine or air), weather data, satellite data, and store location data. All these types of data refer to certain natural, social, or demographic information about their geographic location. This information serves many private and public businesses and institutions.
This data is collected in a number of ways: field data collection, data conversion, remote sensing data processing, and geographical information science (GISc, which is defined as the scientific discipline that studies the techniques to capture, represent, process, and analyze geographic information).
There are many free and open GIS that collect data using different methods of collection. Among these are OpenStreetMaps, NASA’s SEDAC, and Esri Open Data Hub. All of these are sources for other uses of geospatial data, as well.
LIDAR (light detection and ranging), RADAR, hard copy maps, aerial and satellite imagery, and surveying samples are other common sources.
Geospatial data attributes vary depending on the provider and the sub-category of the data in question. Common GIS attributes include object location, POI, traffic congestion level, local tourism statistics, customer lifestyle, renewable resource sites, and natural disasters data.
Geospatial data can provide powerful insight into the population in a certain area. Marketers, business intelligence teams, and executives use the data to determine areas of maximum activity and identify a store or business’s target audience.
Internet and mobile app companies also use this data to enrich existing app usage data sets, enabling demographic analysis of their audience based on their location.
A good indicator of data quality is the reputation of the organizations providing your data. You can generally trust government and systems organizations that provide geospatial data to businesses or other organizations.
Tools like ArcGIS Data Reviewer are made to review and identify errors in data but the most reliable way to identify errors in data is the visual review of irregularities. However, this is not efficient or even possible when your data is very extensive and diverse.
Like with most categories of data, the biggest factors to vet and take into account when selecting geospatial databases are questions of accuracy and update frequency. These are the factors that should be most pertinent when in contact with potential providers. You should also ask for a sample set for testing purposes.
Walgreens pharmacy uses geospatial data to determine the spread of the seasonal flu and target communities suffering the most from the disease. Their maps show where the flu is hitting the hardest before the same information is released by US Disease Control.
Starbucks also depends on geospatial data to fuel its growth, analyzing micro-level location data to find the best place for a new franchise location.
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