Geospatial data is a modern day natural resource in abundant supply. It is also cost effective, helps improve decision making, and powers high performance teams and investments.
Geospatial technology spans a broad expanse of the business landscape, but the entire ecosystem is built on one thing: an exhaustless supply of quality big data. Fortunately, the world's geospatial data reserves are loaded. Some types of data are even open sourced. Why are such large data sets of such high quality so readily available?
As physical location can be gathered via IP address from a landline, spatial data in the form of GPS signals is gathered from common mobile devices. This includes mobile phones, beacons, Wi Fi, raster data (think: satellite images), connected vehicles, and other remote sensing devices that keep track of where people and things are -- the raw fuel for all GIS solutions.
Geospatial data has natural confluence with many other forms of both public and business big data. When one is used to augment the other, the resulting stream is a flow of real world insights that inform investment decisions, detect change sooner, and measure its effects.
What follows are examples of how big data for geospatial and GIS can be applied in common use cases.
Data scientists want to study migration patterns
The primary objective in many cases is to pinpoint areas with growing or depleting populations. For example, by identifying areas with greater outflows, to find out which regions are more susceptible to an economic downturn and thus cities, CRE investors and others can divest or increase investments accordingly. In this case, the data scientist is likely to combine several streams of traditional data and artificial intelligence tools to a large volume and variety of data.
Banks want to increase return on assets
Banks can use the same data urban planners do just by looking at it a different way. One use case is using big geospatial data to drive insights into the movements of people throughout the United States, offering information on regional behavioral differences, permanent vs. temporary moves, and ideal commercial hub locations to be developed. This, in turn powers more efficient CRE transactions, and more accurate demand forecasting in a given regional area.
Home builders want to validate investments
Home builders identify income trends across regions to find the best fit for new developments. Geospatial data processing and big data analytics are used to pinpoint areas experiencing sustainable growth, and to understand regional differences in behavior. Digging into behavior helps draw a line connecting data sources that span rural, urban, and suburban areas, so key market indicators are not missed.
Restaurateurs want to aggressively expand
The competition between KFC and Popeyes is tight, and Popeyes is expanding. Though still the underdog in terms of total foot traffic, Popeyes is strong in the south where state populations are growing, meaning that gap may continue to organically close. At the minimum, the volume of data regionally dictates that Popeyes evolve their presence in the massive Florida market by pushing further into mid-sized cities in the sunshine state.
Big box stores want competitive intelligence
The competition between Target and Costco foot traffic is much closer than it may appear. Repeatedly, the theme in the west, southwest and mountain states is of Costco competing on equal terms with Target in most markets outside Los Angeles. Geospatial big data and analytics reveal that, If Costco wants to build from territorial strength, a growth focus in Colorado and Arizona will be essential.
