Locations are a foundational matrix for geospatial applications, which start with locational reference points and then apply statistical and spatial science and analytics to geographical constructs. What organizations now want to do with this data is to further enrich it so geospatial data can tell a more complete story about the locations that it represents.
“Using locational reference points for analytics dates back to the 1960s, so it certainly isn't new,” says Joe Francica, locational intelligence expert for Pitney Bowes, a provider of analytics and global e-commerce solutions. “But with the advent of applications like Google Maps, 10 or 12 years ago, this data took on greater powers of visualization that have continued along the path of further enriching the content that attaches to locational data.”
Examples of locational data enrichment in geospatial applications include forestry mapping that can now include analysis of trees and ground cover, topography, and even soil composition and moisture content. In the urban arena, 3D city maps with locations of buildings, streets and street construction repair sites are deployed over mobile devices to citizens to help them navigate their routes. In all cases, it is a combination of software and data cultivation that builds greater levels of intelligence into the data.
“GIS departments in city governments are beginning to exploit this enriched locational data for more effective government,” Francica says. “By using enriched geospatial intelligence, they can track and monitor assets, whether these assets are buildings, traffic lights or other points of infrastructure. A fire department can look at incident response logs and determine whether vehicles and stations are deployed for maximum effectiveness.”
Francica believes that if governmental agencies develop a geospatial intelligence foundation that collects and analyzes more statistical and static GIS data, and then add to this a stream of dynamic geospatial data that comes from Internet of Things (IoT) sensors on traffic lights, subway tracks, etc., that agencies can take their geospatial knowledge to new levels.
“The process starts with the setup of baseline location data,” Francica says. “From there, agencies can determine the types of data they wish to append to this locational data base so they can expand their geospatial awareness.”
The process isn't always as easy as it sounds. To be effective, agencies must begin with clearly defined business cases and strategies about what they want to derive from their geospatial information.
“Common uses that we see are agencies wanting to use their geospatial data for purposes of zoning and planning,” Francica says. “For instance, what is the best use of a certain parcel of land? Is the parcel in a flood zone? For the proposed use, what is the future impact likely to be?”
In the private sector, insurance companies might use such data to determine risk factors when setting the rates of property and auto insurance. Telecommunications companies using 3D geospatial enhancement of locational data might use the data to look at heights of structures and general topography in order to determine where to place a communications tower to ensure a line of sight for telecom transmissions. And increasingly, government agencies are letting out construction contracts that require builders to use geospatial intelligence in the form of 3D building site and architectural models.
Companies like Pitney Bowes assist in the process by providing analytics services that help organizations harvest the data that they want — especially the raw data that comes in from real-time IoT sensors.
“The challenge that government agencies and private enterprises have is that there is so much IoT data coming in so quickly and from so many directions, that they often miss opportunities to harness this data for business advantage,” Francica says. “We assist them in the process by providing a cloud-based solution that can capture and harvest the data for them, appending the data that they want to their baseline locational data so their geospatial applications are enriched.”
What is the best way for organizations to improve the quality of information that their geospatial applications are delivering?
“First, get your GIS and geospatial people educated as quickly as you can,” Francica says. “There already is an abundance of data sitting out there that can be incorporated into geospatial systems to enrich them, but you have to know what is available and what you want.”
Second, develop crisp business cases that will help you focus on the data that you want to go after. If you are a telecom provider and you have a service level agreement (SLA) with your customers, you want to make sure that any new tower you invest in will always have a clear line of sight for your data transmissions so you can meet these service levels. If you are a city government and you want to improve the response times of first responders to traffic incidents, you want to know where most of the traffic jams and accidents are occurring.
“It’s all about providing a better quality of life for your customers and your constituents,” Francica says. “If you can master your command of geospatial intelligence so you understand exactly what is going on, you place yourself in a better position to do this.”