What is this Geospatial Data Set and Why You Should Care?
Detailed analysis on geospatial data sets, types and use cases
Let us start with some statistics (and some myth-busting/jargon deconstruct).
80% of the data today has some kind of location component to it.
Have you come across this slide/phrase before? Chances are you have. In fact, this phrase is used world-over to highlight the importance of Location Intelligence or GIS. But this is not new information, this phrase has been in use since 1985 according to this article. The world has since come a long way in terms of the data quality, quantity and the business Use Cases but the marketing gimmick remains.
As a user Location data is powering a host of services we use every day, from checking the ETA to your office as per the traffic to visualizing pollution levels in different parts of your country.
If you are a company that already uses location intelligence (think ride-hailing, ride-sharing, food delivery, ride rentals, e-pharmacy, fleet management, travel and so on) you know how important geographic data is for you.
But even for companies which are not traditionally location-based (or even tech-friendly), GIS data is useful for marketing analytics, revenue mapping, market studies and so on. Some companies use it for personnel tracking or issues tracking. Others use it to derive strategic insights. We know companies of all sizes and industries are using it and using it well. At locale.ai this data is what we process and present to get you specific actionable insights. But to understand what YOU can do with it, it is important to answer the questions:
What exactly is Geo-spatial data? What are the types of datasets under the ambit of Geospatial data that can add meaning to your analyses and where do these come from?
Definitions
Strict terminology suggests that Location data are information about the geographic positions of devices (such as smartphones or tablets) or structures (such as buildings, attractions). While the word geospatial is used to indicate that data that has a geographic component to it. This means that the records in a dataset have locational information tied to them such as geographic data in the form of coordinates, address, city, or ZIP code. GIS data is a form of geospatial data.
Geomatics is the discipline concerned with the collection, distribution, storage, analysis, processing, presentation of geographic data or geographic information.
Geo-spatial data can be both Cross-Sectional (measured at a specific point in time) or Longitudinal/Multitemporal (time-series) data that has geographical coordinates associated with it (latitude, longitude and rarely altitude). Simply speaking, anything captured with location can be useful Geospatial data.
Datatype Variants
The independent variable or the latitude, longitude, and altitude can be of the following types:
- Single point: Useful for scattered data like Points of Interest
- Polyline (Vector): Useful for routes, roads, connections.
- Shapefile (Raster): Useful for administrative boundaries, comparative analysis of sales by region
Functionally, it can be of the following types.
- Commercial (Retail Locations, Point of Sales)
- Socio-Economical (Income groups, Disease Frequency, Crime Rate)
- Infrastructure(Cell Towers, Railway Networks, Cables)
- Human Mobility (GPS coordinates, speed, mode of transportation)
- Financial (Sales, Revenue, Taxation, Profitability, Purchasing Power Parity)
- Demographics (Age, ethnicity, occupation, income level)
- Real Estate (Land Prices, Land availability)
- Road Traffic (Type of Vehicle, Speed, Size of road, Speed limits)
- Points of Interests (Restaurants, Parks, ATMs, Petrol Pumps)
- Asset location (Trucks, Machines, Sensors)
- Environmental (Air Quality, Natural Disasters, Forest cover, rainfall, temperature)
By Source
A lot hinges on the source of the location data. Beacons, GPS, Cell tower triangulation, WiFi — RTT, IP addresses are some standards used to capture this data. The accuracy and reliability differ with the source of the data. Broadly speaking location data can be collected by a company by a combination of these methods:
- Satellite Imagery (Commercial, Military/Navy etc.)
- GPS Satellite & Device on-ground (Phones, beacons, Vehicle GPS)
- Crowdsourced (User entered data — like Google My Business)
- 360 Degree captures — (like Google StreetView)
Use Cases
These datasets are either purchased or collected by companies to generate various use cases. A lot of companies in the space provide APIs and GUIs to play around and test or use these in production. In fact, to end this article let me help you think of a use case in your industry. Broadly speaking there are three Use Cases in this industry (as coined by Google).
- Asset Tracking: If you have moving assets (it can be a cement truck, a biscuit salesman, an insured consignment or delivery boy) you can track them with the help of a sensor (like a phone with GPS or a card with RFID). This can be done real-time or in retrospect. In real-time you could possibly decide the order of their deliveries or their route to be in the most cost-effective way possible. In retrospect, you can analyze your most profitable routes, calculate travel/fuel allowance or costs and improve your field force movement.
- Internal Use Cases: What is meant by Internal is essentially business faced/operations faced use cases. Things like where are my customers, am I selling enough insurance in a particular pin code, Sales numbers by state. You can also correlate different data sets like does temperature affect my ice cream sales, can I use it to forecast sales?
- External Use Cases: External is customer-facing. So how you can think of is can location data help my customer in any way. There are various common examples here like: “Is my customer able to find where his food is?”,”Can she find her cab?” Or a simple store locator (for ATM, Banks, Restaurants, brands etc). To uncommon ones like the exact location of a property, a user might want to buy, its distance or average travel time to the airport/office.
