Types of Data used in Location Intelligence

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Types of Data Used in Location Intelligence

Location Intelligence: Location Data and Beyond

When someone says ‘Location Intelligence’, the concept that would typically come to mind is geographic or location data. And rightfully so, it is, after all, a huge part of it—one might even say, the structural foundation of it. But Location Intelligence (LI) is so much more than just geographic positions on the surface of the Earth. It is a multifarious process that transforms a collection of various sources of geospatial data into conducive insight and business solutions with the help of an indispensable computer system: the GIS (Geographic Information System). 

If location data is the structural foundation of LI, GIS is the mastermind and operator of it. It captures, stores, analyses, and displays seemingly unrelated geospatial (or geospatial-related data) to help individuals and organizations comprehend spatial patterns and relationships. 

LI can accommodate a wide variety of data, ranging from population demographics such as age, ethnicity, recent consumer purchases and internet browsing habits, to information pertaining to landscapes such as locations of lakes, types of vegetation and soil, and so on. It enables the painting of an intricate, comprehensive, and versatile picture of the world as we know it.

So What Does Location Intelligence Encompass?

Location Data 

The base of what constitutes LI, location data could be summed up as a precise and consistent answer to the question: “Where?”. It comprises information regarding the geographic positions of devices (such as smartphones, tablets, etc.) and therefore the humans who operate them, as well as structures, such as buildings, landscapes, and so on. Consequently, people generally believe that location data equals GPS data. In fact, there are several types of location data. 

  1. Digital

Social Media Data

“Dinner Date for Two!” —at Maestro’s Steakhouse #Date #SaturdayNight

Social media offers a rich set of publicly available posts and tweets that can be queried by geo-coordinates, hashtags, trending topics, etc. It captures minimal user profile information using algorithms to identify user demographics, such as age, gender, etc., as well as ambient location information, which is then analyzed to provide personal location intelligence and visualizes possible areas of interest for specific groups of audiences. Such data can be captured from social media platforms such as Twitter and Instagram API.

Point of Sales (POS) Data

Two words - Consumer Behavior. 

POS data is derived from consumer transactions, such as the items they purchased, the amount spent, payment methods, and so on, which is potentially valuable to retailers, investors, and suppliers to understand both item and store-level sales performance with high accuracy and depth. When users click on products, review product information, and purchase products from a website, their information is stored in database tables using Structured Query Language (SQL). Essentially, they know who you are, what you like and are looking for, what you have purchased previously, and what method(s) of payment you used to purchase those products. However, since POS data is decentralized, the challenge of integrating multiple data sources arises.

Bid stream Data

Bid stream data is obtained from ad servers when ads play on applications or websites. Ads can collect locations, IP addresses, and device IDs to provide information on where the ad was served. Where were you when you saw the ad? What kind of device did you use? What operating system does your device have? - these are the questions that can be answered with the help of bid stream data.

  1. Maps Traffic

Point-of-Interest (POI) Data

While most types of location data are focused on identifying devices, their locations, and extracting user information regarding consumers from the foregoing, POI data illustrates the physical locations of structures: businesses, landmarks, restaurants, and so on.

POI data is normally composed of various attributes of the physical location, such as its name, address, coordinates, function, etc. Whenever you look for “Restaurants near me” on Google Maps, a list of places identified as dining places within proximity of your current location (your coordinates) comes up, with the address, contact and franchise information readily available for those restaurants.  

Generally, POI data is used in association with other types of location data to acquire a further understanding of consumer traffic and behavior. Together with store location information, metadata concerning foot-traffic to POIs in terms of the number of visitors is used to help enhance trade area analysis. Moreover, ad-tech companies formulate geofences from the information obtained in POI to assist them in the development of audiences and audience segments. This information can provide insight on who these specific places are interesting to and why.

Road Traffic Data

Gone are the days when people would stand in front of billboards for hours on end, manually counting the number of heads that passed by with a tally counter. Older methods of measurement were not only highly inefficient and time-consuming but also prone to higher levels of inaccuracy and limited the richness of the data obtained. 

The advancement of technology has enabled more innovative and automatic ways to obtain traffic data. Some of the widely used instruments are pneumatic tubes, inductive loops, weigh-in-motion sensors, micro-millimeter wave radar detectors, and video cameras. Traffic data can yield insight on hourly patterns at certain times of day in specific areas, the day-to-day variations of traffic throughout the week, and the season-to-season variations in movement throughout the year. This information is highly beneficial to the marketing world in terms of choosing the right locations, times of day, and periods to play ads.

  1. Smartphone Applications 

Software Development Kit (SDK) Data

“Allow this application to access your device’s location?”

SDKs are source codes embedded into applications by developers that “instruct” the application to gather location data of the device it is installed on. With express user permission, these codes can track a user’s daily habits, potentially divulging valuable and highly accurate insights for businesses. 

Global Positioning System (GPS) Data

The standard for location data, GPS provides geographic positions in the form of latitude-longitude coordinates with the help of hardware on devices (such as car navigation systems, your mobile device, fitness trackers, etc.) and satellites.

GPS can yield highly accurate and precise data in optimal conditions. The keyword here being optimal—the accuracy of data obtained from GPS can decline significantly indoors or in spaces that hinder the view of several satellites and can potentially cause interference.

  1. Internet of Things (IoT) Devices

Weather Data

Can you specifically target consumers with your refreshing new beverage only when the weather is sunny outside? Or target people prone to have bad hair days on humid or rainy days? Or attribute your ad’s success or failure to the weather conditions at the time of your campaign? The answer is yes. Studies show weather is the 2nd biggest influence on consumer behavior, and approximately 30% of the US’s gross domestic product is affected by severe weather conditions each year.

Consumer decision-making is highly driven by weather conditions; it influences our judgments on the type of clothing to wear, where we go, the things we eat and drink, our purchases, and most importantly, our emotional conditions. This makes weather data the perfect tool for contextual targeting. It is virtually the only real-time data set available to marketers which provide an insight into a consumer’s mood, desires, and purchase intent at any given moment.

Weather data providers provide weather data API’s that can analyze weather conditions beyond temperature, such as precipitation rate, pollen count, etc. and can provide not only real-time weather data but also insight on weather patterns and upcoming forecasts.


Beacons are hardware transmitters that can sense other devices within close proximity of them. They are WIFI and Bluetooth based IoT devices that detect smartphone presence in real-time. They collect data regarding locations, as well as the names and birthdays of those device users. Some beacons have onboard cameras available that can be used for video analytics using machine learning based on Computer Vision (shape recognition) technology.

Advances in technology have enabled new possibilities for Out-of-Home Media Measurement. It has led to the emergence of not only more effective, cutting-edge data sources, but also new tools and analytical techniques that provide in-depth insights into the advertising world. However, just as no single ingredient can provide you with the required nutrients and complexity of a balanced meal, no single data source can give you the full picture of your location’s data; to each data source, its own unique perspective, purpose, and of course, its own benefits and drawbacks. In order to form reliable analyses and proper judgments, the integration of several distinct data sources must be implemented—which is, in essence, the very purpose and definition of Location Intelligence. Location Intelligence, akin to a perfectly cooked balanced meal, shall continue to be a force to be reckoned with in the world of advertising.

Related Articles:

  • Content 17: Why Does Location Intelligence Matter in Out-of-Home?

  • Content 19: Benefits and Challenges of using location Intelligence

  • Content 20: How is location intelligence applied in OOH campaigns today?



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