Towards Location-Based Analytics

Juan Huerta is a contributing author to Making Data Meaningful. He is currently a Senior Data Scientist at PlaceIQ where he focuses on location-based analytics. Juan was a speaker at the 2013Business Intelligence Symposiumwhere he spoke on his work of taking large amounts of structured and unstructured data and how he extracts patterns, trends, intelligence and context from this data. He holds a PhD from Carnegie Melon University and resides in the Greater New York City area.

The availability of data incorporating location information is growing. This influx of data has been affected by the emergence and broad adoption of mobile, the intersection of diverse streams of information, the abundance of data-generating and location-enabled devices, and the availability of tools and techniques to extract insights from this type of data, among other things.

In addition to its abundance, location information has proven its value as a proxy for human behavior. Location is a primary marker of consumer intent, both at an individual and segment level. The ebbs and flows resulting from constant movement of mobile devices provide us with a picture from which patterns and insights can be extracted.

Because of these characteristics, it is not surprising that there is an increasing interest across industries in attaining movement-based consumer insights. Marketers, analysts, and decision-makers are realizing the value of this type of data in delivering new types of consumer insights. The possibilities promised by the juxtaposition of information streams relating location, movement, demographics and behavior, are truly exciting.

At the same time, because of its particular dynamic and large-scale nature, the mechanisms, tools and abstractions available for general data do not seem to suffice. Customized approaches to leverage this data are necessary.

Here are a few considerations we need to make when approaching this domain:

Data: We must consider the nature of the data. More specifically, where is the location-related data coming from? To better understand this data, we can categorize it into two types – static and dynamic (i.e., movement data). Static data includes census-related data, satellite photography and maps, business listings, and so on. Dynamic data includes events that occur and are registered as consumers move around in their daily lives. The most important source of dynamic information is the data generated by mobile devices.