It can be challenging to prepare a big data set for analysis. Gravy makes it easier by filtering and flagging location signals during processing with Location Data Forensics, which enables analysts to filter valid data by signal origin, location accuracy, and other key characteristics. There are over a dozen forensic flags that provide analysts with detailed, signal-level insight.

Let’s look at two real-world examples to understand how forensic flags can be used for transportation, logistics, and retail use cases.

“Likely Driving” is a forensic flag that indicates a set of location signals for a mobile device were moving at a rate of speed consistent with a device traveling in a vehicle or train. If an analyst wants to observe traffic patterns in an area of interest, this flag would be the right one to use.  

Here is an example of how one analyst filtered Gravy’s Observations data using the “Likely Driving” flag to see movement patterns between Target’s domestic supply chain locations. By working only with the location signals associated with devices traveling at speed, the analyst was able to gain a high-level view of the relationships between each stop along the route and travel time frames, as well as determine which port the freight originated from domestically.

At this geographic scale, an analyst may not require only high accuracy signals and prefer to retain as many signals as possible for their analysis. To do so, the analyst would choose to include location signals flagged for any accuracy range, as follows:

• “High Accuracy” 
• “Moderate Accuracy” 
• “Low Accuracy”

If a researcher is analyzing movement patterns within a smaller area for a different objective, such as to measure foot traffic at a commercial location of interest, they would want to use a different combination of forensic flags to obtain their ideal data set.  

For example, in this analysis of foot traffic to the Mall of America, the analyst wanted to understand the traffic within this building, rather than any kind of activity on the adjacent roadways. To focus solely on pedestrian traffic, this analyst excluded any location signals flagged as “Likely Driving.”

This analysis also required the inclusion of signals flagged as “Moderate Accuracy” or “High Accuracy” to enable more granular insights into foot traffic patterns between different areas of the mall. In this case, signals with the “Low Accuracy” flag were not used.

The resulting data was then graphed to clearly show the foot traffic pattern at Mall of America between December 2018 and March 2021. Spikes in foot traffic were evident in August and December of 2019 in conjunction with back-to-school and holiday shopping, respectively, as well as a dramatic drop in April of 2020 at the onset of COVID-related shutdowns.

Having a clear view of foot traffic throughout the mall can inform where new stores or restaurants should be placed, where the mall may need better infrastructure to support its high traffic areas, and how foot traffic patterns change over time.

Gravy includes forensic flags with every delivery of Observations data, which makes it easy for analysts to quickly segment the right data for analysis. We only touched on a few of the available forensic flags and possible combinations in this blog. This approach to data preparation can also support many other use cases, such as:

• Market research
• Catchment analysis
• Fraud detection
• Event analytics

Our location intelligence experts can help guide you towards the right combination of forensic flags to use in your next analysis. For more information on Gravy Observations data and forensic flags, speak with one of our location intelligence experts today.