Differentiating Driving Traffic From Foot Traffic in Retail Space Explorer

Point Inside has been using cellphone data to determine the number of people who visit different shopping centers and particular tenants within those venues. As we were looking at this data, we noticed that we were receiving records from nearby highway and road traffic as well as foot traffic within the venues. In some cases, cars passing by were significantly inflating our estimate of how many people visited a venue, which inflated the shopping center's overall popularity ranking. This issue was also plainly visible to Retail Space Explorer users when displaying foot traffic on our venue maps, as shown below. Notice the clear road way pattern within the heat-map which results in distorted visitor counts and for those tenants nearest these roadways as well as a distorted venue popularity rating.



To fix this issue, we made two significant changes. First, we required the cellphone location to be close to a known shopping center building, instead of just being near the venue as a whole. This filtered out roadways that were farther away from the shopping center. To deal with the rest, we started estimating speed by looking at records from the same phone over time and then filtering out records that moved faster than a walking pace. This cleans up even those cases where the road goes directly over or under the venue. Here you can see the improvement after filtering out these types of records. The foot traffic shown below is clearly foot traffic from with in the venue and not those roadways nearby.




Correcting these issues makes our data and our heat maps more accurate and therefor more valuable to both our Retailer and REIT customers.

To find out more about how Point Inside's Retail Space Explorer tool can help you, visit www.pointinside.com/retail-space-explorer or contact us for more information.

Point Inside

About the author

Point Inside: Founded in 2009 and based in Bellevue, WA, Point Inside is led by a team of executives with decades of experience in positioning technologies, platform development, and location-based services for companies such as Amazon, AT&T, Microsoft, MySpace, PayScale, Qualcomm and SAP.


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