# SmartMobility v202303

Release notes summarise changes from the previous product version to this version. This is mostly of interest for existing customers who want to be informed about the latest changes and potential impact on their analysis.

## General​

SmartMobility v202303 comes with 3 major changes:

1. Visitation to locations is now based on a Machine Learning model rather than on data aggregation.
2. To reduce the amount of individual tables, several metrics are bundled together into only 7 datasets. The daily visitation metric is dropped because it contained too much noise.
3. Locations are restricted to the retail industry.

## Differences in this version​

From a data perspective, the biggest changes in this version are due to the new methodology - using a machine learning model to estimate foottraffic. Therefore, we focus on those differences here and investigate changes to the weekly visitation compared to the previous version.

Comparing the new versions' weekly foottraffic to the previous version for the year 2021, we see strong similarities. The mean percentage difference between the versions in absolute volume range between +16% (state level) to -1% (brand level), where a positive number indicates an increased estimate in the new version. We do also observe high similarities when correlating those absolute volumes across venues ($r = 0.98$).

• We removed the daily aggregation of visitation because of high noise level.
• SmartMobility comes by default with a 4-day lag. Meaning that the data is available after 4 days.
• Dynamic Trade Areas are not showing the absolute person_count anymore.
• Cross Visitation focuses now on the venue-to-brand comparison since this was mostly requested by customers.

## Improvements​

• With the switching to a machine learning model to estimate visitation, we improved the robustness of the product. The previous product version was very volatile towards underlying changes in the raw GPS supply.
• Several venues which did not have enough supply (and thus data) previously, are now coming with visitation estimates.
• With the new bundling of tables, there are less tables to download to gain a full data experience.
• We now have the capability to include other data sources that can improve our modelling. Previously, visitation numbers were estimated solely based on GPS data. This is now complemented by further contextual data.

## Known Limitations​

• For a limited amount of locations, the model estimates the same visitation over time (creating a flatline). Locations affected by that belong mostly to the industry categories: convenience, drug or toy stores, or gas stations. These affected stores tend to be small-sized locations. Attention will be directed to those venues in the next revision.
• Visitation estimates are better the more urban the area of interest is. This stems from the fact that mobility data is inherently more available in metropolitan areas. The lager the amount of data, the closer we estimate to the underlying groundtruth of that location.
• We do observe a peak of elevated visitation in October 2022. Thereafter, visitation tends trend too fluctuate more. Nevertheless, it appears that more urban locations are less affected.
• The algorithm has been trained on historical data. Any unpredictable event that severely affects human mobility at local or national scale (e.g., natural catastrophe or pandemic) will not be captured by our algorithm immediately, but requires an update once time has passed.
Important

Overall it is important to highlight that machine learning models will be improved moving forward. With that, this new version is only getting better with a future version and, thus, is less dependent on external factors (like new privacy regulations on GPS data).