Key Metrics & Heatmap
Key Metrics & Heatmaps
propella.civic generates a number of metrics that can be used to analyse the utilisation of any geo-fenced area. These metrics are based on the sample of mobile devices detected within the area for the specified study period. These metrics are best used to support comparative analysis between multiple assets or precincts, but also provide insights into visitation patterns and behaviour (such as average stopping time/dwell time, and repeat visitation).
Set Study Period
The key metrics are defined across rolling 12-month time periods to allow users to understand how they change over time. The Study Period rolling time periods are defined on a 3-monthly basis. In the example below, the Study Period starts on 01/04/2021 and ends on 31/03/2022 (12 months from Study Period start date).
Note that our historical data commences from 01/09/2019. The earliest Study Period within the platform is 01/01/2019. This implies a study period from 01/01/2019 – 31/12/2019, which will only include data from 01/09/2019 – 31/12/2019. Setting the Study Period Start Date to 01/09/2019 will provide the first full 12 months of historical data.
Defined as the number of unique/distinct mobile devices detected within a geo-fenced study area for the study period. These devices may appear multiple times within the study area over the study period (which is measured by the Repeat Visitors metric).
Visit Count (Stays)
The count of identified visits from the sample data during the study period. Visits are derived by grouping together multiple mobile events ("pings") from a single device that occur in the same place for a duration of over one minute - we term this a "stay", but it can be considered a visit in this context.
Average Stopping Time
The median stopping time ("dwell time") calculated for devices that are detected within the study area for the study period. This metric is measured in minutes. Note that this metric relies on the device being detected within the study area, and continuing to generate mobile events (“pings”) to determine the length of stay for the device. This does not always occur. As such, the derived average stopping time is likely to be less than the actual av. stopping time as there may be time spent by a mobile device user in the study area before they registered a mobile event, and they may stay longer than their last observed mobile event. Nevertheless, this metric can provide insight into how dwell time varies across different assets or precincts, or study periods.
Because the identifiers for the mobile devices within our mobility data persist for the life of that device, we have the ability to derive how many times a unique mobile device is detected within the study area during the study period. This metric describes the percentage of devices observed on two or more separate days with the study area (for the study period).