Unique identifier of a POI. Use it to recognise the point of interest or as a foreign key to link different files and/or packages together.
The analysis date on which the KPI is calculated.
The part of the week: weekend or weekdays
The moment of the day used to provide the breakdown of the Popularity KPI as defined below. Please note that time ranges are sometimes overlapping and therefore it is not possibile to calculate the "total" popularity of a given day by adding the popularity figures of each individual timeframe. This choice was made to better represent the time of days that are actually more relevant in real life even if this means not having perfectly clean cuts between the different time intervals.The hours are divided as follows in 24hr format and in the POI time zone:Early Morning: from 04 to 08Late Morning: from 09 to 12Midday: from 11 to 14 Afternoon: from 15 to 18Evening: from 18 to 22Night: from 22 to 04
Proprietary percentage KPI (uncapped range) that measures the popularity of a POI.The score is calculated through the analysis of multiple factors such as the number of geo-localised reviews and social media content contained in the calculation period.
Indeed, we have verified the comparison between the number of individuals transiting from Monday to Friday and the number of individuals transiting on Saturday and Sunday.
No, it is not accurate to assert that the total popularity of the outlet is simply the sum of weekend and weekday traffic, as these two quantities do not combine in a linear fashion.
Neither assumption is correct. The total popularity of the weekday is determined through a proprietary algorithm, and thus, it cannot be directly equated to either averages or sums of time windows.
We do not rely exclusively on one source of information. As a result, there may be discrepancies between the time windows available on one source and those in our Data Packs, leading to potential variations in the data presented.
The determination of popularity across different time intervals is a multifaceted process, influenced by the proportion of individuals present during each specific period, as well as the impact that the point of interest exerts on those individuals. To accurately compute these dynamics, we employ a proprietary algorithm, ensuring a comprehensive and nuanced analysis tailored to our unique parameters.