CrowdTrack: Spatio-temporal estimates of pedestrian concentrations
Locations with greater footfall attract more customers and are likely to be more profitable, especially when new locations fill gaps in the existing network, and provide access to key demographics.
By applying machine learning techniques to open spatial and temporal datasets, CrowdTrack provides spatiotemporal estimates of pedestrian concentrations.
PEDESTRIANS | USER AND CENSUS DATA
CrowdTrack allows users to gain insight into:
when and where urban crowds tend to form
spatial and temporal variations in user datasets, such as differences in store performance or timing of peak sales according to location
desirable locations for new stores or ads, based on footfall, Census data, and existing locations
Users upload .csv files indicating time series observations at specific locations (e.g. sales, customers, etc.), or coordinates of many locations.
User data are displayed on a map of pedestrian concentrations and socioeconomic data.
Users can zoom, pan and select regions and time periods over which all datasets are displayed.
Dr. Kristina Luus, Dublin Institute of Technology
Dr. Sarah Jane Delany, Dublin Institute of Technology
Camille Nadal Université Paul Sabatier de Toulouse (France)
Dr. Caroline Maillet, Dublin Institute of Technology
Marek Bielik, Dublin Institute of Technology
By Kate McCarthy|2022-07-19T12:58:39+00:0019 July 2022|Comments Off on CrowdTrack: Spatio-temporal estimates of pedestrian concentrations