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Foot traffic is a business term to describe the number of customers that enter a point of interest (POI). This work aims to predict future foot traffic: the number of people from each census block group (CBG) that will visit each POI of a study region with potential applications in marketing and advertising. Existing techniques for spatiotemporal prediction of foot traffic use location-based social network data that suffer from sparsity, capturing only a handful of visits per day. This study utilizes highly granular foot traffic data from SafeGraph, a data company that collects mobility data regarding hundreds of millions of visits per day in the United States alone. Using this data, we explore solutions to predict weekly foot traffic data at the POI level. We propose a collaborative filtering approach using tensor factorization on the (POIs x CBGs x Weeks) data tensor. This approach provides us with a de-noised estimation of visits in previous weeks for all POI-CBG pairs. Using this tensor, we explore various time series prediction models: weekly rolling average, weighted weekly rolling average, univariate linear regression, polynomial regression, and long short-term memory (LSTM) recurrent neural networks. Our results show that of all the prediction models, the collaborative filtering step consistently improves prediction results. We also found that a simple weighted average consistently performed better than the more sophisticated approaches. Given this abundance of foot traffic data, this result shows that we can improve the spatiotemporal prediction of foot traffic data by harnessing collaborative filtering. 

An inherent challenge arising in any dataset containing information of space and/or time is uncertainty due to various sources of imprecision. Integrating the impact of the uncertainty is a paramount when estimating the reliability (confidence) of any query result from the underlying input data. To deal with uncertainty, solutions have been proposed independently in the geo-science and the data-science research community. This interdisciplinary tutorial bridges the gap between the two communities by providing a comprehensive overview of the different challenges involved in dealing with uncertain geo-spatial data, by surveying solutions from both research communities, and by identifying similarities, synergies and open research problems.