Optimal Estimation of Census Block Group Clusters to Improve the Computational Efficiency of Drive Time Calculations

Damon Gwinn, Jordan Helmick, Natasha Kholgade Banerjee, Sean Banerjee

2018

Abstract

Location selection determines the feasibility of a new location by evaluating factors such as the drive time of customers, the number of potential customers, and the number and proximity of competitors to the new location. Traditional location selection approaches use census block group data to determine average customer drive times by computing the drive time from each block group to the proposed location and comparing it to all competitors within the area. However, since companies need to evaluate on the order of hundreds of thousands of potential locations and competitors, traditional location selection approaches prove to be computationally infeasible. In this paper we present an approach that generates an optimal set of clusters to speed up drive time calculations. Our approach is based on the insight that in urban areas block groups are comprised of a few adjacent city blocks, making the differences in drive times between neighboring block groups negligible. We use affinity propagation to initially cluster the census block groups. We use population and average distance between the cluster centroid and all points to recursively re-cluster the initial clusters. Our approach reduces the census data for the United States by 80% which provides a 5x speed when computing drive times. We sample 200 randomly generated locations across the United States and show that there is no statistically significant difference in the drive times when using the raw census data and our recursively clustered data. Additionally, for further validation we select 300 random Walmart stores across the United States and show that there is no statistically significant difference in the drive times.

Paper Citation

in Harvard Style

Gwinn D., Helmick J., Kholgade Banerjee N. and Banerjee S. (2018). Optimal Estimation of Census Block Group Clusters to Improve the Computational Efficiency of Drive Time Calculations.In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-294-3, pages 96-106. DOI: 10.5220/0006707800960106

in Bibtex Style

@conference{gistam18,
author={Damon Gwinn and Jordan Helmick and Natasha Kholgade Banerjee and Sean Banerjee},
title={Optimal Estimation of Census Block Group Clusters to Improve the Computational Efficiency of Drive Time Calculations},
booktitle={Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2018},
pages={96-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006707800960106},
isbn={978-989-758-294-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Optimal Estimation of Census Block Group Clusters to Improve the Computational Efficiency of Drive Time Calculations
SN - 978-989-758-294-3
AU - Gwinn D.
AU - Helmick J.
AU - Kholgade Banerjee N.
AU - Banerjee S.
PY - 2018
SP - 96
EP - 106
DO - 10.5220/0006707800960106