Data Density Considerations for Crowd Sourced Population Estimations from Social Media

Samuel Lee Toepke

2017

Abstract

When using social media data for population estimations, data density is of primary concern. A high density of quality, crowd-sourced data in a specified geographic area leads to a more precise estimation. Nonetheless, data acquisition/storage has to be balanced against the provisioned cost/size constraints of the technical implementation and the ability to receive data in that area. This investigation compares hourly population estimations based on Tweet quantity, for several major west coast cities in the United States of America. An estimation baseline is established, and data is artificially removed from the estimation to explore the importance of data density. Experimental data is obtained and stored using an enterprise cloud solution, density observations/results are discussed, and follow-on work is described.

Paper Citation

in Harvard Style

Lee Toepke S. (2017). Data Density Considerations for Crowd Sourced Population Estimations from Social Media . In Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-252-3, pages 35-42. DOI: 10.5220/0006314300350042

in Bibtex Style

@conference{gistam17,
author={Samuel Lee Toepke},
title={Data Density Considerations for Crowd Sourced Population Estimations from Social Media},
booktitle={Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2017},
pages={35-42},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006314300350042},
isbn={978-989-758-252-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Data Density Considerations for Crowd Sourced Population Estimations from Social Media
SN - 978-989-758-252-3
AU - Lee Toepke S.
PY - 2017
SP - 35
EP - 42
DO - 10.5220/0006314300350042