Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2018

Abstract

Urban planners and economists alike have strong interest in understanding the inter-dependency of land use and people flow. The two-pronged problem entails systematic modeling and understanding of how land use impacts crowd flow to an area and in turn, how the influx of people to an area (or lack thereof) can influence the viability of business entities in that area. With cities becoming increasingly sensor-rich, for example, digitized payments for public transportation and constant trajectory tracking of buses and taxis, understanding and modelling crowd flows at the city scale, as well as, at finer granularity such as at the neighborhood level, has now become possible. Integrating such understanding with heterogeneous data such as land use profiles, demographics, and social media, enables richer studies on land use and its interdependence on mobility. In this work, we share findings from our preliminary efforts and identify key lines of research inquiry that can help urban planners towards data-driven policy decisions.

Keywords

Land use, Clustering, Urban computing, Prediction, Urban mobility

Discipline

Databases and Information Systems | Urban Studies and Planning

Research Areas

Data Science and Engineering

Publication

UbiComp '18: Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore, October 8-12

First Page

1079

Last Page

1087

ISBN

9781450359665

Identifier

10.1145/3267305.3274163

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3267305.3274163

Download

Share

COinS