Urban attractors: Discovering patterns in regions of attraction in cities
Name
journal.pone.0250204.pdf
Description
Published version
Size
2.4 MB
Format
Adobe PDF
Checksum (MD5)
75cc8a501cca312105e0e915cdd37c30
Author(s)
Alhazzani, May
Alhasoun, Fahad
Alawwad, Zeyad
González, Marta C
Date Issued
2021
Journal
PLoS ONE
Publisher
Public Library of Science (PLoS)
Version
Final published version
Abstract
Understanding the dynamics by which urban areas attract visitors is important in today’s cities that are continuously increasing in population towards higher densities. Identifying services that relate to highly attractive districts is useful to make policies regarding the placement of such places. Thus, we present a framework for classifying districts in cities by their attractiveness to daily commuters and relating Points of Interests (POIs) types to districts’ attraction patterns. We used Origin-Destination matrices (ODs) mined from cell phone data that capture the flow of trips between each pair of places in Riyadh, Saudi Arabia. We define the attraction profile for a place based on three main statistical features: The number of visitors a place received, the distribution of distance traveled by visitors on the road network, and the spatial spread of locations from where trips started. We used a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We discovered three main types of Urban Attractors in Riyadh during the morning period: Global, which are significant places in the city, Downtown, which contains the central business district, and Residential attractors. In addition, we uncovered what makes districts possess certain attraction patterns. We used a statistical significance testing approach to quantify the relationship between Points of Interests (POIs) types (services) and the patterns of Urban Attractors detected.
MIT Department
Massachusetts Institute of Technology. Center for Computational Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0250204
