A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation
Abstract
The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. Here, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data imply social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.
- Authors:
-
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Computer Science, Columbia University, New York, NY, USA
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1546559
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- International Journal of Geographical Information Science
- Additional Journal Information:
- Journal Volume: 34; Journal Issue: 4; Journal ID: ISSN 1365-8816
- Publisher:
- Informa UK Limited
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 99 GENERAL AND MISCELLANEOUS; Geosocial; points of interest; temporal signatures; social media; cities
Citation Formats
Sparks, Kevin, Thakur, Gautam, Pasarkar, Amol, and Urban, Marie. A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation. United States: N. p., 2019.
Web. doi:10.1080/13658816.2019.1615069.
Sparks, Kevin, Thakur, Gautam, Pasarkar, Amol, & Urban, Marie. A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation. United States. https://doi.org/10.1080/13658816.2019.1615069
Sparks, Kevin, Thakur, Gautam, Pasarkar, Amol, and Urban, Marie. 2019.
"A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation". United States. https://doi.org/10.1080/13658816.2019.1615069. https://www.osti.gov/servlets/purl/1546559.
@article{osti_1546559,
title = {A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation},
author = {Sparks, Kevin and Thakur, Gautam and Pasarkar, Amol and Urban, Marie},
abstractNote = {The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. Here, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data imply social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.},
doi = {10.1080/13658816.2019.1615069},
url = {https://www.osti.gov/biblio/1546559},
journal = {International Journal of Geographical Information Science},
issn = {1365-8816},
number = 4,
volume = 34,
place = {United States},
year = {Tue Jun 04 00:00:00 EDT 2019},
month = {Tue Jun 04 00:00:00 EDT 2019}
}
Web of Science
Works referenced in this record:
Understanding individual human mobility patterns
journal, June 2008
- González, Marta C.; Hidalgo, César A.; Barabási, Albert-László
- Nature, Vol. 453, Issue 7196
Automated identification and characterization of parcels with OpenStreetMap and points of interest
journal, September 2015
- Liu, Xingjian; Long, Ying
- Environment and Planning B: Planning and Design, Vol. 43, Issue 2
How where is when? On the regional variability and resolution of geosocial temporal signatures for points of interest
journal, November 2015
- McKenzie, Grant; Janowicz, Krzysztof; Gao, Song
- Computers, Environment and Urban Systems, Vol. 54
A quantitative analysis of global gazetteers: Patterns of coverage for common feature types
journal, July 2017
- Acheson, Elise; De Sabbata, Stefano; Purves, Ross S.
- Computers, Environment and Urban Systems, Vol. 64
Entrainment of the Human Circadian Clock to the Natural Light-Dark Cycle
journal, August 2013
- Wright, Kenneth P.; McHill, Andrew W.; Birks, Brian R.
- Current Biology, Vol. 23, Issue 16
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
journal, November 1987
- Rousseeuw, Peter J.
- Journal of Computational and Applied Mathematics, Vol. 20
Seasonal and geographical impact on human resting periods
journal, September 2017
- Monsivais, Daniel; Bhattacharya, Kunal; Ghosh, Asim
- Scientific Reports, Vol. 7, Issue 1
An Advanced Systematic Literature Review on Spatiotemporal Analyses of Twitter Data: Spatiotemporal Analyses of Twitter Data - Systematic Literature Review
journal, March 2015
- Steiger, Enrico; de Albuquerque, João Porto; Zipf, Alexander
- Transactions in GIS, Vol. 19, Issue 6
Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries
journal, December 2013
- Sobolevsky, Stanislav; Szell, Michael; Campari, Riccardo
- PLoS ONE, Vol. 8, Issue 12
The Origins of Scaling in Cities
journal, June 2013
- Bettencourt, L. M. A.
- Science, Vol. 340, Issue 6139
Extracting and analyzing semantic relatedness between cities using news articles
journal, August 2017
- Hu, Yingjie; Ye, Xinyue; Shaw, Shih-Lung
- International Journal of Geographical Information Science, Vol. 31, Issue 12
Mining point-of-interest data from social networks for urban land use classification and disaggregation
journal, September 2015
- Jiang, Shan; Alves, Ana; Rodrigues, Filipe
- Computers, Environment and Urban Systems, Vol. 53
Correction: A Tale of Many Cities: Universal Patterns in Human Urban Mobility
journal, September 2012
- Noulas, Anastasios; Scellato, Salvatore; Lambiotte, Renaud
- PLoS ONE, Vol. 7, Issue 9
Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data
journal, November 2015
- Steiger, Enrico; Westerholt, René; Resch, Bernd
- Computers, Environment and Urban Systems, Vol. 54
Cities through the Prism of People’s Spending Behavior
journal, February 2016
- Sobolevsky, Stanislav; Sitko, Izabela; Tachet des Combes, Remi
- PLOS ONE, Vol. 11, Issue 2
Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data
journal, March 2015
- Sloan, Luke; Morgan, Jeffrey; Burnap, Pete
- PLOS ONE, Vol. 10, Issue 3
Geo-located Twitter as proxy for global mobility patterns
journal, February 2014
- Hawelka, Bartosz; Sitko, Izabela; Beinat, Euro
- Cartography and Geographic Information Science, Vol. 41, Issue 3
The human circadian clock entrains to sun time
journal, January 2007
- Roenneberg, Till; Kumar, C. Jairaj; Merrow, Martha
- Current Biology, Vol. 17, Issue 2
Modelling the scaling properties of human mobility
journal, September 2010
- Song, Chaoming; Koren, Tal; Wang, Pu
- Nature Physics, Vol. 6, Issue 10
Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks
journal, April 2010
- Sevtsuk, Andres; Ratti, Carlo
- Journal of Urban Technology, Vol. 17, Issue 1
Identifying residential neighbourhood types from settlement points in a machine learning approach
journal, May 2018
- Jochem, Warren C.; Bird, Tomas J.; Tatem, Andrew J.
- Computers, Environment and Urban Systems, Vol. 69