LIPIcs.GIScience.2023.61.pdf
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Mobility Vitality: Assessing Neighborhood Similarity Through Transportation Patterns In New York City (Short Paper)
Authors
Dan Qiang 
,
Grant McKenzie
-
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Volume:
12th International Conference on Geographic Information Science (GIScience 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Geographic Information Science (GIScience) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-07
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Dan Qiang and Grant McKenzie. Mobility Vitality: Assessing Neighborhood Similarity Through Transportation Patterns In New York City (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 61:1-61:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.61
https://doi.org/10.4230/LIPIcs.GIScience.2023.61
Abstract
Though numerous studies have examined human mobility within an urban environment, few have explored the concept of urban vitality purely through the lens of urban transportation. Given the importance of different modes of transportation within a city, such analysis is necessary. In this short paper, we introduce the novel concept of mobility vitality by integrating human mobility and urban vitality, offering a multilayered framework to assess the degree of transportation and mobility within and between regions. The mobility patterns of three transportation modes, namely subway, taxicab, and bike-share, are first examined independently. These patterns are then aggregated to form the composite measure of static mobility vitality. Through this measure, we evaluate similarities between neighborhoods. Our results observed significant spatial differences in the travel patterns of three transportation modes on weekdays and weekends. Moreover, neighborhoods with high static mobility vitality have relatively similar mobility patterns. Ultimately, this approach aims to find neighborhoods with imbalanced transportation infrastructure or inadequate public.
Subject Classification
ACM Subject Classification
- Information systems → Geographic information systems
- Applied computing → Transportation
Keywords
- mobility vitality
- mobility similarity
- transportation
- bike-sharing
- taxi
- subway
- New York City
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References
- Ezgi Eren and Volkan Emre Uz. A review on bike-sharing: The factors affecting bike-sharing demand. Sustainable Cities and Society, 54:101882, 2020. URL: https://doi.org/10.1016/j.scs.2019.101882.
-
Jane Jacobs. The death and life of great American cities. Random House, New York, 1961.
- Chaogui Kang, Dongwan Fan, and Hongzan Jiao. Validating activity, time, and space diversity as essential components of urban vitality. Environment and Planning B: Urban Analytics and City Science, 48:1180-1197, 2021. URL: https://doi.org/10.1177/2399808320919771.
- Qian Li, Caihui Cui, Feng Liu, Qirui Wu, Yadi Run, and Zhigang Han. Multidimensional Urban Vitality on Streets: Spatial Patterns and Influence Factor Identification Using Multisource Urban Data. ISPRS International Journal of Geo-Information, 11(1):2, 2022. URL: https://doi.org/10.3390/ijgi11010002.
- Grant McKenzie and Daniel Romm. Measuring urban regional similarity through mobility signatures. Computers, Environment and Urban Systems, 89:101684, 2021. URL: https://doi.org/10.1016/j.compenvurbsys.2021.101684.
- Patrizia Sulis, Ed Manley, Chen Zhong, and Michael Batty. Using mobility data as proxy for measuring urban vitality. Journal of Spatial Information Science, 16:137-162, 2018. URL: https://doi.org/10.5311/JOSIS.2018.16.384.
- Yang Xu, Dachi Chen, Xiaohu Zhang, Wei Tu, Yuanyang Chen, Yu Shen, and Carlo Ratti. Unravel the landscape and pulses of cycling activities from a dockless bike-sharing system. Computers, Environment and Urban Systems, 75:184-203, 2019. URL: https://doi.org/10.1016/j.compenvurbsys.2019.02.002.
- Yihong Yuan and Martin Raubal. Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study. International Journal of Geographical Information Science, 30(8):1594-1621, 2016. URL: https://doi.org/10.1080/13658816.2016.1143555.
- Peng Zeng, Ming Wei, and Xiaoyang Liu. Investigating the Spatiotemporal Dynamics of Urban Vitality Using Bicycle-Sharing Data. Sustainability, 106(1):1714, 2020. URL: https://doi.org/10.3390/su12051714.
- Bin Zhang, Shuyan Chen, Yongfeng Ma, Tiezhu Li, and Kun Tang. Analysis on spatiotemporal urban mobility based on online car-hailing data. Journal of Transport Geography, 82:102568, 2020. URL: https://doi.org/10.1016/j.jtrangeo.2019.102568.
- Zhonghao Zhang, Yusi Zhang, Tian He, and Rui Xiao. Urban Vitality and its Influencing Factors: Comparative Analysis Based on Taxi Trajectory Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:5102-5114, 2022. URL: https://doi.org/10.1109/JSTARS.2022.3183176.