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What can high frequency transportation data in Honolulu show us about urban mobility patterns? How are these patterns reflected by transportation type, time, space, and scale?
Background
Since 2018, Columbia University Graduate School of Architecture, Planning and Preservation Adjunct Professor Kaz Sakamoto has been applying a mixed-methods approach to the future of transit-oriented development (TOD) in Honolulu, Hawaii. With a multidisciplinary perspective in its inaugural session, the workshop defined general principles and place-specific tools and tactics around the creation of TODs and topics spanned tactical urbanism, density, climate change, autonomous vehicles, Hawaiʻi place-based culture, and policies restricting development. The second session delved into methodologies to enumerate parking spots in the Ala Moana neighborhood in Honolulu and partnered with technology partner Pebble (formerly Coord) and Ulupono Initiative, a local advocacy and research organization.
Workshop Overview
The high frequency city concept developed by Mike Batty promotes the examination of cities through seconds, minutes, hours, and days in contrast to the low frequency city perceived through years, decades, and centuries. A modern city must be understood through its appropriate rhythm to capture its beating pulse compared to more static representations of cities from the past. The students will get an opportunity to meet with relevant stakeholders from the public, private, and non-profit mobility space in Honolulu. The workshop will also introduce students to urban analytics methodologies to uncover high frequency urban mobility patterns. Some of the methods will include data visualization, network analysis, computer vision, and spatio-temporal methods which will be combined to understand how various modes of transportation mobilize Honolulu.
Project Goals
The goal will be to study high frequency mobility patterns, which aims to:
Methodology and Process
Outputs
Report
Reading Materials
Batty, M. (2020). : High and low frequency cities, big data and urban theory. In The Routledge Companion to Smart Cities. Routledge.
Buyantuyev, A., Wu, J., & Gries, C. (2010). Multiscale analysis of the urbanization pattern of the Phoenix metropolitan landscape of USA: Time, space and thematic resolution. Landscape and Urban Planning, 94(3–4), 206–217. Scopus. https://doi.org/10.1016/j.landurbplan.2009.10.005
Cheng, T., & Adepeju, M. (2014). Modifiable Temporal Unit Problem (MTUP) and Its Effect on Space-Time Cluster Detection. PLOS ONE, 9(6), e100465. https://doi.org/10.1371/journal.pone.0100465
Cöltekin, A., De Sabbata, S., Willi, C., Vontobel, I., Pfister, S., Kuhn, M., & Lacayo, M. (2011). Modifiable temporal unit problem. https://doi.org/10.5167/UZH-54263
Pereira, R. H. M. (2019). Future accessibility impacts of transport policy scenarios: Equity and sensitivity to travel time thresholds for Bus Rapid Transit expansion in Rio de Janeiro. Journal of Transport Geography, 74, 321–332. Scopus. https://doi.org/10.1016/j.jtrangeo.2018.12.005
Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., & González, M. C. (2013). Unraveling daily human mobility motifs. Journal of The Royal Society Interface, 10(84), 20130246. https://doi.org/10.1098/rsif.2013.0246
Xu, Y., Shaw, S.-L., Zhao, Z., Yin, L., Lu, F., Chen, J., Fang, Z., & Li, Q. (2016). Another tale of two cities: Understanding human activity space using actively tracked cellphone location data. Annals of the American Association of Geographers, 106(2), 489–502. Scopus. https://doi.org/10.1080/00045608.2015.1120147
Zhu, R., Zhang, X., Kondor, D., Santi, P., & Ratti, C. (2020). Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility. Computers, Environment and Urban Systems, 81. Scopus. https://doi.org/10.1016/j.compenvurbsys.2020.101483
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