Honolulu, HI Frequency: Urban mobility patterns across time, space, and scale

Aug 10, 2023 – Aug 18, 2023

Honolulu, Hawaii

Kaz Sakamoto

Aerial View of Honolulu by Derek Ford.

Bike Sharing in Waikiki. Photo by Kaz Sakamoto.

Bike Sharing in Waikiki. Photo by Flashpacking America.

Research Question

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.

Map of Honolulu by Kaz Sakamoto.

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:

Gather temporally granular data from various modes of transportation
Investigate how the modifiable temporal unit affects the understanding and interpretation of mobility data
Understand what kind of modal shifts are taking place (when and where)
Holistically view how the road network interacts with various forms of mobility
Provide baseline information that could help define current and future urban plans

Methodology and Process

Data visualization
Network Analysis
Computer Vision
Spatiotemporal Analysis

Outputs

Report

Background on high frequency city and mobility
Data collection of mobility data
Visualization of mobility flows
Analysis of the modifiable temporal unit problem on mobility patterns

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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