Research Profile

Portrait of Kwame Sakamoto

Kwame Sakamoto

Postdoctoral Researcher in Urban Technology

Short Bio

I am an urban technologist studying how data infrastructures shape everyday city services. My work examines equitable deployment of sensor networks and algorithmic governance across mobility, housing, and public works. Since 2018, I have collaborated with city agencies, neighborhood groups, and civic labs on field experiments and open tools.

Research Interests

  • Civic data infrastructures
  • Algorithmic governance for mobility
  • Urban IoT ethics
  • Digital twins for planning
  • Participatory technology design

Short CV

  • 2023–present: Postdoctoral Researcher, Int’l Inst. of Interdisciplinary Development
  • 2020–2023: Urban Data Scientist, City Systems Lab, Metropolis Institute of Design
  • 2017–2020: Research Associate, Urban Data Collaborative, Harbor Tech College
  • 2014–2017: Analyst, Public Innovation Unit, Coastline City Council

Affiliations

  • Int’l Inst. of Interdisciplinary Development
  • Center for Civic Systems, Meridian School of Urban Futures

Education

  • PhD, Urban Informatics, North River Institute of Technology , 2023
  • MSc, City Planning and Analytics, Pacific Graduate School , 2017
  • BSc, Computer Science and Sociology, Central Coast College , 2014

Teaching

  • Urban Tech Studio: Data, Sensors, and Equity
  • Algorithms in the City
  • Digital Twins for Planners
  • Civic Technology and Public Works

Awards

  • City Futures Early Career Award, Urban Systems Network , 2024
  • Best Paper, Civic Data Conference , 2022

Publications

  • Sakamoto, K., Adaptive curb algorithms and equity in last‑mile logistics, Journal of Urban Technologies , 2024.
  • Sakamoto, K.; Lin, A., Community‑led sensor deployments for heat resilience, Proceedings of the Civic Data Conference , 2023.
  • Sakamoto, K.; Duarte, M.; Okoye, N., Digital twins for participatory street redesign, Urban Systems & Society Review , 2022.

Abstract

This project investigates how municipal algorithms allocate scarce curb space and how those choices impact equity. Using a mixed‑methods design—policy document analysis, 48 interviews with practitioners and residents, and a field experiment with community‑installed sensors—the study measures distributional effects of dynamic pricing and loading‑zone prioritization. Results indicate that demand‑only optimization can divert access away from paratransit and small vendors, while adding fairness constraints and community dashboards reduces disparities without significant efficiency loss. The work proposes a governance model combining transparent metrics, participatory calibration, and open audit trails for urban technology deployments.