About me

Welcome to my homepage! I am Yichun Zhou, a fourth-year Ph.D. candidate in Urban Systems at New York University (NYU) Tandon School of Engineering, in the Department of Civil and Urban Engineering. Expected to graduate in May 2025, my research focuses on applying urban analytics to improve sustainability and well-being in cities, particularly through the study of green spaces and mobility patterns. I am also a Research Associate at the Shanghai Key Laboratory of Urban Design and Urban Science (LOUD), NYU Shanghai, and a participant in the Urban Science: Sensing, Complexity, & Informatics doctoral track at the Center for Urban Science and Progress (CUSP).

My work employs computational methods, including machine learning, spatial statistics, and causal inference, to examine urban green spaces, mobility, environmental equity, and health impacts. By analyzing spatial data from sources such as mobile phone data, street view imagery, and georeferenced surveys, I aim to inform planning policies that promote sustained green space use, urban health, and overall urban sustainability. My findings have been published in Landscape and Urban Planning, Urban Forestry & Urban Greening, and Applied Geography, with additional studies under review.

I hold an M.S. in Management of Technology with a Data Analytics specialization from NYU and a B.S. in Computer Science with a Minor in Business Administration from Western Kentucky University. Prior to my Ph.D. journey, I gained industry experience as a Data Science and Program Management Intern at Amazon Web Services, Bosch, and Tencent.

Selected Publications

Visitation-based classification of urban parks through mobile phone big data in Tokyo
Published in Applied Geography, 2024.
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Summary: This study introduces a novel classification of urban parks in Tokyo based on mobile phone data from 300 parks, revealing distinct park types—everyday leisure parks, social destination parks, and seasonal activity parks—each with unique visitation patterns. These findings support targeted park management strategies that align maintenance and amenities with actual usage trends, enhancing green infrastructure planning.

Exploring environmental equity and visitation disparities in peri-urban parks: A mobile phone data-driven analysis in Tokyo
Published in Landscape and Urban Planning, 2024.
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Summary: This study examines environmental equity and visitation disparities in Tokyo’s peri-urban parks using mobile phone data, revealing that extending the park service radius beyond 10 km could reduce inequities in park access. The findings provide insights for developing policies that enhance accessibility and better address visitor needs in urban planning.

“Unfenced” parks and residents’ visit patterns: A regression discontinuity design in Shanghai
Published in Urban Forestry & Urban Greening, 2024.
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Summary: This study employs a regression discontinuity design to examine shifts in park visitation behaviors among Shanghai residents following the sudden easing of COVID-19 containment measures, revealing decreased visits, reduced public transit use, and diminished activity levels, particularly in larger parks and community greenspaces. These insights inform greenspace management strategies during transitional post-pandemic periods.