Tanhua Jin

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Jin

First Name

Tanhua

ORCID

0000-0001-6657-526X

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Publications 1 - 6 of 6

Unsupervised Urban Land Use Mapping with Street View Contrastive Clustering and a Geographical Prior
Che, Lin; Chen, Yizi; Jin, Tanhua; et al. (2025)
SIGSPATIAL '25: Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems
Urban land use classification and mapping are critical for urban planning, resource management, and environmental monitoring. Existing remote sensing techniques often lack precision in complex urban environments due to the absence of ground-level details. Unlike aerial perspectives, street view images provide a ground-level view that captures more human and social activities relevant to land use in complex urban scenes. Existing street view-based methods primarily rely on supervised classification, which is challenged by the scarcity of high-quality labeled data and the difficulty of generalizing across diverse urban landscapes. This study introduces an unsupervised contrastive clustering model for street view images with a built-in geographical prior, to enhance clustering performance. When combined with a simple visual assignment of the clusters, our approach offers a flexible and customizable solution to land use mapping, tailored to the specific needs of urban planners. We experimentally show that our method can generate land use maps from geotagged street view image datasets of two cities. As our methodology relies on the universal spatial coherence of geospatial data ("Tobler's law"), it can be adapted to various settings where street view images are available, to enable scalable, unsupervised land use mapping and updating. The code is available at https://github.com/lin102/CCGP.
Is a 15-Minute City Within Reach? Measuring Multimodal Accessibility and Carbon Footprint in 12 Major American Cities
Jin, Tanhua; Wang, Kailai; Xin, Yanan; et al. (2024)
Land Use Policy
Enhanced efforts in the transportation sector should be implemented to mitigate the adverse effects of CO2 emissions resulting from zoning-based planning paradigms. The concept of a 15-minute city, emphasizing proximity-based planning, holds promise in reducing unnecessary travel and progressing towards carbon neutrality. However, a critical research question remains inadequately explored: to what extent is the 15-minute city concept feasible for American cities? This paper presents a comprehensive framework to evaluate the 15-minute city concept using SafeGraph Point of Interest (POI) check-in data across 12 major American cities. Our findings suggest a prevailing reliance on cars among residents due to the spatial distribution of essential activities beyond convenient walking, cycling, and public transit distances. Nevertheless, there exists significant promise for realizing the 15-minute city vision, given that most residents' daily activities can be accommodated within a 15-minute radius by low-emission modes transportation modes. When comparing cities, it appears that achieving a 15-minute walking city is more feasible for metropolises like New York City, San Francisco, Boston, and Chicago, while proving to be challenging for cities such as Atlanta, Dallas, Houston, and Phoenix. In examing inter-group comparisons, neighborhoods with higher proportion of White residents and higher median incomes tend to have more accessible POIs, with a substantial percentage of activities concentrated within a 15-minute radius. This demographic also shows a greater propensity to fulfill daily activities through walking, cycling, or public transit trips within a 15-minute travel time, thus presenting a greater potential in CO2 reduction compared to African Americans. This study can offer policymakers insight into how far American cities are away from the 15-minute city concept. It also highlights the potential CO2 emissions reductions that could be achieved through successful implementation.
Street matters: Linking perceived street environment to older adults’ bike-sharing
Jin, Tanhua; Wei, Xiaobing; Cheng, Long; et al. (2025)
Travel Behaviour and Society
Shared micromobility has established its role as a viable solution for sustainable transportation worldwide. Despite the widespread discourse on bike-sharing, there remains a paucity of research addressing its utilization among older adults (aged 65 and over). To this end, this research delves into the analysis of docked bike-sharing trip records in Chicago, aiming to understand the impact of land use and perceived street environment (derived from Google Street View Images) on the utilization of bike-sharing services among older adults at both the station and route levels. This study adopts the Extreme Gradient Boosting (XGBoost) method and interprets the results using Shapley Additive explanations (SHAP). Results show that older adults have specific preferences for different land use and perceived street environments when using shared bikes, differing notably from the general user population. Regarding land use effects, older adults are more likely to use shared bikes in areas with high mixed land use and more green spaces. Areas with more cycling lane density also increase older adults’ likelihood to cycle more. For perceived street environments, older adults are prone to streets with high enclosure and low sky openness levels. This research also finds that the route-level perceived street environment has more pronounced marginal effects in comparison to the station-level counterparts. Our findings can provide evidence-based guidance to transportation planners to develop age-friendly transportation systems, thus alleviating the potential inequalities in access to bike-sharing services.
Spatiotemporal Variations of PM₂.₅ Concentration and Its Heterogeneous Relationship with Natural and Humanity Factors in Handan of China
Lu, Die; Jin, Tanhua; Zhang, Xiang; et al. (2023)
2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)
The problem of PM₂.₅ air pollution is increasingly severe in China due to rapid urbanization, especially in developing villages. However, the vast majority of existing research on PM₂.₅ is limited to the provincial or municipal scales and fails to consider regional heterogeneity. This study employs a spatial panel regression model to investigate the spatial spillover effects of township level PM₂.₅. Using Handan, China as a case study, the study examines the effects of various human and natural factors on PM₂.₅ for each sub-unit to discover spatiotemporal interactions. The findings indicate that the PM₂.₅ concentration is significantly influenced by mixed factors and exhibits positive spillover characteristics. In addition, waterfront landscape belts and large-scale ecological parks in central areas may help reduce PM₂.₅ concentration.
Do built environment factors have different effects on ridesourcing usage before and after the COVID-19 pandemic?
Jin, Tanhua; Cheng, Long; Wang, Sicheng; et al. (2023)
Cities
Ridesourcing has undergone a magnificent development pre pandemic and has had a transformative impact on travel behavior and urban mobility. While an individual's travel behavior has been found to be inevitably influenced by the pandemic, how COVID-19 influences the utilization of ridesourcing has rarely been discussed in previous studies. Understanding how COVID-19 has reshaped people's ridesourcing usage may also be helpful for providing updated policy implications in the post-pandemic era. The objective of this research is to investigate the extent to which the impact of the built environment on the utilization of ridesourcing has changed post pandemic, as compared to pre-pandemic period. This study analyzes the spatiotemporal difference in ridesourcing usage before and after the pandemic, and the differential impacts of built environment factors on ridesourcing usage using real-world trip data in Chicago. Generalized additive mixed models (GAMMs) are applied to analyze the nonlinear built environment effects on ridesourcing usage. Results show average ridesourcing usage in the post-pandemic period failed to recover to pre-pandemic trip volumes by June 2022. There are significant differences in the effects of population density, intersection density, land use mix, population employment balance index, and bus accessibility on ridesourcing pick-up usage between the post-pandemic and pre-pandemic periods. For example, population density had an overall positive effect pre pandemic. However, a significant negative effect was observed in areas with extremely high population density post pandemic. The positive effect of land use mix before the pandemic also turns into a negative effect after the pandemic. It seems that COVID-19 is having long-term effects on ridesourcing usage, at least in Chicago. Relevant policies and tailored land-use interventions should be updated regarding the differentiated built environment effects in the post-pandemic era.
Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment
Cao, Jun; Jin, Tanhua; Shou, Tao; et al. (2023)
Urban Planning
Car-dominated daily travel has caused many severe and urgent urban problems across the world, and such travel patterns have been found to be related to the built environment. However, few existing studies have uncovered the nonlinear relationship between the built environment and car dependency using a machine learning method, thus failing to provide policymakers with nuanced evidence-based guidance on reducing car dependency. Using data from Puget Sound regional household travel surveys, this study analyzes the complicated relationship between car dependency and the built environment using the gradient boost decision tree method. The results show that people living in high-density areas are less likely to rely on private cars than those living in low-density neighborhoods. Both threshold and nonlinear effects are observed in the relationships between the built environment and car dependency. Increasing road density promotes car usage when the road density is below 6 km/km2. However, the positive association between road density and car use is not observed in areas with high road density. Increasing pedestrian-oriented road density decreases the likelihood of using cars as the main mode. Such a negative effect is most effective when the pedestrian-oriented road density is over 14.5 km/km2. More diverse land use also discourages people's car use, probably because those areas are more likely to promote active modes. Destination accessibility has an overall negative effect and a significant threshold effect on car dependency. These findings can help urban planners formulate tailored land-use interventions to reduce car dependency.

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Tanhua Jin

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