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A Spatial and Adversarial Representation Learning Approach for Land Use Classification with POIs

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Profiling Urban Streets: A Semi-Supervised Prediction Model Based on Street View Imagery and Spatial Topology

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Scale Effect on Fusing Remote Sensing and Human Sensing to Portray Urban Functions

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Impact of Check-In Data on Urban Vitality in the Macao Peninsula

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Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China

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Spatial Pattern of Functional Urban Land Conversion and Expansion under Rapid Urbanization: A Case Study of Changchun, China

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Study on Urban Spatial Function Mixture and Individual Activity Space From the Perspectives of Resident Activity

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Delineation of the Shanghai Megacity Region of China from a Commuting Perspective: Study Based on Cell Phone Network Data in the Yangtze River Delta

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Flow trace: A novel representation of intra-urban movement dynamics

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Twitter mobility dynamics during the COVID-19 pandemic: A case study of London

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How do urban services facilities affect social segregation among people of different economic levels? A case study of Shenzhen city

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A Deep-Learning-Based Multimodal Data Fusion Framework for Urban Region Function Recognition

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A novel dynamic quantification model for diurnal urban land use intensity

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A simple and direct method to analyse the influences of sampling fractions on modelling intra-city human mobility

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From earth observation to human observation: Geocomputation for social science

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Building-Level Urban Functional Area Identification Based on Multi-Attribute Aggregated Data from Cell Phones—A Method Combining Multidimensional Time Series with a SOM Neural Network

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Multi-type and fine-grained urban green space function mapping based on BERT model and multi-source data fusion

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Refined Urban Functional Zone Mapping by Integrating Open-Source Data

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Revealing temporal stay patterns in human mobility using large‐scale mobile phone location data

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Inferring the heterogeneous effect of urban land use on building height with causal machine learning

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Geographical flows: a fresh perspective on quantifying urban function

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Where renewal works best? Unrevealing spatiotemporal effects of community renewal via DID and RF analysis

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Urban functional zone mapping by coupling domain knowledge graphs and high‐resolution satellite images

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Next location prediction using heterogeneous graph-based fusion network with physical and social awareness

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A Proposed Framework for Identification of Indicators to Model High-Frequency Cities

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Integrating trajectory data and demographic characteristics: a trajectory semantic model for predicting travel flow and conducting interaction analysis

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Relative space-based GIS data model to analyze the group dynamics of moving objects

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Combining individual travel behaviour and collective preferences for next location prediction

Li, Q., Zou, D., & Xu, Y. (2021) · Transportmetrica A Transport Science · doi:10.1080/23249935.2021.1968066 · Cited by 10

Spatiotemporal flow force model of source/sink human mobilities within city

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Gendered urban spaces: Unveiling gender segregation through time, space, and behavior - A case study of Zhuhai, China

Chen, Y., Zhang, H., Wang, G., Jiang, S., Chen, L., Zhang, L., & Tang, G. (2025) · Cities · doi:10.1016/j.cities.2025.106241 · Cited by 9

Sensing the Nighttime Economy–Housing Imbalance from a Mobile Phone Data Perspective: A Case Study in Shanghai

Li, M., Tu, W., & Lu, F. (2022) · Remote Sensing · doi:10.3390/rs14122738 · Cited by 9

Building Function Type Identification Using Mobile Signaling Data Based on a Machine Learning Method

Nie, W., Fan, X., Nie, G., Li, H., & Xia, C. (2022) · Remote Sensing · doi:10.3390/rs14194697 · Cited by 9

Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic

Chen, T., Zhu, D., Cheng, T., Gao, X., & Chen, H. (2023) · PLoS ONE · doi:10.1371/journal.pone.0277913 · Cited by 10

Exploring Budgeted Learning for Data-Driven Semantic Inference via Urban Functions

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A GloVe Model for Urban Functional Area Identification Considering Nonlinear Spatial Relationships between Points of Interest

Chen, Y., Qian, H., Wang, X., Wang, D., & Han, L. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11100498 · Cited by 9

A Comparative Study on Contract Recommendation Model: Using Macao Mobile Phone Datasets

Wang, G., & Wu, N. (2020) · IEEE Access · doi:10.1109/access.2020.2975029 · Cited by 9

Methods of Social Sensing for Urban Studies

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Identification of Urban Functional Areas Based on Point of Interest data and Thiessen Polygons for a Sustainable Urban Management

DÖKER, M. F., Gül, A., Kırlangıçoğlu, C., Ocak, F., & Minaei, M. (2024) · Social Indicators Research · doi:10.1007/s11205-024-03502-9 · Cited by 8

Activity-aware urban area embedding with contrastive learning for intelligent transportation systems applications

Li, G., Feng, T., He, D., Yan, L., & Kim, J. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105252 · Cited by 8

Classifying Urban Functional Zones via a Multilevel Graph Neural Network and Multimodal Geospatial Data

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Insights into People’s Perceptions Towards Urban Public Spaces Through Analysis of Social Media Reviews: A Case Study of Shanghai

Li, L., & Wang, L. (2025) · Buildings · doi:10.3390/buildings15173033 · Cited by 8

Identifying and Recognizing Usage Pattern of Electric Vehicles Using GPS and On-Board Diagnostics Data

Chen, X., Li, K., Zhang, H., Yuan, Q., & Ye, Q. (2020) · ASCE International Conference on Transportation and Development (ICTD) · doi:10.1061/9780784483138.008 · Cited by 8

Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China

Yang, Y., Wang, D., Yan, Z., & Zhang, S. (2021) · Land · doi:10.3390/land10111266 · Cited by 8

A knowledge-based approach for estimating the distribution of urban mixed land use

Li, J., Liu, H., Li, J., Chen, X., & Tao, Z. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2023.2184512 · Cited by 8

Adaptive weighted multi-view subspace clustering method for recognizing urban functions from multi-source social sensing data

Liu, Q., Lu, Z., Huan, W., & Fan, C. (2024) · Geo-spatial Information Science · doi:10.1080/10095020.2024.2356243 · Cited by 9

The IMAGE framework for human mobility science: A comprehensive bibliometric analysis of research trends and frontiers

Zhang, C., Liu, X., Ren, J., Yu, H., Huang, J., & Luo, X. (2025) · Transport Policy · doi:10.1016/j.tranpol.2025.06.028 · Cited by 7

Analysis of Tourist Behavior Patterns and Perceptions in Beijing Based on User-Generated Content Data

Li, X., Yang, T., Meng, B., Chen, S., & Zhang, S. (2025) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-025-09712-z · Cited by 7

Decomposing urban spatial rhythms: A big-small data fusion framework for activity pattern identification

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Geospatial Semantics Analysis of the Qinghai–Tibetan Plateau Based on Microblog Short Texts

Xu, J., & Hu, L. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10100682 · Cited by 7

Potential and real spatial models: Differences and response characteristics from the perspective of flow

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Measuring metro station area's TODness : An exploratory study of Shenyang based on multi‐source urban data

Yi, Z., Zhang, Z., & Wang, H. (2024) · Transactions in GIS · doi:10.1111/tgis.13148 · Cited by 7

Contextual Enrichment of Crowds from Mobile Phone Data through Multimodal Geo-Social Media Analysis

Honzák, K., Schmidt, S., Resch, B., & Ruthensteiner, P. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13100350 · Cited by 7

Trace of pace, place, and space in personal relationships: The chronogeometrics of studying relationships at scale

Spitzberg, B. H. (2019) · Personal Relationships · doi:10.1111/pere.12280 · Cited by 8

Using Point of Interest Data and Satellite Observation for Urban Functional Zone Mapping

Yang, M., Cao, S., Zhang, H., Wu, S., & Zhang, D. (2023) · Sensors and Materials · doi:10.18494/sam4213 · Cited by 7

Crowdsourcing functions of the living city from Twitter and Foursquare data

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UrbanAct-GPT: LLM-Based Activity and Role Semantic Inference from Mobile Phone Signalling Data for City-Scale Weekday Dynamic Urban Structure AnalysisPreprint

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Research of Urban Land Use and Regional Functions Based on Mobile Data Traffic

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Quantify city-level dynamic functions across China using social media and POIs data

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Mapping collective human activity in an urban environment based on mobile phone data

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