2027
[33] Liu, J., Li, X., Meng, B., Zhan, D., & Liu, X. (2027). Mapping public disability-inclusive awareness in Chinese cities: Insights from geotagged social media data. Computers, Environment and Urban Systems, 131, 102536.
2026
[32] Liu, J., Liu, H., Zhan, D., Meng, B., & Liu, X. (2026). Digital pulse of inclusion: spatiotemporal dynamics and influencing factors of public disability attention in China. Humanities & Social Sciences Communications, 13(1), 1051.
[31] Liu, J., Zhan, D., Meng, B., & Liu, X. (2026). Public Perceptions of Urban Inclusivity from a Disability-Friendly Perspective: Attention vs. Satisfaction. Applied Spatial Analysis and Policy, 19(3), 156.
[30] Wang, H., Yu, Y., Liu, J., Cai, J., Zhang, C., & Liu, X. (2026). Structural drivers of taxi deadheading carbon emissions: Spatiotemporal patterns and environmental influences in Jinan City, China. Transport Policy, 104337.
[29] Farasath, H., Liu, J., & Liu, X. (2026). A Review of Machine Learning Modeling Approaches of Spatiotemporal Urbanization and Land Use Land Cover. Smart Cities, 9(5), 74.
[28] Wang, H., Liu, J., Liu, Y., & Liu, X. (2026). Suicide rate under COVID-19’s shadow: Spatiotemporal dynamics and environmental associations in Hong Kong. Cities, 175, 107166.
[27] Guo, Z., Kwan, M. P., Liu, J., & Liu, X. (2026). Investigating extra environmental exposure in bike-sharing trips: spatial patterns and built environment factors. Travel Behaviour and Society, 44, 101241.
[26] Hasan, F., Liu, J., & Liu, X. (2026). A novel attention-enhanced residual ConvLSTM framework for spatiotemporal urban expansion modeling. GeoInformatica, 30(1), 15.
[25] Wang, H., Lai, Z., Liu, J., He, T., & Liu, X. (2026). Comparative analysis of auditory and olfactory complaints: spatiotemporal patterns and multi-scale built environment drivers in urban fringes. Building and Environment, 114803.
[24] Wang, H., Liu, J., Hasan, F., & Liu, X. (2026). Geographies of online food delivery: spatial and cultural perspectives from the South China Sea Region. Computational Urban Science, 6(1), 34.
[23] Hasan, F., Liu, J., & Liu, X. (2026). Deep learning in modeling urban growth dynamics: a systematic literature review. Environmental Modelling & Software, 107000.
[22] Shen, K., Liu, J., & Liu, X. (2026). Social Inequality in Sight: Exploring Urban Visual Perception and Sentiment Across Income Levels. Transactions in GIS, 30(2), e70233.
[21] Liu, J., Shen, K., & Liu, X. (2026). Nonlinear Effects of Multilevel Urban Environments on Traffic Crash Risk: A Multiscale Analysis With Explainable Machine Learning. Transactions in GIS, 30(1), e70200.
[20] Guo, Z., Kwan, M. P., Liu, J., & Liu, X. (2026). Time sensitivity and travel behavior patterns: Insights from Shenzhen’s shared bike data. Journal of Transport Geography, 130, 104479.
2025
[19] Zhan, D., Wang, Y., Wang, D., Zhang, W., & Liu, J. (2025). The nonlinear effects of the subjective and objective built environment on active travel behavior: A case study of Shanghai, China. Health & Place, 95, 103528. (Corresponding author)
[18] Shen, K., Liu, J., & Liu, X. (2025). Understanding the Impact of Street Environments on Traffic Crash Risk from the Perspective of Aging People: An Interpretable Machine Learning Approach. ISPRS International Journal of Geo-Information, 14(7), 248.
[17] Liu, J., Shen, K., Liu, X., & Wu, C. (2025). Unravelling the multiple effects of multilevel neighborhood characteristics on traffic crash risk from a spatiotemporal heterogeneity perspective. Travel Behaviour and Society, 40, 101044.
[16] Hasan, F., Liu, J., & Liu, X. (2025). Hybrid CA–Deep Learning Model for 3D Urban Growth Simulation. Transactions in GIS, 29(7), e70139.
[15] Wang, H., Lai, Z., Wang, S., Liu, J., & Liu, X. (2025). Charting the progress and future directions of disability studies in human geography: A comprehensive systematic review dating back to 1980s. Cities, 161, 105910.
2024
[14] - Liu, J., Lai, Z., Meng, B., Guo, Z., & Liu, X. (2024). Assessing spatial configuration of barrier-free facilities from the perspective of age-friendliness: A case Study of Beijing, China. Applied Geography, 172, 103426.
2023
[13] Liu, J., Meng, B., & Shi, C. (2023). A multi-activity view of intra-urban travel networks: a case study of Beijing. Cities, 143, 104634.
[12] Liu, J., Meng, B., Xu, J., Li, R. (2023). Exploring public transportation supply–demand structure of Beijing from the perspective of spatial interaction network. ISPRS International Journal of Geo-Information, 12(6), 213.
[11] Guo, Z., Liu, J., Zhao, P., Liu, X., & Li, A. (2023). Spatiotemporal heterogeneity of the shared e-scooter–public transport relationships in Stockholm and Helsinki. Transportation Research Part D: Transport and Environment, 122, 103880.
2022
[10] Liu, J., Meng, B., Yang, M., Peng, X., Zhan, D., & Zhi, G. (2022). Quantifying spatial disparities and influencing factors of home, work, and activity space separation in Beijing. Habitat International, 126, 102621.
[9] Liu, H., Liu, J., Li, M., Gou, P., & Cheng, Y. (2022). Assessing the evolution of PM2. 5 and related health impacts resulting from air quality policies in China. Environmental Impact Assessment Review, 93, 106727. (Corresponding author)
[8] Zhan, D., Zhang, Q., Kwan, M.P., Liu, J., Zhan, B., & Zhang, W. (2022). Impact of urban green space on self-rated health: Evidence from Beijing. Frontiers in Public Health, 3025.
[7] Chen, S., Meng, B., Liu, N., Qi, Z., Liu, J., & Wang, J. (2022). Cultural perception of the historical and cultural blocks of Beijing based on weibo photos. Land, 11(4), 495.
[6] Tian, B., Meng, B., Wang, J., Zhi, G., Qi, Z., Chen, S., & Liu, J. (2022). Spatio-temporal patterns of fitness behavior in Beijing based on social media data. Sustainability, 14(7), 4106.
[5] Zhu, Y., Wang, J., Meng, B., Ji, H., Wang, S., Zhi, G., Liu, J., & Shi, C. (2022). Quantifying spatiotemporal heterogeneities in pm2. 5-related health and associated determinants using geospatial big data: a case study in Beijing. Remote Sensing, 14(16), 4012.
2021
[4] Liu, J., Meng, B., Wang, J., Chen, S., Tian, B., & Zhi, G. (2021). Exploring the spatiotemporal patterns of residents’ daily activities using text-based social media data: a case study of Beijing, China. ISPRS International Journal of Geo-Information, 10(6), 389.
[3] Liu, J., Meng, B., Chen, S., Chen, Z., & Zhan, D. (2021). Study on the relationship between dining activities of Beijing residents and urban space based on multi-sources big Data. Human Geography, 36(2), 63-72+183. (in Chinese)
[2] Wang, B., Meng, B., Wang, J., Chen, S., & Liu, J. (2021). Perceiving residents’ festival activities based on social media data: a case study in Beijing, China. ISPRS International Journal of Geo-Information, 10(7), 474.
[1] Zhi, G., Meng, B., Wang, J., Chen, S., Tian, B., Ji, H., … & Liu, J. (2021). Spatial analysis of urban residential sensitivity to heatwave events: case studies in five megacities in China. Remote Sensing, 13(20), 4086.