Mr. Zaheer Abbas is a doctoral researcher in Geography at Sun Yat-Sen University, China, specializing in Geographic Information Systems (GIS) and Remote Sensing (RS). His research focuses on the intersection of human–environment dynamics, socio-spatial inequalities, and environmental disparities, with the overarching aim of developing data-driven, sustainable solutions aligned with the United Nations Sustainable Development Goals (SDGs). Integrating geospatial technologies, big data analytics, and machine learning, his work emphasizes equitable urban development and resilience in rapidly transforming landscapes. He has contributed to interdisciplinary projects exploring urban ecosystem health, flood risk assessment, air pollution disparities, and land use and land cover (LULC) change modeling across China and South Asia. His publications in high-impact international journals such as Environmental Research, Ecological Informatics, Land, and Geomatics, Natural Hazards and Risk reflect his expertise in spatial modeling, sustainability analysis, and environmental monitoring. Professionally, he has applied his analytical skills as a GIS and data analyst in both academic and industrial contexts, utilizing spatial data science to address complex socio-environmental challenges. He actively engages in academic service as a reviewer for several leading journals, including Ecological Indicators, Sustainable Cities and Society, and Scientific Reports. Through his multidisciplinary and technology-driven approach, Abbas aims to advance spatial decision-making processes that promote environmental justice and sustainable urban growth.
Profiles: Scopus | Orcid | Google Scholar
Featured Publications
Yang, Y., Abbas, Z., Zhang, C., Wang, D., & Zhao, Y. (2025). Evolution pattern of urban agglomerations based on Bayesian networks from the perspective of spatial connection: A case study of Guangdong–Hong Kong–Macao Greater Bay Area, China. Applied Spatial Analysis and Policy.
Rizwan, M., Abbas, Z., & Wang, J. (2025, October). The interplay of urban landscape and PM2.5 exposure disparities: A machine learning approach in Shandong, China. Environmental Research.
Zhao, J., Zhang, C., Wang, J., Abbas, Z., & Zhao, Y. (2024, December 31). Machine learning and SHAP-based susceptibility assessment of storm flood in rapidly urbanizing areas: A case study of Shenzhen, China. Geomatics, Natural Hazards and Risk.
Zhao, J., Wang, J., Abbas, Z., Yang, Y., & Zhao, Y. (2023, May 9). Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: A case study of Guangzhou, China. Frontiers in Earth Science.
Abbas, Z., Zhu, Z., & Zhao, Y. (2022). Spatiotemporal analysis of landscape pattern and structure in the Greater Bay Area, China. Earth Science Informatics.