Recording Plan: 

Curated Track: 

Jianwei Huang, Chinese University of Hong Kong

Call For Participation
The intersection of spatial analysis, data science, and social good represents an emerging area with significant potential to address societal challenges. From smart cities to environmental justice, spatial data and geographic information systems (GIS) have become indispensable tools for understanding and mitigating social inequalities, enhancing humanitarian efforts, and promoting community well-being. Can geographers contribute effectively to policymaking in ways that genuinely promote social good (Harvey, 1974)? Recent studies indicate a declining emphasis on social good within data science research, particularly in fields such as artificial intelligence (AI) and machine learning, where the potential for societal benefit remains immense yet underexplored (Abbasi et al., 2023). At the same time, data-driven initiatives led by big tech, often framed as altruistic, may perpetuate patterns of exploitation and inequality through processes of datafication and data colonialism (Viera Magalhães & Couldry, 2021). Consequently, there is a need to encourage meaningful dialogues between quantitative and qualitative geographers. This session aims to bridge the gap between various subdisciplines within geography by inviting researchers, practitioners, and policymakers to submit papers that explore how spatial analysis can be leveraged for social good. We particularly encourage submissions that focus on interdisciplinary approaches, innovative methodologies, and practical applications that address real-world challenges.

Topics of interest include, but are not limited to:
- Spatial data science for social good
- Spatial dimensions of data colonialism
- Participatory spatial analysis and community engagement
- Geography and public policy
- Social media and spatial analysis
- Ethical considerations in spatial analysis

Please email paper titles, 250-word abstracts, author names/affiliations, and PIN to Jianwei Huang (jianweihuang@cuhk.edu.hk) by November 30.

References
Abbasi, A., Chiang, R. H. L., & Xu, J. (2023). Data science for social good. Journal of the Association for Information Systems, 24(6), 1439–1458.
Harvey, D. (1974). What kind of geography for what kind of public policy?. Transactions of the Institute of British Geographers, 18-24.
Viera Magalhães, J., & Couldry, N. (2021). Giving by taking away: Big tech, data colonialism and the reconfiguration of social good. International Journal of Communication, 15, 343-362.

Description:

The intersection of spatial analysis, data science, and social good represents an emerging area with significant potential to address societal challenges. From smart cities to environmental justice, spatial data and geographic information systems (GIS) have become indispensable tools for understanding and mitigating social inequalities, enhancing humanitarian efforts, and promoting community well-being. Can geographers contribute effectively to policymaking in ways that genuinely promote social good (Harvey, 1974)? Recent studies indicate a declining emphasis on social good within data science research, particularly in fields such as artificial intelligence (AI) and machine learning, where the potential for societal benefit remains immense yet underexplored (Abbasi et al., 2023). At the same time, data-driven initiatives led by big tech, often framed as altruistic, may perpetuate patterns of exploitation and inequality through processes of datafication and data colonialism (Viera Magalhães & Couldry, 2021). Consequently, there is a need to encourage meaningful dialogues between quantitative and qualitative geographers. This session aims to bridge the gap between various subdisciplines within geography by inviting researchers, practitioners, and policymakers to submit papers that explore how spatial analysis can be leveraged for social good. We particularly encourage submissions that focus on interdisciplinary approaches, innovative methodologies, and practical applications that address real-world challenges.

Topics of interest include, but are not limited to:
- Spatial data science for social good
- Spatial dimensions of data colonialism
- Participatory spatial analysis and community engagement
- Geography and public policy
- Social media and spatial analysis
- Ethical considerations in spatial analysis

Please email paper titles, 250-word abstracts, author names/affiliations, and PIN to Jianwei Huang (jianweihuang@cuhk.edu.hk) by November 30.

References
Abbasi, A., Chiang, R. H. L., & Xu, J. (2023). Data science for social good. Journal of the Association for Information Systems, 24(6), 1439–1458.
Harvey, D. (1974). What kind of geography for what kind of public policy?. Transactions of the Institute of British Geographers, 18-24.
Viera Magalhães, J., & Couldry, N. (2021). Giving by taking away: Big tech, data colonialism and the reconfiguration of social good. International Journal of Communication, 15, 343-362.

Non-Presenting Participants