Speaker
Description
Addressing socio-spatial inequality in data-poor regions requires innovative urban research and planning to overcome acute data deficiencies. Policymakers and planners face the challenge of accommodating the needs of rapidly growing urban populations, yet data scarcity often exacerbates inequality and hinders effective decision-making. Emerging data—spanning those generated from urban sensing, machine learning, spatial statistical modelling, and high-resolution geospatial resources—offer transformative potentials for deriving actionable insights. This paper explores how big data analytics can be leveraged to navigate the complexities of sustainable urban development and mitigate socio-spatial disparities in data constraint environments. The discussion intends to inform the integration of diverse spatial data sources to inform planning decisions, focusing on enabling equitable and evidence-based urban transformation.
References
Nicoletti, L., Sirenko, M. and Verma, T. (2023). Disadvantaged Communities have Lower Access to Urban Infrastructure. Environment and Planning B: Urban Analytics and City Science, 50(3), pp.831–849.
Piaggesi, S. et al. (2022). Mapping Urban Socioeconomic Inequalities in Developing Countries Through Facebook Advertising Data. Frontiers in Big Data, 5.
Thakuriah, P. (Vonu), Tilahun, N.Y. and Zellner, M. (2017). Big Data and Urban Informatics: Innovations and Challenges to Urban Planning and Knowledge Discovery. In P. (Vonu) Thakuriah, N. Tilahun, & M. Zellner, eds. Seeing Cities Through Big Data: Research, Methods and Applications in Urban Informatics. Cham: Springer International Publishing, pp. 11–45.
Weber, I. et al. (2021). Non-traditional Data Sources: Providing Insights into Sustainable Development. Commun. ACM, 64(4), pp.88–95.
| Keywords | Big data; socio-spatial inequality; decision making: data-scares region |
|---|---|
| Best Congress Paper Award | No |
