Session 33

G7 Understanding Influencers of Fast Urbanization using Non-survey Data

The rate of urbanization and the spatial distribution of people is at times influenced by hidden human agenda that may not be easily revealed survey or experimental data that is expensive (time and resources) to collect. However, non-survey data has been successfully employed to estimate the degree of causal-effect (impact) of interventions (programs) on certain outcomes. Thus, non-survey data can also be employed in urbanization research to generate evidence-based policy and decisions, upon understanding key influencers of urbanization. In support of the “Bangkok as a Method” 2024 SMUS Conference theme, a session is being proposed to bring together studies that employ non-experimental methods, grounded on the quantitative methods, to describe causes of certain spatial distribution of patterns in both the Global North and the Global South. Mainly, the session would invite research papers or articles that employ impact-evaluation (IE) approach to determine/describe influencers of spatial urbanization on the basis of non-survey or administrative data. Non-survey data includes, Geo-coded data; Crime data; Public service data; Utility (water and electricity) data; Social-media data; Traffic data; Road network data; Indigenous data; Satellite images; Cellphone data; Internet data; City rates data etc.