Chapa means urban guide in Portuguese. It is an interdisciplinary urban lab that was established by Kristine Stiphany in the context of a National Science Foundation SBE Postdoctoral Fellowship (#1513395, 2015 - 2017) at The University of Texas at Austin. CHAPA’s primary objective is to complete new connections between data, participation, and urban design in the Latin Global South.
The purpose of CHAPA’s primary study is to measure the impacts of redevelopment (upgrading) on established informal settlements in São Paulo, Brazil. Its goal is to expand redevelopment’s scope to encompass technologies that assimilate urban design, social mobility, and physical transformation within informal settlements across the globe. To these ends, the study is guided by the following major goals:
1 Expand understanding about how historical patterns of redevelopment impact future growth alternatives in established informal settlements.
2 Establish new methods for synthesizing data collection, analysis, and visualization of housing production processes into urban design strategies for adaptive reuse.
3 Promote IT planning tools that enhance civic decision making and participation in urban development, align policy goals with contemporary conditions of informality, and model scenarios for structuring the future of informal settlements.
CHAPA’s approach to research builds off of the Latin American Housing Network at The University of Texas at Austin.
Visualize some of the data collected through a large-scale household survey in São Paulo here, or by clicking on COMUNIDADOS VISUAL DATABASE, above.
See PUBLICATIONS for research outcomes and notable news.
Once again, welcome!
Kristine Stiphany, PhD, AIA, APA
For questions, please contact Kristine Stiphany at firstname.lastname@example.org.
CASE STUDIES Heliopolis and São Francisco / São Paulo, Brazil
DEVELOPMENT TYPES Autoconstrução, Muitrão, Cingapura, Urbanização de Favela
Learn about the project approach and methodology though this film presented at the United Nations Habitat III Conference (2016)