Project Profile: ADRELO
Advancing Resilience in Low Income Housing Using Climate-Change Science and Big Data Analytics
Who?
| Principal Investigators: | Esther Adhiambo Obonyo, The Pennsylvania State University, United States of America (the) |
| Partners: | Daniel Olago, University of Nairobi, Kenya George Odipo, University of Nairobi, Kenya George Onyango Okeyo, De Montfort University, United Kingdom of Great Britain and Northern Ireland (the) Halima Saado, The Kenya Red Cross Society, Kenya Holmer Savastano Junior, University of Sao Paulo, Brazil Kristin Sznajder, The Pennsylvania State University, United States of America (the) Philip Omondi, IGAD Climate Prediction and Application Centre, Kenya Sergio Francisco Santos, University of Sao Paulo, Brazil Shem Oyoo Wandiga, University of Nairobi, Kenya Wang'ombe Wangari, De Montfort University, United Kingdom of Great Britain and Northern Ireland (the) |
| Sponsors: | Brazil (FAPESP) United Kingdom (UKRI) United States (NSF) |
What?
| Full Project Title: | Advancing Resilience in Low Income Housing Using Climate-Change Science and Big Data Analytics |
| Full Call Title: | DR32019 |
| Website: |
Why?
| Project Objective: | Overarching Goals: The project aims at enhancing the resilience of low-income communities living in disaster prone areas. The focus is on low-lying coastal zones that have a high risks of droughts and floods in selected parts of Brazil, East Africa and North America. It develops the geographic and socio-economic knowledge of persons living in slum and riverbed areas by gathering georeferenced data on infrastructures and natural heritage of potential sites. The project team will also investigate technology adoption barriers and diffusion drivers through designing and prototyping an affordable, disaster-resilient, low-income housing system that use sustainable locally-resourced materials. The development of urban spaces is a function of geographic location, economic history, urban development pattern, and governance will have a bearing on resilience. The development (or lack thereof) of an urban center is an outcome of existing social, economic, and political inequities political inequities [59, 14, 34 & 9]. Policy packages for disaster preparedness that do not consider the unique circumstances of vulnerable populations can inadvertently cause harm to low-income households [21 & 63]. Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient-housing urban design and planning policy packages considering projected climate change-related extreme weather scenarios between the current time and 2050. Whilst big data is amenable to long-term climate prediction, data for localized and seasonal predictions is still uncertain and sparse. Machine Learning has potential. Other applications have demonstrated that it can work with either big data or sparse data. The research will contribute to accurately modelling climate and extreme weather events at spatiotemporal level to increases the understanding of climate scientists while empowering policy makers in disaster related decision-making. |
| Call Objective: | The Disaster Risk, Reduction and Resilience (DR3) call responds to the growing need for assessment and reduction of disaster risk, collaborative co-design of resilience strategies with a breadth of stakeholders, and scientifically and technologically enhanced responses to disasters. In the context of this call, disasters are framed as extreme environmental events that negatively impact coupled human-natural systems. The generation of these events may have natural and/or anthropogenic causes. |
Where?
| Regions: | Africa, Europe, North America, South America |
| Countries: | Brazil, Kenya, United Kingdom, United States of America (USA) |
When?
| Duration: | 36 |
| Call Date: | 5 March 2019 |
| Project Award Date: | 04 June 2020 |
