Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains shapefiles of climate risk metrics at Admin Level 2 for three countries: Kenya, Uganda, and Tanzania.
Climate risk metrics are based on population risk exposed for five hazards: inland flooding, coastal flooding, landslides, heatwaves, and droughts.
This dataset was produced as part of the Google.org funded Resilient Planet Initiative pilot project. Further details on the methodology can be found in the accompanying technical note.
Data are in shapefile format. Shapefile column attribute information provided in table below.
Table. Shapefile Column Attributes
Column Name |
Long Name |
Description |
pop |
Population |
Total 2020 population in the administrative region |
rf_abs |
River flood absolute exposure |
Absolute number of people exposed to river floods in the administrative region |
rf_rel |
River flood relative exposure |
Relative number of people exposed to river floods in the administrative region (0-1) |
cf_abs |
Coastal flood absolute exposure |
Absolute number of people exposed to coastal floods in the administrative region |
cf_rel |
Coastal flood relative exposure |
Relative number of people exposed to coastal floods in the administrative region (0-1) |
ls_abs |
Landslide absolute exposure |
Absolute number of people exposed to landslides in the administrative region |
ls_rel |
Landslide relative exposure |
Relative number of people exposed to landslides in the administrative region (0-1) |
hw_abs |
Heatwave absolute exposure |
Absolute number of people exposed to a heatwave on a given day in the administrative region |
hw_rel |
Heatwave relative exposure |
Relative number of people exposed to a heatwave on a given day in the administrative region (0-1) |
dr_abs |
Drought absolute exposure |
Absolute number of people exposed to droughts each year in the administrative region |
dr_rel |
Drought relative exposure |
Relative number of people exposed to droughts each year in the administrative region (0-1) |
rf_abs_rr |
River flood absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute river flood metric |
rf_rel_rr |
River flood relative exposure risk rating |
Risk rating 1 – 5 applied to relative river flood metric |
cf_abs_rr |
Coastal flood absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute coastal flood metric |
cf_rel_rr |
Coastal flood relative exposure risk rating |
Risk rating 1 – 5 applied to relative coastal flood metric |
ls_abs_rr |
Landslide absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute landslide metric |
ls_rel_rr |
Landslide relative exposure risk rating |
Risk rating 1 – 5 applied to relative landslide metric |
hw_abs_rr |
Heatwave absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute heatwave metric |
hw_rel_rr |
Heatwave relative exposure risk rating |
Risk rating 1 – 5 applied to relative heatwave metric |
dr_abs_rr |
Drought absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute drought metric |
dr_rel_rr |
Drought relative exposure risk rating |
Risk rating 1 – 5 applied to relative drought metric |
The file is associated with:
B.A. Han, K.R. Varshney, S. LaDeau, A. Subramanian, K.C. Weathers, J. Zwart. Submitted. Beyond ‘AI for X’: A Synergistic Future for AI and Ecology.
Abstract:
Research in both ecology and artificial intelligence (AI) strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of advances built on a staggered cycle of computational development and ecological adaptation, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change.
The unpredictability of systems-level phenomena and associated challenges in understanding resilience dynamics are critical challenges on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a synergistic convergence research paradigm between ecology and AI. The systems studied in ecology are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behavior that should inspire new, robust AI architectures and methodologies. We share several examples of how challenges in ecological systems modeling will require advances in AI techniques that are themselves inspired by the systems they seek to model.
Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. Here we emphasize the need for more purposeful synergy to accelerate understanding of ecological resilience whilst building the resilience currently lacking in modern AI. There are persistent epistemic barriers that require attention in both disciplines, yet the implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence -- they are critical for both persisting and thriving in an uncertain future.
File list:
AIandML_results_SHARE.csv - contains literature search results from Clarivate Web of Science.
We consider how our society can use data, information and knowledge of the Earth under a broad definition of geoscience to better connect with the Earth system. This is important in our changing world, in particular how geoscience contributes to our response to the societal impacts of the COVID-19 pandemic. Ultimately, informed decisions utilizing the best geoscience data and information provide key parts of our economic, environmental and cultural recovery from the pandemic. The connection to country and more widely connection to our planet and the greater Earth system that comes from personal experience has been especially challenged in 2020. Much of Australia’s population have been encouraged to stay in our homes, first because of major fires and more recently in response to isolation from the COVID-19 pandemic. Although domestic travel became increasingly allowable, international travel has been restricted for much longer. This has increased the importance of trusted data and information initially from domestic locations and for more extended time between countries that are now less accessible. We discuss ways that geoscience governs our discovery and use of minerals, energy and groundwater resources and builds resilience and adaptation to environmental and cultural change. A broad definition of geoscience also includes positioning and location data and information, such as through integrated digital mapping, satellite data and real-time precise positioning. Important here is sharing, with two-way exchange of data, information and knowledge about the Earth, through outreach in geoscience education programs and interactions with communities across Australia, into neighboring countries in Asia and the Pacific, and across the world. An aspiration is for geoscience to inform social license through evidence-based decisions, such as for land and marine access, for a strong economy, resilient society and sustainable environment. At Geoscience Australia, we have developed a ten years strategic plan (Strategy 2028) that guides us to be a trusted source of information on Australia’s geology and geography for government, industry and community decision making. This will contribute to a safer, more prosperous and well-informed Australia and its connection to neighbouring countries, such as in Asia, as well as people that are better connected to country and our planet. Citation: Hill, S., Thorne, J., Przeslawski, R., Mouthaan, R., Lewis, C. The 'new normal' for geoscience in a post-COVID world: connecting informed people with the Earth. Thai Geoscience Journal Volume 2 (2) 2021, p30-37 021 ISSN-2730-2695; DOI-10.14456/tgj.2021.3
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains shapefiles of climate risk metrics at Admin Level 2 for three countries: Kenya, Uganda, and Tanzania.
Climate risk metrics are based on population risk exposed for five hazards: inland flooding, coastal flooding, landslides, heatwaves, and droughts.
This dataset was produced as part of the Google.org funded Resilient Planet Initiative pilot project. Further details on the methodology can be found in the accompanying technical note.
Data are in shapefile format. Shapefile column attribute information provided in table below.
Table. Shapefile Column Attributes
Column Name |
Long Name |
Description |
pop |
Population |
Total 2020 population in the administrative region |
rf_abs |
River flood absolute exposure |
Absolute number of people exposed to river floods in the administrative region |
rf_rel |
River flood relative exposure |
Relative number of people exposed to river floods in the administrative region (0-1) |
cf_abs |
Coastal flood absolute exposure |
Absolute number of people exposed to coastal floods in the administrative region |
cf_rel |
Coastal flood relative exposure |
Relative number of people exposed to coastal floods in the administrative region (0-1) |
ls_abs |
Landslide absolute exposure |
Absolute number of people exposed to landslides in the administrative region |
ls_rel |
Landslide relative exposure |
Relative number of people exposed to landslides in the administrative region (0-1) |
hw_abs |
Heatwave absolute exposure |
Absolute number of people exposed to a heatwave on a given day in the administrative region |
hw_rel |
Heatwave relative exposure |
Relative number of people exposed to a heatwave on a given day in the administrative region (0-1) |
dr_abs |
Drought absolute exposure |
Absolute number of people exposed to droughts each year in the administrative region |
dr_rel |
Drought relative exposure |
Relative number of people exposed to droughts each year in the administrative region (0-1) |
rf_abs_rr |
River flood absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute river flood metric |
rf_rel_rr |
River flood relative exposure risk rating |
Risk rating 1 – 5 applied to relative river flood metric |
cf_abs_rr |
Coastal flood absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute coastal flood metric |
cf_rel_rr |
Coastal flood relative exposure risk rating |
Risk rating 1 – 5 applied to relative coastal flood metric |
ls_abs_rr |
Landslide absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute landslide metric |
ls_rel_rr |
Landslide relative exposure risk rating |
Risk rating 1 – 5 applied to relative landslide metric |
hw_abs_rr |
Heatwave absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute heatwave metric |
hw_rel_rr |
Heatwave relative exposure risk rating |
Risk rating 1 – 5 applied to relative heatwave metric |
dr_abs_rr |
Drought absolute exposure risk rating |
Risk rating 1 – 5 applied to absolute drought metric |
dr_rel_rr |
Drought relative exposure risk rating |
Risk rating 1 – 5 applied to relative drought metric |