Using Disaster Outcomes to Validate Components of Social Vulnerability to Floods: Flood Deaths and Property Damage across the USA

Social vulnerability indicators seek to identify populations susceptible to hazards based on aggregated sociodemographic data. Vulnerability indices are rarely validated with disaster outcome data at broad spatial scales, making it difficult to develop effective national scale strategies to mitigate loss for vulnerable populations. This paper validates social vulnerability indicators using two flood outcomes: death and damage. Regression models identify sociodemographic factors associated with variation in outcomes from 11,629 non-coastal flood events in the USA (2008–2012), controlling for flood intensity using stream gauge data. We compare models with (i) socioeconomic variables, (ii) the composite social vulnerability index (SoVI), and (iii) flood intensity variables only. The SoVI explains a larger portion of the variance in death (AIC = 2829) and damage (R2 = 0.125) than flood intensity alone (death—AIC = 2894; damage—R2 = 0.089), and models with individual sociodemographic factors perform best (death—AIC = 2696; damage—R2 = 0.229). Socioeconomic variables correlated with death (rural counties with a high proportion of elderly and young) differ from those related to property damage (rural counties with high percentage of Black, Hispanic and Native American populations below the poverty line). Results confirm that social vulnerability influences death and damage from floods in the USA. Model results indicate that social vulnerability models related to specific hazards and outcomes perform better than generic social vulnerability indices (e.g., SoVI) in predicting non-coastal flood death and damage. Hazard- and outcome-specific indices could be used to better direct efforts to ameliorate flood death and damage towards the people and places that need it most. Future validation studies should examine other flood outcomes, such as evacuation, migration and health, across scales

Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping

Mapping floods using satellite data is crucial for managing and mitigating flood risks. Satellite imagery enables rapid and accurate analysis of large areas, providing critical information for emergency response and disaster management. Historical flood data derived from satellite imagery can inform long-term planning, risk management strategies, and insurance-related decisions. The Sentinel-1 satellite is effective for flood detection, but for longer time series, other satellites such as MODIS can be used in combination with deep learning models to accurately identify and map past flood events. We here develop a combined CNN--LSTM deep learning framework to fuse Sentinel-1 derived fractional flooded area with MODIS data in order to infer historical floods over Bangladesh. The results show how our framework outperforms a CNN-only approach and takes advantage of not only space, but also time in order to predict the fractional inundated area. The model is applied to historical MODIS data to infer the past 20 years of inundation extents over Bangladesh and compared to a thresholding algorithm and a physical model. Our fusion model outperforms both models in consistency and capacity to predict peak inundation extents.Comment: CVPR 2023: Earthvision Worksho

Violence as an obstacle to livelihood resilience in the context of climate change

Central America continues to be a violent region and is prone to increasing climatic shocks and environmental degradation. This paper explores the non-linear feedback loop between violence and climate shocks on livelihood resilience in El Salvador and Honduras, two countries experiencing high rates of violence. The nature of this complex feedback loop is examined by analysing case studies on the community scale, which include challenges in reconstructing community social capital post-Hurricane Mitch (1998) in Honduras and the importance of social capital in community resilience to Hurricane Ida (2009) in El Salvador. We conclude that social capital is central in communities facing violence in order to enhance livelihood resilience to climate change impacts in Central America

Identifying, projecting, and evaluating informal urban expansion spatial patterns

Informal urban land expansion is produced through a diversity of social and political transactions, yet ‘pixelizable’ data capturing these transactions is commonly unavailable. Understanding informal urbanization entails differentiating spatial patterns of informal settlement from formal growth, associating such patterns with the social transactions that produce them, and evaluating the social and environmental outcomes of distinct settlement types. Demonstrating causality between distinct urban spatial patterns and social-institutional processes requires both high-resolution spatial temporal time-series data of urban change and insights into social transactions giving rise to these patterns. We demonstrate an example of linking distinct spatial patterns of informal urban expansion to the institutional processes each engenders in Mexico City. The approach presented here can be applied across cases, potentially improving land projection models in the rapidly urbanizing Global South, characterized by high informality. We conclude with a research agenda to identify, project, and evaluate informal urban expansion patterns.National Science Foundation12 month embargo; published online: 03 January 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Classifying Narcotrafficking Spatial Event Documents using Transformers

The low signal-to-noise ratio of information in unstructured textual documents remains a challenge in geo-text analyses. While information extraction approaches can be used to identify places, times and people mentioned in text, many of the identified entities may not be related to the analytical scenario, and may mislead spatial models without pre-filtering. On the other hand, detecting relevant spatiotemporal events and their attributes is of high interest to geovisual or computational solutions leveraging textual data. In this paper, we present a classification approach for identifying documents describing the time, locations and attributes of such spatiotemporal events (in Spanish), namely of drug trafficking activities in Honduras. We fine-tune a Spanish-specific and a Multilingual BERT (Bidirectional Encoder Representations from Transformers) model for this task with our limited amounts of training data. Our results indicate high performance of this approach, and the ability of the models to identify and filter documents including spatiotemporal events. The results are noteworthy since all documents in the dataset are related to narcotrafficking events (regardless of class membership), but not all describe a spatiotemporal event, yet the models exhibit high performance in detecting the ones describing the event

Understanding the role of illicit transactions in land-change dynamics

Anthropogenic land use has irrevocably transformed the natural systems on which humankind relies. Advances in remote sensing have led to an improved understanding of where, why and how social and economic processes drive globally important land-use changes, from deforestation to urbanization. The role of illicit activities, however, is often absent in land change analysis. The paucity of data on unrecorded, intentionally hidden transactions makes them difficult to incorporate into spatially specific analyses of land change. We present a conceptual framework of illicit land transactions and a two-pronged approach using remotely sensed data to spatially link illicit activities to land uses

Adaptive pathways and coupled infrastructure: seven centuries of adaptation to water risk and the production of vulnerability in Mexico City

Infrastructure development is central to the processes that abate and produce vulnerabilities in cities. Urban actors, especially those with power and authority, perceive and interpret vulnerability and decide when and how to adapt. When city managers use infrastructure to reduce urban risk in the complex, interconnected city system, new fragilities are introduced because of inherent system feedbacks. We trace the interactions between system dynamics and decision-making processes over 700 years of Mexico City's adaptations to water risks, focusing on the decision cycles of public infrastructure providers (in this case, government authorities). We bring together two lenses in examining this history: robustness-vulnerability trade-offs to explain the evolution of systemic risk dynamics mediated by feedback control, and adaptation pathways to focus on the evolution of decision cycles that motivate significant infrastructure investments. Drawing from historical accounts, archeological evidence, and original research on water, engineering, and cultural history, we examine adaptation pathways of humans settlement, water supply, and flood risk. Mexico City's history reveals insights that expand the theory of coupled infrastructure and lessons salient to contemporary urban risk management: (1) adapting by spatially externalizing risks can backfire: as cities expand, such risks become endogenous; (2) over time, adaptation pathways initiated to address specific risks may begin to intersect, creating complex trade-offs in risk management; and (3) city authorities are agents of risk production: even in the face of new exogenous risks (climate change), acknowledging and managing risks produced endogenously may prove more adaptive. History demonstrates that the very best solutions today may present critical challenges for tomorrow, and that collectively people have far more agency in and influence over the complex systems we live in than is often acknowledged

From cloud to refugee camp : a satellite-based flood analytics case-study in Congo-Brazzaville

In November 2017, floods in Impfondo, Congo forced evacuations and damaged crops, homes, and roads. The World Food Programme (WFP) supported the government’s response by providing food aid but was delayed by one month due to inadequate information. To enable faster flood response, WFP partnered with Cloud to Street to develop a near real-time Congo flood monitoring system in collaboration with the government. The system used precipitation information (GSMaP), and satellites (MODIS, Landsat, Sentinel-2, PlanetScope, and Worldview-3) to estimate flood damage and alert stakeholders via WhatsApp Messenger and an online platform. The system was used to assess flood risk of 16,000 refugees, resulting in the recommendation to move refugees from one high risk site (Makotipoko), reducing the flood exposure of up to 7,000 people. Despite limitations of the flood monitoring system (cloud cover, inaccurate rainfall forecasting, and population data), it provides evidence that satellite-based flood analytics can inform local decision making