Combining remote sensing techniques and field surveys for post‑earthquake reconnaissance missions

Remote reconnaissance missions are promising solutions for the assessment of earthquake induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace

Leveraging Geotagged Social Media to Monitor Spatial Behavior During Population Movements Triggered by Hurricanes

In a world of increased mobility and interconnectedness, the study of spatial behavior becomes more relevant than ever. However, multiple researchers have highlighted that the understanding of these dynamic processes has reached a bottleneck derived from the rigidity of traditional spatial behavior inquiry methods and the unavailability of trustworthy and relevant information. These difficulties are even more prominent during emergencies and disasters as these events often create scenarios where spatial behavior does not follow regular and logical patterns and where conventional mobility datasets are often skewed or not existent. Thus, many scholars working within the spatial behavior sub-discipline are pursuing innovative data collection methods to deepen the understanding of human spatial behavior. Researchers see digital geospatial trace data, also known as passive citizen sensor data, as one of the most promising opportunities to develop and test new hypotheses on spatial behavior. Nevertheless, the application of these new methods has not been fully explored within the hazard/disaster discipline for spatial behavior purposes under stressed situations. This dissertation investigates the suitability of geotagged social media (Twitter) as an innovative approach for the study of spatial behavior of people in stressed contexts and responds to three main research questions: 1) How well do geotagged social media estimate hurricane evacuation compliance? 2) To what extent is geotagged social media amenable for determining hurricane evacuation behavior? 3) How suitable is geotagged social media to evaluate post-disaster displacement and tourist flows? The dissertation therefore not only attempts to develop a new method to estimate the number of movements associated with the different stages of an emergency but also tries to answer long-standing questions about the response of different population sub-groups (residential status, gender, age, race/ethnicity) before, during, and after hurricanes. Results confirm the potential of geotagged social media to tackle some of the deficiencies of traditional approaches, particularly offering more timely, dynamic, and affordable information about the evacuation and post-disaster population movements. In addition, results demonstrate that the Twitter-based approach complements survey-based methods as it permits accessing underrepresented groups in traditional approaches such as the young, short-term residents, and racial/ethnic minorities. Although the representativeness of Twitter samples is still debatable and needs further research, this method to investigate emergency-triggered population movements can ultimately improve our understanding of the response and recovery phases of a disaster

Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

Copyright © The Author(s) 2023. Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace.VM was supported by the Dutch Research Council (NWO), project OCENW.XS5.114. StEER and GHI Data collection was supported by the National Science Foundation (NSF) under Grant CMMI-1841667, the U.S. Geological Survey (USGS) and the U.S. Agency for International Development (USAID), under USGS Cooperative Agreement No. G21AC10343-00 and USAID Award AID-OFDA-T-16-00001, under lead investigator Janise Rodgers

Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace

2016 GREAT Day Program

SUNY Geneseo’s Tenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1010/thumbnail.jp

Disaster Capitalism: Empirical Evidence from Latin America and the Caribbean

Natural disasters are uniquely transformative events. They can drastically transform physical terrain and the lives of those unfortunate enough to be caught in their wrath. However, natural disasters also provide an opportunity to reflect on past failures and, at times, a clean slate to correct those shortcomings. This project takes a political economic approach and recognizes natural disasters as occasions for agenda-setting on behalf of transnational commercial enterprises and market-oriented policy elites. These reformers often use the post-disaster policy space to articulate long-term development strategies based on market fundamentalism, and, more importantly, advance a set of policies consistent with their particular interests. This dissertation delves into that process and identifies the actors, their preferences and the policy outcomes. Using the business conflict model alongside changing transnational processes, this project identifies and traces post-disaster policy making in the Caribbean Basin. It also explores and provides a more nuanced explanation of its effect upon and within certain socioeconomic groups. What becomes apparent is that natural disasters are opportunities to first fracture national economies and then integrate them into transnational processes of capital accumulation. Given that economic viability is increasingly determined by assimilation into the global production processes, reformers in both developed and developing countries use disasters as occasions for re-orienting national economies towards this end. It is within this distorted integrative process that disaster capitalism is located

BIG DATA APPLICATIONS AND CHALLENGES IN GISCIENCE (CASE STUDIES: NATURAL DISASTER AND PUBLIC HEALTH CRISIS MANAGEMENT)

This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis.The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic characteristics and the digital divide on social media engagement during such crises. In addressing the opioid crisis, the dissertation delves into the spatial dynamics of opioid overdose deaths, utilizing Multiscale Geographically Weighted Regression to discern local versus broader-scale determinants. This analysis foregrounds the necessity for targeted public health responses and the importance of localized data in crafting effective interventions, especially within communities that are ethnically diverse and economically disparate. Using Hurricane Irma as a case study, this dissertation analyzes social media activity in Florida in September 2017, leveraging Multiscale Geographically Weighted Regression to explore spatial variations in social media discourse, its correlation with damage severity, and the disproportionate impact on racialized communities. It integrates social media data analysis with political-ecological perspectives and spatial analytical techniques to reveal structural inequalities and political power differentials. The dissertation also tackles the dissemination of false information during the COVID-19 pandemic, examining Twitter activity in the United States from April to July 2020. It identifies misinformation patterns, their origins, and their association with the pandemic\u27s incidence rates. Discourse analysis pinpoints tweets that downplay the pandemic\u27s severity or spread disinformation, while spatial modeling investigates the relationship between social media discourse and disease spread. By concentrating on the experiences of racialized communities, this research aims to highlight and address the environmental and social injustices they face. It contributes empirical and methodological insights into effective policy formulation, with an emphasis on equitable responses to public health emergencies and natural disasters. This dissertation not only provides a nuanced understanding of crisis responses but also advances GIScience research by incorporating social media data into both traditional and critical analytical frameworks

Social Media Influencers- A Review of Operations Management Literature

This literature review provides a comprehensive survey of research on Social Media Influencers (SMIs) across the fields of SMIs in marketing, seeding strategies, influence maximization and applications of SMIs in society. Specifically, we focus on examining the methods employed by researchers to reach their conclusions. Through our analysis, we identify opportunities for future research that align with emerging areas and unexplored territories related to theory, context, and methodology. This approach offers a fresh perspective on existing research, paving the way for more effective and impactful studies in the future. Additionally, gaining a deeper understanding of the underlying principles and methodologies of these concepts enables more informed decision-making when implementing these strategie

2019 EURēCA Abstract Book

Listing of student participant abstracts