2019-2021. EAGER: Fairness-aware Informatics System for Enhancing Disaster Resilience (funded by NSF)
2019-2020. RAPID: Collaborative Research: Data Mining and Fusion Between Unmanned Aerial Systems and Social Media Technologies to Improve Emergency Operations. (funded by NSF)
2018. RAPID: The Changing Roles of Social Media in Disaster Resilience: The Case of Hurricane Harvey. (funded by NSF)
2016-2020. IBSS-L: Understanding Social and Geographical Disparities in Disaster Resilience Through the Use of Social Media. (funded by NSF)
2015-2018. A Synthesis of Resilience Measurement Methods and Indices. (funded by Louisiana Sea Grant)
2014-2016. Community Resilience to Drought Hazard: An analysis of drought exposure, impacts, and adaptation in the south-central United States (funded by U.S. Geological Survey)
2012-2018. CNH: Coupled Natural-Human Dynamics in a Vulnerable Coastal System (funded by NSF)
2010-2014. Development of an Empirical Model for Measuring Community Resilience (funded by USDA-NSF).
2008-2009. Paleotempestology of the Caribbean Region: A multi-proxy, multi-site study of the spatial temporal variability of Caribbean hurricane activity (funded by Inter-American Institute (IAI) for Global Change Research)
2007-2011. Collaborative Research: (DRU) Modeling Business Return Amid Post-Disaster Uncertainties: New Orleans After Katrina (funded by NSF)
EAGER: Fairness-aware Informatics System for Enhancing Disaster Resilience.
This project is a collaboration of Dr. Mingxuan Sun (Principal Investigator), assistant professor in the LSU Division of Computer Science and Engineering, and Dr. Nina Lam (Co-Principal Investigator).
The central goal of this research project is to develop a fairness-aware AI system for emergency management. The project involves formulating and testing reliable principles and methods to adjust the AI algorithms for fairness, a very domain specific challenge. This is especially true in emergency management, where the system has to be able to predict rescue events in real time from large, noisy, and biased data, such as Twitter data. In light of this, the research team will develop a novel point process model for event prediction from streaming data, and it will investigate statistical learning problems when event data are noisy and incomplete. To adjust for the fairness of the prediction algorithm, the team will integrate heterogeneous social and geographical data with varying degrees of granularity and different levels to build a classic event prediction model and to examine correlations between the two approaches. Through comparing the approaches (with and without fairness adjustment) using an empirical example (Hurricane Harvey), the project will reveal the patterns of disparities, if any, and add new knowledge on community resilience and emergency management.
To view the full abstract, please visit the NSF website, and LSU Press release for more information.
RAPID: Collaborative Research: Data Mining and Fusion Between Unmanned Aerial Systems and Social Media Technologies to Improve Emergency Operations.
This project is a collaboration of Dr. Navid Jafari (Principal Investigator), assistant professor in the LSU Department of Civil and Environmental Engineering, and Dr. Nina Lam (Co-Principal Investigator).
This RAPID project will leverage Hurricane Barry as a mechanism for creation of a framework that will be integrated into Emergency Operations Centers (EOCs) to support post-disaster analysis and decision-making. Hurricane Barry was accompanied by extensive flooding in coastal Louisiana communities and this has provided a perishable and voluminous data set of social media and UAV imagery for analysis. The project will develop tools for data mining of the social media and fusion with collected UAV imagery for post-disaster analysis. As part of this project, feedback from EOC operators and decision-makers will be provided that will enable enhancement of algorithms and analyses to support recovery as well as response to social media generated rumors. Data from Beaumont EOC from Hurricane Harvey, which also includes data on the recovery of the community will also be collected. By combining data from both regions, a richer dataset will be produced to make comparative analyses and linkages across space, time, disaster level, and socioeconomic factors. .
To view the full abstract, please visit the NSF website.
RAPID: The Changing Roles of Social Media in Disaster Resilience: The Case of Hurricane Harvey.
This project is a collaboration of researchers from Department of Environmental Sciences, Department of Sociology and Department of Computer Science at Louisiana State University. The team consists of experts in resilience modeling and sustainability modeling — Nina S Lam (PI), environmental sociology — Michelle Meyer (Co-PI), big data and HPC – Seung-Jong Park (Co-PI), environmental policy — Margaret A Reams (Co-PI), and information systems and analysis – Seungwon Yang (Co-PI).
Understanding the changing roles and effects of social media use in disaster events, such as Hurricane Harvey in Texas, will help reduce vulnerability and improve resilience of communities to these disaster events. Hurricane Harvey made landfall on August 25, 2017 near Rockport, Texas as a category-4 hurricane. It lingered over the Houston area and dumped over 50-inches of rainfall, causing widespread flooding and damages in the region. This unprecedented disastrous event reveals many issues, including inadequate flood warning and slow response by agencies. At the same time, a new phenomenon emerged during the Harvey event: many residents in the Houston area resorted to social media to call for rescue from flooded homes when the 911 system was overloaded and could not be connected. This changing use of social media marks Harvey as one of the very first disastrous events in which social media have played an important role in facilitating fast-responding rescue missions. The overarching research question is: how effective is social media in enhancing resilience through its new role in response and rescue, and do we see an increase or decrease in the geographical and social disparities of social media use that may have affected the outcome and the resilience of individuals and communities? This project collects time-sensitive Twitter data and online surveys of individuals and organizations in the flood-affected communities in the Houston region so that they can be used to address this key question.
To view the full abstract, please visit the NSF website.
IBSS-L: Understanding Social and Geographical Disparities in Disaster Resilience Through the Use of Social Media.
This project is a collaboration of researchers from Department of Environmental Sciences, Department of Sociology and Department of Computer Science at Louisiana State University. The team consists of experts in resilience modeling and sustainability modeling — Nina S Lam (PI), environmental sociology — Michelle Meyer (Co-PI), big data and HPC – Seung-Jong Park (Co-PI), environmental policy — Margaret A Reams (Co-PI), and information systems and analysis – Seungwon Yang (Co-PI).
This interdisciplinary research project will examine whether social and geographical disparities exist during the four phrases of emergency management (mitigation, preparedness, response, and recovery). The investigators will use multiple perspectives and scales to address the research questions, including analysis at the community, individual, and organizational scales. Findings from this project will provide valuable insights into the interplay among regional disparities, individual social networks and behavior, and governmental/organizational policies. This project will expand knowledge of whether social media use may serve to overcome or further deepen the social and geographical disparities in each phase of emergency management. The project will enhance understanding of how to conduct efficient mining of social media data in order to produce useful and valid scientific information, thereby advancing both social science and information science research by developing and testing algorithms that can be used to mine noisy and imperfect data from sources like Twitter. The knowledge gained from this project will help develop strategies to reduce disparities, create effective social media campaigns and emergency management outreach, and promote resilience to disasters. The methods used in this project will be applicable to study other disasters in other regions.
For more information, please visit the project website.
To view the full abstract, please visit the NSF website.
A Synthesis of Resilience Measurement Methods and Indices
This project is a collaboration of researchers from Department of Environmental Sciences at Louisiana State University and Department of Geography at University of Hawaii at Manoa. The team consists of experts in resilience and sustainability modeling — Nina S Lam (PI) and geospatial analysis and modeling — Yi Qiang (Co-PI, former postdoctoral researcher at RSGIS lab, LSU).
The goal of this project is to conduct a synthesis study on resilience measurement methods and key indicators used. There are four specific objectives:
- To identify and collect the existing literature about community resilience to derive the knowledge of community resilience definition, measurement methods developed, resilience indicators tested, and adaptive strategies adopted.
- To develop an ontology of resilience measurement and the indicators used through an in-depth content analysis of the literature. The ontology will capture the key knowledge components related to resilience measurement and will serve as a foundation for information management, knowledge sharing, analysis, and modeling.
- To derive the consensus on the community resilience key indicators for improving mitigation and adaptation strategies and provide information on the effectiveness of various resilience measurement models.
- To develop a web-based application for resilience related data management and visualization which will enable users to evaluate the effects of various indicators on the resiliency of communities.
For more information, please visit the project website.
Community Resilience to Drought Hazard: An analysis of drought exposure, impacts, and adaptation in the south-central United States
This project is a collaboration of researchers from Department of Environmental Sciences and Department of Geography & Anthropology at Louisiana State University. The team consists of experts in resilience and sustainability modeling — Nina S Lam (PI), climatology — Robert Rohli (Co-PI), and adaptive governance — Margaret A Reams (Co-PI).
The objectives of this study are to examine: (1) whether drought indices are effective in representing the occurrence of drought events and their actual damages; (2) how the adaptive capacity (i.e. resilience) varies across space and socioeconomic status; and (3) which management tools would be most effective for prevention and damage reduction. The study region includes all the counties (503) in Arkansas, Louisiana, New Mexico, Oklahoma, and Texas.
For more information, please visit the project website.
To view the full abstract, please visit the USGS website.
CNH: Coupled Natural-Human Dynamics in a Vulnerable Coastal System
This project is funded by the NSF Dynamics of Coupled Natural and Human Systems (CNH) Program with a start date of 09/01/12 and an end date of 02/28/2017 (about 4.5 years). Total amount: 1,499,935. PI: Nina S Lam. Co-PI: Kam-biu Liu, Margaret A Reams, Victor H Rivera-Monroy, Yi Jun Xu, David Dismukes, and Kelley Pace, all are LSU faculty.
This interdisciplinary research project will investigate the sustainability of coastal communities that are especially vulnerable to natural resource losses and natural hazards. The investigators will focus on the Lower Mississippi River Basin in Louisiana, which is one of the most vulnerable coasts in the continental United States and the world. The four objectives the researchers will pursue are (1) to develop methods to assess the sustainability or resilience of a coastal system; (2) to develop a system-level model to capture and quantify the dynamic linkages among the major natural and human components; (3) to compare and contrast the resiliency between the northern and southern parts of coastal Louisiana so as to understand the resiliency control factors; and (4) to simulate future scenarios for planning and decision making.
For more information, please visit the project website.
To view the full abstract, please visit the NSF website.
Archived LSU press release.
Development of an Empirical Model for Measuring Community Resilience
This 3-year research is funded jointly by USDA and NSF to PI: Nina Lam and co-PI Margaret Reams, both of Department of Environmental Sciences. The project duration is: 09/15/2010-09/15/2013, with an amount of $390,028.
Despite abundant literature in social-ecological resilience, vulnerability, and hazards and risk assessment, there is yet a convincing approach to quantifying and measuring community resilience. The objectives of this project are: (1) to empirically develop a model to measure community resilience index along the Gulf of Mexico; (2) to compare the resilience indices between urban and rural communities, and between coastal and non-coastal communities; (3) to apply the model using the data at two different spatial scales, county and zip code levels. The development of a meaningful and practical resilience index will help identify aspects of activities that will increase or decrease resilience, thus the model to be developed will serve as a useful tool for sustainable planning and management, especially for coastal and rural communities facing the threats of climate change and other disastrous events.
Archived LSU press release.
Paleotempestology of the Caribbean Region: A Multi-proxy, Multi-site Study of the Spatial and Temporal Variability of Caribbean Hurricane Activity
This 5-year project is currently funded by the Inter-American Institute for Global Change Research (IAI) to Kam-biu Liu (LSU – Oceanography and Coastal Sciences), Nina Lam (LSU – Environmental Sciences), and Sam Bentley (Memory University, New Foundland), with collaborators from several U.S. and Latin American Universities. Total amount: $620,000. The RSGIS Lab is responsible for developing a GIS for the project.
For more information, please visit the project website.
Collaborative Research: (DRU) Modeling Business Return Amid Post-Disaster Uncertainties: New Orleans After Katrina
This collaborative project was funded by NSF Human and Social Dynamics Program to Nina Lam (the Lead PI) and Kelley Pace of LSU ($356,474), Richard Campanella of Tulane University ($121,876), and James LeSage of Texas State University ($144,062). The total amount of funding for the project is: $622, 412. The project duration was from 11/01/2007 to 10/31/2011.
Building on first-hand telephone and street survey data collected through a previous project on New Orleans businesses after Katrina (also funded by NSF), this project developed models to quantify determinants of the decisions by businesses to return to their prior location after a disaster. Special attention was given to the spatial relations between a business, its neighborhood, and businesses located nearby. The project was the first attempt to formally model business connectivity and interdependence in decision making as it pertains to decisions about disaster recovery. The research has augmented both methodological and substantive knowledge. Findings from this research could be applied to planning, mitigation, and the recovery of business in New Orleans as well as in other sites of future disasters.
The project produced a number of publications, visit the NSF website for a detailed list.
Selected publications:
- Lam NSN, Arenas H, Pace RK, LeSage JP, Campanella R. 2012. Predictors of Business Return in New Orleans after Hurricane Katrina. PLoS One 7(10), e47935: 1-8.
- LeSage JP, Pace RK, Lam NSN, Campanella R, Liu X. 2011. New Orleans business recovery in the aftermath of Hurricane Katrina. Journal of Royal Statistics Society A 174, Part 4: 1007-1027.
- Lam NSN, Pace K, Campanella R, LeSage J, Arenas H. 2009. Business return in New Orleans: Decision making amid post-Katrina uncertainty. PLoS One 4(8):e6765.