Nina Lam - US grants

This project seeks to understand the commercial side of resettlement of residents in urban environments after a catastrophe. The overarching research question is: how businesses make spatial decisions on whether to return or relocate and how these decisions in turn impact the landscape and its economy. Specifically, the project will collect and analyze time-critical data on what, where, how, why, and when businesses return to New Orleans following the repopulation of the city after Hurricane Katrina. Both telephone surveys and street surveys of businesses will be conducted periodically. The street surveys will include a complete survey of three major commercial corridors in New Orleans every two weeks, tracking where, when, and what businesses return and survive (or fail). The telephone surveys, conducted every 2~3 weeks for 5 rounds, will target businesses throughout the entire city. In each round, a random sample of 500 complete surveys stratified by census tract and by a few business categories will be conducted. Both sets of survey data will then be mapped and integrated with census, flood damage, and other GIS (geographic information system) data layers. This will provide a valuable, time-critical spatial-temporal data set that can serve to answer a number of specific research questions.

Very little research has focused on collecting time-critical, empirical data on how businesses make spatial decisions on whether they remain or relocate after a catastrophe, especially a catastrophe as deep and wide as we have seen in Hurricane Katrina that affects an entire metropolis of New Orleans. The time-critical data collected for this project will provide unique information on how decisions among businesses are made in this unprecedented case. The coupling and tracking of street and telephone surveys over time will provide vital information for research on human-social-economic dynamics over space and time. The data will serve as an important benchmark dataset for subsequent research and for comparisons with other studies (e.g. studies on decisions made by individuals). Results from this project will also help governmental and planning agencies in devising effective policies for economic recovery in the region.

Relating the socio-economic conditions of urban neighborhoods through remote sensing imagery has been a major research challenge in remote sensing. It is clear from the literature that conventional per-pixel classification methods are not efficient in accurately classifying urban neighborhoods from high-resolution imagery due to the heterogeneous nature of urban environment. Instead, classification methods that utilize the textural characteristics of the image could dramatically improve the classification accuracy. Whether these textural methods can be used to detect the socio-economic conditions of urban neighborhoods remains a key question that this project will address. The goals of this doctoral dissertation research project are threefold: 1) to determine whether neural networks in combination with wavelet analysis are effective for the characterization and classification of urban areas; 2) to determine whether texture patterns can be associated with the social-economic conditions of urban neighborhoods; and 3) to explore the effects of image resolution on the classification accuracy. Two study areas, Atlanta, Georgia and Baton Rouge, Louisiana, will be examined and compared, and high-resolution IKONOS images of the study areas will be used. Fieldwork will be carried out and census and other socio-economic data will be used. Geographic phenomena operate at different scales; hence a multi-resolution approach will be used. Wavelet analysis is an efficient approach to studying textural patterns at different scales, whereas artificial neural networks can learn very complex patterns in the data. A combined methodology that utilizes both methods is expected to outperform other classification methods.

Although wavelet analysis and artificial neural networks have been employed separately to analyze remote sensing images, studies that bring them together are rare and the synergy is yet to be explored. This research will increase our knowledge of textures, texture methods, and their relationships with real features on the ground. The research will also enhance our appreciation of the scale effect on the classification of heterogeneous urban environment from high-resolution imagery. The identification and characterization of urban neighborhoods of different social-economic statuses holds the promises of opening up a new avenue to link remotely sensed images to the social-economic aspects of human activities. The methodology used in the project can be extended to study other social-economic applications such as detecting the sanitary or health conditions on the ground and to other classification scenarios (e.g., non-urban applications), hence will add significantly to the field of remote sensing, image processing, environmental assessment and monitoring, and urban analysis. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

Building on first-hand telephone and street survey data collected through a previous project on New Orleans businesses after Katrina, this project develops models to quantify determinants of the decisions by businesses to return to their prior location after a disaster. Special attention is given to the spatial relations between a business, its neighborhood, and businesses located nearby. Specifically, this project has four goals: (1) Extend current spatial statistical methods to address the ordinal nature of survey data information pertaining to important dependent variables that exhibit spatial dependence in underlying decisions. (2) Use estimates and inferences from the statistical models to explore the relation between business recovery and various disaster related problems that have confronted businesses. (3) Compare alternative spatial strategies for aid distribution to examine approaches that will maximize recovery. (4) Produce estimates and inferences regarding optimal recovery approaches and measures of recovery potential for other locations that will generalize findings for the analysis of disasters in other locations. The project is the first attempt to formally model business connectivity and interdependence in decision making as it pertains to decisions about disaster recovery. The research has the potential to augment both methodological and substantive knowledge. Findings from this research will have major implications for planning, mitigation, and the recovery of business in New Orleans as well as in other sites of future disasters.

The increased availability of high-resolution satellite imagery presents many new opportunities for detailed classification of urban land use and land cover, but it also brings to light the inadequacies of traditional spectral-based classification methods using the maximum-likelihood classifier. Recent research in remote sensing tends to focus on developing new textural methods and alternative classifiers to address this problem, but the potentially promising possibility of optimizing the maximum-likelihood classifier and combining multiple textural characteristics for image classification remains unexplored. Moreover, as an important parameter in texture analysis, the appropriate size of moving window (critical window) used to extract different textural layers and its association with land-use and/or land-cover objects has generated a lot of research, but the quantitative principles remain unclear. The overall goal of this doctoral dissertation research project is to develop an efficient, simple, and robust approach for image classification that has potential for automation. The doctoral candidate's objectives are (1) to develop and test a genetic Bayesian classifier to efficiently boost the performance of the traditional maximum-likelihood classifier by optimizing the prior probabilities with a genetic algorithm; (2) to determine whether a combination of textural indices, such as fractal dimension, lacunarity, and Moran's I, is efficient in classifying heterogeneous urban area, using the genetic Bayesian classifier; and (3) to derive the approximate size of the "critical window" when dealing with these texture indices and high-resolution IKONOS imagery. This project aims to improve urban land-use/land-cover classification accuracy by applying the new approach to high-resolution IKONOS pre- and post-Katrina imagery in New Orleans.

The results of this study will provide a new method to improve land-use/land-cover classification through addressing cutting edge issues like genetic algorithm, Bayesian approach, critical window, fractal geometry, and spatial autocorrelation. The analysis of critical window and texture vector will provide better understanding of the relationships between the texture measures, land-use objects, and sensor resolutions, all of which are critical to the success of texture-aided classification. The emphasis on the development of a simple, quick, and reliable classification method is imperative for future automation and many applications such as rapid monitoring of disastrous and special events. The study area, New Orleans, was severely inundated by Hurricane Katrina in 2005. This project aims to provide a better post-disaster assessment with high-resolution satellite imagery that could help in guiding the recovery and urban planning. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

Mass evacuation of urban areas due to hurricanes is a critical problem that requires extensive basic and applied research. Previous research used a microsimulation approach to simulate individual vehicle and driver behavior but was limited mostly to simulation in a small study area. Very few studies have considered the use of vulnerabilities in the evacuation strategies. There is a need to develop a better approach to integrate the various information (such as vulnerabilities) to better simulate evacuation so that we can evacuate people timely and efficiently. This Doctoral Dissertation Research Improvement project will develop an integrated microsimulation model to simulate hurricane evacuation in New Orleans. The first objective of this project will be to create a two-level regional disaster evacuation model by integrating two agent-based microsimulation models. The first model will treat each vehicle as an agent to simulate regional road network traffic movements on highways, while the second model will use each census block centroid as an agent to simulate the local part of the regional road network traffic. The second project objective will be to evaluate the effectiveness in terms of overall evacuation time and other metrics of three different regional evacuation strategies, including simultaneous evacuation strategy, a staged evacuation strategy based on different community-evacuation vulnerabilities over the study area, and a staged evacuation strategy based on different social-biophysical vulnerabilities.

This research project will contribute to the fields of dynamic spatial modeling, visualization, and disaster and vulnerability science. The incorporation of vulnerabilities into two types of agent-based models will provide more realistic and accurate pictures of how evacuation behavior affects the effectiveness of different evacuation strategies, thus providing useful guidance to policy makers. Although the proposed integrated agent-based model will be based on hurricane evacuation in New Orleans, the methods developed and the knowledge gained from this research will be applicable for other regions and other kinds of disasters. Furthermore, the computer modules developed in this research will be widely distributed to benefit other agent-based traffic microsimulation models. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

Many observers believe that cities and places subject to frequent natural and human disturbance will reach "tipping points" that inhibit their resilience to the level that recovery may not be possible. Understanding the complex relationships among human and natural systems and the thresholds of tipping points are critical to ensuring sustainable development of cities and regions, especially when they are faced with the threats of both long-term climate change and short-term, large-scale disturbance. Until recently, most of the research in the field of post disaster recovery focused on households or whole communities. The empirical study of business responses to disasters is relatively scarce, despite the fact that businesses are a fundamental part of the cities, providing services, jobs, and taxes that are essential for urban sustainability. This doctoral dissertation research project will develop an agent-based simulation model to represent and understand the businesses reopening process in a dynamic environment in New Orleans after Hurricane Katrina. The research has two objectives. First, it will identify the main predictors associated with reopening and estimate their relative importance over time, using an empirical data set collected during studies conducted over the previous five years. A computer simulation model based on the parameters derived from the first objective then will be developed and validated to represent the business-reopening process.

This project will contribute to geography, related spatial sciences, and other fields that examine disaster economy recovery and dynamic modeling. The use of simulation will enhance understanding of the complex recovery process and how a business opening in one location at one point in time will affect business reopening probabilities in other locations in later time periods. The validated simulation model supported by real data will allow researchers and policy makers to design and test recovery policies so that resources can be better allocated. Although the simulation model will be based on post-Katrina New Orleans data, it will be adaptable for other locations affected by disasters of a similar nature. The simulation and visualization computer program developed in this research will be made available to the open source community. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

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. Over the years, this deltaic region has endured multiple threats and disasters, such as land loss, subsidence, sea-level rise, flooding, hurricanes, and oil spills. The disappearance of land is a critical problem in the southern part of the region. During the past decade, significant population and economic growth has occurred in the northern part of the study area, whereas the southern part surrounding New Orleans has experienced population and economic decline. A pressing question is whether southern coastal Louisiana reached a tipping point that makes it too costly to sustain. If so, has the same type of economic and ecosystem functions gradually migrated to the neighboring region to the north? The researchers will quantify and explain these changes using a coupled natural-human system approach, and they will evaluate the implications of those findings for future planning and mitigation. 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.

This project will assess the sustainability of the lower Mississippi River basin in Louisiana using a coupled natural-human system approach. It will increase basic understanding about the key factors affecting the sustainability of a coastal system and how these key factors interact at a system level. The comparison between the two systems in coastal Louisiana will provide new knowledge regarding how the two systems have been coupled in the past that enhances understanding of system interactions in the present and facilitates projections for the future. The set of sustainability assessment and modeling tools and software developed in this project will be valuable educational as well as decision-making tools for students, other researchers, and decision makers. The project will engage a wide audience, including K-12 students and teachers from underserved schools, local governmental officials, environmental groups, and the public. Project findings will inform policy makers and the public about how to increase the resilience of the region, and they will provide scenarios to better plan for the future. The findings also will provide new insights regarding other deltaic regions around the world that are faced with similar types of threats and challenges. This project is supported by the NSF Dynamics of Coupled Natural and Human Systems (CNH) Program.

The most populated cities in the world are located on deltaic coastal floodplains because of their rich fertile soils and plentiful natural resources. River deltas are disappearing at increasing rates due to human-caused changes to sediment supply and river flow, gradual sinking of land, and rising sea level, threatening the sustainability of human settlements on coastal river deltas. River management projects, including those designed to promote navigation and reduce flooding, have in some cases accelerated land loss and increased the threat of hurricane flooding. This project will explore the relationships among human river management, sediment supply, wetland building capacity, coastal flood risk, and human perception of flood risk. Testing the connections between river management, wetland loss, and flood risks will improve prediction of future coastal system states and produce guidelines for how to sustainably manage sediment supply and maintain human settlement in coastal areas. Other broader impact activities will include graduate and undergraduate education, application to public policy, and public and K-12 outreach. These are all unified through the general recognition in Louisiana (like many other deltaic coasts) that the science of deltaic restoration has strong and direct impacts on local welfare and economies. This project is supported as part of the National Science Foundation's Coastal Science, Engineering, and Education for Sustainability program - Coastal SEES.

This project will explore the co-evolution of deltaic landscapes and human system response by focusing on changes in coastal flood risks due to human manipulations of sediment delivery. Three experimental coastal basins in the central Mississippi River Deltaic Plain with distinct histories of sediment delivery by rivers and wetland loss responses will be investigated. An interdisciplinary team of researchers will combine field studies and modeling approaches to characterize: 1) feedbacks between human river management strategies that reduce sediment delivery and corresponding landscape degradation and 2) causal links between landscape degradation resulting from reduced sediment delivery, increased flood risks from hurricane storm surges, and human responses to perceived flood risks. The team will explore historical and future outcomes of river management strategies, including reorganization of human settlement in coastal areas using computer simulations incorporating how sediment supply builds land and human response to flood risks. Testing these system interactions in a modeling framework will produce foundational knowledge that can inform management decisions and promote sustainable human settlements on deltaic landscapes.

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.

Understanding the sources, patterns, and consequences of social and geographical disparities in disaster resilience is critical to building long-term resilient, healthy, and sustainable communities. Traditional resilience analysis has been confined mostly to the use of static data collected at scheduled intervals. With the advent of the "Big Data" era, real-time human response data extracted from social media could provide new opportunities for studying disparities in disaster resilience. The investigators will compare Twitter data from two events, Hurricane Isaac and Hurricane Sandy, both of which occurred in 2012. They will pursue four interrelated research components: (1) development of data-mining algorithms for the evaluation and visualization of Twitter data; (2) analysis of social media and resilience disparities at the community (zip-code) level across the emergency management cycle; (3) an online survey of Twitter and non-Twitter users to understand effects of social media use on individual resilience, social networks, social support, and social disparities; and (4) analysis of bi-directional communication among residents and governmental and non-governmental organizations throughout each event cycle. Through an investigation of the Twitter data before, during, and after the hurricanes coupled with analysis of online surveys, the researchers will be able to address a set of core research questions: (1) Are there distinct geographical and social disparities in the use of social media in disaster resilience? (2) What are the sources and consequences of such disparities? (3) How do these disparities vary across the four phases of the disaster cycle? (4) How do these disparities affect resilience? (5) How can social media data be used to improve resilience? This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition.

Disasters such as Hurricane Barry, Hurricane Harvey and others provide an opportunity to document how emergency operation centers (EOCs) can use technology to respond to a natural disaster by understanding and mitigating the uncertainties involved. The use of social media, Unmanned Autonomous Systems/Unmanned Aerial Vehicles (UAVs), real-time disaster modeling, and widespread connectedness means more efficient analysis and flow of information. Immediate information on the location of most damaged areas of a city or stranded people will save lives. Real-time data allows emergency management to develop more targeted response and post disaster recovery plans, and this is regarded as a technological leap from the previous search and rescue strategies attempted decades ago. The proposed research plan formulated by team will not only collect relevant information from real hazard events, but will also analyze and integrate the data to develop the post disaster management frameworks using advanced technologies including UAVs, and various other modes of data collection. Documenting the current operational inefficiencies, technology gaps, and data analysis limitations of EOCs are important for improvements in disaster preparedness and response. Furthermore, this project has time urgency due to the need to collect and utilize time sensitive data from this recent set of storms in the construction of the framework.

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. This RAPID project will culminate with collecting and archiving (in the National Science Foundation's Natural Hazards Engineering Research Infrastructure DesignSafe) a rich dataset of EOC operations and technology application from Hurricanes Harvey and Barry. However, it is vital that the knowledge gained be reciprocated back to the EOCs so that they can make improvements for the betterment of US citizens. Research team will develop a manual documenting lessons learned for EOC in Beaumont and Louisiana and how to adopt and implement technology. Technology adoption and implementation requires active learning to retain knowledge. Thus, one-day short courses will be developed that will provide UAV and Twitter demonstrations along with examples of data analysis and fusion.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Studying differential COVID-19 responses to trustworthy information will contribute new perspectives and improve our fundamental understanding of the human aspects of information trustworthiness during a pandemic. Although many U.S. residents are heeding restrictive COVID-19 orders and warnings from both official guidance and public health information sources, some individuals are not following official guidance. Simultaneously, the pandemic has spread through major U.S. cities and states in an uneven manner. Moreover, COVID-19 messaging is complicated by evolving guidance and contagion information, and media framing complicates information transmission, shifting public understanding of official guidance. Understanding and evaluating how a population is reacting to these multiple sources of information is a crucial scientific challenge with implications for improving future public policy and more effective crisis mitigation. To better understand differential population responses to this crisis, this interdisciplinary research project will gather Twitter data in Louisiana and Washington State to analyze differential sentiment and language use in responses to health information and public guidance, across assumed age and linked geographic conditions. It will also assess how these differences change given time, location, health, economic, social conditions, and sources of information. Assessing the systematic variation of sentiment and language use across age and then across time, space, context, and information sources will provide vital insight to targeting effective public messaging by public health and government officials during a crisis.

This project will collect three waves of Twitter and associated data to study differential sentiment and language use in responses to health information and public health guidance concerning COVID-19. This interdisciplinary project seeks to advance knowledge about how disparities in information consumption, as represented by sentiment and language use, vary across sources of information and guidance, across time, geography, socio-demographic characteristics, and health conditions. This project will compare sentiment and language use patterns among a population of Twitter users based on the content of their tweets, and analyze how these characteristics evolve through the recognition of the emergency, the peak of the crisis, and the mitigation of the pandemic in the U.S. The project will use computational methods to understand sentiment patterns and language use on Twitter and link tweets and relevant entities to corresponding longitudinal data about trustworthy information sources, including news media sources and official emergency guidance, policies, and orders. The tweets and entities will be classified for relevance (individual, organization, or bot) with derived age and location. Using timestamps and derived location, the project will associate tweets with daily disease-specific rates and annual demographic and socio-economic information. Data about the surrounding information context, including reporting trends and framing of the crisis, sources of health information, and information-seeking behavior about COVID-19 will provide tools for assessing the validity and reliability of our inferences about the patterns of sentiment and language use. Project findings will contribute to the literature on computational methods, disaster communication, misinformation during crisis events, and disaster resilience, informing future guidance on policy and emergency messaging during a crisis.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.