Yi Qiang

Mapping and assessing coastal resilience in the Caribbean region

Assessing the vulnerability and resilience to coastal hazards is a critical worldwide issue, especially for hurricane-prone coastal regions such as the Caribbean. However, the development of a useful metric for vulnerability and resilience assessment has a lot of challenges. Cartography and GIS analysis can contribute effectively to the solution of the issue by integrating natural and human data layers for assessment, mapping, and visualization. This paper uses the new Resilience Inference Measurement (RIM) model to assess the resilience of 25 countries in the Caribbean region to hurricanes. The RIM indices of the countries were computed using three variables representing three dimensions: exposure, damage, and recovery, and eight variables representing social-environmental capacity. The RIM resilience indices were mapped and compared with the vulnerability indices computed in a previous study. The results show that Turks & Caicos Islands had the highest resilience, whereas Montserrat had the lowest. This paper contributes to the hazard literature by demonstrating new vulnerability and resilience assessment methodologies that include validation and enable inference. The paper also contributes to the cartography and GIS literature by demonstrating the need to integrate data and perspectives from multiple disciplines and regions, as well as the ability of geospatial technology, in producing useful decision-making tools for a very pressing societal problem.

Interactive analysis of time intervals in a two-dimensional space

Time intervals are conventionally represented as linear segments in a one-dimensional space. An alternative representation of time intervals is the triangular model (TM), which represents time intervals as points in a two-dimensional space. In this paper, the use of TM in visualising and analysing time intervals is investigated. Not only does this model offer a compact visualisation of the distribution of intervals, it also supports an innovative temporal query mechanism that relies on geometries in the two-dimensional space. This query mechanism has the potential to simplify queries that are difficult to specify using traditional linear temporal query devices. Moreover, a software prototype that implements TM in a geographical information system (GIS) is introduced. This prototype has been applied in a real scenario to analyse time intervals that were detected by a Bluetooth tracking system. This application shows that TM has the potential to support a traditional GIS to analyse interval-based geographical data.

The impact of Hurricane Katrina on urban growth in Louisiana: an analysis using data mining and simulation approaches

ABSTRACT Understanding human dynamics after a major disaster is important to the region’s sustainable development. This study utilized land cover data to examine how Hurricane Katrina has affected the urban growth pattern in the Mississippi Delta in Louisiana. The study analyzed land cover changes from non-urban to urban in three metropolitan areas, Baton Rouge, New Orleans-Metairie, and Hammond, for two time periods, pre-Katrina (2001–2006) and post-Katrina (2006–2010). The study first applied a focal filter to extract continuous urban areas from the scattered urban pixels in the original remote sensing images. Statistical analyses were applied to develop initial functions between urban growth probability and several driving factors. A genetic algorithm was then used to calibrate the transition function, and cellular automata simulation based on the transition function was conducted to evaluate future urban growth patterns with and without the impact of Hurricane Katrina. The results show that elevation has become a much more important factor after Hurricane Katrina, and urban growth has shifted to higher elevation regions. The elevation most probable for new urban growth increased from 10.84 to 11.90 meters. Moreover, simulated future urban growth in this region indicates a decentralized trend, with more growth occurring in more distant regions with higher elevation. In the New Orleans metropolitan area, urban growth will continue to spill across Lake Pontchartrain to the satellite towns that are more than 50 minutes away by driving from the city center.

The continuous spatio-temporal model CSTM as an exhaustive framework for multi-scale spatio-temporal analysis

When studying geographical phenomena, different levels of spatial and temporal granularity often have to be considered. While various approaches have been proposed to analyse geographical data in a multi-scale perspective, they have all focused on either spatial or temporal attributes rather than on the integration of space and time over multiple scales. This study introduces the continuous spatio-temporal model (CSTM), a conceptual model that seeks to address this shortcoming. The presented model is based on (1) the continuous temporal model (CTM), a multi-scale model for temporal information, and (2) the continuous spatial model (CSM), an extension of CTM for multi-scale spatial raster data. At the core of the presented conceptual model is a spatio-temporal evolution element or, in short, stevel, which is described by four variables: (1) pixel location, (2) spatial resolution, (3) temporal interval, and (4) temporal resolution. By varying one or more of these variables, a CSTM-tree consisting of (sets of) stevel arrays is created, forming the basis of an exhaustive CSTM-typology. These arrays can then be used to systematically cluster spatio-temporal information. The value of our approach is illustrated by means of a simplified example of mean temperature evolution. Various suggestions are made for modifications to be developed in future research.

A cyberinfrastructure for community resilience assessment and visualization

Disaster resilience is a major societal challenge. Cartography and GIS can contribute substantially to this research area. This paper describes a cyberinfrastructure for disaster resilience assessment and visualization for all counties in the United States. Aided by the Application Programming Interface-enabled web mapping and component-oriented web tools, the cyberinfrastructure is designed to better serve the US communities with comprehensive resilience information. The resilience assessment tool is based on the resilience inference measurement model. This web application delivers the resilience assessment tool to the users through applets. It provides an interactive tool for the users to visualize the historical natural hazards exposure and damages in the areas of their interest, compute the resilience indices, and produce on-the-fly maps and statistics. The app could serve as a useful tool for decision makers. This app won the top 10 runners-up in the Environmental Systems Research Institute (ESRI) Climate Resilience App Challenge 2014 and the top 5 in the scientific section of the ESRI Global Disaster App Challenge 2014.

Multi-scale analysis of linear data in a two-dimensional space

Many disciplines are faced with the problem of handling time-series data. This study introduces an innovative visual representation for time series, namely the continuous triangular model. In the continuous triangular model, all subintervals of a time series can be represented in a two-dimensional continuous field, where every point represents a subinterval of the time series, and the value at the point is derived through a certain function (e.g. average or summation) of the time series within the subinterval. The continuous triangular model thus provides an explicit overview of time series at all different scales. In addition to time series, the continuous triangular model can be applied to a broader sense of linear data, such as traffic along a road. This study shows how the continuous triangular model can facilitate the visual analysis of different types of linear data. We also show how the coordinate interval space in the continuous triangular model can support the analysis of multiple time series through spatial analysis methods, including map algebra and cartographic modelling. Real-world datasets and scenarios are employed to demonstrate the usefulness of this approach.

Analysing Imperfect Temporal Information in GIS Using the Triangular Model

Abstract Rough set and fuzzy set are two frequently used approaches for modelling and reasoning about imperfect time intervals. In this paper, we focus on imperfect time intervals that can be modelled by rough sets and use an innovative graphic model [i.e. the triangular model (TM)] to represent this kind of imperfect time intervals. This work shows that TM is potentially advantageous in visualizing and querying imperfect time intervals, and its analytical power can be better exploited when it is implemented in a computer application with graphical user interfaces and interactive functions. Moreover, a probabilistic framework is proposed to handle the uncertainty issues in temporal queries. We use a case study to illustrate how the unique insights gained by TM can assist a geographical information system for exploratory spatio-temporal analysis.

Mining Twitter Data for Improved Understanding of Disaster Resilience

Coastal communities faced with multiple hazards have shown uneven responses and behaviors. These responses and behaviors could be better understood by analyzing real-time social media data through categorizing them into the three phases of the emergency management: preparedness, response, and recovery. This study analyzes the spatial–temporal patterns of Twitter activities during Hurricane Sandy, which struck the U.S. Northeast on 29 October 2012. The study area includes 126 counties affected by Hurricane Sandy. The objectives are threefold: (1) to derive a set of common indexes from Twitter data so that they can be used for emergency management and resilience analysis; (2) to examine whether there are significant geographical and social disparities in disaster-related Twitter use; and (3) to test whether Twitter data can improve postdisaster damage estimation. Three corresponding hypotheses were tested. Results show that common indexes derived from Twitter data, including ratio, normalized ratio, and sentiment, could enable comparison across regions and events and should be documented. Social and geographical disparities in Twitter use existed in the Hurricane Sandy event, with higher disaster-related Twitter use communities generally being communities of higher socioeconomic status. Finally, adding Twitter indexes into a damage estimation model improved the adjusted R2 from 0.46 to 0.56, indicating that social media data could help improve postdisaster damage estimation, but other environmental and socioeconomic variables influencing the capacity to reducing damage might need to be included. The knowledge gained from this study could provide valuable insights into strategies for utilizing social media data to increase resilience to disasters.

Changes in Exposure to Flood Hazards in the United States

This article conducts a national, county-based assessment of the changes in population and urban areas in high-risk flood zones from 2001 to 2011 in the contiguous United States. The U.S. Federal Emergency Management Agencys (FEMA) 100-year flood maps, land cover data, and census data were used to extract the proportion of developed (urban) land in flood zones by county at the two time points, and indexes of difference were calculated. Local Morans I statistic was applied to identify hot spots of increase in urban area in flood zones, and geographically weighted regression was used to estimate the population in flood zones from the land cover data. Results show that in 2011, an estimate of about 25.3 million people (8.3 percent of the total population) lived in high-risk flood zones. Nationally, the ratio of urban development in flood zones is less than the ratio of land in flood zones, implying that Americans were responsive to flood hazards by avoiding development in flood zones. This trend varied from place to place, however, with coastal counties having less urban development in flood zones than the inland counties. Furthermore, the contrast between coastal and inland counties increased between 2001 and 2011. Finally, several exceptions from the trend (hot spots) were detected, most notably in New York City and Miami, where significant increases in urban development in flood zones were found. This assessment provides important baseline information on the spatial patterns of flood exposure and their changes from 2001 to 2011. The study pinpoints regions that might need further investigations and better policy to reduce the overall flood risks.

Modeling the Dynamics of Community Resilience to Coastal Hazards Using a Bayesian Network

Studies on how variables of community resilience to natural hazards interact as a system that affects the final resilience (i.e., their dynamical linkages) have rarely been conducted. Bayesian network (BN), which represents the interdependencies among variables in a graph while expressing the uncertainty in the form of probability distributions, offers an effective way to investigate the interactions among different resilience components and addresses the natural–human system as a whole. This article employs a BN to study the interdependencies of ten resilience variables and population change in the Lower Mississippi River Basin (LMRB) at the census block group scale. A genetic algorithm was used to identify an optimal BN where population change, a cumulative resilience indicator, was the target variable. The genetic algorithm yielded an optimized BN model with a cross-validation accuracy of 67 percent over a period of 906 generations. Six variables were found to have direct impacts on population change, including level of threat from coastal hazards, hazard damage, distance to coastline, employment rate, percentage of housing units built before 1970, and percentage of households with a female householder. The remaining four variables were indirect variables, including percentage agriculture land, percentage flood zone area, percentage owner-occupied house units, and population density. Each variable has a conditional probability table so that its impacts on the probability of population change can be evaluated as it propagates through the network. These probabilities could be used for scenario modeling to help inform policies to reduce vulnerability and enhance disaster resilience.