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As one of the most vulnerable coasts in the continental USA, the Lower Mississippi River Basin (LMRB) region has endured numerous hazards over the past decades. The sustainability of this region has drawn great attention from the international, national, and local communities, wanting to understand how the region as a system develops under intense interplay between the natural and human factors. A major problem in this deltaic region is significant land loss over the years due to a combination of natural and human factors. The main scientific and management questions are what factors contribute to the land use land cover (LULC) changes in this region, can we model the changes, and how would the LULC look like in the future given the current factors? This study analyzed the LULC changes of the region between 1996 and 2006 by utilizing an artificial neural network (ANN) to derive the LULC change rules from 15 human and natural variables. The rules were then used to simulate future scenarios in a cellular automation model. A stochastic element was added in the model to represent factors that were not included in the current model. The analysis was conducted for two sub-regions in the study area for comparison. The results show that the derived ANN models could simulate the LULC changes with a high degree of accuracy (above 92 % on average). A total loss of 263 km(2) in wetlands from 2006 to 2016 was projected, whereas the trend of forest loss will cease. These scenarios provide useful information to decision makers for better planning and management of the region.

Time intervals are conventionally represented as linear segments in a onedimensional 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 hard 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 potential of supporting a traditional GIS to analyse interval-based geographical data.

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This paper presents an assessment of community resilience to coastal hazards in the Lower Mississippi River Basin (LMRB) region in southeastern Louisiana. The assessment was conducted at the census block group scale. The specific purpose of this study was to provide a quantitative method to assess and validate the community resilience to coastal hazards, and to identify the relationships between a set of socio-environmental indicators and community resilience. The Resilience Inference Measurement (RIM) model was applied to assess the resilience of the block groups. The resilience index derived was empirically validated through two statistical procedures: K-means cluster analysis of exposure, damage, and recovery variables to derive the resilience groups, and discriminant analysis to identify the key indicators of resilience. The discriminant analysis yielded a classification accuracy of 73.1%. The results show that block groups with higher resilience were concentrated generally in the northern part of the study area, including those located north of Lake Pontchartrain and in East Baton Rouge, West Baton Rouge, and Lafayette parishes. The lower-resilience communities were located mostly along the coastline and lower elevation area including block groups in southern Plaquemines Parish and Terrebonne Parish. Regression analysis between the resilience scores and the indicators extracted from the discriminant analysis suggests that community resilience was significantly linked to multicomponent capacities. The findings could help develop adaptation strategies to reduce vulnerability, increase resilience, and improve long-term sustainability for the coastal region.

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The catastrophic earthquake that struck Sichuan Province, China, in 2008 caused serious damage to Wenchuan County and surrounding areas in southwestern China. In recent years, great attention has been paid to the resilience of the affected area. This study applied the resilience inference measurement (RIM) model to quantify and validate the community resilience of 105 counties in the impacted area. The RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure, damage, and recovery conditions. The model then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county's resilience. Analysis results show that counties located at the epicenter had the lowest resilience, but counties immediately adjacent to the epicenter had the highest resilience capacities. Counties that were farther away from the epicenter returned to normal resiliency quickly. Socioeconomic variables-including sex ratio, per capita GDP, percent of ethnic minority, and medical facilities-were identified as the most influential characteristics influencing resilience. This study provides useful information to improve county resilience to earthquakes and support decision making for sustainable development.