Epidemiological studies have identified associations between mortality and changes in concentration of particulate matter. These studies have highlighted the public concerns about health effects of particulate air pollution. Modeling fine particulate matter PM2.5 exposure risk and monitoring day-to-day changes in PM2.5 concentration is a critical step for understanding the pollution problem and embarking on the necessary remedy. This research designs, implements and compares two inverse distance weighting (IDW)-based spatiotemporal interpolation methods, in order to assess the trend of daily PM2.5 concentration for the contiguous United States over the year of 2009, at both the census block group level and county level. Traditionally, when handling spatiotemporal interpolation, researchers tend to treat space and time separately and reduce the spatiotemporal interpolation problems to a sequence of snapshots of spatial interpolations. In this paper, PM2.5 data interpolation is conducted in the continuous space-time domain by integrating space and time simultaneously, using the so-called extension approach. Time values are calculated with the help of a factor under the assumption that spatial and temporal dimensions are equally important when interpolating a continuous changing phenomenon in the space-time domain. Various IDW-based spatiotemporal interpolation methods with different parameter configurations are evaluated by cross-validation. In addition, this study explores computational issues (computer processing speed) faced during implementation of spatiotemporal interpolation for huge data sets. Parallel programming techniques and an advanced data structure, named k-d tree, are adapted in this paper to address the computational challenges. Significant computational improvement has been achieved. Finally, a web-based spatiotemporal IDW-based interpolation application is designed and implemented where users can visualize and animate spatiotemporal interpolation results.

Geomasking techniques are commonly used to mask the true location information of cases by introducing noise into location data. This study seeks to improve the spatially adaptive random perturbation (SARP) geomasking method by using the actual distribution of the residential addresses (or “risk location”) rather than the people (or “risk population”) to define a perturbation zone. The procedure used in the study also employs a “donut-shaped” perturbation zone, rather than the traditional “pancake-shaped” zone, when displacing a case. The effectiveness of the proposed geomasking methods is assessed in terms of their potential to control for location re-engineering and their ability to maintain the point patterns embedded in the real distribution. The authors conclude that SARP geomasking using the distribution of actual street addresses protects privacy more effectively than geomasking based on population size; the different SARP techniques do not significantly change the clustering patterns on a global level, but the geomasked data tend to be more clustered than the real case distribution.

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Population growth and increasing development pressures are rapidly transforming the river basins across Sub-Saharan Africa. Planning decisions to monitor these landscapes and develop sound environmental management practices will require access to geo-technologies that permit the compilation of multi-date data for land use inventories and detection of change across space and time. This study demonstrates the functionality of these tools using multi-temporal satellite images, 1990 and 2000, acquired for the Densu River basin in Ghana. Change detection methods, based on image differencing and image regression, were used to evaluate the rates of change and identify the areas of significant change over the ten year period. The results show that residential land uses grew substantially during the study period, accounting for nearly two-thirds of the observed changes that occurred in the river basin. The expansion, involving farmland conversion, occurred mainly around Accra and its peri-urban areas. The analysis also confirms the conversion of agricultural land uses from tree crops to food crop farming to meet the demands of the burgeoning urban population. Overall, the findings demonstrate the growing importance of remote sensing and GIS approaches in tackling land use problems in Sub-Saharan Africa.

The purpose of this research is to illustrate a three-component operationalization of the Hazards-of-Place Model (HPM) by integrating urban infrastructure (using the capacity of road networks to facilitate evacuation as an example) to describe place vulnerability. This approach is informed by the HPM first articulated by Cutter (Vulnerability to environmental hazards. Prog Hum Geog. 20:529-39, 1996). The HPM is a conceptual framework through which place vulnerability is defined as a combination of social characteristics (expressed by selected socioeconomic demographics) and geophysical risk (expressed by probabilities of occurrence). Using a geographic information system (GIS), the study models the capacity of road networks to facilitate evacuation and used it as an example of urban infrastructure within which place vulnerability occurs. The output of the model was integrated with a geophysical risk layer and social vulnerability index layer as components for assessing the overall place vulnerability. The threecomponent approach to operationalizing the HPM provides a detailed and nuanced illustration of place-based vulnerability. As an applied tool, the threecomponent approach presents emergency planners with a new method of integrating diverse geographic data when illustrating spatial patterns of vulnerability to environmental hazards.