Environment and public health related data belong to the same category of spatial-temporal data, which contain spatial information about the geographic location and temporal feature of an object in addition to the conventional attribute information describing the object's characteristics. Efficient techniques for analyzing information from the environment and public health related data, the focus of this proposed work, are crucial to organizations, which make decisions based on large spatial-temporal data sets. The applications of efficient models can be found useful in environment conservation and public health. Traditional data analysis and data mining techniques, which do not model spatial context and temporal effect, may lead to residual errors that vary systematically over space and time. The models derived may turn out to be not only biased and inconsistent, but may also be a poor fit to the data set. The traditional approaches towards solving spatial data analysis are to use classical data analysis tools by using spatial lag or error as one of explanatory variables. These techniques maximize classification accuracy, but spatial accuracy may be of more importance. Temporal and attribute accuracies are ignored in most of these predictive models. In addition, these approaches are often computationally expensive and are confounded with a large datasets.
A new computationally efficient spatial-temporal framework, ST-PUMS (Spatial-temporal Prediction Using Map Similarity), which maximize map similarity (including spatial similarity, temporal similarity, and attribute similarity) instead of classification/prediction accuracy was proposed. This work addresses how spatial-temporal autocorrelation, the characteristic property of spatial-temporal data, can be incorporated in the ST-PUMS framework. ST-PUMS framework searches the parameter space of models using a new map-similarity measure that is more appropriate in the context of spatial-temporal data. In addition to modeling spatial accuracy, ST-PUMS can also be extended to incorporate temporal and attribute accuracies in the model. ST-PUMS will provide a solution for the difficulties encountered in analyzing spatial-temporal data. It is able to cope with multidimensional (i.e., spatial, attribute, temporal, etc.) environment and public health related data with complex data structure, and to achieve high efficiency with large volumes of data.
New techniques of the efficient spatial-temporal analysis of environment and public health related data are profound. The proposed framework consists of basic theoretical research as well as rigorous empirical studies to validate all the concepts. The experiments will be driven by a series of increasingly sophisticated case studies, including habitat estimation for bird flu investigation, and asthma hospital admission predictions. The solutions will have a direct impact on several important areas, such as environmental conservation, criminology and justice, real estate management and environmental epidemiology. The concepts, designs, algorithms and strategies are devised to analyze the multiple-dimensional, auto-correlative, large-size spatial-temporal data. The proposed research may also have broad impacts on other natural and social sciences that could take the advantages offered by varieties of spatial-temporal data.
