The Industrial Waste Department (IWD) of the Metropolitan Sewer District in Louisville, Kentucky, had been storing a large amount of various hazardous material data. Due to a lack of GIS training, access to workstations, and the amount and complexity of the data, it was deemed impractical to store this information on a GIS system. Even so, IWD still wanted the ability to exploit ArcInfo and ArcView 2.1 to spatially locate different aspects of hazardous material data. The intent of this paper is to discuss the methods used to get ArcInfo and ArcView 2.1 to "talk" to IWD's Oracle database. This paper will focus on the use of the SQL language to select and extract tabular data and download this data into dBASE 4 and INFO tables for use in ArcInfo and ArcView 2.1 geocoding.
We are exploring ways that GIS can be used to recover the spatial component of risk without extrapolating beyond the known data. Ultimately, interpolation of contaminant data which are rare in space and time is necessary for full evaluation of human risks. Spatial interpolation techniques make assumptions, and may therefore be misleading, incomplete, or incorrect. But to encapsulate human health risk into a single value in a table may be at least as incomplete or misleading, since the spatial relationships among contaminant values are not retained. The presentation of an interpolated contaminant layer together with bar and pie chart symbols placed at actual sample locations distinguishes between measured and derived concentration values, and provides a means of qualitatively evaluating uncertainty. Such a presentation also communicates the spatial weighting of the sampling design. We present an alternative spatial format for the communication of contamination and risk; an array of maps which we call a 'Map Spreadsheet.' The Map Spreadsheet is analogous to a data spreadsheet, except that each cell is a spatially co-registered interpolated map of contaminant concentration. Columns and rows in the Map Spreadsheet could represent alternative ingestion pathways, chemical classes and/or species of contaminants, or years through time, for example. Just as a typical spreadsheet might have a column to the right that represents a row sum or average, or a row at the bottom that represents a column sum or average, so the Map Spreadsheet has column and row totals represented by map algebraic sums or averages of the maps in that row or column. Thus, it would be possible, for example, to view a site contaminated with cesium as a series of yearly maps, as well as a map summarizing average cesium concentration over all years. Alternatively, if columns represent risk due to alternative ingestion pathways, the spatially-explicit risk from cesium inhalation, ingestion, and direct exposure, as well as a summary map of cesium risk from all pathways, could be simultaneously viewed. The 2-dimensional map spreadsheet analogy can be extended into the third dimension as a 'stack' of such spreadsheets, creating a map cube. Three full 'faces' of the Map Cube potentially represent summation map 'cells.' Of course, only a single 2-D 'slice' of a Map Cube could be viewed at a time. The viewer of a Map Spreadsheet obtains a perspective of the combined risk from exposure to multiple contaminants, as well as an idea of the heterogeneity of contamination across space. Such insights are usually not possible with a tabular presentation of single values of human health risk. Several examples, both hypothetical and real-world, will be shown to illustrate some of the potentially useful products resulting from the marriage of GIS and risk assessment.
As environmental risk management gains attention as a core, organizational activity, there is increasing demand for information to support the decision-making process. A diverse amount of data is needed to characterize risks to human health and the environment, including operations, demographics, land use, topography and other key considerations. This presentation describes data needs for performing environmental risk management and identifies sources where data is available to support specific applications.