Design Decisions

Characterizing and comparing heterogeneous data types on the fly across a large geographic scale such as the SCA region required subdividing the region into finer and relatively homogenous sub-regions. Through subdividing, the variations in the data were minimal and the subdivision was relatively easy for interpretation and processing. In literature, this process of subdividing is often referred to as zone mapping. Zone mapping can be achieved by either using some criteria such as natural delineations (e.g., watershed, land cover), geopolitical delineations (e.g., county, city), or by using a fixed grid system. A method of subdividing geospatial data with a fixed grid system has unique advantages over other methods, especially when it comes to undertaking any decision analysis as it coarsely represents the underlying data without the complexities of data size.

In this work, the SCA region (Figure 1) was partitioned into an equal-area hexagonal grid (side length of 0.62 km and area of 1 km2; see Figures 11, 12). Hexagonal grids are preferred over other geometrical shapes (e.g., square or triangle) because they are characterized by elements that do not have gaps or overlaps and the center-to-center distances between neighboring cells are almost equal. Also, hexagonal cells have a topology that is symmetrical, invariant, and of equal area, and can be recursively partitioned into smaller divisions of grids if or when required to represent higher resolution data types.

Figure 11. Hexagonal framework

Figure 12. Snapshot of the 1 km2 hexagonal grid overlaid on a base map showing parts of coastal Mississippi and Louisiana.

The geodatabase was included within the hexagonal grid as an Esri shapefile that stores non-topological geometry and attribute information for the spatial features in the data set. An Esri shapefile consists of a main file (.shp), an index file (.shx), a dBASE table (.dbf), a projection file (.prj), and a metadata file (.xml) that is compliant with the Federal Geographic Data Committee. This data can be read into commercial GIS tools such as ArcMap and open source programs such as QGIS, R, and Python. The Esri shapefile can also be converted into other file formats such as GeoJSON using open source tools.