Issue link: https://read.dmtmag.com/i/84685
Organizing Geographic Space for Effective Analysis BEYONDMAPPING A BY JOSEPH BERRY an aerial image draped over a 3-D terrain perspective. The roads are stored in vector format as an intercon- necting set of line features (vector). The aerial image and elevation relief are stored as numbers in georefer- enced matrices (raster). The positions in a raster image matrix are referred to as "pixels," short for picture elements. The value stored at each pixel corresponds to a displayed color as a combination of red, green and blue hues. For example, the green tone for some of the pixels basic familiarity of the two fundamental data types supporting geotechnology— vector and raster—is important for under- standing map-analysis procedures and capabilities (see "Author's Note," page 11). Vector data are closest to our manual-mapping heritage and familiar to most users, as they characterize geographic space as a "collection of discrete spatial objects" (e.g., points, lines and polygons) that are easy to draw. Raster data, however, describe geographic space as a "continuum of grid-cell values" (e.g., surfaces) that, although easy to conceptualize, require a computer to implement. Generally speaking, vector data are best for tra- ditional map display and geoquery: "where is what" applications that identify existing conditions and characteristics, such as "where are the existing gas pipelines in Colorado" (a descriptive query of existing information). Raster data are best for advanced graphics and map analysis: "why, so what and what if" applications that analyze spatial relationships and patterns, such as "where is the best location for a new pipeline" (a prescriptive model deriving new information). Most vector applications involve the Joseph Berry is a principal in Berry & Associates, consultants in GIS technology. He can be reached via e-mail at jkberry@du.edu. 10 extension of manual mapping and inven- tory procedures that take advantage of modern computers' storage, speed and Internet capabilities (better ways to do things). Raster applications, however, tend to involve entirely new paradigms and procedures for visualizing and analyzing mapped data that advance innovative science (entirely new ways to do things). Rasters and Vectors and Bighorns, Oh My! On the advanced-graphics front, the low- er-left portion of Figure 1 depicts an inter- active Google Earth display of an area in northern Wyoming's Bighorn Mountains, showing local roads superimposed on GEO W ORLD / SEPTEMBE R 2O12 portraying the individual tree in the figure is coded as red = 116, green = 146 and blue = 24. Your eye detects a greenish tone with more green than red and blue. In the tree's shadow toward the northwest, the red, green and blue levels are fairly equally low (dark gray). In a raster image, the objective is to generate a visual graphic of a landscape for visual interpretation. A raster grid is a different type of raster format in which the values indicate characteristics or conditions at each location in the matrix designed for quantitative map analysis (e.g., spatial analysis and statistics). The elevation surface used to construct a tilted relief perspective in a Google Earth display is composed of thousands of matrix values indicating the undulating terrain gradient. The Matrix Revisited Figure 2 depicts a raster grid of the vegetation in the Bighorn area by assigning unique classification values to each cover type. The figure's upper portion isolates the Lodgepole Pine cover type by assigning 0 to all other cover types and displaying the stored matrix values for a small portion of the project area. Although you see the assigned color in the grid map display (green in this example), keep in mind that the computer "sees" the stored matrix of map values. Figure 1. A raster image is composed of thousands of numbers identifying different colors for the "pixel" locations in a rectangular matrix supporting visual interpretation.