Issue link: http://read.dmtmag.com/i/53167
The right side of Figure 2 depicts raster storage of the same cover-type information. Each grid space is assigned a number corresponding to the dominant cover type present—the "cell position" in the matrix determines the location (where), and the "cell value" determines the characteristic/condition (what). Fundamental Concepts Figure 3 depicts the fundamental concepts support- ing raster data. As a comparison between vector and raster data structures, consider how the two approaches represent an elevation surface. In vector, contour lines are used to identify lines of constant elevation, and contour interval polygons are used to identify specified ranges of elevation. Although contour lines are exacting, they fail to describe the intervening surface configuration. Contour intervals describe the interiors, but they overly generalize the actual "ups and downs" of the terrain into broad ranges that form an unrealistic stair-step configuration (center-left portion of Figure 3). As depicted in the figure, rock climbers would need to summit each of the contour-interval "200-foot cliffs" rising from presumed flat mesas. Similarly, surface-water flow presumably would cascade like waterfalls from each contour interval "lake" like a Spanish multi-tiered fountain. The remainder of Figure 3 depicts the basic raster/grid organizational structure. Each grid map is termed a map layer, and a set of georegistered layers constitutes a map stack. All the map layers in a project conform to a common analysis frame with a fixed number of rows and columns at a specified cell size that can be positioned anywhere in geographic space. As in the case of the elevation surface in the lower-left portion of Figure 3, a continuous gradient is formed with subtle elevation differences that allow hik- ers to step from cell to cell while considering relative steepness. In addition, surface water can be mapped to sequentially stream from a location to its steepest downhill neighbor, thereby identifying a flow path. The underlying concept of this data structure is that grid cells for all map layers precisely coincide, and by simply accessing map values at a row/column location, a computer can "drill down" through the map layers, noting their characteristics. Similarly, noting the map values of surrounding cells identifies the characteristics within a location's vicinity on a given map layer or set of map layers. In fact, the preponderance of spatial data is easily and best represented as grid-based continuous map surfaces that are preconditioned for use in map analysis and modeling. The computer does the heavy lifting of computation—what's needed is a new Imagery/LIDAR Special Issue Figure 3. Organizational considerations and terminology are necessary for grid-based mapped data. JANUAR Y 2O12 / WWW . GEOPLA CE . COM 11 Figure 2. A basic data structure is described for vector and raster map types. generation of creative minds that goes beyond map- ping to "thinking with maps" within this less-familiar, quantitative framework—a SpatialSTEM environment. Author's Note: My involvement in map analysis/modeling began in the 1970s with doctoral work in computer-assisted analysis of remotely sensed data a couple of years before civilian satellites. The extension from digital-imagery clas- sification using multivariate statistics and pattern-recognition algorithms in the 1970s to a comprehensive grid-based mathematical structure for all forms of mapped data in the 1980s was a natural evolution. See www.innovativegis.com, selecting "Online Papers" for a link to a 1986 paper on "A Mathematical Structure for Analyzing Maps" that serves as an early introduction to a comprehensive framework for map analysis/modeling.