Data Elements and Models¶
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The concepts of data elements, data interpretation, and data sources are explained. Although today multiple data element maps are commonly superposed (combined) by computer processing, analog methods can be used instead in a more limited way. The meaning of geospatial data types is considered.
Data Elements and Models¶
As evident from the above diagram, we take information from various conventional sources, along with data from Landsat or similar Earth-observing satellites, and often combine it to produce data elements, which we further convolve to produce interpretation maps and other output types. Then these outputs become viable parts of environmental resource planning, site selection, and other outcomes that benefit from modeling.
Interpretative maps are derivative, i.e., they result from decisions to produce new object categories, stemming from combinations of several others. For example, a map may plot the likelihood of runoff erosion, which we can deduce from maps showing soil properties, topography, rainfall, stream patterns, and other related factors. Variants of this map type are allocative or suitability maps (e.g., transportation routing), evaluative maps (e.g., earthquake damage potential), and predictive maps (e.g., 100-year flood coverage).
` <>`__15-5: Let’s say that you have, during one of your vacations in the western desert, found some enticing but still elusive evidence that an area you traversed during a hike had some unusual surface expressions of possible minable silver ore in a body near, but below, the surface. State some appropriate steps you would take to end up with a Data Interpretation map that you could use to persuade some investors to help you “stake a claim” and then develop the ore body. `ANSWER <Sect15_answers.html#15-5>`__
Until the 1970s, the traditional way of using geospatial data, making decisions based on combining information sources, and applying models by laying maps on a table was cumbersome. Interpreters inspected these maps individually, and mentally compared data sets - visual and tabular - while seeking to narrow parts of an area to locations that they pinpointed as suitable on several defining maps. In principle, they could physically overlay two or three maps, treating several relevant themes by placing them on a light table (particularly if they were rephotographed as transparencies) to check on pre-selected, favorable conditions, where patterns on the maps superposed in a positive way. This is, of course, inherently unwieldy and inefficient, and often the interpreter would simply glance back and forth between maps. One can improve the interrelationship process by laying a grid over each map and extracting information according to some relative merit or weighting, organized by location within the grid. Managing these data, usually in tables, proved labor-intensive, slow, and often ineffective.
|Operator using a Bausch and Lomb Zoom Transfer Scope to project a photo scene onto a base map; this helps in recognizing features in the photo (or a space image) by registering them to identified geospatially coincident elements in the map. |
Obviously, this form of image combination is severely limited to two images. However, one or more additional layers are feasible, if we display map and photo reproductions in other colors, but multiple registration becomes difficult.
Primary Author: Nicholas M. Short, Sr. email: nmshort@nationi.net