The PP&L data sets were somewhat dated, having been acquired over a period of years. One of the main contributions from Landsat was to foster an updated land use map. For this MSS false color composite, a "cookie-cut" section representing the service area within which the site must be located appears like this:

This June 1977 scene was then run through a supervised classification, in which training sites were selected from other data bases and field observations. This map results:

Drawing upon the PP&L data elements, and the updated classification, a site selection model developed particularly for this study was then applied to the data base. Six of the 43 elements were chosen as the primary criteria for establishing suitability: (1) Landforms; (2) Groundwater Supply; (3) Soil porosity; (4) Ease of Excavation; (5) Foundation Stability; (6) Distance to Surface Water (limits: 15 cells maximum linear distance). Other factors, including several socio-economic ones, were included as secondary determinants.

The next illustration uses single colors (against a yellow-gray background; river in blue as a location reference) to mark areas derived from the model analysis that were acceptable or unacceptable under the conditions set.

Panel A highlights areas (red) of high acceptability under all conditions. In B, the yellow area denotes groundwater unfavorability. In C, the few blue dots point to areas subject to unsatisfactory porosity infill rates. The purplish-red area in D refer to an area to be avoided because of foundation instability. The green in the E panel associates with landforms in which slope steepness rules out any use for a site. F contains large areas in light-blue that relate to difficult excavation.

These elements can be combined, along with others, to produce a Best Site map, made by creating a binary mask (0s and 1s for reject or accept) for each data plane and then adding all together to form this composite image:

Note that only one small area (a white square, with enlarged details in the inset) broadly meets all these conditions and the constraint of proximity to a large surface water supply (and avenue for discharge of waste water). This area is a lowlands just south of the junction of the Juniata River with the Susquehanna that is high enough to avoid most floods. It lies beween the frontal Blue Mountain (south) and Mahony Ridge (north). Although much of the surrounding areas is now built up along Route 11-15, the immediate area selected for the site has only two small towns, Duncannon and Perdix, on either side and still has open lands amenable to development. A good supply of labor is nearby, including Harrisburg (whose northern suburbs are about 10 km [6 miles] away.

Admittedly, the above example is a rather simplistic exercise. But it does successfully illustrate the manner in which GIS supports various modes of decision-making. It also demonstrates the role of timely space imagery in the process.

If you are new to GIS, you now should know enough to begin practicing on your own. There is a Web Site, sponsored by REGIS (Research Program for Environmental Planning and Geographic Information Systems), a group of geographers at the University of California at Berkeley, that has data sets - maps, aerial photos, space imagery - of the San Francisco Bay Area which can be displayed, combined, and used to output new products. This is the BAGIS program done with a version of GRASS called GRASSlinks, developed at UCB by Dr. Susan Huse and her colleagues. It's both fun and a challenge to work with. Access it here (http://www.regis.berkeley.edu/gldev/regis.html) and follow the instructions to build your skills at deriving meaningful maps that divulge how GIS "does its thing". Links at this site also guide you to some of the projects being carried out by this group.

In the last two decades, GIS has burgeoned into a major international industry. It has become the tool of choice for many applications of map-based spatial data to problem solving in the Information Age. Its emergence has largely paralleled that of remote sensing and the two aid each other symbiotically. In the GIS realm, remote sensing contributes a subset of input information whose value is mainly in updating land cover types. As a measure of the import now attached to GIS, it has grown into a staple of the Geography curriculum in our colleges and is probably more widely taught than remote sensing itself. We trust that this section has proffered some insight as to why this is so today.

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