One goal of the FRIRP is to educate decisionmakers regarding the issues related to infrastructure resources. Decisionmakers need to understand that aggregate occurs only in limited areas; that the quality of the aggregate, in part, controls its use; and that aggregate quality changes from deposit to deposit. Some decisionmakers are not familiar with geology, aggregate resources, or resource maps. Consequently, visualization is a very important part of the process. Geographic information systems (GIS) are valuable tools for use in outreach and visualization. This demonstration looks at part of the FRIRP study area along the Cache la Poudre River from Ft. Collins to Greeley, Colorado.

    An artificially sun-shaded relief image created from a digital elevation model was prepared for the Cache la Poudre River area. This was combined with highway and hydrological digital line graphs (DLG) to form the base for other digital maps. The first data layer added to the base map was the location of landforms underlain by sand and gravel (figure 1).

    The "hot-link" feature of the GIS was employed to demonstrate graphically some of the geologic aspects of sand and gravel resources. The hot-link feature allows you to associate an image with a map feature. For example, the locations of pits were plotted in the database, and bar charts were linked to pit locations to demonstrate grain size distribution of deposits (figure 2). Cross sections were prepared across the river valleys to help demonstrate that the thickness of the deposits change from place to place. The locations of the cross sections were plotted in the data base, and the cross sections were connected to the section lines by using the hot-link tool (figure 3).

    Decisionmakers need to understand that gravel particle size affects the use of the aggregate. For example, in the United States, most cement concrete requires gravel of at least ¾-inch. Most asphalt requires particles with angular faces (obtained through crushing), which commonly requires starting with gravel of at least 1½-inch. The downstream change in particle size can clearly be demonstrated by linking photographs of pit faces to pit locations along the stream valleys (figure 4). Gravel at the upstream end of the image (left-hand photograph) is about as large as a person’s head, and gravel size particles are predominant. Gravel at the downstream end of the image (right-hand photograph) is about as large as a fist, and the deposit includes many sand lenses.

    Decision Support Systems (DSS) assists decisionmakers in assessing the implications and opportunities of alternate land scenarios. The FRIRP aggregate project described above provides some of the earth science data, and the GIS provides a platform for implementing a DSS. Using a DSS, we developed a land-use scenario by adding three resource-related components to the data set - development, demand, and transportation. These components are editable and can be added or deleted for individual scenario exercises.

    Use of the DSS to estimate aggregate resource requirements for a new housing and commercial development is shown in figure 5. First, a polygon was drawn on the map view (by using simple GIS procedures) that identified a new development area. The user is prompted to input the number of housing units per acre. The DSS then calculated the aggregate required to construct the new houses, infrastructure, and supporting commercial development. The calculations were based on the size of the development area (determined by the DSS), the number of dwellings per acre (input by the user), and an average aggregate requirement per housing unit (provided by the authors).

   The DSS can also analyze aggregate resources in a new aggregate extraction area. Again, a polygon was drawn on the map to identify the resource extraction area. The DSS searched for conflicting land uses and for permitting requirements (figure 6).  Those items are displayed as restrictions. Resource reserves were calculated on the basis of aggregate distribution, thickness, and quality information derived from other GIS data layers. To calculate pit life, the DSS combined those data with information regarding production rates, which was input by the user. All calculations were based on formulas and data supplied by the scenario developer (the authors) (figure 7).

    Using the line-drawing function of the DSS, the route to transport the aggregate from the extraction area to the use area was identified. The DSS queried the "new development area" to determine aggregate needed. It calculated the cost of transporting aggregate along the indicated route, as well as such other factors such as accident exposure, fuel consumption, and determined a hypothetical value for emissions. These calculations were based on miles traveled (determined by the DSS), aggregate required for the new developments (determined by the DSS), and other data provided by the scenario developer (the authors) (figure 8).

    An extremely versatile function of the DSS is a feature that allows a user to create a polygon around an area of interest and return information regarding the contents of that polygon. We created four evaluation tools for this scenario (shown on the right side of figure 9). Those tools can summarize the results of various land-use options regarding aggregate resources, demand, and transportation and can demonstrate the results either as numbers or as graphics. For this example, we implemented the area summary tool. By using this tool, an area can be identified on the map, and information regarding aggregate resources within that area can be returned in a graphical format.

    First we zoomed in on a detailed part of the map and created a polygon around an area on the map (upper left part of figure 9). Occurring within that polygon are four different landforms that are underlain with gravel. By enabling the area evaluation tool (right side of figure 9), the DSS identified what types of landforms were contained within the polygon, determined how many acres each landform comprised, and displayed the results in graphical format. The DSS can query any layer of the map project, regardless of whether or not it is active, or even visible.

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