Viewshed of Analysis of Native American Sacred Landscapes

Viewshed Analysis of Native American Sacred Landscapes Dr. David M. Diggs, Dr. Robert Brunswig, and Sara Jo Lambert Departments of Geography and Anthropology University of Northern Colorado, Greeley, CO Abstract. Rocky Mountain National Park (RMNP), USA, is rich in Native American sites, many believed to have served religious purposes. Field and consultation data have been incorporated into successive generations of a Geographic Information System (GIS) project designed to model and predict the spatial distribution of sacred sites and ritual features believed to have constituted long-lost landscapes. In recent iterations of the RMNP sacred landscape modeling research, a Weights-of-Evidence site location predictive model was developed. This model showed the strong influence of the relative visibility of five sacred landmarks. This paper uses multi-feature and multi-landmark viewshed analysis techniques to evaluate the relative and absolute visibility of the above mentioned five landmarks. Landmarks are assessed both individually, in total, and in strength of visibility. The research suggests that both strength of visibility and total number of landmarks visible are strongly associated with the location of sacred features. Introduction Since 1998 the University of Northern Colorado (UNC) has conducted archaeological surveys and research in Rocky Mountain National Park. Early results of these interrelated projects suggested that many of these sites had been associated with past ritual/ceremonial activities (Brunswig 2003). This was supported by earlier research that had identified areas within and just outside the park boundaries that were known or thought to be sacred landmarks to Arapahoe, Ute, and other Native American groups (see for example discussions in Benedict 1985; McBeth 2007; Toll 2003). Many of the archaeological sites within the park include rock features such as cairns, circles, and U-shaped formations thought to have been used by the Ute or Arapahoe. Consultations with Ute elders confirmed these observations (Brunswig 2003). Five purported sacred landmarks are used in the present study to determine viewshed and intervisibility analysis to and from 190 features (collectively found in 31 archeological sites) within Rocky Mountain National Park, Colorado. Viewshed and intervisibility analysis has been employed by many archaeologists to assess the characteristics of archeological sites. A few examples include: Jones’ (2006) use of viewshed analysis to explain settlement patterns and the importance of defensibility to Iroquois settlement locations; Ogburn (2006) applied a target size-sensitive fuzzy viewshed approach to assess Inca storehouses in the Saraguro region of Ecuador; and, perhaps of greater relevance to the present study, Hartley and Vawser’s (2005) work on Native American sacred features in Wyoming’s Hunt and Sheep Mountains. Wheatley and Gillings (2002) also discuss the use of viewshed and visibility analysis in archeology along drawbacks in using viewshed analysis. The authors’ interest in conducting viewshed and visibility analysis of suspected sacred landmarks stems not only from previous research and Native American consultations, but also from recent GIS modeling work. From 2006 through 2009 the authors developed a weights-of- evidence predictive model of potential sacred sites based upon 183 suspected sacred features in RMNP (Brunswig, Diggs, and Chady 2009; Diggs and Brunswig 2006 and 2009). During this research we found that overall intervisibility of these sacred landmarks appeared to be highly predictive of sacred site locations. Our viewshed/intervisibility data inputs into the sacred sites model, however, were relatively coarse. Sacred landmarks were not examined on an individual basis, nor did we attempt to measure the strength of visibility of each landmark from each 1 feature. The remainder of this paper details our attempt to provide a more meaningful analysis of sacred landmark visibility from 190 archeological features in RNNP. Figure 1. Rocky Mountain National Park, Colorado with sacred features and landmarks. Methodology Intervisibility of five purported sacred landmarks from 190 suspected sacred features (in 31 sites) were analyzed. Figure one shows locations of the five landmarks and the 190 features (many features are very close to each other). Known as Beaver Mountain to the Ute, Longs Peak is thought to be a spiritually powerful place as it shows up in Ute mythology (Brunswig 2003). Due to its proximity, Mount Meeker is considered as one sacred site (with Longs). Long’s and Meeker Peaks are dominant peaks in RMNP. The Lava Cliffs area sits at approximately 3,700+ meters elevation. It contains a number of major trail and pre-historic corridors and cross- roads giving it religious, as well as utilitarian significance (Brunswig 2003). For this reason there is a concentration of sacred sites found at Lava Cliffs. Grand Lake was referred to as Spirit or Holy Lake by Arapaho who visited the site in 1914 and is considered sacred for the Ute as well (Toll 2003). Specimen Mountain was considered sacred to the Arapaho who called it “Mountain Smokes” (Toll 2003). It is likely that it also held spiritual significance for the Ute. Old Man Mountain, a massive granite outcrop in Estes Park is another known sacred landmark to the 2 Arapahoe used in our analysis. Arapahoe did vision quests in the area and evidence on the mountain indicates that other Native Americans visited the area before the early 19th century when the Arapahoe arrived (Benedict 1985; McBeth 2007; Toll 2003). Each of the above described sacred landmarks covers an area ranging from a few dozen square meters to over a square kilometer. The most common method in ArcGIS 3D Analyst is to use a single point for viewshed/visibility creation. A single point and its visibility, however, doesn’t adequately describe the overall visibility of a mountain or lake (which covers area). A possible solution would be to create a grid of points with regular spacing across the landmark surface. In doing so, it is important to ask the questions could a person see only a small part of a mountain (weak visibility), or could the same person see a significant part of the mountain peak and its surrounding area (strong visibility)? In both cases, visibility of the number or percentage of vertices would provide some measure of the strength of visibility. ArcGIS 3D Analyst, however, does not directly measure visibility to multiple points that represent a single landmark. Our solution was to devise a methodology that could provide some measure of the strength of visibility. The process is detailed below ArcGIS 3D analyst does not measure visibility to or from polygons. 3D Analyst, however, is able to determine the visibility of an arc and its associated vertices. A polygon outline feature class was created around each sacred landmark that we deemed as being an important part of its visibility. A polygon grid was then created with the extent set to that polygon feature’s outline (thus, avoiding a grid that covered the entire national park). Each polygon grid was unique to its associated sacred landmark. Grid cell sizes ranged from 20 to 200 meters. Hawth’s Analysis Tools for ArcGIS was used for this early work (cf. Beyer 2011). A given sacred landmark’s polygons was then converted into polylines by creating a new polylines shapefile in ArcCatalog and then by copying the polygons and pasting them into the new polylines shapefile. The process is detailed in ESRI Knowledge Base article 21395 (ESRI 2011). The new polyline shapefile (one for each sacred landmark) must be dissolved so that the grid of lines is essentially one continuous arc. This is accomplished by creating a new field (for example: name) in the polyline shapefile’s table and calculating or inputting the same value (for example: 1) for all records. Once this is done, ArcGIS’s Dissolve tool is used to dissolve the multiple records into one—in essence the grid of lines become one long arc (see Figure 2). Two additional fields were then added to the each of the five sacred landmark dissolved polyline shapefiles. Two fields, OFFSETA and OFFSETB were added with a value of 7 (meters) for each. This adds 7 meters to each sacred landmark vertex analyzed (OFFSETA) and 7 meters to each analysis cell (OFFSETB). A National Elevation Dataset (NED) 1/3 Arc Second DEM was used for the analysis. According to USGS NED 1/3 Second DEMs can have a vertical accuracy of +/- 7 meters. For this reason OFFSETA and OFFSETB were set to 7. This added a vertical distance in meters to the observation point z- values (OFFSETA) and to the z-value of each raster cell (OFFSETB). Figure 2. Grid of lines (now one line) used to represent Grand Lake. 3 Once each sacred landmark was processed in the above manner, the viewshed tool was run for each of the five sacred landmark dissolved polyline shapefiles. In 3D Analyst the input raster was our DEM, the observer feature was a given sacred landmark polyline file. The typical 3D Analyst output file was added to the map. But this map only shows Visible and Not Visible (see Figure 3). Thus, if only one vertex of a landmark polyline shapefile is visible, the output cell is treated as visible for display purposes while, in reality, the landmark has very weak visibility. The attribute table displayed in Figure 4 indicates that the strength of visibility varies substantially within the visible areas. The VALUE field represents the number of vertices visible from a cell. The COUNT file shows the total number of cells with that value. This particular viewshed had a possible 107 vertices that could be viewed from a given cell. Figure 3. Viewshed from Specimen Figure 4. Viewshed table from Specimen Mountain, Rocky Mountain National Mountain, Rocky Mountain National Park. Park. The viewshed from Specimen Mountain, for instance, can now be associated with the shapefile/feature class of 190 features.

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