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74 Table 28. Descriptive statistics of the clude coarse-scale or preliminary analyses that can be used in CrimeStat clusters delineating deer rapid assessments to identify wildlifetransportation conflicts vehicle collision hotspot clusters on or to streamline planning and implementation of wildlife and State Route 89, Sierra County, California. safety needs. They are preliminary by nature, but are useful in Mean cluster initial examinations of the relationships between wildlife Number of vehicle collisions and the natural and man-made environ- Highway length SD clusters (km) ment around them. The type of data needed to identify the Route 89 9 1.34 0.26 location of hotspots for wildlifevehicle collisions need not be spatially accurate, because mitigation measures usually locations on SR 89 shows a high degree of overlap of DVC address problematic areas that cover several miles of highway. points. As was the case with the simple plots made of WVCs For this reason, data accurate to the 1.0 mile-marker is suffi- in the Canadian Rocky Mountains, identification of the cient. Existing agency carcass data are sufficient. actual hotspot location was difficult. The excessive overlap Bridge rebuilding and retrofits are excellent examples where and what appears to be continuous clustering of DVC points hotspot information can be utilized to identify areas where was most likely a result of the high number and density of highway improvement projects can improve motorist safety DVCs for the relatively short stretch of highway. Note that the and habitat connectivity for wildlife. The periodic reconstruc- California DVC data were obtained from a 26-year period tion of highway bridges that span waterways are excellent along 53 km (~33 mi) of highway, compared to more than opportunities to benefit from structural work projects to im- 500 points from the Canadian study area obtained from more prove wildlife and fish passage along riparian corridors by than 250 km (~155 mi) of highway during a 7-year period. widening bridge spans or habitat enhancement.98 Nine CrimeStat clusters with a mean length of 1.34 0.26 Today, state transportation planning exercises such as STIP km (Table 28) were created on California SR 89 and occupied (Statewide Transportation Improvement Program) are identi- more than half of the 18 km section. Hotspots were associated fying key areas for transportation infrastructure investments. with a variety of terrain types, but largely with mountainous At the same time, state natural resource agencies have been terrain. Some of the hotspot clusters appear to be associated mandated by Congress to develop comprehensive wildlife con- with valley bottom habitats, but a substantial amount can be servation plans that address a full array of wildlife and habitat linked with river courses in rugged terrain. Given the large conservation issues.98 Coordination of both network plans in a number of hotspots identified along SR 89, management timely and integrated fashion would be a significant contribu- would need to prioritize which ones represented real safety tion to streamlining environmental concerns in transportation and wildlife conservation concerns. The large 26-year dataset planning. A recent example of integrating agency roadkill clouds the picture by having numerous DVCs on one stretch information with standard GIS data for sustainable trans- of highway. A sequential analysis of DVC hotspots in 5-year portation planning took place in Vermont.9 The transportation increments would help identify trends and patterns in department (VTrans) developed a centralized database of hotspot distribution and bring to light the more problematic roadkilled wildlife carcass, wildlife road crossing, and related sections of highway. habitat data for individual species throughout the state. To expand and improve wildlife carcass reporting data, a partner- Interpretation, Appraisal, and Applications ship and recording procedures were developed with VTrans field and district staff enabling them to record a new array of GIS Linkages to Hotspot Data wildlife carcass information. With their wildlife carcass infor- The collection of wildlifevehicle collision carcass data is mation they performed a GIS-based wildlife linkage habitat important for many reasons, but serves as important baseline analysis using landscape-scale data to identify or predict the lo- information to guide the planning and management of road- cation of potentially significant Wildlife Linkage Habitats way safety. Wildlifevehicle collision data can be used to (WLHs) associated with state roads throughout Vermont. The quickly identify coarse-scale problematic areas on roads, as project relied on readily available GIS data including (1) land demonstrated with the techniques just discussed, and help use and land cover data, (2) data on developed or built areas, guide efficient planning and decision making if transporta- and (3) contiguous or "core" habitat data obtained from the tion improvement plans encompass WVC hotspots. This re- University of Vermont. The components that composed the port has explored ways GIS-based information can be linked overall GIS data layers were then ranked in accordance with to hotspot data and their applications. With the hotspot their relative significance to creating potential WLH. The data collected and stored in a database format, the next logi- analysis, in conjunction with the newly updated wildlife carcass cal step is to look at the types of GIS data that can be used to data, provided a science-based planning guide that will aid perform analyses for transportation management. These in- VTrans in understanding, addressing, and mitigating the