Statewide Integrated Traffic Record System (SWITRS) data suggest that Native Americans are a disproportionately high-risk population for traffic injury, therefore it is vital to improve road safety for California’s tribal populations, as well as for all population groups in California that may travel to the state’s rancherias and reservations.
SafeTREC (Safe Transportation Research and Education Center) at UC Berkeley has been heavily involved in efforts to improve crash data reporting and collection as well as obtaining collision data from various sources.
SafeTREC project staff has geocoded the crash locations data gathered from the SWITRS, the primary source of state-level crash data for California agencies. The following maps are created with SWITRS crash data points from 2005 to 2014, clipped by tribal boundary shapefile with 5 miles buffer that is obtained from Bureau of Indian Affairs and further processed by project staff.
The crash data points have many layers of spatial and temporal information attached to them. The multiplicity of data embedded can be displayed in various ways to reveal patterns to inform tribal transportation policy makers.
This first map charts all collisions that happened between 2005 and 2014 within 5 miles buffer from all tribes in California. Darker red indicates crashes that involve fatalities and lighter yellow indicates crashes that involve minor injuries. By manipulating color ramps and opacity level, I'm hoping to achieve the goal to visualize spatial pattern and provide details about around which tribe the crash occured as they click on the markers. Ideally the legend in lower right corner would read ‘fatal’, ‘severe injury’ etc. instead of 1 through 4 which is the internal code used by SWITRS system. If I had more time I would clean up the data in R or Python beforehands.
In the second map, I explored the spatial pattern of crashes throughout the week. I experimented with Torque and had trouble converting date format in string to the format that CartoDB accepts with timestamp, so I opted to display crashes by day in the week. It appears that crashes occur more frequently during mid-week near tribes in Northern California, whereas they occur equally frequent near tribes in Southern California.
In the last map, I changed the basemap to one with a lighter background to make the tribal boundaries 'pop'. I added the tribal boundary shapefile on top of crash data after digging around for multilayer map CartoDB tutorial. In this map, I'm hoping to map the pattern of fatal crashes in relation to actual tribal lands, so I experimented a bit with SQL and Heatmap settings. Most fatal crashes occur on major roadways within periphery of the tribes instead of on tribal lands possibly due to lower speed on rural roads.