Using hotspot analyses to locate bicycle incident clustering : analysis of bicycle incidents with automobiles between 2011-2015

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Using hotspot analyses to locate bicycle incident clustering : analysis of bicycle incidents with automobiles between 2011-2015
Johnson, Jonathan
Place of Publication:
Denver, Colo.
University of Colorado Denver
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General Note:
Completed for GEOG4081, University of Colorado Denver, Department of Geography and Environmental Sciences
General Note:
GIS Day 2016

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Source Institution:
University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
Copyright Jonathan Johnson. Permission granted to University of Colorado Denver to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.


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Data Sources: DRCOG Regional Data Catalog Bicycle Neworks 2014 Building Planometrics 2010 Census Block Data Denver City and County Open Data Trac Accidents Statistical Neighborhoods Trac Signals ESRI U.S. Roadways Projection: NAD 1983 StatePlane Colorado FIPS 0502 (US Feet) Author: Jonathan Johnson GEOG4081: Cartography Project Poster University of Colorado-Denver, Department of Geography and Environmental Sciences This section of the map highlights a popular area by the 16th street walking mall in the Central Business District neighborhood of Denver. What is unique in this frame is the high incident occurance at the intersection of 15th and Champa streets. Both roadways at this section have bicycle infrastructure, with 15th having a buered bicycle lane as of August 2013, and Champa utilizing a dedicated bicycle lane. Interestingly, the incidents reported here are all after the installation of the buered lane, with ve in 2014 and four in 2015. Further analysis is required in order to determine what is specically occuring here because bicycle infrastructure should create safety for bicyclists. One potential reason to explain this outcome is due to more bicyclists using the corridor because of the availability of infrastructure. The above frame is taken from in between the Union Station and Five Points neighborhoods highlights 17 incidents occuring between the years of 2011-2015. While almost all intersections are trac-device controlled, the speed limit on 20th and Larimer streets are 35 miles per hour. From the intersection at 20th and Larimer, there is roughly 200 feet in between each light. The higher speed limits could be attributing to incident occurance in this area. Larimer, Market, and Blake streets are all one way travel directions, with no shoulder for cyclists, and both travel lanes are bounded by parking spaces for business patrons, creating dooring hazards possibly. Larimer has a protected bike lane 4 blocks east of this image, allowing for rider protection into the city, but once into the city that protection dissolves, and riders merge with 35 MPH trac. 0 0.025 0.05Miles Incident Location Trac Signal 25 MPH Roadway 35 MPH Roadway Multi-Purpose Sidewalk No Infrastructure Present, Bikes Allowed Bicycle Lane Present Protected Bicycle LaneCoors Field 1 2 North Capitol Hill CBD Five Points Whittier City Park WestCheesman ParkCapitol Hill Civic Center Lincoln Park Auraria Union Station Highland 1 5 Accidents 6 8 Accidents 9 12 Accidents 13 15 Accidents 16 19 Accidents No DataShown above is the second hotspot analysis done, utilizing equal-area shnet polygons to bound areas of high incident rates. The yellow and orange boxes highlight the two areas of highest incidence, and oer the study areas examined in the next two maps. The orange box is oset from the red area as many of the accidents reported in this bound were incorrectly placed according to the address in the data le. In order to maintain accuracy, these incidents were updated to being correct in the focus maps. 0 0.25 0.5MilesFigure 3: Locating Study Regions by Hotspot Analysis Using Equal Area Polygons2 1 Figure 2: Optimized Hot Spot Analysis, Showing Aggregated Incidents Cold Spot 99% Condence Cold Spot 95% Condence Cold Spot 90% Condence Not Signicant Hot Spot 90% Condence Hot Spot 95% Condence Hot Spot 99% CondenceThis is one of two hotspot analyses conducted with Denvers incident data. Each point is aggregated with surrounding incidents within 373.5 feet to determine if there is statistically relevant clustering. This conrms the downtown area to represent statistically relevant incidents. 0 2.5 5 This map displays two sets of important data the numbers in each neighborhood represents the total bicycle incidents reported between 2011-2015. Incidents are only reported when there is bodily harm or monetary damages exceeding $1,000. The colors represent the percent of incident occurance per capita using 2010 Census data the downtown region unsurprisingly has higher incidents rates. 0% 0.01% 0.02% 0.03%Figure 1: Incidents by Neighborhood, Percent Chance of Incidents per Capita 0 2.5 5 Incidents By Year 293 292 278 258 268 2011 2012 2013 2014 2015 (Figures 4 & 5): Examine two specic areas to display where incidents occur the most, and examine factors that could be leading to thehigh incidence rate. (Figure 1): Map out trac incidents occuring between the years 2011-2015 by neighborhood. Include census data from 2010, in order to display per capita incidents rates. Process (Figure 2): Execute the rst hotspot analysis, in order to determine where incident occurance is high. Aggregate the features into points as one level of study. (Figure 3): Execute a second hotspot analysis, aggregating the incident data into an equal area grid to attempt to identify smaller regions of study. While not a suggestion of a trend occuring, the fact that incidents rose between is cause for concern, and should not be taken lightly. Denver, Colorado is going through a revitalization as many citizens seek to live closer to work and leisure activities, prompting population gains in the metropolitan region. The roadway network as a result has seen increases in usage. As the city attempts to promote other forms of transportation in order to decrease single occupancy vehicle use, it is important to monitor the overall safety of other roadway users such as bicyclists. Understanding where bicycle incidents occur frequently is important for city and transportation planners when devising strategies to increase safety for bicyclists who utilize the same roadways as automobiles. For bicycling to grow as a transportation method, it is of utmost importance that safety be prioritized in the City of Denver, and combining GIS and hotspot analyses can help planners strategize improvements accordingly. This study uses hotspot analyses to locate areas of bicycle incident clustering with high statistical signicance, in order to highlight areas that transportation planners could focus on to improve safety of bicyclists. Using this process could allow city planners to eectively protect one specic group of roadway users, who are likely to incur signicant harm or damage when interacting with vehicles on the roadway. Abstract Using Hotspot Analyses to Locate Bicycle Incident Clustering Analysis of Bicycle Incidents With Automobiles between 2011-2015