This Clay Notebook project is designed to process and analyze California's crash data (CCRS) with a focus on specific areas and time periods. Our primary goals are to analyze trends on Telegraph Ave before and after the implementation of traffic calming measures and to examine current crash patterns on Grand Ave, including the affected demographics.
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Process California’s Crash Data (CCRS):
- Analyze trends on Telegraph Ave before and after traffic calming measures were implemented.
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Examine Current Crash Patterns on Grand Ave:
- Investigate the affected demographics, such as age groups.
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Visualize and Interpret Findings:
- Create visualizations to help interpret the analyzed data and present the findings in an understandable manner.
- CSV files with the Oakland-only data are saved in the project.
- some basic "figure out how to make graphs" stuff has happened.
- Define the Telegraph Ave project (what section of the street do we want to look at?)
- Look at Telegraph Ave before/after the project
Are we still helpful for my friends? What might be the story here? Do we have a method to notice some places where there has been an increase compared to the whole city?
Model for a local count? probabilistic/statistical models. compare to the city. Ratio of decrease overall, vs ration of degrees on Telegraph.
What percentage of the injuries are on telegraph? Could try to localize the peaks? Near schools?
Time series compares to the city and what it means Come up with some questions to challenge tools,
Looking into ratios (local count/city) What visualization do we want? Given a point in the city- open an iframe with a street view. usability of the storytelling
Local questions? The story is the important part.
Can we use zipcde or neighborhood to narrow down? School zones? SR2S? Maybe ask them?
How did crash rates change on Telegraph Ave before and after the lane reduction?
Did the number of injuries and fatalities decrease after the redesign?
What types of crashes (e.g., pedestrian-involved, cyclist-involved, rear-end collisions) were most affected?
Did crash severity change (e.g., more minor crashes, fewer fatal ones)?
What are the most common types of crashes on Grand Ave?
Who is most affected (age groups, pedestrian/cyclist involvement, vehicle occupants)?
What locations along Grand Ave see the most crashes?
Are there trends in crash timing (e.g., peak hours, weekends vs. weekdays)?
How do crash rates on Grand Ave compare to pre-road-diet Telegraph?
How do total crash numbers compare between the two corridors?
Are the types of crashes similar, or does one street see different patterns?
Who is most at risk on each street?
Based on Telegraph’s data, what safety improvements could be predicted for Grand Ave if a similar lane reduction were implemented?