Three analytical dimensions โ spatial distribution, temporal patterns, and enforcement outcomes โ drawn from San Francisco Police Department stop data. Each section includes a data table and three visualizations.
Across the dataset, traffic stops are not evenly distributed across San Francisco. The clearest pattern is concentration: stop activity clusters in selected districts, rises during specific parts of the day, and leads to different outcomes depending on stop context and population group.
This page is designed to connect those three dimensions into one story. Rather than treating each stop as an isolated event, the visualizations show how geography, time, and post-stop decision-making combine into a broader enforcement pattern.
| # | District | Code | Stop Count | Share of Total | Relative Volume |
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| Hour | Stop Count | % of Daily Total | Volume |
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| Day | Stop Count | % of Weekly Total | Volume |
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| Month | Stop Count | % of Annual Total | Volume |
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| Stop Reason | Total Stops | Warning % | Citation % | Arrest % | Search Hit Rate |
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The strongest cross-section finding is unevenness. Stops are concentrated in selected districts, cluster in recognizable time windows, and lead to different outcomes depending on why the stop happened and who was stopped.
That makes the StopAtlas story more than a set of counts. It is a way of showing that traffic enforcement operates through patterns in place, time, and procedure, which is exactly what the later interactive milestones should help users explore.