Working With Emergency Call Records In Rural Pennsylvania
I spent about three weeks last winter pulling and cleaning 911 dispatch logs for a county outside Pittsburgh. The goal was straightforward: map out when police and fire calls spike, when they dip, and whether there are seasonal or hourly patterns that might actually matter for staffing decisions. What I found wasn't dramatic, but it was useful. Here is how I did it. The data source matters more than anything else. In Venango County and most rural Pennsylvania counties, 911 records come through the county sheriff's office or a regional dispatch center. They are typically stored as CSV files or Excel spreadsheets, sometimes PDFs if the municipality is stuck in 2008. You have to request them formally. In Pennsylvania, the Right-to-Know Law (formerly the Freedom of Information Law) covers these records, though dispatch centers sometimes push back on volume requests by claiming operational sensitivity. It rarely holds up legally, but it slows you down. My first mistake was assuming the data would be clean. It is not. Call timestamps are often logged in different formats depending on which dispatcher entered them. Some use 24-hour clock, some use AM/PM with inconsistent spacing. Dates range from MM/DD/YYYY to YYYY-MM-DD. Call types are categorized differently between police and fire divisions, and the same incident might appear in both tables with slightly different codes. I spent two days just writing a normalization script in Python before I touched any visualization.
I used pandas for the cleaning, then matplotlib and seaborn for the visualizations. Here is the rough structure of what I built:
- Load both police and fire CSV files separately
- Standardize all timestamp columns to datetime objects using a single format string with fallback parsing
- Map call type codes to human-readable labels using a lookup table I built from the department's published code manual
- Drop rows where the timestamp was missing entirely — roughly 3 to 5 percent in my dataset, which is higher than I would like to admit
- Merge the two datasets on date only, not time, since some calls span across midnight and lose their original day assignment
Once the data was cleaned, the patterns emerged quickly. Hourly volume showed the expected shapes — a lull between 2 AM and 6 AM, a morning ramp starting around 7, and a second peak between 4 PM and 8 PM. But the interesting part was the seasonal split. Fire calls in Venango County dropped sharply during mild winters and spiked during the deep cold snaps, which tracked closely with heating equipment failures and carbon monoxide events. Police calls told a different story, trending upward in summer months and on weekends, which aligns with everything you already know about how small-town crime distributes. I also cross-referenced the call data against the county's reported holiday schedule and local event calendar. There was a clear drop on Thanksgiving and Christmas Day for both services, but the dropoff was steeper for police than fire. Fire responded to structure calls on Christmas morning that police simply did not see. That mismatch between the two datasets is probably the most important thing I found, and it took me about twelve hours of work to surface it. One edge case that almost broke the analysis involved a single dispatcher who logged every call with the same timestamp down to the second. It looked like forty-seven calls arrived at exactly 3:14 PM on a Tuesday in March. They did not. I caught it because the call type codes were sequential and the distance fields were all null. The workaround was to use the call sequence numbers as a proxy for chronological order and spread the timestamps by one-minute intervals within the same hour block. It is a rough fix, but it prevented a massive clustering artifact from skewing the hourly heat map. I flagged the adjustment in my methodology notes so anyone reviewing the work would know what happened.
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What The Patterns Actually Mean
Calling data reveals rhythm, not reason. The hourly and daily cycles show you when people need help. They do not tell you why. That requires joining the dispatch records to something else — weather data, economic indicators, even local social media posts if you are willing to deal with the noise. I pulled NOAA precipitation and temperature data for the nearest station and ran a simple correlation. The relationship between freezing temperatures and fire calls was stronger than I expected, with a Pearson coefficient around 0.62 during December through February. Police calls showed almost no correlation with weather variables. Cold does not make people call 911 less, but it does seem to make houses catch fire more. Another counter-intuitive finding: the busiest month for police calls was not summer. It was October. The summer spike I expected was there, but October had a secondary bump I could not explain without more context. Looking into it, the data suggested it aligned with a local high school football season, which tracks with increased domestic dispute calls and minor traffic incidents in the surrounding township. That kind of insight does not come from the raw call log alone. It comes from knowing the community. There are serious limitations to this approach. Dispatch data captures only what gets reported. A significant portion of neighborhood disputes, quality-of-life complaints, and even some property crimes in rural areas never reach the 911 system. They go through non-emergency lines or are handled through direct officer contact. Your hourly distribution will therefore underrepresent the true shape of community activity, especially during off-peak hours when patrol visibility is lower. Fire data has a similar blind spot, though structure fires and medical emergencies tend to be reported more consistently because the threshold for calling 911 is lower for those events.
Another problem is the lack of geographic precision in many rural dispatch systems. Venango County's data only included road names and mile markers, not GPS coordinates. That made it impossible to build a proper spatial heat map. I worked around it by grouping calls into township-level buckets, which gave a reasonable approximation but lacked the granularity I would have preferred. If your county has full GIS-linked records, skip straight to a kernel density estimate. If you are working with road names, aggregate to the nearest meaningful geography and accept the loss of detail. For anyone wanting to replicate this, I would recommend starting with a small time window — one quarter is plenty to test your pipeline. Running the full year on a first pass will expose formatting inconsistencies you did not anticipate and waste time you could spend building a cleaner model. I initially tried to process eighteen months of data in one shot and ended up rewriting my entire normalization script after I discovered a column rename that affected only the second half of the dataset. The code I used is available on GitHub under a permissive license. It includes the cleaning functions, the time-series aggregation, and the basic visualization templates I relied on. If you are working with a different Pennsylvania county, you will need to adjust the call type mappings to match that department's coding system. The structure of the data is similar enough that the core pipeline should carry over with minimal changes. Just verify your timestamp columns first. That will save you the most trouble.
Repository link: github.com/agnostudio/pa_dispatch_analysis Ultimately, the value of this kind of work is not in the charts themselves. It is in the questions the charts force you to ask. Why does fire call volume jump on the first freezing weekend of November? Why do police calls cluster around certain intersections on Friday nights? Those answers do not live in the spreadsheet. They live in the community, and getting them requires stepping away from the data long enough to talk to the people who actually respond to the calls.
