Oxford Fire Department: 2008 Service Analysis. Prepared for the Subcommittee studying the Oxford Division of Fire. April 8, 2009

Transcription

1 Oxford Fire Department: 2008 Service Analysis Prepared for the Subcommittee studying the Oxford Division of Fire. April 8, 2009

2 Background The data are from the OFD s Firehouse database. The Firehouse database maintains current information on incident calls as well as personnel information. Only includes Jan.1 Dec. 31, This data was developed and prepared in order to answer specific questions regarding Oxford s fire and emergency medical service in 2008.

3 Data Limitations Descriptive quality data, not inferential. In other words is great for describing what has happened in the past, but is a poor predictor for the future and other hypothetical situations. Data appears to be reasonably accurate. However, there are small gaps and errors present in the database. In part, these gaps and errors exist because of data entry errors and omissions. These gaps are nominal in nature.

4 Process Exported Firehouse Data to Excel spreadsheets Converted files and put data into ArcMap 9.3 (GIS). Geocoded address responses. Developed relational database. Exported that database to SPSS for statistical analysis.

5 Major Variables Included in Data Jurisdiction Townships, Oxford, & Miami U. Response Time Alarm time Arrival time (minutes) Distance Straight line from OFD Emergency Type based on classification used by OFD (modified MVA s as a separate type). Shift Two 12-hour shifts changing at 7am and 7 pm.

6 Outline and Purpose In the last meeting there were several requests for more information about the nature of out current services. The following areas of inquiry will be treated in this presentation: Overall Response Times and Geography Calls by political jurisdiction Calls by Miami University Response time differential between Volunteers and Part-time Staff

7 Summary Data All Reponses EMS responses represent the lion s share of workload. EMS responses accounted for a full two-thirds (66%) of all responses. 93% of all Fire and EMS responses were within Oxford and Oxford Township. 97% of all Fire and EMS responses were within a 5-mile radius of the Fire Station. Mean response time for 2008 was 8.30 minutes (from alarm to arrival on scene).

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9 All Responses by Type Emergency Type Valid Fire Explosion EMS Hazardous Condition Service Call Good Intent Call False Alarm Motor Vehicle Accident Total Cumulative Frequency Percent Valid Percent Percent

10 All Responses by Type

11 Response by Jurisdiction Township Valid 0 Oxford Milford Reily Hanover Wayne NA Total Cumulative Frequency Percent Valid Percent Percent Oxford Township (Not including the City of Oxford) Valid Other Oxford Township Only Total Cumulative Frequency Percent Valid Percent Percent

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14 Average Response Time Summary - Response Time In Minutes (excluding times entered as 0) res_time Valid N (listwise) N Range Minimum Maximum Mean Std. Deviation Variance

15 Response Times (minutes)

16 Miami Property Summary Miami Property was identified through the Butler County Auditor s records of ownership. Responses were considered on Miami University Property if the address was located on a parcel owned by Miami University or occurred with 50 feet of their property line. The 50-foot addition was include easements for sidewalks and the like. Miami Property Valid Other Miami Total Cumulative Frequency Percent Valid Percent Percent

17 Miami Comparison False Alarms Non-Miami Property - Emergency by Type Valid Fire Explosion EMS Hazardous Condition Service Call Good Intent Call False Alarm Motor Vehicle Accident Total Cumulative Frequency Percent Valid Percent Percent Miami Property - Emergency by Type Valid Fire Explosion EMS Hazardous Condition Service Call Good Intent Call False Alarm Motor Vehicle Accident Total Cumulative Frequency Percent Valid Percent Percent

18 Comparison between Shifts Compares two months and response times based on shifts. Shift 1 are the hours between 7 am and 7 pm. Shift 2 are the hours between 7 pm and 7 am. It compares February and December. In February there were no part-time staff on station. Starting in August, part-time staff were hired.

19 Case Processing Summary February res_time Shift2 1 2 Cases Valid Missing Total N Percent N Percent N Percent % 0.0% % % 0.0% %

20 February Shift Comparison Descriptives res_time Shift2 1 Mean 95% Confidence Interval for Mean Lower Bound Upper Bound Statistic Median Variance Std. Deviation Minimum Maximum Range Interquartile Range Mean 95% Confidence Interval for Mean Lower Bound Upper Bound Median Variance Std. Deviation Minimum Maximum Range Interquartile Range

21 Case Processing Summary December res_time Shift2 1 2 Cases Valid Missing Total N Percent N Percent N Percent % 0.0% % % 0.0% %

22 December Shift Comparison Descriptives res_time Shift2 1 Mean 95% Confidence Interval for Mean Lower Bound Upper Bound Statistic Median Variance Std. Deviation Minimum Maximum Range Interquartile Range Mean 95% Confidence Interval for Mean Lower Bound Upper Bound Median Variance Std. Deviation Minimum Maximum Range Interquartile Range

23 Conclusions Overall Response times appear very reasonable given the large coverage area. However, the descriptive statistical comparison between part-time and volunteer response times are significant. Miami University, no matter how the data is parsed, is a significant consumer of resources. Miami is also a significant source of false alarms. Mean response time for 1 st Shift (comparing February and December) improved by nearly 3 minutes and 30 seconds.