I work for a fire department in Florida and the fire chief posed a question to me; At any given moment in time during the calendar year 2018, how many fire trucks are busy, how many are available, how many are standing by and how many are out of service? Using the SQL Server database I was able to distill this information into tabular data.
Software available to me.
Also, because the data is in SQL Server I can parse out the date time stamp by hour, weekday, month, etc. Then Using Python or Excel I can generate statistical tests and univariate statistics and charts. I have modest experience with R. Therefore my tools are SQL Server, R, Python and Excel.
For the first record with the primary key 21546912 at the time of january 1, 2018 at 00:02:31.800 there was 1 fire truck working, 9 fire trucks are available for work and 0 are standing by or out of service. For the entire year I have 104,179 observations showing at exactly that time stamp how many are working, available or standing by or out of service. I have enclosed 30 records as a comma separated values file below my question for you to see a sample.
The Question !
So what do I do with this data? I have already generated histograms showing the frequency of FireTrucksWorking, etc. Is there a statistical test such as regression that I can perform on this data? How do I bring meaning to this dataset? Are there any general patterns to the dataset that I can discover? Can I create a probability model out of this data such as; at 08:00 during the month of July there is x% probability of N fire trucks working ? Would you use queueing theory on this dataset?
I am open to all suggestions. At this point I have a large dataset but it is really just a jumble of numbers, how do I generate meaning out of these numbers?
This is cross-posted in Mathematics Exchange. But here I make the following revision. I am looking for some type of knowledge discovery that will show me something about my data set the naked eye cannot see or the human brain cannot discern. For example: "there is a much higher likelihood of working during the hours of 08:00 to 12:00 than the rest of the day". "Are there patterns in my data I do not recognize"? Do you have any thoughts on this?
Thirty sample records of data
PrimaryKey,DateTimeStamp,FireTrucksWorking,FireTrucksAvailableForWork,FireTrucksStandingBy,FireTrucksOutOfService 21546912,2018-01-01 00:02:31.800,1,9,0,0 21546921,2018-01-01 00:04:46.720,1,9,0,0 21546950,2018-01-01 00:09:39.400,1,9,0,0 21546951,2018-01-01 00:09:47.320,2,8,0,0 21546955,2018-01-01 00:11:16.780,3,7,0,0 21546959,2018-01-01 00:12:04.840,2,8,0,0 21546962,2018-01-01 00:12:09.030,3,7,0,0 21546963,2018-01-01 00:12:14.470,3,7,0,0 21546966,2018-01-01 00:12:17.790,3,7,0,0 21546967,2018-01-01 00:12:21.240,2,8,0,0 21546970,2018-01-01 00:12:40.240,2,8,0,0 21546973,2018-01-01 00:12:46.720,2,8,0,0 21546990,2018-01-01 00:14:24.610,2,8,0,0 21547002,2018-01-01 00:16:03.130,2,8,0,0 21547036,2018-01-01 00:19:59.450,1,9,0,0 21547043,2018-01-01 00:21:21.950,0,10,0,0 21547064,2018-01-01 00:24:50.470,0,10,0,0 21547065,2018-01-01 00:25:13.000,0,10,0,0 21547165,2018-01-01 00:43:31.130,0,10,0,0 21547344,2018-01-01 01:15:00.980,1,9,0,0 21547361,2018-01-01 01:16:58.320,1,9,0,0 21547383,2018-01-01 01:21:38.130,1,9,0,0 21547421,2018-01-01 01:30:42.250,0,10,0,0 21547436,2018-01-01 01:33:30.320,1,9,0,0 21547442,2018-01-01 01:33:48.470,1,9,0,0 21547449,2018-01-01 01:33:58.780,1,9,0,0 21547474,2018-01-01 01:37:30.550,1,9,0,0 21547484,2018-01-01 01:39:12.350,1,9,0,0 21547487,2018-01-01 01:40:41.290,0,10,0,0 21547510,2018-01-01 01:47:02.420,0,10,0,0