29,637 Bookings for Whatcom County Jail covering the 4.5 year period from 1/1/2011 to 7/1/2015. The vertical axis represents 17,256 unique names. 5,314 of those unique names had multiple bookings for the period with a range of 2 - 22 bookings per individual. Each unique name receives a random color for their bookings. See an animation of this chart here. See R code here. See notes on data/methodology at end. Click on the Charts to enlarge.
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This post discusses recidivism in Whatcom County jail. Recidivism is a primary concern for law and justice and citizens. The bottom line on 'recidivism' is this: so many re-offend at such high rates that some criminology professors fundamentally doubt whether imprisonment has any significant effect on prohibiting crime. Literature on this subject is abundant[1,2,3,4]. Some of us have come to the conclusion that imprisonment itself is the primary cause of recidivism; a counter intuitive analysis that has little political chance of wide scale adoption. I am looking at 'booking recidivism' or booking 'return buckets' in Whatcom County over the 4.5 year period from 1/1/2011 to 7/1/2015. (e.g. How many times a user is "booked" in that period. For this definition of recidivism, I am counting multiple bookings or returns to jail per individual (user). These aren't necessarily convictions. What I describe are 'return buckets' to our jail. Here are granular views at the 'return buckets' by user booking counts for these 4.5 years. Click to enlarge chart.
Above: Chart of data table below showing of bookings per 'return bucket' (e.g. 'bookings count'/ unique individual for the 4.5 year period).
Below: Data table showing number of Users @ booking Number/per user with product of Users and Number (User * Number) = Count of total bookings.
Far Below: Density plots for Users in red and Count in blue.
Users Number Count
1 1 22 22
2 0 21 0
3 0 20 0
4 0 19 0
5 1 18 18
6 3 17 51
7 3 16 48
8 3 15 45
9 12 14 168
10 16 13 208
11 14 12 168
12 28 11 308
13 40 10 400
14 69 9 621
15 89 8 712
16 141 7 987
17 216 6 1296
18 327 5 1635
19 567 4 2268
20 1172 3 3516
21 2612 2 5224
22 11942 1 11942
The tables below show that Whatcom County books the equivalent amount of 2% or more (1 out 50!) of its own population each year. (See column Unique.TotPop) In the tables below, I project totals for 2015 for all fields: charge, booked, unique. To project 2015, I doubled the the half year total. For Total Population (TotPop) I added the 2013-2014 increase to 2014 to create a 2015 estimate.
Years Charge Booked Unique TotPop
1 2011 11333 6860 4989 203329
2 2012 9650 6523 4723 204827
3 2013 9577 6677 4890 206248
4 2014 10520 6348 4466 208351
5 2015 13304 6458 5190 210454
The table below divides Charge, Booked, Unique by Total Population:
Years Charge.TotPop Booked.TotPop Unique.TotPop
[1,] 2011 0.05573725 0.03373842 0.02453659
[2,] 2012 0.04711293 0.03184639 0.02305848
[3,] 2013 0.04643439 0.03237365 0.02370932
[4,] 2014 0.05049172 0.03046782 0.02143498
[5,] 2015 0.06321571 0.03068604 0.02466097
The table below is derived from the two tables above.
Years Charge Booked Unique TotPop Years Charge.TotPop Booked.TotPop Unique.TotPop
1 2011 11333 6860 4989 203329 2011 0.05573725 0.03373842 0.02453659
2 2012 9650 6523 4723 204827 2012 0.04711293 0.03184639 0.02305848
3 2013 9577 6677 4890 206248 2013 0.04643439 0.03237365 0.02370932
4 2014 10520 6348 4466 208351 2014 0.05049172 0.03046782 0.02143498
5 2015 13304 6458 5190 210454 2015 0.06321571 0.03068604 0.02466097
Level Count
15orGTR 11
13orGTR 39
10orGTR 121
5orGTR 963
3orGTR 2702
2orGTR 5314
Notes on Data:
The Whatcom County Inmate press releases were used to compile this data. Unique identifiers were created from paste First, Middle, Last Names. In R:
paste(Booked201x$FirstMiddle,Booked201x$Last)
Unique numerical IDs weren't appended to the press releases until December 2013. Data may be subject to revision. For R code see here.
3 comments:
Thanks Ryan. Good stuff to contemplate
Ryan, Thanks for the information. When looking at the total booking by Whatcom population, 2%, did you take into account that single person's will be booked multiple times? So, how many persons were booked throughout the year a single time versus more than once?
Thanks again.
I used unique.totpop for the 2% or more figure so that should be unique (unique.(firstname+middlename+lastname) / total pop). However, I created that primary key because not all firstname+middlename+lastnmes had a unique id. firstname + middlename + lastname collisions could have happened because people can share firstname+middlename+lastname. To be honest, it would be more likely with this population that the same person was named differently for different bookings. Best I could do without consistent unique (numerical IDs).
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