Last Known Address: Processing and Applications. Ryan Kling, Ari Lewenstein, Gerry Gaes, Mandy Wong, Walter Campbell

Transcription

1 Last Known Address: Processing and Applications Ryan Kling, Ari Lewenstein, Gerry Gaes, Mandy Wong, Walter Campbell NCRP Annual Meeting 2017

2 Introduction In 2015 the NCRP requested last known address prior to incarceration for prison admission (Part A) and PCCS admission (Part E) records Record Type Number of States Part A 22 Part E 13 Part B 6 Part D 5 Part F 2 Abt Associates pg 2

3 Motivation Potential research questions Background of new admissions Prison assignment and facilitation of visitation Re-entry planning Fugitive apprehension Other questions of interest Abt Associates pg 3

4 Outline Data processing County-level mapping and correlates Spatial econometrics Your suggestions for further work Abt Associates pg 4

5 States Providing Last Known Address: Prison Abt Associates pg 5

6 States Providing Last Known Address: PCCS Abt Associates pg 6

7 Steps to Process Address Information 1. Parse addresses 2. Spell check city names 3. Standardize street types and other abbreviations 4. Geocode 1. SAS 2. Google API 5. Check addresses against list of known facilities 6. Add additional information Abt Associates pg 7

8 Forms of Addresses Submitted to the NCRP Separate address, city, state, and zip code variables GA, HI, IN, IA, KS, LA, MA, NM, OK, PA, RI, TN Single string with delimiters 55 Wheeler St, Cambridge, MA, AR, CA, CO, KY, WV Single string without delimiters, but regular format 55 Wheeler St Cambridge MA NV Single string with non-standardized format 55 Wheeler St Cambrige MA AL, AZ, NJ, SC Only zip codes: ME, MO Abt Associates pg 8

9 Geocoding Results: Quality of Geocodes Prison PCCS Quality of Geocode N Percent N Percent Street match 167, % 75, % Zip centroid match 42, % 28, % City match 6, % 2, % Multiple city match % 4 0.0% No geocode 7, % 2, % Not really an address 13, % 9, % Abt Associates pg 9

10 Addresses Geocoded at Least at Street Midpoint: Prison Abt Associates pg 10

11 Addresses Geocoded at Least at Street Midpoint: PCCS Abt Associates pg 11

12 Geocoding Results: Type of Address Prison PCCS Type of Address N Percent N Percent Home, mobile home, or apartment 156, % 70, % Prison 3, % % Jail 2, % % Other Correctional Facility 1, % % Treatment Center % % Hospital % % Homeless Shelter % % No Residence (Homeless) 1, % % Other % % Abt Associates pg 12

13 Frequently Reported Addresses Address in a medium-size city in a southwestern state 19 offenders report this address This belongs to a manufacturing company but Across the street is the Albuquerque Opportunity Center, a men s shelter, and the same street, but the opposite direction (e.g. W vs E) is a halfway house Midwestern rural address 10 offenders report this address Appears to be a campground Abt Associates pg 13

14 Frequently Reported Addresses with Sex Offenders Two smaller-size cities in the southeast Both are motels 9 offenders in the first, 6 were sex offenders 7 offenders in the second, 5 were sex offenders Abt Associates pg 14

15 Sentencing County and Last Known Address Prison Admissions Abt Associates pg 15

16 Sentencing County and Last Known Address Prison Admissions Abt Associates pg 16

17 Sentencing County and Last Known Address Prison Admissions Abt Associates pg 17

18 Incarceration Rates by County Abt Associates pg 18

19 Incarceration Rates in Counties Surrounding Atlanta Abt Associates pg 19

20 Incarceration Rates in Block Groups Surrounding Atlanta Abt Associates pg 20

21 Spatial Dependency of Prison Admissions There is likely a spatial dependency of prison admissions between bordering places, like neighborhoods Show counties, but this could be replicated with block groups, or neighborhoods if you have the spatial files Analyses performed with GeoDa provided as open source software from spatial.chicago.edu Abt Associates pg 21

22 Moran s I for Number of Admissions to Prison from a County Moran s I is a global spatial statistic indicating spatial association over all the spatial units The correlation between the count of admissions to prison within a county and spatially lagged counts of admissions in surrounding counties Significant Moran s I indicates a spatial correlation, which can indicate A spatial effect of admissions from surrounding counties on a specific county or There is spatial heterogeneity that must be taken into account in spatial regression analyses or There is spatial heterogeneity and spatial dependence Abt Associates pg 22

23 Moran s I for Number of Admissions to Prison from a County This diagram has brushed counties shown in red on the right graph where high values of prison admissions are related to high values of spatially lagged prison admission These brushed counties are the counties surrounding Atlanta Abt Associates pg 23

24 LISA Map: Georgia counties that have spatial dependency LISA Local Indicators of Spatial Association red and blue counties indicate statistical significance in each county separately Red counties indicate that a high number of prison admissions are spatially related to a high number of admission from spatially contiguous counties Blue counties indicate that a low number of prison admissions are spatially related to a low number of admission from spatially contiguous counties This graph shows the LISA significant counties with the same brushed counties from the prior slide Abt Associates pg 24

25 LISA Map: Georgia counties that have spatial dependency This graph shows the color coded significance levels for each county note that some high/high and some low/low have the highest significance levels in dark green. Abt Associates pg 25

26 Conclusion Addresses add a new dimension to NCRP data collection Use for research questions such as prison assignment, re-entry programming, etc. Are there suggestions for other analyses? Abt Associates pg 26

27 Contact Information Ryan Kling, Abt Associates Josh Markman, BJS Program Manager Abt Associates pg 27

28 2017, Abt Associates Inc