{ "id": "2000-november-inmate-imports", "slug": "2000-november-inmate-imports", "url": "https://dwillis.github.io/uplink/article/2000-november-inmate-imports.html", "year": 2000, "month": "November", "monthNum": 11, "headline": "Inmate imports", "kicker": "CRIMINAL JUSTICE", "author_name": "Greg Paraham", "author_title": "Researcher", "affiliation": "The Commercial Appeal", "summary": "A reporter and researcher collaborate to analyze prison data by matching Wisconsin inmate records with out-of-state prison populations to investigate whether privatized Tennessee prisons disproportionately house violent offenders. The project required extensive data preparation, including converting disparate data sources into linked FileMaker databases for analysis.", "keywords": [ "data", "prison", "investigation" ], "topics": [ "Investigative Methods", "Crime & Justice", "Data & Databases" ], "technologies": [ "Word", "Access", "FileMaker Pro" ], "full_text": "Good training.\n\nThat phrase from military jargon, which means learning something valuable from a difficult task, best describes what happened when an investigative reporter asked if I could help translate some prison data he was expecting.\n\nI said yes without hesitation. As a researcher, I had worked with the reporter on other successful projects involving electronic public records.\n\nThen I discovered the condition of the data. Some of it was zipped across several disks. Some of it was on printouts and had to be scanned and then edited. All of it would need translation to a database manager because of the expected large number of records.\n\nThe next few weeks promised to be interesting.\n\nProject description\n\nThe reporter's task was to identify prison inmates from the Wisconsin prison system that had been exported to privatized prisons in our state. He suspected that two for-profit Tennessee prisons were receiving more murderers and sex offenders than other states that accepted out-of-state inmates.\n\nHe sought to verify this by using a master list of prisoners provided by the Wisconsin Department of Corrections along with the inmate population lists from prisons in Oklahoma, Minnesota, Tennessee and Texas. Matching the lists would pinpoint the identities and locations of out-of-state inmates as well as their offenses.\n\nData preparation\n\nMy task was to get the data in shape for the analysis, which turned out to be the easy part. After unzipping the disks, I used Microsoft Word and discovered that I had a 69-megabyte, field-length delimited blob of text that represented the entire inmate population in the Wisconsin prison system.\n\nI used Microsoft Access and the field-length explanation sheet sent by the Wisconsin prison official with the data to create a tab-delimited text file.\n\nI decided to use FileMaker Pro for Windows to analyze the data, because I am more familiar with its field calculation, database linking, layout and scripting (programming) capabilities.\n\nImporting the data created a 72,610 record database with 53 fields of information for each inmate. This number reflected current and past prisoners. We identified inmates who had been released to exclude them from the data set, reducing the data to 20,673 records.\n\nI created field calculations for text strings and dates and indexed necessary fields to aid in querying and sorting the records later.\n\nMeanwhile, the reporter scanned the inmate lists he received from the five prisons being examined. This data was later proofread, formatted and uploaded to another FileMaker database.\n\nThe databases were linked by the inmate's Wisconsin Department of Corrections identification number, which identified the inmates housed in the five out-of-state prisons.\n\nAfter we checked for the correct inmate name and date of birth for records that matched, we had 4,268 records that were needed for the analysis.\n\nAn immediate concern arose when we found conflicting locations for some inmates. The master list did not always reflect recent relocations. The reporter ran these down and the correct locations were added to our databases.\n\nWe were faced also with several numeric codes for prison names, sentences, offenses, race, etc. Two code types, prison name and offense identification, were used in creating lookup files that were linked to the main database. When codes matched, text was programmed to appear along with the code making it easier to identify locations of inmates and their offenses.\n\nThe offense identification codes were contained in as many as 16 fields within a record. This was confusing because some codes were duplicated within the record. We couldn't determine if any code was primary. Searching the database would be cumbersome if each field had to be included in a query.\n\nFurther examination of the data provided a solution, however. Another set of coded fields with numeric indicators for assaults, sex offenses, drug offenses, escape attempts and life sentences (indicating murderers) could be searched instead of the offense codes.\n\nTesting the data\n\nThe reporter and I spent a few hours over a couple of days testing the databases to ensure that the results of queries made sense. We checked that the number of inmates in the out-of-state prisons matched the hard-copy printouts received from prison officials. We double-checked that the prison names and codes matched because the name data was keyed and copied into data fields. We searched the names of inmates from the various prisons to verify their locations, offenses and sentences.\n\nData analysis\n\nI decided at this point to use FileMaker's scripting and layout capabilities to create the queries and reports based on the reporter's data requests. Querying the data was reduced to using the pull-down menu and selecting the appropriate search.\n\nWe used eight pieces of information from the records to create a profile of the type of prisoner being sent to our state:\n\nLife sentences for murder convictions\nSex offense indicators\nAssault indicators\nDrug offense indicators\nMaximum security rating of inmates\nLarge sentences\nPrior felony convictions\nEscape indicators\n\nQuerying the data and then sorting by prison identification code and inmate counts verified the reporter's suspicions. Private prisons were indeed making Tennessee a repository for violent criminals exported from crowded public prisons in Wisconsin.\n\nOf the five facilities under examination, one Tennessee prison was receiving the majority of Wisconsin's most offensive exiled offenders.\n\nOnce the reporter had the results of the data analysis, he was able to interview prison officials and present our findings for confirmation and comment.\n\nThe data gave us the names of some killers who were in prison for multiple charges in addition to murder. I searched the archives of newspapers in Wisconsin and the Associated Press to get background. We also used the names to find (in public records databases) relatives of inmates who might want to talk about an exported loved one.\n\nGood training indeed.", "provenance": { "extracted_by": "anthropic/claude-sonnet-4-6", "extracted_at": "2026-07-05", "verified": true }, "similar": [ { "slug": "2000-november-the-following-stories-also-address-jails-and-prison-problems", "score": 0.287 }, { "slug": "2000-julyaugust-repeat-offenders", "score": 0.186 }, { "slug": "1998-december-correctional-care", "score": 0.159 }, { "slug": "2000-may-whos-teaching-our-kids", "score": 0.145 }, { "slug": "1999-september-catch-and-release", "score": 0.137 } ], "html_url": "https://dwillis.github.io/uplink/article/2000-november-inmate-imports.html", "json_url": "https://dwillis.github.io/uplink/article/2000-november-inmate-imports.json" }