The purpose of the project was to (1) determine whether the combined dynamic (SOTIPS) and static risk assessment (Static-99R) tools better predicted sexual recidivism than either alone, and (2) determine whether the tools could be implemented successfully in more representative populations. Previous research has established a "status quo" for risk assessments. This study was set within the context of the developing sexual offender risk prediction field, where investigators explored reliable and valid means to assess what have been termed "dynamic risk factors." Instruments that identify the specific psychological risk factors present in the individual offender ought to allow treatment for that individual to be tailored to these specific needs, thus increasing its effectiveness. Thus, instruments have been designed to: Assess psychological factors that are empirically related to sexual recidivism, thus creating a basis for selecting treatment targets Show robust incremental predictive validity relative to Static-99R or other measures of static risk factors Measure change in a way that is convincingly related to sexual recidivism Incorporate and point risk managers towards some of the factors identified in the desistance literature Improve the effectiveness of treatment in reducing sexual recidivism Enrollment of sex offenders in the evaluation study began in April 2013. To be included, offenders needed to be Static-99R eligible (an adult male convicted of a contact or non-contact sex offense with an identifiable victim), mentally cognizant, released to community supervision, and at least 18 years old in January 2013 in Maricopa County and April 2013 in New York City.

In 2023, Texas had the highest number of forcible rape cases in the United States, with 15,097 reported rapes. Delaware had the lowest number of reported forcible rape cases at 194. Number vs. rate It is perhaps unsurprising that Texas and California reported the highest number of rapes, as these states have the highest population of states in the U.S. When looking at the rape rate, or the number of rapes per 100,000 of the population, a very different picture is painted: Alaska was the state with the highest rape rate in the country in 2023, with California ranking as 30th in the nation. The prevalence of rape Rape and sexual assault are notorious for being underreported crimes, which means that the prevalence of sex crimes is likely much higher than what is reported. Additionally, more than a third of women worry about being sexually assaulted, and most sexual assaults are perpetrated by someone the victim knew.

The National Crime Victimization Survey (NCVS), previously called the National Crime Survey (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. Beginning in 1992, the survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual assault, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, motor vehicle theft, and vandalism. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations. This dataset represents the concatenated version of the NCVS on a collection year basis for 1992-2023. A collection year contains records from interviews conducted in the 12 months of the given year. Under the collection year format, victimizations are counted in the year the interview is conducted, regardless of the year when the crime incident occurred.For additional information on the dataset, please see the documentation for the data from the most current year of the NCVS, ICPSR Study 38962.

This is a dataset of prisoner mugshots and associated data (height, weight, etc). The copyright status is public domain, since it's produced by the government, the photographs do not have sufficient artistic merit, and a mere collection of facts aren't copyrightable.

The source is the Illinois Dept. of Corrections. In total, there are 68149 entries, of which a few hundred have shoddy data.

It's useful for neural network training, since it has pictures from both front and side, and they're (manually) labeled with date of birth, name (useful for clustering), weight, height, hair color, eye color, sex, race, and some various goodies such as sentence duration and whether they're sex offenders.

Here is the readme file:

---BEGIN README---
Scraped from the Illinois DOC.

https://www.idoc.state.il.us/subsections/search/inms_print.asp?idoc=
https://www.idoc.state.il.us/subsections/search/pub_showfront.asp?idoc=
https://www.idoc.state.il.us/subsections/search/pub_showside.asp?idoc=

paste <(cat ids.txt | sed 's/^/http:\/\/www.idoc.state.il.us\/subsections\/search\/pub_showside.asp\?idoc=/g') <(cat ids.txt| sed 's/^/ out=/g' | sed 's/$/.jpg/g') -d ' ' > showside.txt
paste <(cat ids.txt | sed 's/^/http:\/\/www.idoc.state.il.us\/subsections\/search\/pub_showfront.asp\?idoc=/g') <(cat ids.txt| sed 's/^/ out=/g' | sed 's/$/.jpg/g') -d ' ' > showfront.txt
paste <(cat ids.txt | sed 's/^/http:\/\/www.idoc.state.il.us\/subsections\/search\/inms_print.asp\?idoc=/g') <(cat ids.txt| sed 's/^/ out=/g' | sed 's/$/.html/g') -d ' ' > inmates_print.txt

aria2c -i ../inmates_print.txt -j4 -x4 -l ../log-$(pwd|rev|cut -d/ -f 1|rev)-$(date +%s).txt

Then use htmltocsv.py to get the csv. Note that the script is very poorly written and may have errors. It also doesn't do anything with the warrant-related info, although there are some commented-out lines which may be relevant.
Also note that it assumes all the HTML files are located in the inmates directory., and overwrites any csv files in csv if there are any.

front.7z contains mugshots from the front
side.7z contains mugshots from the side
inmates.7z contains all the html files
csv contains the html files converted to CSV

The reason for packaging the images is that many torrent clients would otherwise crash if attempting to load the torrent.

All CSV files contain headers describing the nature of the columns. For person.csv, the id is unique. For marks.csv and sentencing.csv, it is not.
Note that the CSV files use semicolons as delimiters and also end with a trailing semicolon. If this is unsuitable, edit the arr2csvR function in htmltocsv.py.

There are 68149 inmates in total, although some (a few hundred) are marked as "Unknown"/"N/A"/"" in one or more fields.

The "height" column has been processed to contain the height in inches, rather than the height in feet and inches expressed as "X ft YY in."
Some inmates were marked "Not Available", this has been replaced with "N/A".
Likewise, the "weight" column has been altered "XXX lbs." -> "XXX". Again, some are marked "N/A".

The "date of birth" column has some inmates marked as "Not Available" and others as "". There doesn't appear to be any pattern. It may be related to the institution they are kept in. Otherwise, the format is MM/DD/YYYY.

The "weight" column is often rounded to the nearest 5 lbs.

Statistics for hair:
43305 Black
17371 Brown
2887 Blonde or Strawberry
2539 Gray or Partially Gray
740 Red or Auburn
624 Bald
396 Not Available
209 Salt and Pepper
70 White
7 Sandy
1 Unknown

Statistics for sex:
63409 Male
4740 Female

Statistics for race:
37991 Black
20992 White
8637 Hispanic
235 Asian
104 Amer Indian
94 Unknown
92 Bi-Racial
4

Statistics for eyes:
51714 Brown
7808 Blue
4259 Hazel
2469 Green
1382 Black
420 Not Available
87 Gray
9 Maroon
1 Unknown
---END README---

Here is a formal summary:

---BEGIN SUMMARY---
Documentation:

1. Title: Illinois DOC dataset

2. Source Information
   -- Creators: Illinois DOC
   -- Illinois Department of Corrections
   1301 Concordia Court
   P.O. Box 19277
   Springfield, IL 62794-9277
   (217) 558-2200 x 2008
   -- Donor: Anonymous
   -- Date: 2019

3. Past Usage:
   -- None

4. Relevant Information:
   -- All CSV files contain headers describing the nature of the columns. For person.csv, the id is unique. For marks.csv and sentencing.csv, it is not.
   -- Note that the CSV files use semicolons as delimiters and also end with a trailing semicolon. If this is unsuitable, edit the arr2csvR function in htmltocsv...

This project undertook the systematic collection of first-generation data concerning the nature, extent, and seriousness of child sexual exploitation (CSE) in the United States. The project was organized around the following research objectives: (1) identification of the nature, extent, and underlying causes of CSE and the commercial sexual exploitation of children (CSEC) occurring in the United States, (2) identification of those subgroups of children that were at the greatest risk of being sexually exploited, (3) identification of subgroups of adult perpetrators of sex crimes against children, and (4) identification of the modes of operation and other methods used by organized criminal units to recruit children into sexually exploitative activities. The study involved surveying senior staff members of nongovernment organizations (NGOs) and government organizations (GOs) in the United States known to be dealing with persons involved in the transnational trafficking of children for sexual purposes. Part 1 consists of survey data from nongovernment organizations. These were local child and family agencies serving runaway and homeless youth. Part 2 consists of survey data from government organizations. These organizations were divided into local, state, and federal agencies. Local organizations included municipal law enforcement, county law enforcement, prosecutors, public defenders, and corrections. State organizations included state child welfare directors, prosecutors, and public defenders. Federal organizations included the Federal Bureau of Investigation, Federal Public Defenders, Immigration and Naturalization Service, United States Attorneys, United States Customs, and the United States Postal Service. Variables in Parts 1 and 2 include the organization's city, state, and ZIP code, the type of services provided or type of law enforcement agency, how the agency was funded, the scope of the agency's service area, how much emphasis was placed on CSEC as a policy issue or a service issue, conditions that might influence the number of CSEC cases, how staff were trained to deal with CSEC cases, how victims were identified, the number of children that experienced child abuse, sexual abuse, pornography, or other exploitation in 1999 and 2000 by age and gender, methods of recruitment, family history of victims, gang involvement, and substance abuse history of victims.

The dataset contains year-, state-, type-of-crime- and gender-wise compiled data on the number of different types of crimes which were committed against children and the number of victims who were affected by the same crimes. The different types of crimes covered in the dataset include kidnapping and abduction crimes such as kidanapping and abduction for the purpose of murder, begging, ransom, compelling for marriage, procuration of minor girls, importation of girls from foreign countries, missing deemed as kidnapped, etc., fatal crimes such as murder, attempt to commit murder, muder with rape, abetment of suicide of child, infanticide, foeticide, trafficking and sexual crimes such buying and selling of minors for prostitution, use of children for pornography, transmiting sexual content and material involving children in sexually explicit acts, sexual assualt, penetrative sexual assault, rape, and other crimes such as child labour, child marriage, exposure, abandaonment, simple hurt, grievous hurt, insult and assualt of damage modesty, crimes under juvenile justice act and transplantation of organs act, etc.

A. SUMMARY These data represent hate crimes reported by the SFPD to the California Department of Justice. Read the detailed overview of this dataset here.

What is a Hate Crime? A hate crime is a crime against a person, group, or property motivated by the victim's real or perceived protected social group. An individual may be the victim of a hate crime if they have been targeted because of their actual or perceived: (1) disability, (2) gender, (3) nationality, (4) race or ethnicity, (5) religion, (6) sexual orientation, and/or (7) association with a person or group with one or more of these actual or perceived characteristics. Hate crimes are serious crimes that may result in imprisonment or jail time.

B. HOW THE DATASET IS CREATED How is a Hate Crime Processed?
Not all prejudice incidents including the utterance of hate speech rise to the level of a hate crime. The U.S. Constitution allows hate speech if it does not interfere with the civil rights of others. While these acts are certainly hurtful, they do not rise to the level of criminal violations and thus may not be prosecuted. When a prejudice incident is reported, the reporting officer conducts a preliminary investigation and writes a crime or incident report. Bigotry must be the central motivation for an incident to be determined to be a hate crime. In that report, all facts such as verbatims or statements that occurred before or after the incident and characteristics such as the race, ethnicity, sex, religion, or sexual orientations of the victim and suspect (if known) are included. To classify a prejudice incident, the San Francisco Police Department’s Hate Crimes Unit of the Special Investigations Division conducts an analysis of the incident report to determine if the incident falls under the definition of a “hate crime” as defined by state law. California Penal Code 422.55 - Hate Crime Definition

C. UPDATE PROCESS These data are updated monthly.

D. HOW TO USE THIS DATASET This dataset includes the following information about each incident: the hate crime offense, bias type, location/time, and the number of hate crime victims and suspects. The data presented mirrors data published by the California Department of Justice, albeit at a higher frequency. The publishing of these data meet requirements set forth in PC 13023.

E. RELATED DATASETS California Department of Justice - Hate Crimes Info California Department of Justice - Hate Crimes Data

The National Incident-Based Reporting System (NIBRS) is a part of the Uniform Crime Reporting Program (UCR), administered by the Federal Bureau of Investigation (FBI). In the late 1970s, the law enforcement community called for a thorough evaluative study of the UCR with the objective of recommending an expanded and enhanced UCR program to meet law enforcement needs into the 21st century. The FBI fully concurred with the need for an updated program to meet contemporary needs and provided its support, formulating a comprehensive redesign effort. Following a multiyear study, a "Blueprint for the Future of the Uniform Crime Reporting Program" was developed. Using the "Blueprint," and in consultation with local and state law enforcement executives, the FBI formulated new guidelines for the Uniform Crime Reports. The National Incident-Based Reporting System (NIBRS) was implemented to meet these guidelines. NIBRS data as formatted by the FBI are stored in a single file. These data are organized by various segment levels (record types). There are six main segment levels: administrative, offense, property, victim, offender, and arrestee. Each segment level has a different length and layout. There are other segment levels which occur with less frequency than the six main levels. Significant computing resources are necessary to work with the data in its single-file format. In addition, the user must be sophisticated in working with data in complex file types. While it is convenient to think of NIBRS as a hierarchical file, its structure is more similar to a relational database in that there are key variables that link the different segment levels together. NIBRS data are archived at ICPSR as 11 separate data files per year, which may be merged by using linkage variables. Prior to 2013 the data were archived and distributed as 13 separate data files, including three separate batch header record files. Starting with the 2013 data, the FBI combined the three batch header files into one file. Consequently, ICPSR instituted new file numbering for the data. NIBRS data focus on a variety of aspects of a crime incident. Part 2 (formerly Part 4), Administrative Segment, offers data on the incident itself (date and time). Each crime incident is delineated by one administrative segment record. Also provided are Part 3 (formerly Part 5), Offense Segment (offense type, location, weapon use, and bias motivation), Part 4 (formerly Part 6), Property Segment (type of property loss, property description, property value, drug type and quantity), Part 5 (formerly Part 7), Victim Segment (age, sex, race, ethnicity, and injuries), Part 6 (formerly Part 8), Offender Segment (age, sex, and race), and Part 7 (formerly Part 9), Arrestee Segment (arrest date, age, sex, race, and weapon use). The Batch Header Segment (Part 1, formerly Parts 1-3) separates and identifies individual police agencies by Originating Agency Identifier (ORI). Batch Header information, which is contained on three records for each ORI, includes agency name, geographic location, and population of the area. Part 8 (formerly Part 10), Group B Arrest Report Segment, includes arrestee data for Group B crimes. Window Segments files (Parts 9-11, formerly Parts 11-13) pertain to incidents for which the complete Group A Incident Report was not submitted to the FBI. In general, a Window Segment record will be generated if the incident occurred prior to January 1 of the previous year or if the incident occurred prior to when the agency started NIBRS reporting. As with the UCR, participation in NIBRS is voluntary on the part of law enforcement agencies. The data are not a representative sample of crime in the United States. Recognizing many differences in computing resources and that many users will be interested in only one or two segment levels, ICPSR has decided to make the data available as multiple files. Each NIBRS segment level in the FBI's single-file format has been made into a separate rectangular ASCII data file. Linkage (key) variables are used to perform analyses that involve two or more segment levels. If the user is interested in variables contained in one segment level, then the data are easy to work with since each segment level file is simply a rectangular ASCII data file. Setup files are available to read each segment level. Also, with only one segment level, the issue of