These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study was designed to investigate whether the presence of CCTV cameras can reduce crime by studying the cameras and crime statistics of a controlled area. The viewsheds of over 100 CCTV cameras within the city of Philadelphia, Pennsylvania were defined and grouped into 13 clusters, and camera locations were digitally mapped. Crime data from 2003-2013 was collected from areas that were visible to the selected cameras, as well as data from control and displacement areas using an incident reporting database that records the location of crime events. Demographic information was also collected from the mapped areas, such as population density, household information, and data on the specific camera(s) in the area. This study also investigated the perception of CCTV cameras, and interviewed members of the public regarding topics such as what they thought the camera could see, who was watching the camera feed, and if they were concerned about being filmed.

The data on crime occurring in Philadelphia County is from the Philadelphia Police Department. The Philadelphia Inquirer has organized the data into a maps and charts. The data can be searched by year and neighborhood.

In 2023, the District of Columbia had the highest reported violent crime rate in the United States, with 1,150.9 violent crimes per 100,000 of the population. Maine had the lowest reported violent crime rate, with 102.5 offenses per 100,000 of the population. Life in the District The District of Columbia has seen a fluctuating population over the past few decades. Its population decreased throughout the 1990s, when its crime rate was at its peak, but has been steadily recovering since then. While unemployment in the District has also been falling, it still has had a high poverty rate in recent years. The gentrification of certain areas within Washington, D.C. over the past few years has made the contrast between rich and poor even greater and is also pushing crime out into the Maryland and Virginia suburbs around the District. Law enforcement in the U.S. Crime in the U.S. is trending downwards compared to years past, despite Americans feeling that crime is a problem in their country. In addition, the number of full-time law enforcement officers in the U.S. has increased recently, who, in keeping with the lower rate of crime, have also made fewer arrests than in years past.

description: Crime Data from 2006 to the present; abstract: Crime Data from 2006 to the present

These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed. This study examines municipal crime levels and changes over a nine year time frame, from 2000-2008, in the fifth largest primary Metropolitan Statistical Area (MSA) in the United States, the Philadelphia metropolitan region. Crime levels and crime changes are linked to demographic features of jurisdictions, policing arrangements and coverage levels, and street and public transit network features.

This study was designed to explore school culture and climate and their effects on school disorder, violence, and academic performance on two levels. At the macro level of analysis, this research examined the influences of sociocultural, crime, and school characteristics on aggregate-level school violence and academic performance measures. Here the focus was on understanding community, family, and crime compositional effects on disruption and violence in Philadelphia schools. This level included Census data and crime rates for the Census tracts where the schools were located (local data), as well as for the community of residence of the students (imported data) for all 255 schools within the Philadelphia School District. The second level of analysis, the intermediate level, included all of the variables measured at the macro level, and added school organizational structure and school climate, measured with survey data, as mediating variables. Part 1, Macro-Level Data, contains arrest and offense data and Census characteristics, such as race, poverty level, and household income, for the Census tracts where each of the 255 Philadelphia schools is located and for the Census tracts where the students who attend those schools reside. In addition, this file contains school characteristics, such as number and race of students and teachers, student attendance, average exam scores, and number of suspensions for various reasons. For Part 2, Principal Interview Data, principals from all 42 middle schools in Philadelphia were interviewed on the number of buildings and classrooms in their school, square footage and special features of the school, and security measures. For Part 3, teachers were administered the Effective School Battery survey and asked about their job satisfaction, training opportunities, relationships with principals and parents, participation in school activities, safety measures, and fear of crime at school. In Part 4, students were administered the Effective School Battery survey and asked about their attachment to school, extracurricular activities, attitudes toward teachers and school, academic achievement, and fear of crime at school. Part 5, Student Victimization Data, asked the same students from Part 4 about their victimization experiences, the availability of drugs, and discipline measures at school. It also provides self-reports of theft, assault, drug use, gang membership, and weapon possession at school.

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.

Version 5 release notes:
Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. Version 4 release notes:
Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent.
Made the 'population' column which is the total population in that agency.

Version 3 release notes:
Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes:
Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.

Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency.
Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.).

The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

These data were prepared in conjunction with a project using Bureau of Labor Statistics data (not provided with this collection) and the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program data to examine the relationship between unemployment and violent crime. Three separate time-series data files were created as part of this project: a national time series (Part 1), a state time series (Part 2), and a time series of data for 12 selected cities: Baltimore, Buffalo, Chicago, Columbus, Detroit, Houston, Los Angeles, Newark, New York City, Paterson (New Jersey), and Philadelphia (Part 3). Each data file was constructed to include 82 monthly time series: 26 series containing the number of Part I (crime index) offenses known to police (excluding arson) by weapon used, 26 series of the number of offenses cleared by arrest or other exceptional means by weapon used in the offense, 26 series of the number of offenses cleared by arrest or other exceptional means for persons under 18 years of age by weapon used in the offense, a population estimate series, and three date indicator series. For the national and state data, agencies from the 50 states and Washington, DC, were included in the aggregated data file if they reported at least one month of information during the year. In addition, agencies that did not report their own data (and thus had no monthly observations on crime or arrests) were included to make the aggregated population estimate as close to Census estimates as possible. For the city time series, law enforcement agencies with jurisdiction over the 12 central cities were identified and the monthly data were extracted from each UCR annual file for each of the 12 agencies. The national time-series file contains 82 time series, the state file contains 4,083 time series, and the city file contains 963 time series, each with 228 monthly observations per time series. The unit of analysis is the month of observation. Monthly crime and clearance totals are provided for homicide, negligent manslaughter, total rape, forcible rape, attempted forcible rape, total robbery, firearm robbery, knife/cutting instrument robbery, other dangerous weapon robbery, strong-arm robbery, total assault, firearm assault, knife/cutting instrument assault, other dangerous weapon assault, simple nonaggravated assault, assaults with hands/fists/feet, total burglary, burglary with forcible entry, unlawful entry-no force, attempted forcible entry, larceny-theft, motor vehicle theft, auto theft, truck and bus theft, other vehicle theft, and grand total of all actual offenses.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Camden County, NJ (DISCONTINUED) (FBITC034007) from 2009 to 2020 about Camden County, NJ; crime; violent crime; property crime; Philadelphia; NJ; and USA.

In an attempt to inform and advance the literature on co-offending, this study tracked through time the patterns of criminal behavior among a sample of offenders and their accomplices. This study consists of a random sample of 400 offenders selected from all official records of arrest (N=60,821) for offenders under age 18 in Philadelphia in 1987. Half of the offenders selected committed a crime alone and half committed a crime with an accomplice. Criminal history data from January 1976 to December 1994 were gathered for all offenders in the sample and their accomplices.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Gloucester County, NJ (DISCONTINUED) (FBITC034015) from 2009 to 2020 about Gloucester County, NJ; crime; violent crime; property crime; Philadelphia; NJ; and USA.

Source:

Creator: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA -- culled from 1990 US Census, 1995 US FBI Uniform Crime Report, 1990 US Law Enforcement Management and Administrative Statistics Survey, available from ICPSR at U of Michigan. -- Donor: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA -- Date: July 2009

Data Set Information:

Many variables are included so that algorithms that select or learn weights for attributes could be tested. However, clearly unrelated attributes were not included; attributes were picked if there was any plausible connection to crime (N=122), plus the attribute to be predicted (Per Capita Violent Crimes). The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units.

The per capita violent crimes variable was calculated using population and the sum of crime variables considered violent crimes in the United States: murder, rape, robbery, and assault. There was apparently some controversy in some states concerning the counting of rapes. These resulted in missing values for rape, which resulted in incorrect values for per capita violent crime. These cities are not included in the dataset. Many of these omitted communities were from the midwestern USA.

Data is described below based on original values. All numeric data was normalized into the decimal range 0.00-1.00 using an Unsupervised, equal-interval binning method. Attributes retain their distribution and skew (hence for example the population attribute has a mean value of 0.06 because most communities are small). E.g. An attribute described as 'mean people per household' is actually the normalized (0-1) version of that value.

The normalization preserves rough ratios of values WITHIN an attribute (e.g. double the value for double the population within the available precision - except for extreme values (all values more than 3 SD above the mean are normalized to 1.00; all values more than 3 SD below the mean are normalized to 0.00)).

However, the normalization does not preserve relationships between values BETWEEN attributes (e.g. it would not be meaningful to compare the value for whitePerCap with the value for blackPerCap for a community)

A limitation was that the LEMAS survey was of the police departments with at least 100 officers, plus a random sample of smaller departments. For our purposes, communities not found in both census and crime datasets were omitted. Many communities are missing LEMAS data.

Attribute Information:

'(125 predictive, 4 non-predictive, 18 potential goal) ', ' communityname: Community name - not predictive - for information only (string) ', ' state: US state (by 2 letter postal abbreviation)(nominal) ', ' countyCode: numeric code for county - not predictive, and many missing values (numeric) ', ' communityCode: numeric code for community - not predictive and many missing values (numeric) ', ' fold: fold number for non-random 10 fold cross validation, potentially useful for debugging, paired tests - not predictive (numeric - integer) ', ' population: population for community: (numeric - expected to be integer) ', ' householdsize: mean people per household (numeric - decimal) ', ' racepctblack: percentage of population that is african american (numeric - decimal) ', ' racePctWhite: percentage of population that is caucasian (numeric - decimal) ', ' racePctAsian: percentage of population that is of asian heritage (numeric - decimal) ', ' racePctHisp: percentage of population that is of hispanic heritage (numeric - decimal) ', ' agePct12t21: percentage of population that is 12-21 in age (numeric - decimal) ', ' agePct12t29: percentage of population that is 12-29 in age (numeric - decimal) ', ' agePct16t24: percentage of population that is 16-24 in age (numeric - decimal) ', ' agePct65up: percentage of population that is 65 and over in age (numeric - decimal) ', ' numbUrban: number of people living in areas classified as urban (numeric - expected to be integer) ', ' pctUrban: percentage of people living in areas classified as urban (numeric - decimal) ', ' medIncome: median household income (numeric - may be integer) ', ' pctWWage: percentage of households with wage or salary income in 1989 (numeric - decimal) ', ' pctWFarmSelf: percentage of households with farm or self employment income in 1989 (numeric - decimal) ', ' pctWInvInc: percentage of households with investment / rent income in 1989 (numeric - decimal) ', ' pctWSocSec: percentage of households with social security income in 1989 (numeric - decimal) ', ' pctWPubAsst: pe...

This survey was conducted by the Center for Urban Affairs and Policy Research at Northwestern University to gather information for two projects that analyzed the impact of crime on the lives of city dwellers. These projects were the Reactions to Crime (RTC) Project, which was supported by the United States Department of Justice's National Institute of Justice as part of its Research Agreements Program, and the Rape Project, supported by the National Center for the Prevention and Control of Rape, a subdivision of the National Institute of Mental Health. Both investigations were concerned with individual behavior and collective reactions to crime. The Rape Project was specifically concerned with sexual assault and its consequences for the lives of women. The three cities selected for study were Chicago, Philadelphia, and San Francisco. A total of ten neighborhoods were chosen from these cities along a number of dimensions -- ethnicity, class, crime, and levels of organizational activity. In addition, a small city-wide sample was drawn from each city. Reactions to crime topics covered how individuals band together to deal with crime problems, individual responses to crime such as property marking or the installation of locks and bars, and the impact of fear of crime on day-to-day behavior -- for example, shopping and recreational patterns. Respondents were asked several questions that called for self-reports of behavior, including events and conditions in their home areas, their relationship to their neighbors, who they knew and visited around their homes, and what they watched on TV and read in the newspapers. Also included were a number of questions measuring respondents' perceptions of the extent of crime in their communities, whether they knew someone who had been a victim, and what they had done to reduce their own chances of being victimized. Questions on sexual assault/rape included whether the respondent thought this was a neighborhood problem, if the number of rapes in the neighborhood were increasing or decreasing, how many women they thought had been sexually assaulted or raped in the neighborhood in the previous year, and how they felt about various rape prevention measures, such as increasing home security, women not going out alone at night, women dressing more modestly, learning self-defense techniques, carrying weapons, increasing men's respect of women, and newspapers publishing the names of known rapists. Female respondents were asked whether they thought it likely that they would be sexually assaulted in the next year, how much they feared sexual assault when going out alone after dark in the neighborhood, whether they knew a sexual assault victim, whether they had reported any sexual assaults to police, and where and when sexual assaults took place that they were aware of. Demographic information collected on respondents includes age, race, ethnicity, education, occupation, income, and whether the respondent owned or rented their home.

Crime incidents from the Philadelphia Police Department. Part I crimes include violent offenses such as aggravated assault, rape, arson, among others. Part II crimes include simple assault, prostitution, gambling, fraud, and other non-violent offenses.

V6 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. V5 release notes: Changes the word "larceny" to "theft" in column names - eg. from "act_larceny" to "act_theft."Fixes bug where state abbrebation was NA for Washington D.C., Puerto Rico, Guam, and the Canal Zone.Fixes bug where officers_killed_by_accident was not appearing in yearly data. Note that 1979 does not have any officers killed (by felony or accident) or officers assaulted data.Adds aggravated assault columns to the monthly data. Aggravated assault is the sum of all assaults other than simple assault (assaults using gun, knife, hand/feet, and other weapon). Note that summing all crime columns to get a total crime count will double count aggravated assault as it is already the sum of existing columns. Reorder columns to put all month descriptors (e.g. "jan_month_included", "jan_card_1_type") before any crime data.Due to extremely irregular data in the unfounded columns for New Orleans (ORI = LANPD00) for years 2014-2016, I have change all unfounded column data for New Orleans for these years to NA. As an example, New Orleans reported about 45,000 unfounded total burglaries in 2016 (the 3rd highest they ever reported). This is 18 times largest than the number of actual total burglaries they reported that year (2,561) and nearly 8 times larger than the next largest reported unfounded total burglaries in any agency or year. Prior to 2014 there were no more than 10 unfounded total burglaries reported in New Orleans in any year. There were 10 obvious data entry errors in officers killed by felony/accident that I changed to NA.In 1974 the agency "Boston" (ORI = MA01301) reported 23 officers killed by accident during November.In 1978 the agency "Pittsburgh" (ORI = PAPPD00) reported 576 officers killed by accident during March.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during April.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during June.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during April.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during June.In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by accident during May.In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by felony during May.In 1996 the agency "Ruston" in Louisiana (ORI = LA03102) reported 30 officers killed by felony during September.In 1997 the agency "Washington University" in Missouri (ORI = MO0950E) reported 26 officers killed by felony during March.V4 release notes: Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Makes all column names lowercase.Change some variable namesMakes values in character columns lowercase.Adds months_reported variable to yearly data.Combines monthly and yearly files into a single zip file (per data type).V3 release notes: fixes a bug in Version 2 where 1993 data did not properly deal with missing values, leading to enormous counts of crime being reported. Summary: This is a collection of Offenses Known and Clearances By Arrest data from 1960 to 2016. Each zip file contains monthly and yearly data files. The monthly files contain one data file per year (57 total, 1960-2016) as well as a codebook for each year. These files have been read into R using the ASCII and setup files from ICPSR (or from the FBI for 2016 data) using the package asciiSetupReader. The end of the zip folder's name says what data type (R, SPSS, SAS, Microsoft Excel CSV, Stata) the data is in. The files are lightly cleaned. What this means specifically is that column names and value labels are standardized. In the original data column names were different between years (e.g. the December burglaries cleared column is "DEC_TOT_CLR_BRGLRY_TOT" in 1975 and "DEC_TOT_CLR_BURG_TOTAL" in 1977). The data here have standardized columns so you can compare between years and combine years together. The same thing is done for values inside of columns. For example, the state column gave state names in some years, abbreviations in others. For the code uses to clean and read the data, please see my GitHub file h

Check out the Crime Maps and Stats Application, an online application that displays summary statistics and enables mapping of recent incidents within a radius of an address. Also see this Crime Incidents Visualization.View metadata for key information about this dataset.Part I crimes include violent offenses such as aggravated assault, rape, arson, among others. Part II crimes include simple assault, prostitution, gambling, fraud, and other non-violent offenses.Please note that this is a very large dataset. To see all incidents, download all datasets for all years.If you are comfortable with APIs, you can also use the API links to access this data. You can learn more about how to use the API at Carto’s SQL API site and in the Carto guide in the section on making calls to the API.For questions about this dataset, contact publicsafetygis@phila.gov. For technical assistance, email maps@phila.gov.

To explore the relationship between alcohol, drugs, and crime in the electronic dance music and hip hop nightclub scenes of Philadelphia, Pennsylvania, researchers utilized a multi-faceted ethnographic approach featuring in-depth interviews with 51 respondents (Dataset 1, Initial Interview Qualitative Data) and two Web-based follow-up surveys with respondents (Dataset 2, Follow-Up Surveys Quantitative Data). Recruitment of respondents began in April of 2005 and was conducted in two ways. Slightly more than half of the respondents (n = 30) were recruited with the help of staff from two small, independent record stores. The remaining 21 respondents were recruited at electronic dance music or hip hop nightclub events. Dataset 1 includes structured and open-ended questions about the respondent's background, living situation and lifestyle, involvement and commitment to the electronic dance music and hip hop scenes, nightclub culture and interaction therein, and experiences with drugs, criminal activity, and victimization. Dataset 2 includes descriptive information on how many club events were attended, which ones, and the activities (including drug use and crime/victimization experiences) taking place therein. Dataset 3 (Demographic Quantitative Data) includes coded demographic information from the Dataset 1 interviews.

Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. The data sets here combine all data from the years 1992-2015 into a single file. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data. The zip file contains the data in the following formats and a codebook: .csv - Microsoft Excel.dta - Stata.sav - SPSS.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

!!!WARNING~~~This dataset has a large number of flaws and is unable to properly answer many questions that people generally use it to answer, such as whether national hate crimes are changing (or at least they use the data so improperly that they get the wrong answer). A large number of people using this data (academics, advocates, reporting, US Congress) do so inappropriately and get the wrong answer to their questions as a result. Indeed, many published papers using this data should be retracted. Before using this data I highly recommend that you thoroughly read my book on UCR data, particularly the chapter on hate crimes (https://ucrbook.com/hate-crimes.html) as well as the FBI's own manual on this data. The questions you could potentially answer well are relatively narrow and generally exclude any causal relationships. ~~~WARNING!!!For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 8 release notes:Adds 2019 and 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last UCR hate crime data they release. Changes .rda file to .rds.Version 7 release notes:Changes release notes description, does not change data.Version 6 release notes:Adds 2018 dataVersion 5 release notes:Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), made all character values lower case, reordered columns. I also generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

Abstract (en): The purpose of this data collection was to follow a birth cohort born in Philadelphia during 1958 with a special focus on delinquent activities as children and as adults. The respondents were first interviewed in DELINQUENCY IN A BIRTH COHORT IN PHILADELPHIA, PENNSYLVANIA, 1945-1963 (ICPSR 7729). Part 1 offers basic demographic information, such as sex, race, date of birth, church membership, age, and socioeconomic status, on each cohort member. Two files supply offense data: Part 2 pertains to offenses committed while a juvenile and Part 3 details offenses as an adult. Offense-related variables include most serious offense, police disposition, location of crime, reason for police response, complainant's sex, age, and race, type of victimization, date of offense, number of victims, average age of victims, number of victims killed or hospitalized, property loss, weapon involvement, and final court disposition. Part 4, containing follow-up survey interview data collected in 1988, was designed to investigate differences in the experiences and attitudes of individuals with varying degrees of involvement with the juvenile justice system. Variables include individual histories of delinquency, health, household composition, marriage, parent and respondent employment and education, parental contacts with the legal system, and other social and demographic variables. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Checked for undocumented or out-of-range codes.. All children born in Philadelphia during 1958. 2006-01-12 All files were removed from dataset 5 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 5 and flagged as study-level files, so that they will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Office of Juvenile Justice and Delinquency Prevention. When using the Juvenile Offense file (Part 2), users should exclude from analyses any records of offenses committed when the offender was over 17 years of age. All records included in this file represent police contacts. Only a subset of these cases represent true offenses or violations of the Pennsylvania Crime Code. The variable EVENTYPE distinguishes between true offenses and cases that are police contacts only. The crime code fields can also be used to distinguish true offense charges from charges that represent police contacts only. Police contacts are those designated in the crime code value labels by an asterisk directly following the equal sign. For example, "1001 = COUNTERFEIT" represents a true offense, while "2624 = *RUNAWAY" represents a police contact only. To link the interview data from the survey file with either the juvenile delinquency history or adult criminal history databases, the user should utilize the LINKAGE DATABASE, provided in the Follow-Up Interview machine-readable codebook. A data collection instrument is available only for Part 4, the Follow-Up Interview data.Producers: Sellin Center for Studies in Criminology and Criminal Law and National Analysts, Division of Booz-Allen and Hamilton, Inc., Philadelphia, PA, 1990.