Content

This dataset is about Philadelphia, PA and includes average house sales price in a number of neighborhoods. The attributes of each neighborhood we have include the crime rate ('CrimeRate'), miles from Center City ('MilesPhila'), town name ('Name'), and county name ('County').

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.

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.

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. 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.

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.

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.

Cheltenham PA, Crime Data

Cheltenham is a home rule township bordering North Philadelphia in Montgomery County. It has a population of about 37,000 people. You can find out more about Cheltenham on wikipedia.

Cheltenham's Facebook Groups. contains postings on crime and other events in the community.

Getting Started

Reading Data is a simple python script for getting started.

If you prefer to use R, there is an example Kernel here.

Proximity to Philadelphia

This township borders on Philadelphia, which may or may not influence crime in the community. For Philadelphia crime patterns, see the Philadelphia Crime Dataset.

Reference

Data was obtained from socrata.com

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

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.

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.

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. 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: .dta - Stata.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

This study sought to assess changes in the volume and types of homicide committed in Philadelphia, Phoenix, and St. Louis from 1980 to 1994 and to document the nature of those changes. Three of the eight cities originally studied by Margaret Zahn and Marc Riedel (NATURE AND PATTERNS OF HOMICIDE IN EIGHT AMERICAN CITIES, 1978 [ICPSR 8936]) were revisited for this data collection. In each city, police records were coded for each case of homicide occurring in the city each year from 1980 to 1994. Homicide data for St. Louis were provided by the St. Louis Homicide Project with Scott Decker and Richard Rosenfeld as the principal investigators. Variables describing the event cover study site, year of the case, date and time of assault, location of fatal injury, method used to kill the victim, and circumstances surrounding the death. Variables pertaining to offenders include total number of homicide and assault victims, number of offenders arrested, number of offenders identified, and disposition of event for offenders. Variables on victims focus on whether the victim was killed at work, if the victim was using drugs or alcohol, the victim's blood alcohol level, and the relationship of the victim to the offender. Demographic variables include age, sex, race, and marital status of victims and offenders.

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.

!!!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 10 release notes:Adds 2022 dataVersion 9 release notes:Adds 2021 data.Version 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.

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.

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.

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.

This study measured the difference that defense counsel made to the outcome of homicide and death penalty cases. One in five indigent murder defendants in Philadelphia were randomly assigned representation by the Defender Association of Philadelphia while the remainder received court-appointed private attorneys. This study's research design utilized this random assignment to measure how defense counsel affected murder case outcomes. The research team collected data on 3,157 defendants charged with murder in Philadelphia Municipal Court between 1995-2004, using records provided by the Philadelphia Courts (First Judicial District of Pennsylvania). Data were also obtained from the Philadelphia Court of Common Pleas, the Pennsylvania Unified Judicial System web portal, the National Corrections Reporting Program, and the 2000 Census. This study contains a total of 47 variables including public defender representation, defendant demographics, ZIP code characteristics, prior criminal history, case characteristics, case outcomes, and case handling procedures.

Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled “sharing of information” between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns. In this situation, standard prior choices often yield overly smooth parameter estimates, which can ultimately produce mis-calibrated forecasts. To prevent potential over-smoothing, we introduce a prior that partitions the set of neighborhoods into several clusters and encourages spatial smoothness within each cluster. In terms of model implementation, conventional stochastic search techniques are computationally prohibitive, as they must traverse a combinatorially vast space of partitions. We introduce an ensemble optimization procedure that simultaneously identifies several high probability partitions by solving one optimization problem using a new local search strategy. We then use the identified partitions to estimate crime trends in Philadelphia between 2006 and 2017. On simulated and real data, our proposed method demonstrates good estimation and partition selection performance. Supplementary materials for this article are available online.

This data collection was designed to assess the effects of the New York Juvenile Offender Law on the rate of violent crime committed by juveniles. The data were collected to estimate the deterrent effects of the law and to permit the use of an interrupted time-series model to gauge the effects of intervention. The deterrent effects of the law are assessed on five types of violent offenses over a post-intervention period of 75 months using two comparison time series to control for temporal and geographical characteristics. One time series pertains to the monthly juvenile arrests of 16- to 19-year-olds in New York City, and the other covers monthly arrests of juveniles aged 13 to 15 years in Philadelphia, Pennsylvania, the control jurisdiction. Included in the collection are variables concerning the monthly rates of violent juvenile arrests for homicide, rape, assault, arson, and robbery for the two juvenile cohorts. These time series data were compiled from records of individual police jurisdictions that reported monthly arrests to the Uniform Crime Reporting Division of the Federal Bureau of Investigation.