View metadata for key information about this dataset.The PSA boundaries replaced a much smaller boundary, Sectors in 2009. In several Districts, PSA's split Sector boundaries and therefore a historical comparison would not necessarily be accurate. These polygon features were created by the Crime Mapping & Analysis Unit and they represent an initiative by the Philadelphia Police Department that started in 2009. A Police District can have up to 4 PSA's and as few as 2. There is a Police Lieutenant assigned to each PSA and officers are limited to patrol their specific boundary with the goal of familiarity with the area residents and businesses.See also the related datasets:Police DistrictsPolice DivisionsFor questions about this dataset, contact publicsafetygis@phila.gov. For technical assistance, email maps@phila.gov.

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.

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.

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