An interactive public crime mapping application providing DC residents and visitors easy-to-understand data visualizations of crime locations, types and trends across all eight wards. Crime Cards was created by the DC Metropolitan Police Department (MPD) and Office of the Chief Technology Officer (OCTO). Special thanks to the community members who participated in reviews with MPD Officers and IT staff, and those who joined us for the #SaferStrongerSmarterDC roundtable design review. All statistics presented in Crime Cards are based on preliminary DC Index crime data reported from 2009 to midnight of today’s date. They are compiled based on the date the offense was reported (Report Date) to MPD. The application displays two main crime categories: Violent Crime and Property Crime. Violent Crimes include homicide, sex abuse, assault with a dangerous weapon (ADW), and robbery. Violent crimes can be further searched by the weapon used. Property Crimes include burglary, motor vehicle theft, theft from vehicle, theft (other), and arson.CrimeCards collaboration between the Metropolitan Police Department (MPD), the Office of the Chief Technology Officer (OCTO), and community members who participated at the #SafterStrongerSmarterDC roundtable design review.

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 dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit https://crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

The Urban Institute undertook a comprehensive assessment of communities approaching decay to provide public officials with strategies for identifying communities in the early stages of decay and intervening effectively to prevent continued deterioration and crime. Although community decline is a dynamic spiral downward in which the physical condition of the neighborhood, adherence to laws and conventional behavioral norms, and economic resources worsen, the question of whether decay fosters or signals increasing risk of crime, or crime fosters decay (as investors and residents flee as reactions to crime), or both, is not easily answered. Using specific indicators to identify future trends, predictor models for Washington, DC, and Cleveland were prepared, based on data available for each city. The models were designed to predict whether a census tract should be identified as at risk for very high crime and were tested using logistic regression. The classification of a tract as a "very high crime" tract was based on its crime rate compared to crime rates for other tracts in the same city. To control for differences in population and to facilitate cross-tract comparisons, counts of crime incidents and other events were converted to rates per 1,000 residents. Tracts with less than 100 residents were considered nonresidential or institutional and were deleted from the analysis. Washington, DC, variables include rates for arson and drug sales or possession, percentage of lots zoned for commercial use, percentage of housing occupied by owners, scale of family poverty, presence of public housing units for 1980, 1983, and 1988, and rates for aggravated assaults, auto thefts, burglaries, homicides, rapes, and robberies for 1980, 1983, 1988, and 1990. Cleveland variables include rates for auto thefts, burglaries, homicides, rapes, robberies, drug sales or possession, and delinquency filings in juvenile court, and scale of family poverty for 1980 through 1989. Rates for aggravated assaults are provided for 1986 through 1989 and rates for arson are provided for 1983 through 1988.

Context

Introduction: The dataset used for this experiment is real and authentic. The dataset is acquired from UCI machine learning repository website [13]. The title of the dataset is ‘Crime and Communities’. It is prepared using real data from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crimedata from the 1995 FBI UCR [13]. This dataset contains a total number of 147 attributes and 2216 instances.

The per capita crimes variables were calculated using population values included in the 1995 FBI data (which differ from the 1990 Census values).

Content

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 crime attributes (N=18) that could be predicted are the 8 crimes considered 'Index Crimes' by the FBI)(Murders, Rape, Robbery, .... ), per capita (actually per 100,000 population) versions of each, and Per Capita Violent Crimes and Per Capita Nonviolent Crimes)

predictive variables : 125 non-predictive variables : 4 potential goal/response variables : 18

Acknowledgements

http://archive.ics.uci.edu/ml/datasets/Communities%20and%20Crime%20Unnormalized

U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files),

U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United States (Computer File) (1995)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Data available in the dataset may not act as a complete source of information for identifying factors that contribute to more violent and non-violent crimes as many relevant factors may still be missing.

However, I would like to try and answer the following questions answered.

1. Analyze if number of vacant and occupied houses and the period of time the houses were vacant had contributed to any significant change in violent and non-violent crime rates in communities

2. How has unemployment changed crime rate(violent and non-violent) in the communities?

3. Were people from a particular age group more vulnerable to crime?

4. Does ethnicity play a role in crime rate?

5. Has education played a role in bringing down the crime rate?

It is important for the community to understand what is – and is not – a hate crime. First and foremost, the incident must be a crime. Although that may seem obvious, most speech is not a hate crime, regardless of how offensive it may be. In addition, a hate crime is not a crime, but a possible motive for a crime.It can be difficult to establish a motive for a crime. Therefore, the classification as a hate crime is subject to change as an investigation proceeds – even as prosecutors continue an investigation. If a person is found guilty of a hate crime, the court may fine the offender up to 1½ times the maximum fine and imprison him or her for up to 1½ times the maximum term authorized for the underlying crime.While the District strives to reduce crime for all residents of and visitors to the city, hate crimes can make a particular community feel vulnerable and more fearful. This is unacceptable, and is the reason everyone must work together not just to address allegations of hate crimes, but also to proactively educate the public about hate crimes.The figures in this data align with DC Official Code 22-3700. Because the DC statute differs from the FBI Uniform Crime Reporting (UCR) and National Incident-Based Reporting System (NIBRS) definitions, these figures may be higher than those reported to the FBI.Each month, an MPD team reviews crimes that have been identified as potentially motivated by hate/bias to determine whether there is sufficient information to support that designation. The data in this document is current through the end of the most recent month.The hate crimes dataset is not an official MPD database of record and may not match details in records pulled from the official Records Management System (RMS).Unknown or blank values in the Targeted Group field may be present prior to 2016 data. As of January 2022, an offense with multiple bias categories would be reflected as such.Data is updated on the 15th of every month.

This study investigates rates of serious crime for selected public housing developments in Washington, DC, Phoenix, Arizona, and Los Angeles, California, for the years 1986 to 1989. Offense rates in housing developments were compared to rates in nearby areas of private housing as well as to city-wide rates. In addition, the extent of law enforcement activity in housing developments as represented by arrests was considered and compared to arrest levels in other areas. This process allowed both intra-city and inter-city comparisons to be made. Variables cover study site, origin of data, year of event, offense codes, and _location of event. Los Angeles files also include police division.

The purpose of this project was to conduct an evaluation of the impact on crime of the closing, renovation, and subsequent reopening of selected public housing developments under the United States Department of Housing and Urban Development's (HUD) Housing Opportunities for People Everywhere (HOPE VI) initiative. The study examined crime displacement and potential diffusion of benefits in and around five public housing developments that, since 2000, had been redeveloped using funds from HUD's HOPE VI initiative and other sources. In Milwaukee, Wisconsin, three sites were selected for inclusion in the study. However, due to substantial overlap between the various target sites and displacement zones, the research team ultimately decided to aggregate the three sites into a single target area. A comparison area was then chosen based on recommendations from the Housing Authority of the City of Milwaukee (HACM). In Washington, DC, two HOPE VI sites were selected for inclusion in the study. Based on recommendations from the District of Columbia Housing Authority (DCHA), the research team selected a comparison site for each of the two target areas. Displacement areas were then drawn as concentric rings ("buffers") around the target areas in both Milwaukee, Wisconsin and Washington, DC. Address-level incident data were collected for the city of Milwaukee from the Milwaukee Police Department for the period January 2002 through February 2010. Incident data included all "Group A" offenses as classified under National Incident Based Reporting System (NIBRS). The research team classified the offenses into personal and property offenses. The offenses were aggregated into monthly counts, yielding 98 months of data (Part 1: Milwaukee, Wisconsin Data). Address-level data were also collected for Washington, DC from the Metropolitan Police Department for the time period January 2000 through September 2009. Incident data included all Part I offenses as classified under the Uniform Crime Report (UCR) system. The data were classified by researchers into personal and property offenses and aggregated by month, yielding 117 months of data (Part 2: Washington, DC Data). Part 1 contains 15 variables, while Part 2 contains a total of 27 variables. Both datasets include variables on the number of personal offenses reported per month, the number of property offenses reported per month, and the total number of incidents reported per month for each target site, buffer zone area (1000 feet or 2000 feet), and comparison site. Month and year indicators are also included in each dataset.

The dataset contains records of felony crime incidents recorded by the District of Columbia Metropolitan Police Department in 2016. Visit mpdc.dc.gov/page/data-and-statistics for more information.

The District of Columbia offers several interactive online visualizations highlighting data and information from various fields of interest such as crime statistics, public school profiles, detailed property information and more. The web visualizations in this group present data coming from agencies across the Government of the District of Columbia. Click each to read a brief introduction and to access the site. This app is embedded in https://opendata.dc.gov/pages/dashboards.

This data collection effort examined the influence of drug use on three key aspects of offenders' criminal careers in violence: participation, frequency of offending, and termination rate. A random sample of arrestees was taken from those arrested in Washington, DC, during the period July 1, 1985, to June 6, 1986. The sample was stratified to overrepresent groups other than Black males. Drug use was determined by urinalysis results at the time of arrest, as contrasted with previous studies that relied on self-reports of drug use. The research addresses the following questions: (1) Does drug use have an influence on participation in violent criminal activity? (2) Does drug use influence the frequency of violent offending? (3) Is there a difference in the types and rates of violent offending between drug-using offenders who use stimulants and those who use depressants? Variables regarding arrests include date of arrest, drug test result, charges filed, disposition date, disposition type, and sentence length imposed. Demographic variables include race, sex, birthdate, and place of birth.

V8 release notes: Adds 2017 data.V7 release notes: Removes SPSS (.sav) and Excel (.csv) files. Changes column names for clearances to "tot_clr_..." to make explicit that this is all clearances, not just adult clearances. The formatting of the monthly data has changed from wide to long. This means that each agency-month has a single row. The old data had each agency being a single row with each month-crime (e.g. jan_act_murder) being a column. Now there will just be a single column for each crime (e.g. act_murder) and the month can be identified in the month column. Adds a month column and a date column. This date column is always set to the first of the month. It is NOT the date that a crime occurred or was reported. It is only there to make it easier to create time-series graphs that require a date input. Removes all card columns. This was done to reduce file size. Reorders crime columns to the order of assaults/deaths of officers, actual crimes, total clearance, clearance under age 18, unfounded. Within each category the columns are alphabetized.Monthly data and yearly data are now in different zip folders to download.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 report

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

Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: Number of offences - provides a …Show full descriptionCrime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

The data contain records of charges filed against defendants whose cases were filed by United States attorneys in United States district court during fiscal year 2003. The data are charge-level records, and more than one charge may be filed against a single defendant. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central Charge file. The charge-level data may be linked to defendant-level data (extracted from the EOUSA Central System file) through the CS_SEQ variable, and it should be noted that some defendants may not have any charges other than the lead charge appearing on the defendant-level record. The Central Charge and Central System data contain variables from the original EOUSA files as well as additional analysis variables, or "SAF" variables, that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html