This map shows a comparable measure of crime in the United States. The crime index compares the average local crime level to that of the United States as a whole. An index of 100 is average. A crime index of 120 indicates that crime in that area is 20 percent above the national average.The crime data is provided by Applied Geographic Solutions, Inc. (AGS). AGS created models using the FBI Uniform Crime Report databases as the primary data source and using an initial range of about 65 socio-economic characteristics taken from the 2000 Census and AGS’ current year estimates. The crimes included in the models include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. The total crime index incorporates all crimes and provides a useful measure of the relative “overall” crime rate in an area. However, these are unweighted indexes, meaning that a murder is weighted no more heavily than a purse snatching in the computations. The geography depicts states, counties, Census tracts and Census block groups. An urban/rural "mask" layer helps you identify crime patterns in rural and urban settings. The Census tracts and block groups help identify neighborhood-level variation in the crime data.------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

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.

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. Researchers compiled datasets on prison admissions and releases that would be comparable across places and geocoded and mapped those data onto crime rates across those same places. The data used were panel data. The data were quarterly or annual data, depending on the location, from a mix of urban (Boston, Newark and Trenton) and rural communities in New Jersey covering various years between 2000 and 2010. The crime, release, and admission data were individual level data that were then aggregated from the individual incident level to the census tract level by quarter (in Boston and Newark) or year (in Trenton). The analyses centered on the effects of rates of prison removals and returns on rates of crime in communities (defined as census tracts) in the cities of Boston, Massachusetts, Newark, New Jersey, and Trenton, New Jersey, and across rural municipalities in New Jersey. There are 4 Stata data files. The Boston data file has 6,862 cases, and 44 variables. The Newark data file has 1,440 cases, and 45 variables. The Trenton data file has 66 cases, and 32 variables. The New Jersey Rural data file has 1,170 cases, and 32 variables.

The Boston Housing Dataset

The Boston Housing Dataset is a derived from information collected by the U.S. Census Service concerning housing in the area of Boston MA. The following describes the dataset columns: 1. CRIM- per capita crime rate by town 2. ZN- proportion of residential land zoned for lots over 25,000 sq.ft. 3. INDUS- proportion of non-retail business acres per town. 4. CHAS- Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5. NOX- nitric oxides concentration (parts per 10 million) 6. RM- average number of rooms per dwelling 7. AGE- proportion of owner-occupied units built prior to 1940 8. DIS- weighted distances to five Boston employment centres 9. RAD- index of accessibility to radial highways 10. TAX- full-value property-tax rate per $10,000 11. PTRATIO- pupil-teacher ratio by town 12. B- 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13. LSTAT- % lower status of the population 14. MED- Median value of owner-occupied homes in **1000's

The Domestic Violence Research Project was a pilot study designed to examine the dynamics of domestic violence within two of the ten police districts that comprise the city of Boston. The objectives were to collect data on domestic violence in greater detail than previously possible, conduct various analyses on this information, and determine how the findings could best be used to improve the police, prosecutorial, and social service responses to domestic violence. Data for 1993 are a stratified random sample of reported domestic violence incidents occurring throughout the year. The sample represents approximately 27 percent of the domestic violence incidents reported in 1993 for the two districts studied, B3 and D4. The 1994 data include all reported incidents occurring in the two districts during the period May to July. After the incident selection process was completed, data were collected from police incident reports, follow-up investigation reports, criminal history reports, and court dockets. Variables include arrest offenses, time of incident, location of incident, witnesses (including children), nature and extent of injuries, drug and alcohol use, history of similar incidents, whether there were restraining orders in effect, and basic demographic information on victims and offenders. Criminal history information was coded into five distinct categories: (1) violent offenses, (2) nonviolent offenses, (3) domestic violence offenses, (4) drug/alcohol offenses, and (5) firearms offenses.

Characteristics:

• Number of Instances: 506

• Number of Attributes: 13 numeric/categorical predictive. The Median Value (attribute 14) is the target.

• Attribute Information (in order): 1. CRIM per capita crime rate by town 2. ZN proportion of residential land zoned for lots over 25,000 sq.ft. 3. INDUS proportion of non-retail business acres per town 4. CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) 5. NOX nitric oxides concentration (parts per 10 million) 6. RM average number of rooms per dwelling 7. AGE proportion of owner-occupied units built prior to 1940 8. DIS weighted distances to five Boston employment centres 9. RAD index of accessibility to radial highways 10. TAX full-value property-tax rate per $10,000 11. PTRATIO pupil-teacher ratio by town 12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13. LSTAT % lower status of the population 14. PRICE Median value of owner-occupied homes in $1000's

• Missing Attribute Values: None

• Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset. This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. You can find the original research paper here.

This study surveyed immigrant and non-immigrant populations residing in high Latino population communities in order to: Assess the nature and pattern of bias motivated victimization. Explore the co-occurrence of bias motivated victimization with other forms of victimization. Measure reporting and help-seeking behaviors of individuals who experience bias motivated victimization. Identify cultural factors which may contribute to the risk of bias victimization. Evaluate the effect of bias victimization on negative psychosocial outcomes relative to other forms of victimization. The study's sample was a community sample of 910 respondents which included male and female Latino adults across three metropolitan areas within the conterminous United States. These respondents completed the survey in one of two ways. One set of respondents completed the survey on a tablet with the help of the research team, while the other group self-administered the survey on their own mobile device. The method used to complete the survey was randomly selected. A third option (paper and pencil with an administrator) was initially included but was removed early in the survey's deployment. The survey was administered from May 2018 to March 2019 in the respondent's preferred language (English or Spanish). This collection contains 1,620 variables, and includes derived variables for several scales used in the questionnaire. Bias victimization measures considered both hate crimes (e.g. physical assault) and non-criminal bias events (e.g. racial slurs) and allowed the respondent to report multiple incidents, perpetrators, and types of bias victimization. The respondents were asked about their help-seeking and reporting behaviors for the experience of bias victimization they considered to be the most severe and the measures considered both formal (e.g. contacting the police) and informal (e.g. communicating with family) help-seeking behaviors. The victimization scale measured exposure to traumatic events (e.g. witnessing a murder) as well as experiences of victimization (e.g. physical assault). Acculturation and enculturation scales measured topics such as the respondent's use of Spanish and English and their consumption of media in both languages. The variables pertaining to acculturative stress considered factors such as feelings of social isolation, experiences of racism, and conflict with family members. The variables for mental health outcomes measured symptoms of anger, anxiety, depression, and disassociation.

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. Within the past fifteen years, policymakers across the country have increasingly supported criminal justice reforms designed to reduce the scope of mass incarceration in favor of less costly, more evidence-based approaches to preventing and responding to crime. One of the primary reform efforts is the Justice Reinvestment Initiative (JRI), a public-private partnership through which state governments work to diagnose the primary drivers of their state incarceration rates, reform their sentencing policies to send fewer nonviolent offenders to prison, and reinvest the saved money that used to go into prisons into alternatives to incarceration, instead. This mixed-methods study sought to assess public opinion about the justice reinvestment paradigm of reform and to determine whether exposure to racialized and race-neutral cues affects people's willingness to allocate money into criminal justice institutions versus community-based social services in order to reduce and prevent crime.