This study explores the relationship between crime and neighborhood deterioration in eight neighborhoods in Chicago. The neighborhoods were selected on the basis of slowly or rapidly appreciating real estate values, stable or changing racial composition, and high or low crime rates. These data provide the results of a telephone survey administered to approximately 400 heads of households in each study neighborhood, a total of 3,310 completed interviews. The survey was designed to measure victimization experience, fear and perceptions of crime, protective measures taken, attitudes toward neighborhood quality and resources, attitudes toward the neighborhood as an investment, and density of community involvement. Each record includes appearance ratings for the block of the respondent's residence and aggregate figures on personal and property victimization for that city block. The aggregate appearance ratings were compiled from windshield surveys taken by trained personnel of the National Opinion Research Center. The criminal victimization figures came from Chicago City Police files.

The Department of Justice launched Operation Weed and Seed in 1991 as a means of mobilizing a large and varied array of resources in a comprehensive, coordinated effort to control crime and drug problems and improve the quality of life in targeted high-crime neighborhoods. In the long term, Weed and Seed programs are intended to reduce levels of crime, violence, drug trafficking, and fear of crime, and to create new jobs, improve housing, enhance the quality of neighborhood life, and reduce alcohol and drug use. This baseline data collection effort is the initial step toward assessing the achievement of the long-term objectives. The evaluation was conducted using a quasi-experimental design, matching households in comparison neighborhoods with the Weed and Seed target neighborhoods. Comparison neighborhoods were chosen to match Weed and Seed target neighborhoods on the basis of crime rates, population demographics, housing characteristics, and size and density. Neighborhoods in eight sites were selected: Akron, OH, Bradenton (North Manatee), FL, Hartford, CT, Las Vegas, NV, Pittsburgh, PA, Salt Lake City, UT, Seattle, WA, and Shreveport, LA. The "neighborhood" in Hartford, CT, was actually a public housing development, which is part of the reason for the smaller number of interviews at this site. Baseline data collection tasks included the completion of in-person surveys with residents in the target and matched comparison neighborhoods, and the provision of guidance to the sites in the collection of important process data on a routine uniform basis. The survey questions can be broadly divided into these areas: (1) respondent demographics, (2) household size and income, (3) perceptions of the neighborhood, and (4) perceptions of city services. Questions addressed in the course of gathering the baseline data include: Are the target and comparison areas sufficiently well-matched that analytic contrasts between the areas over time are valid? Is there evidence that the survey measures are accurate and valid measures of the dependent variables of interest -- fear of crime, victimization, etc.? Are the sample sizes and response rates sufficient to provide ample statistical power for later analyses? Variables cover respondents' perceptions of the neighborhood, safety and observed security measures, police effectiveness, and city services, as well as their ratings of neighborhood crime, disorder, and other problems. Other items included respondents' experiences with victimization, calls/contacts with police and satisfaction with police response, and involvement in community meetings and events. Demographic information on respondents includes year of birth, gender, ethnicity, household income, and employment status.

Ranking of each LGA in NSW based on the rate for selected offences. Rankings are available for the most recent 5 years of data. Ranking of each LGA in NSW based on the rate for selected offences. Rankings are available for the most recent 5 years of data.

The purpose of the study was to evaluate the effects of Neighborhood Watch signs on perceived crime rates, likelihood of victimization, community safety, and estimates of home and community quality. Part 1 (Study One Data) assessed the causal impact of Neighborhood Watch sign presence and content on perceptions of the community. Three Neighborhood Watch signs were incorporated into a series of slide show presentations. The signs utilized the traditional orange and white color scheme with black text and were used to represent an injunctive norm alone, a low descriptive norm for crime, or a high descriptive norm for crime. Digital color images of a for-sale home and the surrounding neighborhood of a middle class community in North San Diego County were shown to 180 undergraduates recruited from the Psychology Department's Human Participant Pool, and from other lower division general education courses at California State University, San Marcos, between July and November of 2005. Three of the slide shows were designated as Neighborhood Watch communities with one of the three sign types posted, and the fourth slide show served as a control with no posted crime prevention signs. Each slide show consisted of 20 images of the home and community, along with four instruction slides. Part 2 (Study Two Data) replicated the basic effect from Study 1 and extended the research to examine the moderating role of community social economic status (SES) on the effects of the Neighborhood Watch signs. Participants were 547 undergraduate students recruited from the Psychology Department's Human Participant Pool, and from other lower division general education courses at California State University and Palomar Community College in San Marcos, between January and September 2006. A total of 12 slide shows were utilized in Study Two, such that each of the four sign conditions from Study One was represented across each of the three communities (Low, Middle, and High SES). Part 3 (Study Three Data) examined the potential for the physical condition of the Neighborhood Watch signs posted in the community to convey normative information about the presence and acceptance of crime in the community. Participants were 364 undergraduate students recruited from the Psychology Department's Human Participant Pool, and from other lower division general education courses at California State University and Palomar Community College in San Marcos, between October 2006 and March 2007. Study Three used the same generic (Injunctive Norm, Program Only) sign that was utilized in Studies One and Two. However, three variations (new, aged, and defaced) of the sign were used. The surveys used for Study One, Study Two, and Study Three, were identical. The data include variables on perceived crime rates, perceived likelihood of victimization, perceived community safety, community ratings, self-protective behavior, burglar's perspective, manipulation check, and demographics of the respondent.

Assessment of the safety of both person and property from actions or events that cause damage, harm or impede access to needed resources. This component is measured using population standardized valuations of population density in flood hazard zones, number of severe thunderstorms and tornados, number of tropical storms and hurricanes, property crime rates, and violent crime rates.

Higher scores indicate greater community well-being. The scores are grouped into high, medium, and low classes using a 3-class Natural Jenks scheme. This classification scheme was applied to the scores of all the counties across all years for safety indicator. The analysis provides insight into the individual counties' well-being, comparisons among counties and comparisons across time.

Recorded crime figures for CSP areas. Number of offences for the last two years, percentage change, and rates per 1,000 population for the latest year.

This method returns total crime rates, crime rates by crime types, area ratings by total crime, and area ratings by crime type for small areas (Lower Layer Super Output Areas, or LSOAs) and Local Authority Districts (LADs). The results are determined by the inclusion of the submitted postcode/coordinates/UPRN within the corresponding LSOA or LAD.

All figures are annual (for the last 12 months).

The crime rates are calculated per 1,000 resident population derived from the census 2021.

The dataset is updated on a monthly basis, with a 3-month lag between the current date and the most recent data.

Data is no longer provided by the Calgary Police Service. To access latest data click here. This data is considered cumulative as late-reported incidents are often received well after an offence has occurred. Therefore, crime counts are subject to change as they are updated. Crime count is based on the most serious violation (MSV) per incident. Violence: These figures include all violent crime offences as defined by the Centre for Canadian Justice Statistics Universal Crime Reporting (UCR) rules. Domestic violence is excluded. Break and Enter: Residential B&E includes both House and ‘Other’ structure break and enters due to the predominantly residential nature of this type of break in (e.g. detached garages, sheds). B&Es incidents include attempts.

The survey charted Finnish opinion and experiences on policing, public safety and security, victimisation, and services in the neighbourhood. First, the respondents were asked whether certain factors diminishing the attractiveness of the neighbourhood (e.g. drug dealing, vandalism, drinking in public places) occurred in the area. They were asked how often they walk outside in the population centre of their municipality in the evenings or at night, how safe they felt walking outside in their neighbourhood or in the population centre of the municipality late at night during weekend, and how safe they felt alone at home after dark. Views were probed on how serious a threat crime was in the neighbourhood. Experience of crime was investigated by asking whether the respondents had been victims of certain crimes (e.g. actual or attempted car or vehicle theft, housebreaking, other type of theft, violent robbery, threat of violence at work, domestic violence, rape) during the past three years, and where this had happened. Fear of crime was charted by asking how worried the respondents were about particular crimes (e.g. housebreaking, fire, traffic accident, rape or sexual harassment, being offered drugs, being threatened with violence) happening to them. Experiences of the police were studied by asking whether the respondents had had contact with the police as crime victims, witnesses or suspects, while getting a driving licence or a new passport, or during traffic control. The respondents rated with a scale of 4-10 how well the police performed in its tasks (crime prevention, traffic control, handling domestic violence situations, solving crimes, etc.). They also rated how important various police services and tasks were. Worry over certain things happening to them or in general in the future (e.g. loneliness, financial difficulties, illness, cuts in health or police services, exclusion, international terrorism) was charted. Views on whether certain measures would increase the safety of the neighbourhood were studied. The respondents rated the services of the municipality, including care of persons with drug or alcohol problems, child care, youth services, road maintenance, environmental protection, etc. General trust in people and satisfaction with the neighbourhood were charted. Background variables included the respondent's gender, year of birth, education, employment status, type of accommodation, housing tenure, type of neighbourhood, number of children aged under 18 living at home, jurisdictional district, and province and region of residence.

ObjectiveGiven the limited information about how neighborhood environment relates to physical activity (PA) in Hispanic families, this work examined cross-sectional associations between perceived neighborhood environment and PA of Hispanic parents and children.MethodsParticipants were 137 Hispanic parent-child dyads (children aged 6–11 years) in South Phoenix, AZ, USA. Parents completed a survey about their own and their child's PA, and perceptions of neighborhood environment (i.e., scores of walking/cycling, neighborhood aesthetics, traffic safety, and crime rate) using NEWS survey. Participants also wore an accelerometer for 7 days.ResultsChildren engaged in 60 min of moderate-to-vigorous PA (MVPA) on 2.3, and parents in 30 min of MVPA on 2.1 days per weeks. Additionally, children engaged in 104.4 min, and parents in 65.3 min of accelerometer-assessed MVPA per day. Participants rated their neighborhood (range 0–4) as favorable regarding walking/cycling (mean score 3.1), aesthetics (2.4), traffic safety (2.5), and crime rate (3.1). In Spearman correlation analyses, better neighborhood aesthetics was associated with higher accelerometer-assessed MVPA in children (r = 0.25, p = 0.04). Multiple linear regression analyses revealed an association between traffic safety and parent-reported MVPA in children (standardized beta coefficient 0.19, p = 0.03). No further associations between scores of neighborhood environment and physical activity in either children or parents were observed.ConclusionOur findings may underscore the importance of neighborhood aesthetics and traffic safety for PA engagement in children. Longitudinal studies are needed to confirm our observations, and to untangle potential mechanisms linking neighborhood environment and PA in understudied populations such as Hispanics.

City Planning Staff built a GIS model to help prioritize locations in the city for safety improvements based on severity of crashes and need of the surrounding community. Essentially, the model aggregated crash data points into street segments, weights the crashes by severity, and factors other context, like proximity to schools, senior housing, buses, into a final score.Both the final index score and the various aggregate crash numbers are useful for understanding safety conditions on Cleveland's streets and identifying the worst problem intersections.MethodologyBreak up Cleveland's street network in comparable segments including intersections.Aggregate crash data within those segments (sum crashes by severity type)Score the crash history for the segment (Crash_Score_Total)Identify context points of interest in vicinity (schools, senior centers, bus stops so far) of the street.Score the context for how many things are nearby that require extra proactive attention.Identify the social health and vulnerability of the street segment using CDC's Social Vulnerability Index and how it ranks within ClevelandCombine the crash score and context score.Boost scores based on social vulnerability, e.g. elevate streets in neighborhoods experiencing more poverty, racial discrimination, housing and transportation challenges.Data GlossaryFor all crashes: Click here, then click on "Fields" to view documentation.For bike and pedestrian classes: Click here, then click on "Fields" to view documentation.Update FrequencyNeverContactsCleveland City Planning Commission

The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

Crime severity index (violent, non-violent, youth) and weighted clearance rates (violent, non-violent), Canada, provinces, territories and Census Metropolitan Areas, 1998 to 2024.

The Vision Zero Neighborhood Survey Web Map showcases detailed roadway safety feedback from Miami-Dade residents, categorized by Driving Behavior, Infrastructure Needs, Non-Motorized Options, Visibility, and Law Enforcement Concerns. This map visualizes residents’ ratings on their perceived safety while walking, biking, using transit, and driving in their neighborhoods, along with their suggested safety improvements and preferred tools for implementation

Please see data table https://data.tempe.gov/datasets/tempegov::police-sentiment-survey-detail-1/about for continued data updates. This table was deprecated 11/3/2022.-----------------------------------------------This data supports the 1.05 Feeling of Safety in Your Neighborhood and 2.06 Police Trust Score performance measures.This data is the result of a community survey of approximately 500 residents collected electronically and monthly by Elucd on behalf of Tempe Police Department. The scores are provided to TPD monthly in PDF form, and are then transferred to Excel for Open Data. The trust score is a 0 to 100 measure, and is a combination of two questions: How much do you agree with this statement? The police in my neighborhood treat people with respect. How much do you agree with this statement? The police in my neighborhood listen to and take into account the concerns of local residents.The safety score is a 0 to 100 measure, and scores residents' feelings of safety in their neighborhood.The performance measure pages are available at 1.05 Feeling of Safety in Your Neighborhood and 2.06 Police Trust Score.Additional InformationSource: ElucdContact (author): Carlena OroscoContact E-Mail (author): Carlena_Orosco@tempe.gov Contact (maintainer): Carlena OroscoContact E-Mail (maintainer): Carlena_Orosco@tempe.gov Data Source Type: ExcelPreparation Method: This data is from a citizen survey collected monthly by Elucd and provided in Excel for publication.Publish Frequency: MonthlyPublish Method: ManualData Dictionary

Police recorded crime figures by Police Force Area and Community Safety Partnership areas (which equate in the majority of instances, to local authorities).

Toronto Neighbourhoods Boundary File includes Crime Data by Neighbourhood. Counts are available at the offence and/or victim level for Assault, Auto Theft, Bike Theft, Break and Enter, Robbery, Theft Over, Homicide, Shootings and Theft from Motor Vehicle. Data also includes crime rates per 100,000 people by neighbourhood based on each year's Projected Population by Environics Analytics.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario..In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

This statistic shows the crime severity index value of metropolitan areas in Canada in 2023. As of 2023, the crime severity index in Saskatoon, Saskatchewan, stood at 116.31.

This data supports the 1.05 Feeling of Safety in Your Neighborhood and 2.06 Police Trust Score performance measures.This data is the result of a community survey of approximately 500 residents collected electronically and monthly by Zencity on behalf of Tempe Police Department. The scores are provided to TPD monthly in PDF form, and are then transferred to Excel for Open Data. The trust score is a 0 to 100 measure, and is a combination of two questions: How much do you agree with this statement? Trust-Respect: The police in my neighborhood treat people with respect. How much do you agree with this statement? Trust-Listen: The police in my neighborhood listen to and take into account the concerns of local residents.The safety score is a 0 to 100 measure, and scores residents' feelings of safety in their neighborhood.The performance measure pages are available at 1.05 Feeling of Safety in Your Neighborhood and 2.06 Police Trust Score.Additional InformationSource: ZencityContact (author): Carlena OroscoContact E-Mail (author): Carlena_Orosco@tempe.gov Contact (maintainer): Carlena OroscoContact E-Mail (maintainer): Carlena_Orosco@tempe.gov Data Source Type: Zencity REST APIPreparation Method: This data is from a citizen survey collected monthly by Zencity and provided in an automated survey feed to the City of Tempe.Publish Frequency: MonthlyPublish Method: Zencity REST API Automated Survey Feed Updates ArcGIS Online feature layer.Data Dictionary

Ranking of the 694 datazones within Glasgow throughout 2012 on indices of community safety to form the Community Safety Index of Priority. Lower values of the Community Safety Index or component indices indicate the least safe datazones throughout the city. The 20 indices are rankings which cover alcohol and drugs, anti-social behaviour, vandalism, fire, accidents, assaults and theft as described in the data dictionary. The Intermediate Geography names are also included. , datazones nest directly into intermediate geographies and local authorities but do not fit exactly into higher geographies like multi-member wards. Data supplied 2014-02-14 by Community Safety Glasgow Licence: None csg-community-safety-index-of-priority.xlsx - https://dataservices.open.glasgow.gov.uk/Download/Organisation/408de737-97bd-4cf2-a0a8-850fded6b427/Dataset/8b132419-4ee0-464d-97cc-f5dc91e8a87b/File/ebb8a6e8-6281-4237-b128-ac64244c5bf4/Version/46efc4de-ce5c-49a0-bc79-7642db23e777