This method returns Crystal Roof’s proprietary crime rate map overlays. These overlays are taken directly from our main Crime Rates map.

The overlays are circular PNG images, available in 1,000, 1,500, or 2,000-meter radii.

You can request overlays showing either total crime rates or crime rates for a specific crime type (controlled by the variant parameter).

About Crystal Roof’s Crime Rates Map

• Crime rates are calculated for small geographic areas known as Lower Layer Super Output Areas (LSOAs).
• Rates are calculated per 1,000 residents, using population data from the 2021 Census.
• Crime levels are grouped into 10 categories using our proprietary algorithm, which balances both the distribution of crime values and the number of areas with similar rates. These categories are not standard deciles.
• All figures represent annual data (covering the most recent 12 months).
• The dataset is updated monthly, with a three-month lag between the current date and the most recent available data.

Integration examples

• Google Maps integration example
• Leaflet integration example

Thi

This dataset accompanies the study Crime Metrics in Ibiza: Alternative Models and the Impact of the Floating Population, which analyzes crime trends in Ibiza between 2019 and 2024. The dataset provides comprehensive information on crime rates, population fluctuations, and alternative methodologies for calculating crime incidence in a region characterized by strong seasonal variations. Traditional crime rates are usually calculated based on census population data, which does not account for temporary residents, tourists, or seasonal workers. To address this limitation, two alternative methodologies were applied, incorporating floating population estimates to refine crime rate calculations and provide a more accurate representation of criminal activity on the island.

The dataset is structured into multiple sheets, each containing specific variables related to crime and population estimates. It includes official census population data sourced from the Spanish National Statistics Institute (INE) and crime rates derived from these figures. Additionally, the dataset contains estimated figures for tourism accommodation, based on statistics from the Balearic Institute of Statistics (IBESTAT). Using these estimates, a floating population adjustment has been applied, which allows for a recalculated crime rate that considers the significant impact of tourism on the island’s demographics.

A second approach within the dataset estimates the population using urban waste production data, sourced from the Consell d’Eivissa. Since the amount of waste generated is closely linked to population density, this methodology provides an alternative way to estimate the real number of people present on the island at any given time. The crime rates have been recalculated accordingly, providing an additional perspective on the relationship between demographic fluctuations and crime trends.

The dataset is derived from multiple authoritative sources, including official crime statistics from the Spanish Ministry of the Interior, census population data from INE, and detailed tourism and accommodation figures from IBESTAT. The urban waste methodology is based on data provided by the Consell d’Eivissa, which records the volume of waste generated by municipalities on a yearly basis. By integrating these diverse data sources, the dataset offers a more precise and adaptable model for understanding crime dynamics in a tourism-dependent region.

The methodologies applied in this dataset highlight the importance of accounting for floating populations when analyzing crime rates. The traditional crime rate model, which only considers permanent residents, tends to overestimate crime levels in regions with large seasonal populations. The tourism-based adjustment method corrects this by incorporating official and unofficial accommodation figures, while the urban waste-based method offers an alternative approach by estimating the real-time population based on resource consumption. Both approaches reveal significant differences between conventional crime rates and adjusted figures, emphasizing the need for policymakers to adopt more refined methodologies when developing crime prevention strategies.

This dataset is released under the Creative Commons Attribution 4.0 (CC-BY 4.0) license, allowing for its free use, redistribution, and modification, provided that proper attribution is given. Researchers, policymakers, and criminologists are encouraged to use this dataset to further explore crime trends in tourism-heavy regions and to develop more accurate statistical models for crime analysis.

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

This is a development key figure, see questions and answers on kolada.se for more information. Deviation from model calculated value for reported violent crimes. The model calculation is based on various structural factors in the municipality. The model calculation is compared to the indicator indicating reported violent crimes. The data are taken from the Swedish Crime Prevention Council’s official crime statistics and highlight the crime based on the crimes reported to and handled by police, customs, prosecutors, courts and prison services. Crimes that are not reported are therefore not included in the criminal statistics. Offences that have occurred before, but were reported during the accounting year are included in the statistics, as well as offences reported in Sweden but committed abroad. To a lesser extent, there are also reported offences that in later investigations do not turn out to be a crime reported. The category of violent crime includes murder, manslaughter, child murder and ill-treatment with and without fatal outcome, rape including aggravated rape, gross violation of peace, gross violation of women’s rights, violence against officials, and robbery including aggravated robbery. The number of reported offences has then been adjusted with data from Statistics Sweden on the population of each municipality. Three-year average (year T-2 to year T).

Analysis of ‘Crime in Context, 1975-2015’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/marshallproject/crime-rates on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Is crime in America rising or falling? The answer is not as simple as politicians make it out to be because of how the FBI collects crime data from the country’s more than 18,000 police agencies. National estimates can be inconsistent and out of date, as the FBI takes months or years to piece together reports from those agencies that choose to participate in the voluntary program.

To try to fill this gap, The Marshall Project collected and analyzed more than 40 years of data on the four major crimes the FBI classifies as violent — homicide, rape, robbery and assault — in 68 police jurisdictions with populations of 250,000 or greater. We obtained 2015 reports, which have yet to be released by the FBI, directly from 61 of them. We calculated the rate of crime in each category and for all violent crime, per 100,000 residents in the jurisdiction, based on the FBI’s estimated population for that year. We used the 2014 estimated population to calculate 2015 crime rates per capita.

Acknowledgements

The crime data was acquired from the FBI Uniform Crime Reporting program's "Offenses Known and Clearances by Arrest" database for the year in question, held at the National Archives of Criminal Justice Data. The data was compiled and analyzed by Gabriel Dance, Tom Meagher, and Emily Hopkins of The Marshall Project; the analysis was published as Crime in Context on 18 August 2016.

--- Original source retains full ownership of the source dataset ---

These data tables contain estimates from the British Crime Survey (BCS) broken down by a number of demographic characteristics they can be used to calculate the rates and numbers of different crime types, as well as levels of certain perception measures also covered by the BCS.

From April 2012, responsibility for the publication of crime statistics will move from the Home Office to the Office for National Statistics (ONS).

Between 1982 and September 2024, 82 out of the 151 mass shootings in the United States were carried out by White shooters. By comparison, the perpetrator was African American in 26 mass shootings, and Latino in 12. When calculated as percentages, this amounts to 54 percent, 17 percent, and eight percent respectively. Race of mass shooters reflects the U.S. population Broadly speaking, the racial distribution of mass shootings mirrors the racial distribution of the U.S. population as a whole. While a superficial comparison of the statistics seems to suggest African American shooters are over-represented and Latino shooters underrepresented, the fact that the shooter’s race is unclear in around nine percent of cases, along with the different time frames over which these statistics are calculated, means no such conclusions should be drawn. Conversely, looking at the mass shootings in the United States by gender clearly demonstrates that the majority of mass shootings are carried out by men. Mass shootings and mental health With no clear patterns between the socio-economic or cultural background of mass shooters, increasing attention has been placed on mental health. Analysis of the factors Americans considered to be to blame for mass shootings showed 80 percent of people felt the inability of the mental health system to recognize those who pose a danger to others was a significant factor. This concern is not without merit – in over half of the mass shootings since 1982, the shooter showed prior signs of mental health issues, suggesting improved mental health services may help deal with this horrific problem. Mass shootings and guns In the wake of multiple mass shootings, critics have sought to look beyond the issues of shooter identification and their influences by focusing on their access to guns. The majority of mass shootings in the U.S. involve firearms which were obtained legally, reflecting the easy ability of Americans to purchase and carry deadly weapons in public. Gun control takes on a particular significance when the uniquely American phenomenon of school shootings is considered. The annual number of incidents involving firearms at K-12 schools in the U.S. was over 100 in each year since 2018. Conversely, similar incidents in other developed countries exceptionally rare, with only five school shootings in G7 countries other than the U.S. between 2009 and 2018.

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.

This data collection contains a revised SMSA (Standard Metropolitan Statistical Area) aggregate version of the FBI's Uniform Crime Reports (UCR) statistics gathered from 1966-1976, in which original UCR agency records are combined to produce several types of crime rates, by SMSA, for eight crimes. The data were prepared by the Hoover Institution for Economic Studies of the Criminal Justice System, at Stanford University. The data in the file are an aggregation of all relevant law enforcement reporting agencies into 291 SMSAs, and corresponding approximate aggregations of crime rates and dispositions. Each record contains crime rates for one SMSA in one specific year, with data including annual statistics of eight index crimes, i.e., murder, manslaughter, rape, robbery, assault, burglary, larceny, and motor vehicle theft. Calculations include offense-based clearance rates (the number of clearances of juvenile clearances per reported offense), clearance-based rates (the number of persons charged per offense cleared by arrest), and charge-based rates (the number of persons whose cases were disposed in a particular manner per person charged). A related study is UNIFORM CRIME REPORTS, 1966-1976 (ICPSR 7676).

This dataset includes the Crime Data by Neighbourhood. Counts are available for Assault, Auto Theft, Break and Enter, Robbery, Theft Over, Homicide and Shooting & Firearm Discharges. Data also includes the crime rate per 100,000 population calculated using the population estimates provided by Environics Analytics. Following the standard definition by StatsCan, crime rate is calculated as the crime count per 100,000 population* per year. This metric facilitates the comparisons of crime between geographic areas with different size of populations. Comparing to crime count, crime rate provides a fairer comparison of the crime over time by taking into account the change in population in the region. Note: Fields have been included for the new 158 City of Toronto Neighbourhoods structure *Population figures reflect only the resident population of a region. The temporary population such as the commuters and business patrons are not included.

Context

Crime Incident Reports (August 2015 - July 2020) by Boston Police Department

Content

Crime incident reports are provided by Boston Police Department (BPD) to document the initial details surrounding an incident to which BPD officers respond. This is a dataset containing records from the new crime incident report system, which includes a reduced set of fields focused on capturing the type of incident as well as when and where it occurred. Records in the new system begin in June of 2015.

Acknowledgements

The dataset is maintained by Analyze Boston and Boston Police Department - https://data.boston.gov/dataset/crime-incident-reports-august-2015-to-date-source-new-system.

Inspiration

Geospatial clustering of the locations to identify, which area has higher crime rates. Calculating the trend of the crime across the years and identifying crimes which are more common. Exploring if variables like Time of the Day, Day of the Week, Weather conditions affect crime rates.

Violent and property crime rates per 100,000 population for San Mateo County and the State of California. The total crimes used to calculate the rates for San Mateo County include data from: Sheriff's Department Unincorporated, Atherton, Belmont, Brisbane, Broadmoor, Burlingame, Colma, Daly City, East Palo Alto, Foster City, Half Moon Bay, Hillsborough, Menlo Park, Millbrae, Pacifica, Redwood City, San Bruno, San Carlos, San Mateo, South San Francisco, Bay Area DPR, BART, Union Pacific Railroad, and CA Highway Patrol.

The Uniform Crime Reporting (UCR) Program has been the starting place for law enforcement executives, students of criminal justice, researchers, members of the media, and the public at large seeking information on crime in the nation. Part I categorizes incidents in two categories: violent and property crimes. Aggravated assault, forcible rape, murder, and robbery are classified as violent crime, while burglary, larceny-theft, and motor vehicle theft are classified as property crimes. This dataset contains FBI Uniform Crime Reporting (UCR) Part I crime data for the last 40 years in Greensboro, North Carolina. The crime rate or index is calculated on a per 100,000 resident basis.A crime rate describes the number of crimes reported to law enforcement agencies per 100,000 residents. A crime rate is calculated by dividing the number of reported crimes by the total population; the result is multiplied by 100,000. For example, in 2013 there were 496 robberies in Greensboro and the population was 268,176 according to the SBI estimate. This equals a robbery crime rate of 185 per 100,000 general population.496/268,176 = 0.00184953165085615 x 100,000 = 184.95The Greensboro Police Department is comprised of 787 sworn and non-sworn employees dedicated to the mission of partnering to fight crime for a safer Greensboro. We believe that effectively fighting crime requires everyone's effort. With your assistance, we can make our city safer. Wondering what you can do?Take reasonable steps to prevent being victimized. Lock your car and home doors. Be aware of your surroundings. If something or someonefeels out of the ordinary, go to a safe place.Be additional eyes and ears for us. Report suspicious or unusual activity, and provide tips through Crime Stoppers that can help solve crime.Look out for your neighbors. Strong communities with active Neighborhood Watch programs are not attractive to criminals. By taking care of the people around you, you can create safe places to live and work.Get involved! If you have children, teach them how to react to bullying, what the dangers of texting and driving are, and how to safely use the Internet. Talk with your older relatives about scams that target senior citizens.Learn more about GPD. Ride along with us. Participate in the Police Citizens' Academy. Volunteer, apply for an internship, or better yet join us.You may have heard about our philosophy of neighborhood-oriented policing. This is practice in policing that combines data-driven crime analysis with police/citizen partnerships to solve problems.In the spirit of partnership with the community, our goal is to make the Greensboro Police Department as accessible as possible to the people we serve. Policies and procedures, referred to as directives, are rules that all Greensboro Police Department employees must follow in carrying out the mission of the department. We will update the public copy of the directives in a timely manner to remain consistent with new policy and procedure updates.

data culled from http://www.disastercenter.com/crime/uscrime.htm accessed October 28 2014. These are rate per 100,000 people, so not technically per capita, but the trend lines will be identical in a strict per capita calculation (the scale for the y axis merely changes)

This method returns historical annual crime rates (total and by crime type) for the years starting 2011. The results are determined by the inclusion of the submitted postcode/coordinates/UPRN within the corresponding LSOA, LAD or region.

The crime rates are calculated per 1,000 resident population derived from the census 2011 and 2021.

The dataset is updated annually.

Dataset showing reported crime counts and rates by offense category for Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington counties. Crime rates are calculated using Census estimates of each county's resident population.

In the 2023/24 reporting year, West Yorkshire Police reported a crime rate of 121.7 crimes per 1,000 population, the highest crime rate among the provided police force areas whose territories include large cities. Greater Manchester Police reported a crime rate of 117.7 crimes per 1,000 population, and had the second-highest crime rate during this year.

Number of personal violent and property crimes in Pierce County.

Only specific crimes are highlighted in the crime rates presented here. These numbers represent total numbers of reported crimes in each category (not arrests which may occur over a prolonged period).

The following categories represent the personal violent crimes considered in this data: Murder, Manslaughter, Forcible Sex, Assault, Kidnapping/Abduction, Human Trafficking, and Robbery.

The following categories represent the property crimes considered in this data: Burglary, Theft, Arson, and Destruction of Property.

Each set of crimes is totaled, then the rate per 1,000 people is calculated using the total # of crimes and the current population of each jurisdiction per year as provided in the same report.

This is a voluntary program and as such, some law enforcement agencies do not participate or have only recently participated, which is also reflected in this table.

This dataset includes the Crime Data by Neighbourhood. Counts are available for Assault, Auto Theft, Break and Enter, Robbery, Theft Over, Homicide and Shooting & Firearm Discharges. Data also includes the crime rate per 100,000 population calculated using the population estimates provided by Environics Analytics. Following the standard definition by StatsCan, crime rate is calculated as the crime count per 100,000 population* per year. This metric facilitates the comparisons of crime between geographic areas with different size of populations. Comparing to crime count, crime rate provides a fairer comparison of the crime over time by taking into account the change in population in the region. Note: Fields have been included for the new 158 City of Toronto Neighbourhoods structure *Population figures reflect only the resident population of a region. The temporary population such as the commuters and business patrons are not included.

Clearance rates for FBI index crimes in 2017, 2020, and 2021 in the San Diego region. For each category, clearance rates are calculated by dividing the number of crimes solved by the total amount of crimes committed.