The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_60 _100.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_100 _250.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_250 _plus.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'total_crime': total crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'total_violent _crime': total violent crime - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft - 'tot_prop _crime': total property crime - 'arson': arson

Photo by David von Diemar on Unsplash

This data set is no longer compiled by the Ministry of the Solicitor General. Property crimes are typically non-violent in nature and include: * breaking and entering * motor vehicle theft * theft over $5,000 (non-motor vehicle) * theft under $5,000 (non-motor vehicle) * mischief The data can be accessed from Statistics Canada.

In 2023, there were 906,176 cases of property crimes reported in California - the highest in the country. Texas, New York, Florida, and Washington rounded out the top five states in the U.S. for property crimes in that year.

In 2023, the violent crime rate in the United States was 363.8 cases per 100,000 of the population. Even though the violent crime rate has been decreasing since 1990, the United States tops the ranking of countries with the most prisoners. In addition, due to the FBI's transition to a new crime reporting system in which law enforcement agencies voluntarily submit crime reports, data may not accurately reflect the total number of crimes committed in recent years. Reported violent crime rate in the United States The United States Federal Bureau of Investigation tracks the rate of reported violent crimes per 100,000 U.S. inhabitants. In the timeline above, rates are shown starting in 1990. The rate of reported violent crime has fallen since a high of 758.20 reported crimes in 1991 to a low of 363.6 reported violent crimes in 2014. In 2023, there were around 1.22 million violent crimes reported to the FBI in the United States. This number can be compared to the total number of property crimes, roughly 6.41 million that year. Of violent crimes in 2023, aggravated assaults were the most common offenses in the United States, while homicide offenses were the least common. Law enforcement officers and crime clearance Though the violent crime rate was down in 2013, the number of law enforcement officers also fell. Between 2005 and 2009, the number of law enforcement officers in the United States rose from around 673,100 to 708,800. However, since 2009, the number of officers fell to a low of 626,900 officers in 2013. The number of law enforcement officers has since grown, reaching 720,652 in 2023. In 2023, the crime clearance rate in the U.S. was highest for murder and non-negligent manslaughter charges, with around 57.8 percent of murders being solved by investigators and a suspect being charged with the crime. Additionally, roughly 46.1 percent of aggravated assaults were cleared in that year. A statistics report on violent crime in the U.S. can be found here.

The property crime rate measures the number of Part 1 crimes identified as being property-based (burglary and auto theft) that are reported to the Police Department. These incidents are per 1,000 residents in the neighborhood to allow for comparison across areas. Source: Baltimore Police Department Years Availabile: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

The data are provided are the Maryland Statistical Analysis Center (MSAC), within the Governor's Office of Crime Control and Prevention (GOCCP). MSAC, in turn, receives these data from the FBI's annual Uniform Crime Reports.

This project was designed to isolate the effects that individual crimes have on wage rates and housing prices, as gauged by individuals' and households' decisionmaking preferences changing over time. Additionally, this project sought to compute a dollar value that individuals would bear in their wages and housing costs to reduce the rates of specific crimes. The study used multiple decades of information obtained from counties across the United States to create a panel dataset. This approach was designed to compensate for the problem of collinearity by tracking how housing and occupation choices within particular locations changed over the decade considering all amenities or disamenities, including specific crime rates. Census data were obtained for this project from the Integrated Public Use Microdata Series (IPUMS) constructed by Ruggles and Sobek (1997). Crime data were obtained from the Federal Bureau of Investigation's Uniform Crime Reports (UCR). Other data were collected from the American Chamber of Commerce Researchers Association, County and City Data Book, National Oceanic and Atmospheric Administration, and Environmental Protection Agency. Independent variables for the Wages Data (Part 1) include years of education, school enrollment, sex, ability to speak English well, race, veteran status, employment status, and occupation and industry. Independent variables for the Housing Data (Part 2) include number of bedrooms, number of other rooms, building age, whether unit was a condominium or detached single-family house, acreage, and whether the unit had a kitchen, plumbing, public sewers, and water service. Both files include the following variables as separating factors: census geographic division, cost-of-living index, percentage unemployed, percentage vacant housing, labor force employed in manufacturing, living near a coastline, living or working in the central city, per capita local taxes, per capita intergovernmental revenue, per capita property taxes, population density, and commute time to work. Lastly, the following variables measured amenities or disamenities: average precipitation, temperature, windspeed, sunshine, humidity, teacher-pupil ratio, number of Superfund sites, total suspended particulate in air, and rates of murder, rape, robbery, aggravated assault, burglary, larceny, auto theft, violent crimes, and property crimes.

Comprehensive crime statistics for Orange County including homicides, violent crime, property crime, and city-by-city breakdowns with five-year trend analysis.

In undertaking this data collection, the principal investigators sought to determine (1) whether police enforcement against drug crimes, specifically heroin crimes, had any influence on the rates of nondrug crimes, and (2) what effect intensive law enforcement programs against drug dealers had on residents where those programs were operating. To achieve these objectives, data on crime rates for seven successive years were collected from police records of 30 cities in Massachusetts. Data were collected for the following offenses: murder, rape, robbery, assault, larceny, and automobile theft. The investigators also interviewed a sample of residents from 3 of those 30 cities. Residents were queried about their opinions of the most serious problem facing people today, their degree of concern about being victims of crime, and their opinions of the effectiveness of law enforcement agencies in handling drug problems.

Property Crime Rate In the San Diego Region From 1980 - 2023.

This statistic shows the rate of property crimes in Canada in 2023, by metro area. There were roughly 4,362.09 reported property crimes per 100,000 residents in Canada's Edmonton, Alberta metropolitan area in 2023.

This dataset provides comprehensive data on violent and property crimes in Maryland counties from 1975 to the present. It includes various crime metrics such as murder, rape, robbery, aggravated assault, burglary, larceny theft, motor vehicle theft, and more.

Columns Information

JURISDICTION

• Description: County name in Maryland.
• Data Type: String

YEAR

• Description: Year of the data.
• Data Type: Integer

POPULATION

• Description: Population of the county.
• Data Type: Integer

MURDER

• Description: Number of murders.
• Data Type: Integer

RAPE

• Description: Number of rapes.
• Data Type: Integer

ROBBERY

• Description: Number of robberies.
• Data Type: Integer

AGG. ASSAULT

• Description: Number of aggravated assaults.
• Data Type: Integer

B & E

• Description: Number of burglaries (breaking and entering).
• Data Type: Integer

LARCENY THEFT

• Description: Number of larceny thefts.
• Data Type: Integer

M/V THEFT

• Description: Number of motor vehicle thefts.
• Data Type: Integer

GRAND TOTAL

• Description: Total number of crimes.
• Data Type: Integer

PERCENT CHANGE

• Description: Percent change in total crimes from the previous year.
• Data Type: Float

VIOLENT CRIME TOTAL

• Description: Total number of violent crimes.
• Data Type: Integer

VIOLENT CRIME PERCENT

• Description: Percent of violent crimes out of total crimes.
• Data Type: Float

VIOLENT CRIME PERCENT CHANGE

• Description: Percent change in violent crimes from the previous year.
• Data Type: Float

PROPERTY CRIME TOTALS

• Description: Total number of property crimes.
• Data Type: Integer

PROPERTY CRIME PERCENT

• Description: Percent of property crimes out of total crimes.
• Data Type: Float

PROPERTY CRIME PERCENT CHANGE

• Description: Percent change in property crimes from the previous year.
• Data Type: Float

OVERALL CRIME RATE PER 100,000 PEOPLE

• Description: Crime rate per 100,000 people.
• Data Type: Float

OVERALL PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in crime rate per 100,000 people from the previous year.
• Data Type: Float

VIOLENT CRIME RATE PER 100,000 PEOPLE

• Description: Violent crime rate per 100,000 people.
• Data Type: Float

VIOLENT CRIME RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in violent crime rate per 100,000 people from the previous year.
• Data Type: Float

PROPERTY CRIME RATE PER 100,000 PEOPLE

• Description: Property crime rate per 100,000 people.
• Data Type: Float

PROPERTY CRIME RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in property crime rate per 100,000 people from the previous year.
• Data Type: Float

MURDER PER 100,000 PEOPLE

• Description: Murder rate per 100,000 people.
• Data Type: Float

RAPE PER 100,000 PEOPLE

• Description: Rape rate per 100,000 people.
• Data Type: Float

ROBBERY PER 100,000 PEOPLE

• Description: Robbery rate per 100,000 people.
• Data Type: Float

AGG. ASSAULT PER 100,000 PEOPLE

• Description: Aggravated assault rate per 100,000 people.
• Data Type: Float

B & E PER 100,000 PEOPLE

• Description: Burglary (breaking and entering) rate per 100,000 people.
• Data Type: Float

LARCENY THEFT PER 100,000 PEOPLE

• Description: Larceny theft rate per 100,000 people.
• Data Type: Float

M/V THEFT PER 100,000 PEOPLE

• Description: Motor vehicle theft rate per 100,000 people.
• Data Type: Float

MURDER RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in murder rate per 100,000 people from the previous year.
• Data Type: Float

RAPE RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in rape rate per 100,000 people from the previous year.
• Data Type: Float

ROBBERY RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in robbery rate per 100,000 people from the previous year.
• Data Type: Float

AGG. ASSAULT RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in aggravated assault rate per 100,000 people from the previous year.
• Data Type: Float

B & E RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in burglary (breaking and entering) rate per 100,000 people from the previous year.
• Data Type: Float

LARCENY THEFT RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in larceny theft rate per 100,000 people from the previous year.
• Data Type: Float

M/V THEFT RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in motor vehicle t...

Crime data assembled by census block group for the MSA from the Applied Geographic Solutions' (AGS) 1999 and 2005 'CrimeRisk' databases distributed by the Tetrad Computer Applications Inc. CrimeRisk is the result of an extensive analysis of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, CrimeRisk provides an accurate view of the relative risk of specific crime types at the block group level. Data from 1990 - 1996,1999, and 2004-2005 were used to compute the attributes, please refer to the 'Supplemental Information' section of the metadata for more details. Attributes are available for two categories of crimes, personal crimes and property crimes, along with total and personal crime indices. Attributes for personal crimes include murder, rape, robbery, and assault. Attributes for property crimes include burglary, larceny, and mother vehicle theft. 12 block groups have no attribute information. CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes included in the database are the "Part I" crimes and include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level. Part II crimes are not reported in the detail databases and are generally available only for selected areas or at high levels of geography. In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative "overall" crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values. The block group boundaries used in the dataset come from TeleAtlas's (formerly GDT) Dynamap data, and are consistent with all other block group boundaries in the BES geodatabase. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Comprehensive crime statistics for Los Angeles County including homicides, property crime, robbery, assault, and neighborhood-by-neighborhood breakdowns with five-year trend analysis.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Platte County, MO (DISCONTINUED) (FBITC029165) from 2004 to 2020 about Platte County, MO; crime; violent crime; property crime; Kansas City; MO; and USA.

The Division of Criminal Justice Services (DCJS) collects crime reports from more than 500 New York State police and sheriffs’ departments. DCJS compiles these reports as New York’s official crime statistics and submits them to the FBI under the National Uniform Crime Reporting (UCR) Program. UCR uses standard offense definitions to count crime in localities across America regardless of variations in crime laws from state to state. In New York State, law enforcement agencies use the UCR system to report their monthly crime totals to DCJS. The UCR reporting system collects information on seven crimes classified as Index offenses which are most commonly used to gauge overall crime volume. These include the violent crimes of murder/non-negligent manslaughter, forcible rape, robbery, and aggravated assault; and the property crimes of burglary, larceny, and motor vehicle theft. Firearm counts are derived from taking the number of violent crimes which involve a firearm. Population data are provided every year by the FBI, based on US Census information. Police agencies may experience reporting problems that preclude accurate or complete reporting. The counts represent only crimes reported to the police but not total crimes that occurred. DCJS posts preliminary data in the spring and final data in the fall.

This comprehensive dataset provides a deep dive into crime statistics across the United States. It encompasses a wide range of crime types, spanning from violent offenses like homicide and assault to property crimes such as theft and burglary. The data is meticulously organized, offering insights into crime trends, geographical variations, and temporal patterns.

This dataset aggregates Seattle Police Department crime statistics with spatial ZIP code boundaries and US Census data to determine the property crime rate per 1,000 residents. The following sources were used to create this dataset:

King County ZIP Code Boundaries

Source: https://data-seattlecitygis.opendata.arcgis.com/datasets/SeattleCityGIS::zip-codes/explore

King County provides approximate ZIP code boundaries, updated quarterly and published by the city of Seattle.

SPD Crime Data: 2008-Present

Source: https://data.seattle.gov/Public-Safety/SPD-Crime-Data-2008-Present/tazs-3rd5

The Seattle Police Department publishes data for reported crimes from 2008 to the present, refreshed daily. This data includes whether the crime is classified as against a person, against property, or against society.

2020 ACS 5-year estimates for population

Source: https://data.census.gov/cedsci/table?q=population%20seattle&g=0400000US53%248600000&tid=ACSDP5Y2020.DP05

The US Census Department American Community Survey (ACS) publishes 5-year estimates of population by a variety of geographies, including ZIP Code Tabulation Areas (ZCTAs), geographic approximations of each ZIP code.

Data Processing

Using the pandas and geopandas libraries within python, the following processing steps were followed to prepare this dataset: - Converted the date and time reported field in the SPD dataset to a datetime object and extracted the year - Filtered to crimes reported between 2008 and 2021 - Filtered to only crimes against property - Dropped rows with null values for year, crime against category, longitude, or latitude - Performed a spatial join using the latitude and longitude for each report in the SPD data to append a ZIP code from the King County ZIP Code boundary shapefile - Summarized to calculate a count of property crimes reported for each combination of year and ZIP code - Summarized by ZIP code to calculate the count of years with at least one crime reported and the total number of property crimes reported - Calculated the average number of property crimes reported per year in each ZIP code - Merged with the ACS population estimates - Calculated the number of property crimes reported per year per 1,000 population for each zip code

Photo by Justus Hayes: https://www.pexels.com/photo/a-bicycle-chained-to-a-metal-post-6355944/

In 2023, murder and manslaughter charges had the highest crime clearance rate in the United States, with 57.8 percent of all cases being cleared by arrest or so-called exceptional means. Motor vehicle theft cases had the lowest crime clearance rate, at 8.2 percent. What is crime clearance? Within the U.S. criminal justice system, criminal cases can be cleared (or closed) one of two ways. The first is through arrest, which means that at least one person has either been arrested, charged with an offense, or turned over to the court for prosecution. The second way a case can be closed is through what is called exceptional means, where law enforcement must have either identified the offender, gathered enough evidence to arrest, charge, and prosecute someone, identified the offender’s exact location, or come up against a circumstance outside the control of law enforcement that keeps them from arresting and prosecuting the offender. Crime in the United States Despite what many people may believe, crime in the United States has been on the decline. Particularly in regard to violent crime, the violent crime rate has almost halved since 1990, meaning that the U.S. is safer than it was almost 30 years ago. However, due to the FBI's recent transition to a new crime reporting system in which law enforcement agencies voluntarily report crime data, it is possible that figures do not accurately reflect the total amount of crime in the country.

The data are provided are the Maryland Statistical Analysis Center (MSAC), within the Governor's Office of Crime Control and Prevention (GOCCP). MSAC, in turn, receives these data from the Maryland State Police's annual Uniform Crime Reports.