Official UK police crime data by postcode, including crime types, locations, and outcomes

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.

Retirement Notice: This item is in mature support as of June 2023 and will be retired in December 2025. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.This map shows the total crime index in the U.S. in 2022 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The layer uses 2020 Census boundaries. The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indices Permitted use of this data is covered in the DATA section of the EsriMaster Agreement (E204CW) and these supplemental terms.

Visit the interactive Crime Mapping Tool and prepare your own tailored crime report showing the latest maps, graphs and data on crimes, victims and offenders in NSW LGAs, suburbs or postcodes.

*Note: prior to June 2021 there were three additional crime tools available providing data for Local Government Areas on crime trends, crimes by premises and LGA crime rankings. These tools are no longer supported; this information is available in the Crime Mapping Tool.

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

This map shows the total crime index in the U.S. in 2020 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indicesThe values are all referenced by an index value. The index values for the US level are 100, representing average crime for the country. A value of more than 100 represents higher crime than the national average, and a value of less than 100 represents lower crime than the national average. For example, an index of 120 implies that crime in the area is 20 percent higher than the US average; an index of 80 implies that crime is 20 percent lower than the US average.Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layersPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Data is broken out by year into separate CSV files. Note the file grouping by year is based on the crime's Date Reported (not the Date Occurred).Older cases found in the 2003 data are indicative of cold case research. Older cases are entered into the Police database system and tracked but dates and times of the original case are maintained.Data may also be viewed off-site in map form for just the last 6 months on communitycrimemap.comData Dictionary:

Field Name

Field Description

Incident Number

the number associated with either the incident or used as reference to store the items in our evidence rooms

Date Reported

the date the incident was reported to LMPD

Date Occurred

the date the incident actually occurred

Badge ID

Badge ID of responding Officer

Offense Classification

NIBRS Reporting category for the criminal act committed

Offense Code Name

NIBRS Reporting code for the criminal act committed

NIBRS_CODE

the code that follows the guidelines of the National Incident Based Reporting System. For more details visit https://ucr.fbi.gov/nibrs/2011/resources/nibrs-offense-codes/view

NIBRS Group

hierarchy that follows the guidelines of the FBI National Incident Based Reporting System

Was Offense Completed

Status indicating whether the incident was an attempted crime or a completed crime.

LMPD Division

the LMPD division in which the incident actually occurred

LMPD Beat

the LMPD beat in which the incident actually occurred

Location Category

the type of location in which the incident occurred (e.g. Restaurant)

Block Address

the location the incident occurred

City

the city associated to the incident block location

Zip Code

the zip code associated to the incident block location

Contact:LMPD Open Records lmpdopenrecords@louisvilleky.gov

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/

Quarterly recorded crime reports and datasets

The quarterly recorded crime reports are available from 2004 and annually from 1997. They contains statistics and graphs relating to the 62 offences BOCSAR reports on, with trends rates and ratios for LGAs and Statistical Areas.

The datasets are produced quarterly for all of NSW and broken down by LGA, postcode and suburb for the 62 offences. The data includes incident counts by month from 1995

This dataset provides detailed information on the 2019 Index of Multiple Deprivation (IMD) for Birmingham, UK. The data is available at the postcode level and includes the Lower Layer Super Output Area (LSOA) information.Data is provided at the LSOA 2011 Census geography.The decile score ranges from 1-10 with decile 1 representing the most deprived 10% of areas while decile 10 representing the least deprived 10% of areas.The IMD rank and decile score is allocated to the LSOA and all postcodes within it at the time of creation (2019).Note that some postcodes cross over LSOA boundaries. The Office for National Statistics sets boundaries for LSOAs and allocates every postcode to one LSOA only: this is the one which contains the majority of residents in that postcode area (as at 2011 Census).

The English Indices of Deprivation 2019 provide a detailed analysis of relative deprivation across small areas in England. The Crime Deprivation dataset is a key component of this index, measuring the risk of personal and material victimization at the local level. This dataset includes indicators such as recorded crimes for violence, burglary, theft, and criminal damage. It helps identify areas with high levels of crime, guiding policy interventions and resource allocation to improve safety and reduce crime rates.

Postcode-based crime statistics for crimes against the person and crimes against property.

The Crime statistics datasets contain all offences against the person and property that were reported to police in that respective financial year.

For more information please see the original spreadsheet.

Source: The data was downloaded from data.sa.gov.au and summarised by the Adelaide Data Hub to generate monthly Crime Statistics datasets at Offence Level 3. The data was spatialised using South Australian suburbs boundaries which was dissolved to reflect the postcode boundaries.

Open Postcode Geo is a postcode dataset and API optimised for geocoding applications. You can use Open Postcode Geo to geocode a dataset, geocode user input, and therefore build a proximity search.

Data is derived from the ONS (Office for National Statistics) postcode database and is free to use, subject to including attributions to ONS, OS (Ordinance Survey) and Royal Mail.

Information is also provided on a range of topics, including education, health, crime, business, etc.

Postcodes can be entered at area, district, sector, and unit level - see Postcode map for the geographical relationship between these.

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police.

The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public.

The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose.

Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria.

The Crime Statistics Agency releases quarterly and annual crime data at four geographical levels; State, Police region, Local Government Area (LGA), and Postcode.

Data Classification - http://www.crimestatistics.vic.gov.au/home/about+the+data/classifications/

Glossary and Data Dictionary - http://www.crimestatistics.vic.gov.au/home/about+the+data/data+dictionary/

Level of education, social benefits claimants and crime suspects by 4-digit postcode zone, 2011-2012 (Dutch only)

Esri's Crime Indexes data incorporates information from the AGS national CrimeRisk database that is based on an extensive analysis of several years of crime incidents reported by most US law enforcement jurisdictions. The Crime Indexes database includes standardized indexes for a range of serious crimes against both persons and property. The data vintage is 2019. All attributes are available at the following geography levels: State, County, Tract, Block Group, ZIP Code, Place, CBSA and DMA. Attributes include total crime index, personal crime index, and other indexes for serious crimes. To view ArcGIS Online items using this service, including the terms of use, visit http://goto.arcgisonline.com/demographics5/USA_Crime.

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police. \r
\r
The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public. \r
\r
The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose. \r
\r
Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria.\r
\r
The Crime Statistics Agency releases quarterly and annual crime data at four geographical levels; State, Police region, Local Government Area (LGA), and Postcode.\r
\r
Data Classification - https://www.crimestatistics.vic.gov.au/about-the-data/classifications \r
\r
Glossary and Data Dictionary - https://www.crimestatistics.vic.gov.au/about-the-data/glossary-and-data-dictionary\r

The Justice Equity Need Index (JENI), by Advancement Project California, offers a means to map out the disparate burden that criminalization and a detention-first justice model place on specific communities. The index includes the following indicators:System Involvement: The system-involved population by ZIP Code results in direct needs for justice equity, as measured by adult and youth probation. Indicators: Adult Probation (per 1,000 people); Youth Probation (per 1,000 people) Inequity Drivers: Root inequities across communities that contribute to racial and economic disparities as seen in incarceration and policing. Indicators: Black, Latinx, AIAN, and NHPI Percentages of Population (average percentile); Unemployment Rate (%); Population aged 25+ without a High School Diploma (%); Population below 200% of the Federal Poverty Level (%); Violent Crime Rate (per 1,000 people) Criminalization Risk: Conditions where the criminal justice system has historically taken a detention-first, prevention-last approach. Indicators: Mental Health Hospitalizations (per 1,000 people); Substance Use-Related Hospitalizations (per 1,000 people); Homelessness Rate (per 1,000 people) Learn more at https://www.catalystcalifornia.org/campaign-tools/maps-and-data/justice-equity-need-index.Supervisorial Districts, SPAs, and CSAs determined by ZIP Code centroid.

Dataset Description: South Australia Crime Data 2022-2023🕵️‍♂️

Overview: Dive into the depths of criminal activities with the South Australia Crime Statistics 2022-2023 dataset. This comprehensive collection encapsulates a detailed breakdown of crimes reported across various suburbs, offering insights into offences against persons and properties. Ideal for enthusiasts and practitioners of data science, criminology, and policy-making, this dataset opens up possibilities for a wide array of analyses.

What We Can Do with the Data: - Perform trend analysis to understand crime patterns over time. - Conduct geographical analysis to identify crime hotspots. - Explore relationships between different types of crimes. - Develop predictive models for crime occurrence or escalation. - Inform public policy and community safety strategies.

Column Descriptors: - Reported Date: Date of crime report (e.g., 01/07/2022). - Suburb - Incident: Suburb where the incident occurred. - Postcode - Incident: Postcode of the incident location. - Offence Level 1 Description: Broad categorization of the offence. - Offence Level 2 Description: More specific categorization of the offence. - Offence Level 3 Description: Detailed description of the offence. - Offence count: Number of times the offence occurred.

Tips for Processing Data: - Convert 'Reported Date' to datetime format for any time-series analysis. - Handle missing values in 'Suburb - Incident' and 'Postcode - Incident'. - Check and clean any duplicate or inconsistent records. - Consider normalizing or standardizing 'Offence count' if used in predictive modeling.

Acknowledgements: We extend our deepest gratitude to the South Australian authorities and South Australian Police for their commitment to transparency and public safety by making this dataset available. Your dedication greatly aids in educational, social, and scientific advancements.

For more information and to access the original dataset, please visit the South Australia Crime Statistics.

By delving into this dataset, you're not just learning about data manipulation and analysis; you're also uncovering the stories behind the numbers, contributing to a safer and more informed community. Happy analyzing!

Report Volume Data The report counts in this data set are broken down by activity sector, report type, the year and month of receipt, and reporting entity location. The reporting entity location is represented by the forward sortation area (FSA) component of the Canadian postal code (i.e. the first three characters of the Canadian postal code, e.g. “K1P”) that designates the postal district where the reporting entity is located. ##Modernization of Reporting Forms The Financial Transactions and Reports Analysis Centre of Canada (FINTRAC) is engaged in a multi-year initiative to implement important changes to its reporting forms. Part of this modernization now allows reporting entities to submit reports containing transactions from multiple locations, increasing efficiency and reducing burden. This will change how FINTRAC publishes statistics for reports that have been modernized. FINTRAC implemented the new Large Cash Transaction Report in October 2023. As such, reporting volume statistics for the Large Cash Transaction Report will be published at a national level for quarters 3 and 4 of 2023-2024 (from October to December, and January to March, respectively). The publication of this dataset has been temporarily paused to accommodate the transition to new reporting forms. This transition is part of our ongoing efforts to enhance the quality and consistency of the data we collect. ##Protecting the Identity of Reporting Entities By law, FINTRAC must protect the identity of the persons and entities that are required to submit financial transaction reports to the Centre under the Proceeds of Crime (Money Laundering) and Terrorist Financing Act. In keeping with this responsibility to protect information, FINTRAC cannot provide more specific geographic data than is contained in this data set. Whenever possible, the data set includes the full FSA to identify the location of reporting entities submitting reports to FINTRAC. However, in any case where a certain location contains fewer than five reporting entities, or fewer than five reports submitted, only partial characters of the FSA are shown. This means that certain FSAs may contain only one or two characters (e.g., K or K1) instead of the standard three characters (e.g., K1P). In rare cases, it was not possible to provide a reporting entity location or report count without risking revealing the identity of reporting entities in a given activity sector or identifying a specific report, and so the data is provided at a national level only. All FSA levels are hierarchically inclusive. This means, for example, that the total number of report counts for K1 includes all reports submitted within all FSAs that start with K1 (i.e. K1A, K1B, K1C), including those that may not be visible because they include fewer than five reporting entities in a given activity sector. E&OE