This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited.

The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://bit.ly/rk5Tpc.

Context

The City of Orlando hosts an Open Data Portal where municipal agencies may share data for public consumption.

Acknowledgements

The Orlando Police Department has provided this dataset, derived from their internal records management system.

Crimes by type aggregated to census blocks for years 2007-2011. Original report data from OPD, cleaned, processed and geocoded by Urban Strategies Council

AUSTIN POLICE DEPARTMENT DATA DISCLAIMER Please read and understand the following information.

This dataset contains a record of incidents that the Austin Police Department responded to and wrote a report. Please note one incident may have several offenses associated with it, but this dataset only depicts the highest level offense of that incident. Data is from 2003 to present. This dataset is updated weekly. Understanding the following conditions will allow you to get the most out of the data provided. Due to the methodological differences in data collection, different data sources may produce different results. This database is updated weekly, and a similar or same search done on different dates can produce different results. Comparisons should not be made between numbers generated with this database to any other official police reports. Data provided represents only calls for police service where a report was written. Totals in the database may vary considerably from official totals following investigation and final categorization. Therefore, the data should not be used for comparisons with Uniform Crime Report statistics. The Austin Police Department does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided. Pursuant to section 552.301 (c) of the Government Code, the City of Austin has designated certain addresses to receive requests for public information sent by electronic mail. For requests seeking public records held by the Austin Police Department, please submit by utilizing the following link: https://apd-austintx.govqa.us/WEBAPP/_rs/(S(0auyup1oiorznxkwim1a1vpj))/supporthome.aspx

Since 1930, the Federal Bureau of Investigation (FBI) has administered the Uniform Crime Reporting Program (UCR). The UCR was designed to provide nationally representative estimates of the level and change in level of crimes known to and recorded by the p

The Oakland Police Department provides crime data to the public through the City of Oakland’s Crime Watch web site. This site presents the data in a geographic format, which allows users of the information to produce maps and/or reports.

The file that you are about to electronically download, copy, or otherwise retrieve by other means is a tabular representation of the same data without maps or reporting capabilities. Be advised that the exact address of each crime has been substituted with the block address to protect the privacy of the victim.

This dataset, provided by the Redding Police Department in response to a Public Records Act request, contains demographic information on victims of Part 1 violent and property crimes. The data is pulled from the department’s current records management system and covers the period from May 2, 2023, to the present. It was not possible to include data from the prior system, as the old system did not associate specific crimes with individual victims. Therefore, the dataset only includes information from the current system.

The spreadsheet, titled "Victim Part 1 Crime Demographics 2022-2023," includes details on victims involved in reported incidents of violent and property crimes. Each victim is assigned a unique identifier in the "SeqNo" column, with "0" representing the first victim in an incident, "1" the second victim, and so on. This structure ensures clear identification of victims in relation to the associated crime data.

The dataset is formatted in Excel and contains various fields that allow for demographic analysis of victims of major crimes within the Redding area during the specified period.

About SSOCSThe School Survey on Crime and Safety is a national survey of principals in U.S. public elementary, middle, and high schools. The survey covers topics such as:Frequency and types of crimes occurring at schoolDisciplinary actions allowed and used in schoolsPolicies and practices designed to prevent or reduce crime in schoolsCharacteristics of school climate related to safetyThe SSOCS was first conducted in the 1999-2000 school year. Data has been collected every other school year from 2004 to 2010 and from 2016 to 2020. The SSOCS data collection in the 2021-22 school year is the final planned collection.About SCSThe School Crime Supplement (SCS) is part of the National Crime Victimization Survey (NCVS), which is led by the Bureau of Justice Statistics (BJS) at the U.S. Department of Justice. NCES collaborates with BJS to survey students ages 12 to 18 years old in grades 6 through 12 about crime and safety at school and in the surrounding neighborhood.The SCS asks students about topics such as:Fighting, bullying, and hate-related behaviorPrevalence of guns and weaponsGangs at schoolAvailability of alcohol and drugs at schoolFear and avoidance behaviorsThe SCS was first conducted in 1989, followed by administrations in 1995 and 1999, and was conducted every other year thereafter, from 2001 to 2019. Data collection was postponed during the COVID-19 pandemic and resumed in 2022. The next collection of SCS is scheduled for 2025.Data OrganizationThe categories from the original tables library are represented as folders. Each table has its own folder within the category folders. Each table has an excel (XLS) and excel SE file associated with it. The catalog CSV in the top level folder provides a crosswalk between the original table names and the current folder names.Additionally, there are folders for Questionnaires and Reports from the SCS and SSOCS, which were downloaded from the NCES website. There is also a folder of SSOCS data users manuals, also downloaded from the NCES resource library. The SCS users manuals are already available in ICPSR, see listing here: https://nces.ed.gov/programs/crime/scs/data/.

Crime reports for various years.

Data from police reports of bias incidents for Old Bridge, New Jersey in Middlesex County from January 2018 - May 2021.

This dataset comes out of a broader project on ethnicity, crime and justice in England 1700-1825 which is described below. The aim of this part of the project was to use this data to explore the extent to which different ethnic groups were treated differently by the courts as well as to measure differences between groups in their involvement as accused.

Although modern criminological research has established that race and ethnicity have a deep impact on the workings of the criminal justice system, no substantial historical work has yet been on this subject. By analysing the impact of ethnicity on patterns of recorded crime and on decision-making at every point in the criminal justice system during the eighteenth and early nineteenth centuries, this project aims to provide this vital comparative perspective. It focuses primarily on London, with its growing black and Irish population, and asks the following questions - Were these ethnic groups over-represented among those accused of property crime, violent crime etc? And were they more likely to be found guilty and to receive harsher punishments? It also looks at the experiences of ethnic groups as prosecutors and victims. Was there, for example, more sympathy for black victims than for Irish ones, or was there a fairly systematic bias against almost all migrant groups? Were they subjected to particular types of ethnically motivated crimes? More generally, by looking both at patterns of decision-making, and at the language used when ethnic minorities appeared in court as prosecutors, victims or accused, the aim is to gain a deeper understanding of attitudes towards and discourses about race and ethnicity in this period. The dataset includes data about all the offenders awaiting Old Bailey trial in the period 1791-1805 as listed in the National Archives Home Office records HO26.1-11. The particular dataset contains 10 fields of coded information including gender, age, place of birth, ethnicity, occupation, crime, verdict and punishment for 11927 accused - all of those for which information exists 1791-1805 (with one small exception explained in the notes). This data is taken from the National Archives series HO 26. It covers the first 11 volumes of that series which have some missing volumes but cover the period Oct 1791-Dec 1805.

Prison Inmates in India

Demographics, Age, Education, Caste, Wages, Rehabilitation, Technical Info

By Rajanand Ilangovan [source]

About this dataset

This dataset provides a detailed view of prison inmates in India, including their age, caste, and educational background. It includes information on inmates from all states/union territories for the year 2019 such as the number of male and female inmates aged 16-18 years, 18-30 year old inmates and those above 50 years old. The data also covers total number of penalized prisoners sentenced to death sentence, life imprisonment or executed by the state authorities. Additionally, it provides information regarding the crimehead (type) committed by an inmate along with its grand total across different age groups. This dataset not only sheds light on India’s criminal justice system but also highlights prevelance of crimes in different states and union territories as well as providing insight into crime trends across Indian states over time

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How to use the dataset

This dataset provides a comprehensive look at the demographics, crimes and sentences of Indian prison inmates in 2019. The data is broken down by state/union territory, year, crime head, age groups and gender.

This dataset can be used to understand the demographic composition of the prison population in India as well as the types of crimes committed. It can also be used to gain insight into any changes or trends related to sentencing patterns in India over time. Furthermore, this data can provide valuable insight into potential correlations between different demographic factors (such as gender and caste) and specific types of crimes or length of sentences handed out.

To use this dataset effectively there are a few important things to keep in mind: •State/UT - This column refers to individual states or union territories in India where prisons are located •Year – This column indicates which year(s) the data relates to •Both genders - Female columns refer only to female prisoners while male columns refers only to male prisoners •Age Groups – 16-18 years old = 21-30 years old = 31-50 years old = 50+ years old •Crime Head – A broad definition for each type of crime that inmates have been convicted for •No Capital Punishment – The total number sentenced with capital punishment No Life Imprisonment – The total number sentenced with life imprisonment No Executed– The total number executed from death sentence Grand Total–The overall totals for each category

By using this information it is possible to answer questions regarding topics such as sentencing trends, types of crimes committed by different age groups or genders and state-by-state variation amongst other potential queries

Research Ideas

• Using the age and gender information to develop targeted outreach strategies for prisons in order to reduce recidivism rates.
• Creating an AI-based predictive model to predict crime trends by analyzing crime head data from a particular region/state and correlating it with population demographics, economic activity, etc.
• Analyzing the caste of inmates across different states in India in order to understand patterns of discrimination within the criminal justice system

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: SLL_Crime_headwise_distribution_of_inmates_who_convicted.csv | Column name | Description | |:--------------------------|:---------------------------------------------------------------------------------------------------| | STATE/UT | Name of the state or union territory where the jail is located. (String) | | YEAR | Year when the inmate population data was collected. (Integer) ...

All crime data for Vital Signs indicators are provided by the Baltimore City Police Department. BNIA-JFI normalizes this data by population to establish crime rates. Normalizing data allows for the rates to reflect the concentration of the crime relative to the population in the area and allows for comparison between areas of different populations.

This map is an an old format. For a better user experience, we recommend using https://data.cityofchicago.org/d/ahwe-kpsy, instead.

Map of records from the Crimes - 2001 to Present dataset.

Please see the description section of the full dataset for further information about the data.

The Old Bailey Proceedings 1674-1913 represent the largest body of direct recorded speech by non-elite people ever created. The Old Bailey Voices dataset (OBV) consists of a full text corpus and summary data for 21000 trials reported in the Proceedings between 1780 and 1880. The dataset was created for the Voices of Authority research theme of the [Digital Panopticon project, in order to explore changing speech patterns in the courtroom.The Old Bailey Corpus project headed by Magnus Huber added linguistic tagging to a large sample of the Proceedings data. OBV has recombined the linguistic corpus with trial data to enable Digital Panopticon researchers to associate individual defendants with their spoken words (or silences) in court and long-term outcomes.

The Oakland Police Department provides crime data to the public through the City of Oakland’s Crime Watch web site. This site presents the data in a geographic format, which allows users of the information to produce maps and/or reports.

The file that you are about to electronically download, copy, or otherwise retrieve by other means is a tabular representation of the same data without maps or reporting capabilities. Be advised that the exact address of each crime has been substituted with the block address to protect the privacy of the victim.

The Oakland Police Department provides crime data to the public through the City of Oakland’s Crime Watch web site. This site presents the data in a geographic format, which allows users of the information to produce maps and/or reports. The file that you are about to electronically download, copy, or otherwise retrieve by other means is a tabular representation of the same data without maps or reporting capabilities. Be advised that the exact address of each crime has been substituted with the block address to protect the privacy of the victim.