In 2023, the District of Columbia had the highest reported violent crime rate in the United States, with 1,150.9 violent crimes per 100,000 of the population. Maine had the lowest reported violent crime rate, with 102.5 offenses per 100,000 of the population. Life in the District The District of Columbia has seen a fluctuating population over the past few decades. Its population decreased throughout the 1990s, when its crime rate was at its peak, but has been steadily recovering since then. While unemployment in the District has also been falling, it still has had a high poverty rate in recent years. The gentrification of certain areas within Washington, D.C. over the past few years has made the contrast between rich and poor even greater and is also pushing crime out into the Maryland and Virginia suburbs around the District. Law enforcement in the U.S. Crime in the U.S. is trending downwards compared to years past, despite Americans feeling that crime is a problem in their country. In addition, the number of full-time law enforcement officers in the U.S. has increased recently, who, in keeping with the lower rate of crime, have also made fewer arrests than in years past.

An interactive public crime mapping application providing DC residents and visitors easy-to-understand data visualizations of crime locations, types and trends across all eight wards. Crime Cards was created by the DC Metropolitan Police Department (MPD) and Office of the Chief Technology Officer (OCTO). Special thanks to the community members who participated in reviews with MPD Officers and IT staff, and those who joined us for the #SaferStrongerSmarterDC roundtable design review. All statistics presented in Crime Cards are based on preliminary DC Index crime data reported from 2009 to midnight of today’s date. They are compiled based on the date the offense was reported (Report Date) to MPD. The application displays two main crime categories: Violent Crime and Property Crime. Violent Crimes include homicide, sex abuse, assault with a dangerous weapon (ADW), and robbery. Violent crimes can be further searched by the weapon used. Property Crimes include burglary, motor vehicle theft, theft from vehicle, theft (other), and arson.CrimeCards collaboration between the Metropolitan Police Department (MPD), the Office of the Chief Technology Officer (OCTO), and community members who participated at the #SafterStrongerSmarterDC roundtable design review.

The Office of Gun Violence Prevention (OGVP) shares real-time gun violence data to increase government transparency, improve the public's awareness, and support community-based gun violence prevention and reduction partners. All District crime data is available through Crime Cards. The dashboards below focus on gun violence only. The data in these dashboards is updated daily at 7:40AM with the incidents from the day before. View data covering 7-Day Look-back of Gun Violence and Year-to-date Gun Violence.All statistics presented here are based on preliminary DC criminal code offense definitions. The data do not represent official statistics submitted to the FBI under the Uniform Crime Reporting program (UCR) or National Incident Based Reporting System (NIBRS). All preliminary offenses are coded based on DC criminal code and not the FBI offense classifications. Please understand that any comparisons between MPD preliminary data as published on this website and the official crime statistics published by the FBI under the Uniform Crime Reporting Program (UCR) are inaccurate and misleading. The MPD does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information. The MPD will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. Read complete data notes at buildingblocks.dc.gov/data.

These data were prepared in conjunction with a project using Bureau of Labor Statistics data (not provided with this collection) and the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program data to examine the relationship between unemployment and violent crime. Three separate time-series data files were created as part of this project: a national time series (Part 1), a state time series (Part 2), and a time series of data for 12 selected cities: Baltimore, Buffalo, Chicago, Columbus, Detroit, Houston, Los Angeles, Newark, New York City, Paterson (New Jersey), and Philadelphia (Part 3). Each data file was constructed to include 82 monthly time series: 26 series containing the number of Part I (crime index) offenses known to police (excluding arson) by weapon used, 26 series of the number of offenses cleared by arrest or other exceptional means by weapon used in the offense, 26 series of the number of offenses cleared by arrest or other exceptional means for persons under 18 years of age by weapon used in the offense, a population estimate series, and three date indicator series. For the national and state data, agencies from the 50 states and Washington, DC, were included in the aggregated data file if they reported at least one month of information during the year. In addition, agencies that did not report their own data (and thus had no monthly observations on crime or arrests) were included to make the aggregated population estimate as close to Census estimates as possible. For the city time series, law enforcement agencies with jurisdiction over the 12 central cities were identified and the monthly data were extracted from each UCR annual file for each of the 12 agencies. The national time-series file contains 82 time series, the state file contains 4,083 time series, and the city file contains 963 time series, each with 228 monthly observations per time series. The unit of analysis is the month of observation. Monthly crime and clearance totals are provided for homicide, negligent manslaughter, total rape, forcible rape, attempted forcible rape, total robbery, firearm robbery, knife/cutting instrument robbery, other dangerous weapon robbery, strong-arm robbery, total assault, firearm assault, knife/cutting instrument assault, other dangerous weapon assault, simple nonaggravated assault, assaults with hands/fists/feet, total burglary, burglary with forcible entry, unlawful entry-no force, attempted forcible entry, larceny-theft, motor vehicle theft, auto theft, truck and bus theft, other vehicle theft, and grand total of all actual offenses.

Context

Introduction: The dataset used for this experiment is real and authentic. The dataset is acquired from UCI machine learning repository website [13]. The title of the dataset is ‘Crime and Communities’. It is prepared using real data from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crimedata from the 1995 FBI UCR [13]. This dataset contains a total number of 147 attributes and 2216 instances.

The per capita crimes variables were calculated using population values included in the 1995 FBI data (which differ from the 1990 Census values).

Content

The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units. The crime attributes (N=18) that could be predicted are the 8 crimes considered 'Index Crimes' by the FBI)(Murders, Rape, Robbery, .... ), per capita (actually per 100,000 population) versions of each, and Per Capita Violent Crimes and Per Capita Nonviolent Crimes)

predictive variables : 125 non-predictive variables : 4 potential goal/response variables : 18

Acknowledgements

http://archive.ics.uci.edu/ml/datasets/Communities%20and%20Crime%20Unnormalized

U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files),

U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United States (Computer File) (1995)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Data available in the dataset may not act as a complete source of information for identifying factors that contribute to more violent and non-violent crimes as many relevant factors may still be missing.

However, I would like to try and answer the following questions answered.

1. Analyze if number of vacant and occupied houses and the period of time the houses were vacant had contributed to any significant change in violent and non-violent crime rates in communities

2. How has unemployment changed crime rate(violent and non-violent) in the communities?

3. Were people from a particular age group more vulnerable to crime?

4. Does ethnicity play a role in crime rate?

5. Has education played a role in bringing down the crime rate?

This data collection effort examined the influence of drug use on three key aspects of offenders' criminal careers in violence: participation, frequency of offending, and termination rate. A random sample of arrestees was taken from those arrested in Washington, DC, during the period July 1, 1985, to June 6, 1986. The sample was stratified to overrepresent groups other than Black males. Drug use was determined by urinalysis results at the time of arrest, as contrasted with previous studies that relied on self-reports of drug use. The research addresses the following questions: (1) Does drug use have an influence on participation in violent criminal activity? (2) Does drug use influence the frequency of violent offending? (3) Is there a difference in the types and rates of violent offending between drug-using offenders who use stimulants and those who use depressants? Variables regarding arrests include date of arrest, drug test result, charges filed, disposition date, disposition type, and sentence length imposed. Demographic variables include race, sex, birthdate, and place of birth.

THIS DATASET WAS LAST UPDATED AT 8:10 AM EASTERN ON AUG. 23

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

This collection presents survey data from 12 cities in the United States regarding criminal victimization, perceptions of community safety, and satisfaction with local police. Participating cities included Chicago, IL, Kansas City, MO, Knoxville, TN, Los Angeles, CA, Madison, WI, New York, NY, San Diego, CA, Savannah, GA, Spokane, WA, Springfield, MA, Tucson, AZ, and Washington, DC. The survey used the current National Crime Victimization Survey (NCVS) questionnaire with a series of supplemental questions measuring the attitudes in each city. Respondents were asked about incidents that occurred within the past 12 months. Information on the following crimes was collected: violent crimes of rape, robbery, aggravated assault, and simple assault, personal crimes of theft, and household crimes of burglary, larceny, and motor vehicle theft. Part 1, Household-Level Data, covers the number of household respondents, their ages, type of housing, size of residence, number of telephone lines and numbers, and language spoken in the household. Part 2, Person-Level Data, includes information on respondents' sex, relationship to householder, age, marital status, education, race, time spent in the housing unit, personal crime and victimization experiences, perceptions of neighborhood crime, job and professional demographics, and experience and satisfaction with local police. Variables in Part 3, Incident-Level Data, concern the details of crimes in which the respondents were involved, and the police response to the crimes.

V8 release notes: Adds 2017 data.V7 release notes: Removes SPSS (.sav) and Excel (.csv) files. Changes column names for clearances to "tot_clr_..." to make explicit that this is all clearances, not just adult clearances. The formatting of the monthly data has changed from wide to long. This means that each agency-month has a single row. The old data had each agency being a single row with each month-crime (e.g. jan_act_murder) being a column. Now there will just be a single column for each crime (e.g. act_murder) and the month can be identified in the month column. Adds a month column and a date column. This date column is always set to the first of the month. It is NOT the date that a crime occurred or was reported. It is only there to make it easier to create time-series graphs that require a date input. Removes all card columns. This was done to reduce file size. Reorders crime columns to the order of assaults/deaths of officers, actual crimes, total clearance, clearance under age 18, unfounded. Within each category the columns are alphabetized.Monthly data and yearly data are now in different zip folders to download.V6 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. V5 release notes: Changes the word "larceny" to "theft" in column names - eg. from "act_larceny" to "act_theft."Fixes bug where state abbrebation was NA for Washington D.C., Puerto Rico, Guam, and the Canal Zone.Fixes bug where officers_killed_by_accident was not appearing in yearly data. Note that 1979 does not have any officers killed (by felony or accident) or officers assaulted data.Adds aggravated assault columns to the monthly data. Aggravated assault is the sum of all assaults other than simple assault (assaults using gun, knife, hand/feet, and other weapon). Note that summing all crime columns to get a total crime count will double count aggravated assault as it is already the sum of existing columns. Reorder columns to put all month descriptors (e.g. "jan_month_included", "jan_card_1_type") before any crime data.Due to extremely irregular data in the unfounded columns for New Orleans (ORI = LANPD00) for years 2014-2016, I have change all unfounded column data for New Orleans for these years to NA. As an example, New Orleans reported about 45,000 unfounded total burglaries in 2016 (the 3rd highest they ever reported). This is 18 times largest than the number of actual total burglaries they reported that year (2,561) and nearly 8 times larger than the next largest reported unfounded total burglaries in any agency or year. Prior to 2014 there were no more than 10 unfounded total burglaries reported in New Orleans in any year. There were 10 obvious data entry errors in officers killed by felony/accident that I changed to NA.In 1974 the agency "Boston" (ORI = MA01301) reported 23 officers killed by accident during November.In 1978 the agency "Pittsburgh" (ORI = PAPPD00) reported 576 officers killed by accident during March.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during April.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during June.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during April.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during June.In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by accident during May.In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by felony during May.In 1996 the agency "Ruston" in Louisiana (ORI = LA03102) reported 30 officers killed by felony during September.In 1997 the agency "Washington University" in Missouri (ORI = MO0950E) reported 26 officers killed by felony during March.V4 release notes: Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Makes all column names lowercase.Change some variable namesMakes values in character columns lowercase.Adds months_reported variable to yearly data.Combines monthly and yearly files into a single zip file (per data type).V3 release notes: fixes a bug in Version 2 where 1993 data did not properly deal with missing values, leading to enormous counts of crime being report

V6 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. V5 release notes: Changes the word "larceny" to "theft" in column names - eg. from "act_larceny" to "act_theft."Fixes bug where state abbrebation was NA for Washington D.C., Puerto Rico, Guam, and the Canal Zone.Fixes bug where officers_killed_by_accident was not appearing in yearly data. Note that 1979 does not have any officers killed (by felony or accident) or officers assaulted data.Adds aggravated assault columns to the monthly data. Aggravated assault is the sum of all assaults other than simple assault (assaults using gun, knife, hand/feet, and other weapon). Note that summing all crime columns to get a total crime count will double count aggravated assault as it is already the sum of existing columns. Reorder columns to put all month descriptors (e.g. "jan_month_included", "jan_card_1_type") before any crime data.Due to extremely irregular data in the unfounded columns for New Orleans (ORI = LANPD00) for years 2014-2016, I have change all unfounded column data for New Orleans for these years to NA. As an example, New Orleans reported about 45,000 unfounded total burglaries in 2016 (the 3rd highest they ever reported). This is 18 times largest than the number of actual total burglaries they reported that year (2,561) and nearly 8 times larger than the next largest reported unfounded total burglaries in any agency or year. Prior to 2014 there were no more than 10 unfounded total burglaries reported in New Orleans in any year. There were 10 obvious data entry errors in officers killed by felony/accident that I changed to NA.In 1974 the agency "Boston" (ORI = MA01301) reported 23 officers killed by accident during November.In 1978 the agency "Pittsburgh" (ORI = PAPPD00) reported 576 officers killed by accident during March.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during April.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during June.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during April.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during June.In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by accident during May.In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by felony during May.In 1996 the agency "Ruston" in Louisiana (ORI = LA03102) reported 30 officers killed by felony during September.In 1997 the agency "Washington University" in Missouri (ORI = MO0950E) reported 26 officers killed by felony during March.V4 release notes: Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Makes all column names lowercase.Change some variable namesMakes values in character columns lowercase.Adds months_reported variable to yearly data.Combines monthly and yearly files into a single zip file (per data type).V3 release notes: fixes a bug in Version 2 where 1993 data did not properly deal with missing values, leading to enormous counts of crime being reported. Summary: This is a collection of Offenses Known and Clearances By Arrest data from 1960 to 2016. Each zip file contains monthly and yearly data files. The monthly files contain one data file per year (57 total, 1960-2016) as well as a codebook for each year. These files have been read into R using the ASCII and setup files from ICPSR (or from the FBI for 2016 data) using the package asciiSetupReader. The end of the zip folder's name says what data type (R, SPSS, SAS, Microsoft Excel CSV, Stata) the data is in. The files are lightly cleaned. What this means specifically is that column names and value labels are standardized. In the original data column names were different between years (e.g. the December burglaries cleared column is "DEC_TOT_CLR_BRGLRY_TOT" in 1975 and "DEC_TOT_CLR_BURG_TOTAL" in 1977). The data here have standardized columns so you can compare between years and combine years together. The same thing is done for values inside of columns. For example, the state column gave state names in some years, abbreviations in others. For the code uses to clean and read the data, please see my GitHub file h

Abstract

The survey focuses upon citizens' experiences of civil wrongs and criminal offences and their use of formal and informal dispute resolution mechanisms to obtain redress.

BACKGROUND

The World Bank began its engagement on legal and judicial reform in Bangladesh with the Legal and Judicial Capacity Building Project (the project commenced in 2001 and has been extended until December 2008), a Government strategy supporting the reform agenda in this package was adopted in 2000. The project was a product of its time, and focused on a series of technocratic reforms to the civil justice system (improving the commercial legal framework, increasing court efficiency (strengthening court administration, improving case management, strengthening judicial training), upgrading infrastructure and facilities, establishing capacity in law reform and legal drafting, and attempting to establish and support a legal aid framework.).

The last decade has seen a significant evolution in the Bank's approach to the overall governance agenda in its client countries. It has also witnessed a broadening of the Bank's agenda to “demand side” interventions and pro-poor justice, and a new interpretation of the Articles of Agreement which comprehends that working on criminal justice and human rights is within the Bank's mandate. Since the 2000/2001 World Development Report, the Bank has adopted a definition of poverty that incorporates vulnerability, exposure to risk, voicelessness and powerlessness, seeing poverty as multi-dimensional -- the absence of “fundamental freedoms of action and choice”. So, the poverty reduction aspiration is logically also one which incorporates the notion of increasing human security and individual dignity/reducing vulnerability. The Articles of Association were interpreted to comprehend criminal justice and human rights issues as within the Bank's mandate in separate legal opinions of the General Counsel in early 2006.

At the same time, there has been a shift in the Government's stated policy priorities to reform of the criminal justice sector and enhancing affordable justice for the poor. The PRSP of 2005-8 proposes a number of institutional reforms in the justice sector (embracing the judiciary, police, public prosecution system and prison reform) as well as initiatives to increase access to justice, develop informal mechanisms of dispute resolution, and meaningful progress on the separation of the judiciary from the executive. Only the last of these matters has been the subject of significant progress, one of the governance reforms introduced by the Caretaker Government during 2007.

Other donors in Bangladesh have shifted their attention to a number of interventions relating to access to justice for the poor, after limited success with the formal institutions involved in the administration of justice. In fact, reform of legal institutions has met with scant success anywhere in the world. A World Bank assessment concluded that “less overall progress has been made in judicial reform and strengthening than in almost any other area of policy or institutional reform: James H. Anderson, David S. Bernstein and Cheryl W. Gray, Judicial Systems in Transition Economies: Assessing the Past, Looking to the Future (Washington DC, World Bank, 2005).

When the existing project concludes at the end of 2008, the Bank is interested in designing a new intervention in this field. However, there needs to be a greater evidence base about the existing state of play before preparatory work on a new project can begin. While a literature review reveals a multitude of analyses of Bangladesh's legal system, much of this material is doctrinal, with little empirical work and practically no work which engages with the political economy of institutional reform. Few initiatives have been informed by hard analysis of the day to day experiences of citizens in dealing with civil and criminal wrongs on the one hand and the embedded political, economic and cultural incentives that surround institutional change on the other.

What is proposed is a set of empirical investigations that is closely tailored to the initial literature review's findings. A survey would provide insights into the dispute resolution experiences and needs of the bulk of citizens in the country. Qualitative work would probe the current institutional responses (both formal and informal) - how the institutions operate and why, the incentive structures within, the dynamics of institutional change. Through the results of this work, the Bank will be better equipped to put the two parts of the puzzle together (basic institutional reform and ensuring that the poor are benefited) in planning any future interventions.

RATIONALE

The rationale for the survey lies in the paucity of robust data regarding citizens' experience of civil wrongs and crime and about their experiences and perceptions of formal and informal institutions involved in dispute resolution (including NGO service-providers). As is the case in many developing countries, official statistics cannot be relied upon, due to the chronic under-reporting of crime - in fact, some countries undertake or use crime victimisation surveys in the absence of any other reliable basis upon which to develop public policy in this area. The existing record-keeping practices of NGO service-providers often catalogue numbers of cases processed but fail to disaggregate this data or to collect meaningful statistics about the incidence of crimes and civil wrongs more generally. Thus, this survey could establish a baseline for monitoring purposes that could be repeated in coming years.

After sifting through the existing empirical work, several recent surveys stand out as worthwhile background. Survey work on dispute resolution and legal systems tends to be folded into larger “high-end” governance surveys. This genre of surveys usefully outlines the dimensions of governance problems in Bangladesh including, at a general level, the relationship of institutions that enforce laws and resolve disputes. Three surveys more specifically probe law and order and human security issues, one of which is being finalized at the present time. Another survey draws on the data bases of four prominent legal aid NGOs to provide a profile of perceptions of beneficiaries of the services of those NGOs. And another probes public opinion more broadly with respect to alternative dispute resolution mechanisms.

Collectively, the existing surveys are useful; they provide glimpses into the institutional pathologies of law enforcement and dispute resolution from a citizen's perspective and potential policy prescriptions and programmatic interventions. But they have certain limitations for the purposes of examining very broadly the contours of dispute resolution at informal and formal levels, the enforcement of norms, and citizens' behaviour in response to the civil and criminal wrongs that increase their vulnerability and reduce control and predictability over their lives:
(i) a narrow topical focus; (ii) the sample size is insufficient to show regional differentiation, that could be expected to be substantial; (iii) the sample pool is bounded geographically and by beneficiaries of on-going NGO programs; (iv) the surveys potentially have a bias toward empirically justifying an on-going activity; and/or (v) donor pressure in terms of time frame and methodology employed.

Finally, a lot of the social change in Bangladesh over the last three decades is not adequately documented in the scholarship on the justice-poverty nexus. It thus does not capture the effects of increased urbanization, the breakdown in the authority of traditional mediators (and thus presumably compliance with the outcomes of traditional dispute resolution) as well as the penetration of partisan political patronage into the fabric of collective social life down to the village level in the period since 1991. Recent years have also witnessed the growth in the variety of dispute resolution fora available to parts of the population, especially with the rise of community legal service providers. The latter term refers to NGOs, which in the Bangladesh context provide a variety of dispute resolution services in addition to assisting clients with legal advice and representation in the courts where appropriate.

OBJECTIVES

The broad objectives of the survey have been identified through the literature review and are designed to supplement existing knowledge:

A. To provide a national and regionally representative profile of civil disputes and crimes and their impacts, by gathering data on: i. Reported personal and household experience of civil disputes and crimes: type, frequency, severity ii. Community security and social cohesion profile: knowledge of civil disputes and crimes in the locality (type, frequency, severity) as well as social harmony (trust, confidence, collective action, feeling of safety etc.) iii. Which legal violations (criminal actions, human rights violations and civil wrongs) are the most serious for the average citizen (viz. that reduce to the greatest extent feelings of control over, and predictability in planning, one's life or for which redress is difficult/impossible to obtain.).
iv. Self-help strategies, routine practices for avoiding exposure to civil and criminal wrongs, and the impacts on individual citizens of institutional failure. This includes assessing the impact of chronic conditions of crime and violence on coping strategies and pre-emptive behaviour which may have negative consequences for economic and social well-being. These include risk-averse economic behaviour, incorporation into exploitative social networks or patron-client relationships, violent and other forms of vigilante or retaliatory behaviour. This will enable a fuller assessment of the extent of 'unmet need'. v.

In 2020, more than 8,300 cases of intimate partner violence were registered in Bogotá, D.C. The capital of Colombia was followed by the department of Antioquia as the departments with the largest number of partner violence. Furthermore, the overall number of IPV cases reported in Colombia has decreased in recent years.