The "Bangladesh crime dataset from 2010 - 2019" is a comprehensive dataset that contains information on various types of crimes reported in different regions of Bangladesh over the past decade. The dataset includes 17 columns and 147 rows, with each row representing a particular region and year.

The "Bangladesh crime dataset from 2010 - 2019" is a rich and comprehensive dataset that provides insights into crime trends and patterns in different regions of Bangladesh over the past decade. With 17 columns and 147 rows, the dataset covers a wide range of crime types, including robbery, murder, kidnapping, burglary, and more. It also includes information on the number of cases resolved through speedy trials and the recovery of weapons, narcotics, and other contraband. This dataset is an invaluable resource for researchers, policymakers, and law enforcement officials who want to gain a deeper understanding of crime in Bangladesh and develop evidence-based strategies to address it. Whether you're a data scientist, a journalist, or just someone interested in exploring crime data, this dataset is sure to provide valuable insights and opportunities for analysis.

Overall, this dataset provides a valuable resource for analyzing and understanding crime trends and patterns in Bangladesh over the past decade.

Historical dataset showing Bangladesh crime rate per 100K population by year from 2000 to 2018.

Dataset

This dataset was created by Nahian Alvy

Released under MIT

Contents

Context

This dataset contains information about crimes happening in Bangladesh in the zonal area from 2010-2019.

Content

With a bigger population comes the bigger challenge in handling social values. Crimes are one of the major problems for a small country with a big population like Bangladesh. Here for every 4000 people, 1 police are assigned if we calculate it statistically. This dataset will give an overview look of the criminal nature of Bangladesh.

Acknowledgements

This dataset is collected from the Bangladesh Police website.

Inspiration

To find the most common patterns and frequencies using statistical method.

In 2018, there were approximately **** homicide victims per 100,000 of the population in Bangladesh. In comparison, there were approximately *** homicide victims per 100,000 of the population in Bangladesh in 2014.

Type of data: Crime records in CSV format with numerical and textual values.

Data format: CSV.

Number of samples: 6,574 instances.

Crimes considered: Murder, Rape, Assault, Robbery, Kidnap, Body Found.

Number of classes: Six (corresponding to the crime categories).

Distribution of instances: Varies across crime types based on real-world occurrences.

How data are acquired: • Crime data collected from newspapers. • Socioeconomic data sourced from the National Census. • Weather data retrieved from a Weather API.

Data source locations: Bangladesh.

Where applicable: Suitable for crime classification, forecasting, and crime analysis.

📁Dataset Overview

This dataset contains comprehensive crime statistics reported across various divisions, ranges, and metropolitan areas in Bangladesh from 2010 to 2025. It provides a valuable insight into crime trends over 15 years, helping researchers, policymakers, journalists, and data enthusiasts to understand the dynamics of public safety in the country. This dataset contains trends based on years

📌 Key Features:

✅ Timeframe: 2010 to 2025 ✅ Regional Breakdown: DMP=Dhaka Metropolitan Police CMP=Chattogram Metropolitan Police KMP=Khulna Metropolitan Police RMP=Rajshahi Metropolitan Police BMP=Barishal Metropolitan Police SMP=Sylhet Metropolitan Police RPMP=Rangpur Metropolitan Police GMP=Gazipur Metropolitan Police Dhaka Range Mymensingh Range Chittagong Range Sylhet Range Khulna Range Barishal Range Rajshahi Range Rangpur Range Ralway Range

📚 Columns : 'Names of Unit', 'Dacoity', 'Robbery', 'Murder', 'Speedy Trial', 'Riot', 'Woman & Child Repression', 'Kidnapping', 'Police Assault', 'Burglary', 'Theft', 'Other Cases', 'Arms Act', 'Explosive Act', 'Narcotics', 'Smuggling', 'year', 'Total Cases'

🧠 Possible Use Cases: 📈 Trend Analysis of criminal activity over the years

🗺️ Regional comparison of crime rates

🧾 Policy analysis for law enforcement effectiveness

📊 Data visualization and dashboard projects

🤖 Training data for machine learning models (e.g., crime prediction, anomaly detection)

📎 Source: Compiled and processed from publicly available crime records and reports by law enforcement agencies in Bangladesh.

📁Dataset Overview

This dataset contains comprehensive crime statistics reported across various divisions, ranges, and metropolitan areas in Bangladesh from 2020 to 2025. It provides a valuable insight into crime trends over six years, helping researchers, policymakers, journalists, and data enthusiasts to understand the dynamics of public safety in the country.

📌 Key Features:

✅ Timeframe: 2020 to 2025

✅ Regional Breakdown:

DMP=Dhaka Metropolitan Police CMP=Chattogram Metropolitan Police KMP=Khulna Metropolitan Police RMP=Rajshahi Metropolitan Police BMP=Barishal Metropolitan Police SMP=Sylhet Metropolitan Police RPMP=Rangpur Metropolitan Police GMP=Gazipur Metropolitan Police Dhaka Range Mymensingh Range Chittagong Range Sylhet Range Khulna Range Barishal Range Rajshahi Range Rangpur Range Ralway Range

📚 Columns :

'Names of Unit', 'Dacoity', 'Robbery', 'Murder', 'Speedy Trial', 'Riot', 'Woman & Child Repression', 'Kidnapping', 'Police Assault', 'Burglary', 'Theft', 'Other Cases', 'RC Arms Act', 'RC Explosive Act', 'RC Narcotics', 'RC Smuggling', 'Date' RC: Recovery Cases

🧠 Possible Use Cases: 📈 Trend Analysis of criminal activity over the years

🗺️ Regional comparison of crime rates

🧾 Policy analysis for law enforcement effectiveness

📊 Data visualization and dashboard projects

🤖 Training data for machine learning models (e.g., crime prediction, anomaly detection)

📎 Source: Compiled and processed from publicly available crime records and reports by law enforcement agencies in Bangladesh.

Content

Bangladesh, a densely populated country in South Asia, has been grappling with various crimes and security challenges. Despite some improvements in law enforcement, the country still faces several challenges related to crime and safety. Crime statistics in Bangladesh suggest that the country has a high rate of crime, particularly in urban areas.

The crime statistics of Bangladesh between 2010 and 2019 reveal a mixed trend in terms of the incidence of different types of crimes. According to the dataset, which is based on official records of the Bangladesh Police there has been an overall increase in the number of crimes reported over the last decade, with a few notable fluctuations in certain categories.

The types of Offences are: - Unit Name - Year - Dacoity - Robbery - Murder - Speedy Trial - Riot - Woman & Child Repression - Kidnapping - Police Assault - Burglary - Theft - Other Cases - Arms Act - Explosive - Narcotics - Smuggling - Total Cases

Overall, while crime remains a significant challenge in Bangladesh, the government is taking steps to improve law enforcement and security. However, more needs to be done to address the root causes of crime, such as poverty and social inequality, and ensure the safety of citizens.

Acknowledgements

Data as published from:(https://www.police.gov.bd/)

Comprehensive dataset containing 15 verified Crime victim service businesses in Bangladesh with complete contact information, ratings, reviews, and location data.

Meta Data Fields and Details This dataset contains detailed metadata about rape cases reported in Bangladesh during the year 2013-2023. The dataset is intended for researchers, policymakers, and organizations focused on understanding and addressing sexual violence. It includes information on the circumstances of each incident, the legal proceedings, the conditions of the victims, and the responses from the police and community. Each entry in the dataset provides a wealth of information that can be used for in-depth analysis and research. The metadata includes fields such as: Total Sample Size: This dataset comprises a total of 2,813 rows, each representing an individual case reported in various districts across Bangladesh. Temporal Coverage: Minimum Date: February 22, 2013 Maximum Date: April 10, 2023 Data Description: incident_details: A description of the incident, including key events and actions taken by the perpetrators. legal_proceedings: court: The name of the court handling the case. judge: The name of the judge presiding over the case. confession_date: The date when the confession was made. confession_details: Details about the confession given by the accused. victim_education_status: The education status of the victim (e.g., student, graduate). location: district: The district where the incident occurred. upazila: The sub-district or upazila where the incident occurred. road: The specific road or location within the district. victim_condition: The condition or status of the victim (e.g., stable, critical, student). is_fatal_weapon_used_in_rape: Indicates whether a fatal weapon was used in the incident (true/false). police_response: Details about the actions taken by the police in response to the incident. is_romantic_relationship: Indicates whether the incident involved a romantic relationship between the victim and the perpetrator (true/false). time_of_incident_hour: The hour when the incident occurred, if available. attacker_details: Information about the attackers, including names, ages, aliases, occupations, and residences. local_leaders_involved_in_arbitration: Indicates whether local leaders were involved in arbitration (true/false). pressure_from_attackers_family: Details about any pressure exerted by the attacker's family on the victim or their family. medical_response: Details about the medical response provided to the victim (e.g., medical examination, autopsy). community_response: Details about the community's response to the incident. is_pressure_from_attackers_family: Indicates whether there was pressure from the attacker's family (true/false). date_of_incident: The date when the incident occurred. legal_perspective: Legal perspective or interpretation related to the incident, if available. is_victim_family_filed_complaint_by_themselves: Indicates whether the victim's family filed the complaint themselves (true/false). victim_age: The age of the victim. police_involvement: Details about the involvement of the police in the case. outcome_of_arbitration: The outcome of any arbitration involving local leaders. officer_in_charge: The name of the officer in charge of the case. victim_marital_status: The marital status of the victim. victim_action: Actions taken by the victim in response to the incident (e.g., filing a complaint). number_of_attackers: The number of attackers involved in the incident. police_statement: Statements given by the police regarding the incident. motive_behind_not_filing_complaint: The motive behind not filing a complaint, if applicable. time_of_incident: The specific time of the incident, if available. public_response: The response of the public to the incident. Example Meta Data Entry for Version 2 { "victim":{ "status":"Alive", "age":19, "gender":"Female", "condition":"Garment worker", "marital_status":"Unknown", "education_status":"Unknown", "family_economic_status":"Unknown", "physical_injuries":"None", "psychological_support_provided":"Unknown" }, "attackers":{ "number_of_attackers":1, "details":[ { "name":"মুরাদ হোসেন", "age":33, "gender":"Male", "alias":"None", "occupation":"None", "residence":"None", "relationship_to_victim":"Stranger" } ] }, "incident":{ "date":"Unknown", "time":"Night", "time_hour":"11 PM", "location":"নগরের বায়েজিদ বোস্তামী থানার জালালাবাদ রেললাইনের পাশে", "details":"এক পোশাকশ্রমিক নিজের সাহসিকতায় ধর্ষণের হাত থেকে নিজেকে রক্ষা করেন। ধর্ষণের চেষ্টাকারী যুবককে পা দিয়ে ধাক্কায় দূরে সরিয়ে দেন। পরে পাহাড়ের ঝোপে লুকিয়ে থাকেন। এর মধ্যে পুলিশ এসে ধর্ষণের চেষ্টাকারী যুবককে গ্রেপ্তার করে। গতকাল রাতে নগরের বায়েজিদ বোস্তামী থানার জালালাবাদ রেললাইনের পাশে এ ঘটনা ঘটে। রাত ১১টায় কারখানায় ছুটি হওয়ার পর এক সহকর্মীকে নিয়ে ফৌজি ফ্লাওয়ার মিলসংলগ্ন রেললাইনের পাশে বাসা দেখতে যাচ্ছিলেন। ওই সময় মুরাদ হোসেন তাঁর সহকর্মীকে আটকে মারধর করতে থাকেন। পরে মুরাদের সহযোগীরা তাঁর সহকর্মীকে নিয়ে যান। এদিকে মুরাদ তাঁকে টেনেহিঁচড়ে রেললাইনের পাশে পাহাড়ে নিয়ে যান। একপর্যায়ে তাঁকে মাটিতে ফেলে দেন। ওই সময় তিনি পা দিয়ে মুরাদের মুখে আঘাত করেন। এতে মুরাদ যখন মাটিতে পড়ে যান,...

Problem Statement

Crime is a very common issue in the context of Bangladesh. In this dataset, listed over 10 years of crime in the whole country. Anyone can be started with this dataset.

Acknowledgements

Thanks, Bangladesh police for this information. link http://www.police.gov.bd/en/crime_statistic/year/2019

Inspiration

Dataset created for inspiration of crime statics of Bangladeshi crime.

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.

The "Bangladesh Rape Cases Data" dataset contains detailed information on rape cases reported in various districts of Bangladesh. This dataset is valuable for analyzing trends, patterns, and regional distributions of reported rape cases over a decade. It can be utilized by researchers, policymakers, and social scientists to study and address the issue of rape in Bangladesh. Total Sample Size: This dataset comprises a total of 2,813 rows, each representing an individual case reported in various districts across Bangladesh. Data Description: headline: Type: String Description: The headline of the news article reporting the rape case. It provides a brief summary of the incident. district-tag: Type: String Description: The district where the incident occurred. This helps in identifying the geographical distribution of the cases. division-tag: Type: String Description: The division of Bangladesh to which the district belongs. This is useful for broader regional analysis. subdistrict-tag: Type: String Description: The specific subdistrict or locality within the district where the incident occurred. This column may contain missing values if the subdistrict is not specified. id: Type: String (UUID format) Description: A unique identifier for each news article, ensuring that each entry can be distinctly referenced. url: Type: String Description: The web link to the original news article, allowing users to access the full report for more detailed information. last-published-at: Type: DateTime Description: The date and time when the news article was last published, helping to understand the timeline of the reported cases. offset: Type: Integer Description: An offset value for the article, potentially indicating its position in a larger dataset or the order of processing. content: Type: String Description: The main content of the news article, providing detailed information about the incident. Temporal Coverage: Minimum Date: February 22, 2013 Maximum Date: April 10, 2023 The dataset spans over a decade, allowing for a comprehensive temporal analysis of the reported cases. Potential Uses: Trend Analysis: Analyze how the frequency of reported cases changes over time. Geographical Analysis: Identify regions with higher or lower reporting rates. Content Analysis: Examine the language and details provided in the headlines and content to understand the nature of reporting. Correlation Studies: Investigate possible correlations between reported cases and other socio-economic factors. Data Quality and Considerations: Missing Values: Some columns, such as subdistrict-tag, may contain missing values where specific information was not provided. Data Source: The data is sourced from news articles, so it may be influenced by reporting biases and the availability of news coverage.

This dataset comprises survey responses collected from individuals regarding the increasing incidents of rape in Bangladesh. The responses were gathered using a form and contain insights into the participants' views on the causes, prevention methods, and personal experiences related to rape and sexual harassment. The dataset consists of a total of 116 entries and 11 columns. Here is a detailed description of the columns: Timestamp: The date and time when the survey response was submitted. Gender: The gender of the respondent. Reasons for the increase in rape cases: Respondents' opinions on why rape incidents have increased in recent years in Bangladesh. Why do girls fall victim to rape?: Respondents' views on why girls become victims of rape. Other reasons why girls fall victim to rape: Additional reasons provided by respondents on why girls become victims of rape. Have you known anyone in your surroundings who has been a victim of harassment or unpleasant situations?: Indicates whether respondents know someone who has been a victim of harassment or unpleasant situations. Description of harassment experiences in surroundings: Descriptions of harassment experiences shared by respondents. Number of female members in the respondent's family: The number of female members in the respondent's family. Do you think it is possible to create a rape-free society?: Respondents' opinions on whether a rape-free society is achievable. How do you think rape can be prevented?: Suggestions provided by respondents on how to prevent rape. What do you think should be done from your position?: Actions that respondents believe they can take to contribute to preventing rape.

With approximately *** million prisoners, China had by far the biggest prison population across the Asia-Pacific region in 2022. In contrast, less than ************ people were incarcerated in Brunei and Timor-Leste, respectively. Prison populations and total populationsThe varying size of prison populations throughout Asia-Pacific can be attributed to the size of the general populations across the region's countries and territories. With a population of over *** billion, China is the most populous country in the world. Despite the disparity in population size, Bhutan, which had one of the smallest prison populations in APAC in 2022, had a higher serious assault rate than other Asia-Pacific counties. Crime ratesApart from the general population size, there are other factors which can be taken into consideration, such as a diversity in justice systems. Therefore, a comparison of crime throughout the region can be challenging. Although China had a higher prison population, it had a lower intentional homicide rate compared to other Asia-Pacific countries and territories. New Zealand, Singapore, and Hong Kong have the lowest corruption index scores in the region, whereas countries including Bangladesh, Cambodia, and North Korea have recorded the highest scores.

Context

This dataset contain data about armed conflict locations & event data in Bangladesh from the beginning of 2001 until Novmber 2021. The 'fatalities' feature can be used as a target to model for predictions.

Acknowledgements

We thank ACLED for providing this data. Find ACLED here : https://acleddata.com/#/dashboard

Licence

Non-Commercial Licenses - ACLED’s full dataset is available for use free of charge by noncommercial entities and organizations (e.g., non-profit organizations, government agencies, academic institutions) using the data for non-commercial purposes, subject to these Terms of Use. Non-commercial licenses may also be granted to for-profit media outlets or journalists citing ACLED’s content in works of journalism; provided that such works are made available to the general public and benefit public discourse on the topic, subject to ACLED’s prior, written approval.

Inspiration

How many fatalities based on event type and subtype? What to expect when each actor is involved in conflict? What regions are impacted the most? What are the events that manifest more fatalities? Can we model and predict fatalities based on the features we have? Can we forecast the upcoming year's crime rate?

Bangladesh: Thefts per 100,000 people: Pour cet indicateur, The UN office on drugs and crime fournit des données pour la Bangladesh de 2005 à 2006. La valeur moyenne pour Bangladesh pendant cette période était de 9 thefts per 100,000 people avec un minimum de 9 thefts per 100,000 people en 2005 et un maximum de 9 thefts per 100,000 people en 2005.

Can You Solve Those two crimes? 🕵️‍♂️

🧩On 2023-07-29, a horrific crime shook the city of Khulna. A young woman, Anika Rahman, was found unconscious and severely injured in a dark alley behind an abandoned building. The evidence was scarce, but the few clues left behind whispered the truth.

🧩On 2022-05-17, A young woman was found unconscious near an abandoned warehouse. A torn piece of fabric and footprints were discovered at the scene.

Here is your database investigation.db and investigation.sql

Can you find rapists of those rape case ?

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F21667819%2F30b7dba3c557b6f9281c49fb5cd13b92%2Fsmart-detective-look-through-magnifying-glass-free-vector.jpg?generation=1740767035947923&alt=media" alt="">

Detailed Description of investigation.db (Rape Case Investigation Database)

The investigation.db database is designed for a forensic investigation of a rape case in Bangladesh in 2021. It contains five interrelated tables—persons, crimes, statements, school, and messenger_chats. Each table holds crucial data that aids in tracking the crime, identifying suspects, verifying witness statements, and analyzing digital evidence to determine the real culprit.

Table Descriptions

1. persons Table

   • Stores details of individuals involved in the case (suspects, victims, witnesses).
   • Attributes:
     • person_id (Primary Key) – Unique identifier for each person.
     • name – Full name of the person.
     • age – Age of the person.
     • gender – Gender identity.
     • address – Residential address.
     • phone – Contact number.
     • blood_type – Blood group, useful for forensic matching.
     • skin_color – Skin tone description for identification purposes.
     • tattoo - Person has tattoo or not ( 1 means yes 0 means no)

2. crimes Table

   • Holds records of various crimes, including rape, with detailed descriptions.
   • Attributes:
     • crime_id (Primary Key) – Unique identifier for each crime.
     • crime_type – Specifies the type of crime (e.g., Rape, Murder, Theft).
     • location – Where the crime took place.
     • date – Date of the crime occurrence.
     • description – Detailed narrative of the crime, including forensic clues.

3. statements Table

   • Contains statements given by witnesses or persons related to a crime.
   • Attributes:
     • statement_id (Primary Key) – Unique identifier for each statement.
     • person_id (Foreign Key) – Links the statement to a person in the persons table.
     • statement – Text of the statement, possibly containing evidence or witness testimony.

4. school Table

   • Stores school details, useful for identifying individuals through school records.
   • Attributes:
     • school_id (Primary Key) – Unique school identifier.
     • name – School name.
     • address – School location.
     • student_id (Foreign Key) – Links a student to the persons table.
     • height – Height of the student, which can help in suspect identification.

5. messenger_chats Table

   • Contains digital conversations that may serve as crucial evidence in the investigation.
   • Attributes:
     • chat_id (Primary Key) – Unique identifier for each chat.
     • sender_id (Foreign Key) – Links the sender to the persons table.
     • message – Content of the chat message.
     • timestamp – Date and time of the message.

Context

This dataset contains data from 2010 to 2019 about various criminal activities in Bangladesh.

Content

The data represents different criminal activities and which unit took the case.

• Unit_Name: Police Unit from a different region

• Dacoity: The number of violent robberies by an armed gang that year under a specific police unit.

• Robber: The number of robberies that took place that year under a specific police unit.

• Murder: The number of murders that took place that year under a specific police unit.

• Speedy Trial: The number of criminal trials held after a minimal delay that year under a specific police unit.

• Riot: The number of riots that took place that year under a specific police unit.

• Women&Children_Represion: The number of women or children who faced domestic violence that year under a specific police unit.

• Kidnapping: The number of kidnappings that took place that year under a specific police unit.

• Police_Assult: The number of kidnappings that took place that year under a specific police unit.

• Burglary

• Theft

• Other_cases

• Arms_act

• Explosive_act

• Narcotic_act

• Smuggling

• Tot(arm+exp+nar+smu)

• Total

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

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Inspiration

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