The dataset contains state- and city-wise data on the number of types of registered transport and non-transport vehicles in the cities of India with over one million population.

The dataset contains state- and city-wise data on the number of types of registered non-transport vehicles in the cities of India with over one million population. The different types of vehicles covered in the dataset include Two-Wheelers - Scooters, Mopeds, Motor Cycles -, Cars, Jeeps, Omni Buses, Tractors, Trailers, and Others

The dataset contains year-, state- and city-wise historicallycompiled data on the number of cyber crimes committed in India in cities with over one million population, categorized by motives with which the crimes were committed. The different types of crimes covered in the dataset include Personal Revenge, Anger, Fraud, Extortion, Causing Disrepute, Prank, Sexual Exploitation, Political Motives, Terrorist Activities, Inciting Hate against, Country, Disrupt Public Service, Sale or Purchase illegal drugs or other items, Developing own business or interest, Spreading Piracy, Psycho or Pervert, Steal Information, Abetment to Suicide, Terrorist Recruitment, Terrorist Funding, Illegal Gain, Revenge, Insult to Modesty of Women, Extortion or Blackmailing, Prank or Satisfaction of Gaining Control, Inciting Hate Crimes Against Community, Piracy, Steal Information for Espionage, Serious Psychiatric Illness viz. Perversion, etc., Revenge or Settling scores, Greed or Money, Fraud or Illegal Gain, Eve Teasing or Harassment, etc.

We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self-reported data. Our survey of Bangalore slums covered 36 slums across the city. The slums were chosen based on stratification criteria which included the geographical location of the slums, whether the slums were resettled or rehabilitated, slums in planned localities, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified with the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries.In 2010, an estimated 860 million people were living in slums worldwide with around 60 million added to the slum population between 2000 and 2010. In 2011, 200 million people in urban Indian households were considered to live in slums. To identify the poor is to be able to deliver benefits to them. Unfortunately, there is a paucity of highly granular data at the level of individual slums. We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self-reported data. Our survey of Bangalore slums covered 36 slums across the city. The slums were chosen based on stratification criteria which included the geographical location of the slums, whether the slums were resettled or rehabilitated, slums in planned localities, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified in the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries. The data was captured in paper questionnaires with handwritten responses, with most answers coded into structured replies, in addition to a few open-ended questions. The data collected from this survey underwent cleaning and was stored in a relational database for further analysis. Specifically, the data was vetted by the enumerators and research team by randomly picking households and a site visit with field verification was carried out. Once the data was verified by the surveyors, the filled-in questionnaires were translated to English and then digitized by an independent group. The research team then carried out two rounds of validation, in the first round, the data was checked for consistency and outliers and in the second round, the research team coordinated with the enumerators to validate any discrepancies.

The dataset contains year-, state- and city-wise historically compiled data on the total number of cyber crimes which have been committed in indian cities with over one million population

The dataset contains year-, state- and city-wise historically compiled data on the number of cyber crimes committed in violation of Information Technology Act (IT Act) in Indian cities with over one million population. The different types of cyber crimes covered in the dataset include Tampering Computer Source documents, Cyber Terrorism, Publication or transmission of obscene or sexually explicit act in electronic form, Interception or Monitoring or decryption of Information, Unauthorized access or attempt to access to protected computer system, Abetment to Commit Offences, Publication or Transmission of Obscene or Sexually Explicit Act, etc. in Electronic Form, Breach of Confidentiality or Privacy and Disclosure of Information in Breach of Lawful Contract , Hacking, Obscene Publication or Transmission in Electronic Form, Failure in Obtaining Licence or Digital Signature by misrepresentation or suppression of fact, Publishing false digital Signature Certificate, Fraud Digital Signature, Breach of confidentiality or privacy, other computer related offences such as Ransomware, Offences other than Ransomware, Dishonestly receiving stolen computer resource or communication device, Identity Theft, Cheating by personation by using computer resource, Violation of Privacy, Failure Of compliance or orders of certifying Authority, To assist to decoy or the information in interception by Government Agency, Hacking crimes such as Loss or damage to computer resource or utility, Publication or transmission of Obscene or Sexually Explicit Act in Electronic Form involving Children and Adults, etc.

The dataset contains year-, state- and city-wise compiled data on the number of cyber crimes which have been committed in India in cities with over one million population and categorized by their status in courts. The different types of status of cyber crime court cases covered in the dataset include number of cases pending trial from the previous and current years, cases abated by court, withdrawn from prosecution, compounded or compromised, disposed off by plea bargaining, quashed,discharged, acquitted, completed trails, disposed off without trial, stayed or sent to record room, convicted cases from previous year and current year, etc.

The datasert contains year-, state- and city-wise historiclally compiled data on the number of cyber crimes committed in violation of Indian Penal Code (IPC) in Indian cities with over one million population. The different types of cyber crimes covered in the dataset include Abetment of Suicide - Online, Cyber Stalking or Bullying of Women or Children, Data theft, Cheating, Forgery, Defamation or Morphing (IPC r/w Indecent Representation of Women Act), Fake Profile (IPC r/w SLL), Counterfeiting, Cyber Blackmailing or Threatening, Fake News on Social Media, Other Offences (r/w IT Act), Fabrication of False Evidence/Destruction of Electronic Records for, Evidence, Offences By or Against Public Servant, False Electronic Evidence, Destruction of Electronic Evidence, Crimes of Property or Mark such as Counterfeiting, Tampering, Currency or Stamps, Crimes of Fraud such as Crimes related to Credit or Debit Card, Any Time Machines (ATMs), Online Banking Fraud, OTP Frauds, Crimes of Criminal Breach of Trust or Fraud such as crimes of Credit or Debit card, Crimes of Counterfeiting of Currecy, Stamps and Tampering, etc.