WS4D Datathon: Concept and Details

Concept Note for the SafeCity Data Visualisation Challenge

WS4D Datathon http://cognitive.iiitb.ac.in/ws4d-datathon-and-phd-colloquium/

Data:

The key dataset(s) pertain to information gathered from India, and provided by the Red Dot Foundation.

  1. Reports: time, place, type of event, report
  2. MobileApp: time, place, type of event

Reference articles https://safecity.in/publications/research-papers/  pertain to the following topics:

  1. Use of ML/AI to find the type of event (touching/groping/sexual invites/commenting/etc.) from the reports; a study on the diverse forms of sexual harassment
  2. Street violence
  3. Gender-based violence in public transport
  4. Women’s strategies to address assault and violence
  5. Study of crowdsourced data

Challenge themes:

The following points are for processing data and analyzing it deliberately, and using the knowledge to create a compelling visualization as a narrative/summary (preferably) or a tool.  The visualization (tool) must be shareable on social media to spread awareness and to inspire action against gender-based violence and others.

  1. Theme-Mythbusters: Time-related clustering/visualization or integration of time (time of day, evolution over time) with spatial and categories of crime – ( http://maps.safecity.in/ ): This will help us debunk the myths of where and when different kinds of sexual violence tend to take place. Hence, the challenge starts with picking/identifying a myth as a hypothesis, and demonstrating if the data confirm it or not. 
  2. Theme-MirrorMirrorOnTheWall: Comparison of Indian cities with others in the world where data is available: this will give us a sense of India’s position in sexual violence across different parameters captured in the existing datasets. For example, do we see a concentration of specific kinds of violence in India? Such data help us make aware of specific social structures within which sexual crime takes place. 
  3. Theme-Mash-up: Integration with other relevant datasets — police data, sex ratio, etc. available for a specific city. This will help us understand the overall situation of the safety and status of women in a city.  Such data will be crucial in shaping institutional strategies for coping with the incidence of sexual violence.  

For Theme-MythBusters, relevant myths (as a sample):

  1. Gender-based violence of all forms is highly prevalent in Delhi.
  2. Gender-based violence occurs in dimly lit streets and at night.
  3. Sexual violence and harassment occur only in very crowded or very deserted regions.
  4. Not many women get distressed with non-physical forms of violence.

For Theme-MirrorMirrorOnTheWall, relevant datasets and sources:

  1. https://evaw-global-database.unwomen.org/en/countries
  2. New York City crime: https://data.cityofnewyork.us/Public-Safety/NYC-crime/qb7u-rbmr
  3. Country and World data: consolidated as an excel sheet by Red Dot Foundation using multiple sources: http://worldpopulationreview.com/countries/rape-statistics-by-country/

https://data.oecd.org/inequality/violence-against-women.htm

https://data.gov.in

For Theme-Mash-up, relevant datasets and sources:

  1. social indicators: the general status of women in a specific city, for example, sex ratio, gender-segregated literacy rates, rate of female workforce participation. 
    1. Demographics data with gender segregation – raw data: http://censusindia.gov.in/2011census/population_enumeration.html
    2. Report: Women and Men in India:
      1. 2017: http://www.indiaenvironmentportal.org.in/files/file/women%20and%20men%20in%20India%202017.pdf
      2. 2018: http://www.mospi.gov.in/sites/default/files/publication_reports/Women%20and%20Men%20%20in%20India%202018.pdf
    3. http://www.mospi.gov.in/statistical-year-book-india/2017/171
    4. https://data.gov.in/search/site?query=gender
    5. Districtwise Education Data 2015-16 based on sex ratio, male/female literacy, schools by category, boys/girls schools by category, male/female teachers by category, etc.
    6.  Rural Female broad employment status
    7. Urban female broad employment status
    8. Women prisoners with children
    9.  Statewise schools with female teachers
    10. Statewise registered cases against stalking, rape, acid attacks
    11. Financial assistance provided to OBC women
    12.  Budgetary allocation for women safety
    13. State level literacy rate
  2. infrastructure indicators: the general state of law and order, safety in public spaces, gender-based crime, street lights, CCTV cameras, etc.
    1. Street lighting: https://data.gov.in/resources/stateut-wise-no-led-street-lights-installed-under-street-lighting-national-programme-slnp
    2. Crime against women:
      1. https://data.gov.in/catalog/crime-committed-against-women?filters%5Bfield_catalog_reference%5D=86920&format=json&offset=0&limit=6&sort%5Bcreated%5D=desc
      2. https://data.humdata.org/dataset/crime-trends-and-operations-of-criminal-justice-systems-un-cts-sexual-violence
      3. Crime against Women in Metropolitan Cities — tables from a book chapter. [provided separately as a pdf].

Deliverables:

A compelling visual narrative to be shared on social media:

  1. Appropriate fonts and color palettes
  2. Situation-sensitive text, e.g. without victim shaming
  3. Use of popular NLP tools in python, visualization tools like D3.js, Tableau, etc.

For further queries: datathon2020@iiitb.ac.in

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