Difference between revisions of "Not Sure Yet"

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! style="font-weight: bold;background: #141414;color:#fbfcfd;width: 30%;" | Dataset/Source
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! style="font-weight: bold;background: #141414;color:#fbfcfd;width: 20%;" | Dataset/Source
! style="font-weight: bold;background: #141414;color:#fbfcfd;width: 20%" | Data Attributes
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| <center>District-wise Crimes Committed Against Women, 2015 <br/><br/><br/>
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| <center>District-wise Crimes Committed Against Women, 2015 <br/><br/><br/> </center>
(https://data.gov.in/catalog/district-wise-crimes-committed-against-women?filters%5Bfield_catalog_reference%5D=87613&format=json&offset=0&limit=6&sort%5Bcreated%5D=desc) </center>
 
 
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Latest revision as of 14:59, 8 October 2019


Team

 

Proposal

 

Poster

 

Application

 

Research Paper


<--- Go Back to Project Groups

PROBLEM STATEMENT


India has always been known for its high crime rate of violent crimes against women.

MOTIVATION


Our main motivation is to

OBJECTIVES


Our objectives of this project are as follows:

  1. Gain insight on the issue of violent crimes against women in India
  2. Study the effect of various socioeconomic factors on the number of crimes committed against women


DATA SOURCES


We have obtained the following datasets for this research:

Dataset/Source Data Attributes Purpose
District-wise Crimes Committed Against Women, 2015



  • State/UT
  • Sl No.
  • District
  • Year
  • Rape
  • Attempt to commit Rape
  • Kidnapping & Abduction_Total
  • Dowry Deaths
  • Assault on Women with intent to outrage her Modesty_Total
  • Insult to the Modesty of Women_Total
  • Cruelty by Husband or his Relatives
  • Importation of Girls from Foreign Country
  • Abetment of Suicides of Women Dowry Prohibition Act, 1961
  • Indecent Representation of Women (P) Act, 1986
  • Protection of Children from Sexual Offences Act
  • Protection of Women from Domestic Violence Act, 2005
  • Immoral Traffic Prevention Act
  • Total Crimes against Women
The dataset would provide the crime rate for each type of crime against women, at a district-level. We can then aggregate the data to find trends.


LITERATURE REVIEW



CONSIDERATION & VISUAL SELECTION




BRAINSTORMING SESSIONS



TECHNOLOGIES



TIMELINE



COMMENTS