Difference between revisions of "IS428-AY2019-20T1 Group09-Proposal"

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The tools we will be using for this Project is as follows: <br>
The technologies we will be using for this Project is as below:
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==<div style="background:#143c67; padding:15px; font-weight: bold; line-height: 0.3em;letter-spacing:0.5em;font-size:20px"><font color=#fbfcfd face="Century Gothic"><center>CHALLENGES</center></font></div>==
 
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* Unfamiliarity of Visualization Technologies such as Tableau, R,Rshiny etc.
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Lack of proficiency in using R and R Shiny
 
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* Attending Workshop on R and Rshiny
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* Complete DataCamp courses on the relevant technologies
* Hands-on Practice with different technologies.
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* Watch tutorial videos
* Peer Learning.
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* Read the documentation
  
 
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* Data Cleaning & Transformation from messy data.
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District Crime Rates are in separate files, with different data attributes.
 
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* Clean the data to ensure that the columns are similar
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* Consolidate the data into one file for the years 2001-2015.
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Difficulty in understanding some of the data attributes due to its local context, such as the different acts for protection against women found in some of our datasets.
  
* Organize Meeting Sessions to meet and do data cleaning and transformation together.
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* Split the work between team members.
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* Conduct more research on India and its history of crimes against women to get a better understanding of the data.
* Python Scripting to find Top 10, Sort data and translate.
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Difficulty in finding socioeconomic factors by state level
  
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* Look to different data sources to find more socioeconomic factors for consideration.
* Integrating Relevant Data from Multiple Sources Proposed Solution.
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The socioeconomic factors identified may not be indicative of the crime rate against women in India, as there may not be a relationship between the two.
  
* Working together to decide on what data to extract or eliminate.
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* Trial and test for one set of data together before splitting the work.
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* Do EDA to discover any correlation between each socioeconomic factor and the crime rate, then select the relevant ones from there
 
 
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Revision as of 13:14, 12 October 2019

Logo.png
Team Name


Team

 

Proposal

 

Poster

 

Application

 

Research Paper


<--- Go Back to Project Groups

PROBLEM STATEMENT


According to the Thomson Reuters Foundation Annual Poll, India is ranked as the world’s most dangerous country for women. This is not surprising, as India has had a long-standing history of violence against women, which is deeply rooted in certain cultural practices such as female infanticide and acid attacks.

Even with the increasing public outcry regarding such discrimination and the enactment of laws protecting women, the number of crimes committed against women is increasing steadily over the years.

MOTIVATION


India is one of the world’s fastest growing economies. It is currently the seventh richest country in the world, and is projected to be the third largest economy in the world. Despite its rapid growth and development, women in India still suffer from long-standing gender inequality and are the victims to brutal and inhumane crimes. Hence, there is a need to analyse various socioeconomic factors to garner insights on the root causes for crimes against women to understand why this phenomenon is so.

OBJECTIVES


Our objectives of this project are as follows:

  1. Provide an overview of the issue of crimes against women in India
  2. Draw comparisons to study the differences in crime rates between different states
  3. Study the effect of various socioeconomic factors on the number of crimes committed against women

We hope to achieve these objectives by developing interactive visualisations which can help us to understand the increasing trend of crimes against women, and what factors may contribute to such crime rates.

DATA SOURCES


We have obtained the following datasets for this research:

Dataset/Source Data Attributes Purpose
District-wise Crimes Committed Against Women, 2015
(Click to View Data)


District-wise Crimes Committed Against Women, 2014
(Click to View Data)
  • 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.
dstrCAW_2013
(Click to View Data)


dstrCAW_1 (2001-2012)
(Click to View Data)
  • STATE/UT
  • DISTRICT
  • Year
  • Rape
  • Kidnapping and Abduction
  • Dowry Deaths
  • Assault on women with intent to outrage her modesty
  • Insult to modesty of Women
  • Cruelty by Husband or his Relatives
  • Importation of Girls
This data set will be used to understand the general demographic of international visitors coming to Korea from 2007 - 2018. We will be able to gain descriptive insights on the visitor demographics by Age Range.
Entry by nationality by age

(2007 - 2018)


(Click to View Data)
  • City
  • Age Range
  • Date
This data set will be used to understand the general demographic of international visitors coming to Korea from 2007 - 2018. We will be able to gain descriptive insights on the visitor demographics by Age Range.


LITERATURE REVIEW



CONSIDERATION & VISUAL SELECTION




BRAINSTORMING SESSIONS



TECHNOLOGIES


The tools we will be using for this Project is as follows:

G9 technologies.png


CHALLENGES


Challenges Mitigation Plan

Lack of proficiency in using R and R Shiny

  • Complete DataCamp courses on the relevant technologies
  • Watch tutorial videos
  • Read the documentation

District Crime Rates are in separate files, with different data attributes.

  • Clean the data to ensure that the columns are similar
  • Consolidate the data into one file for the years 2001-2015.

Difficulty in understanding some of the data attributes due to its local context, such as the different acts for protection against women found in some of our datasets.

  • Conduct more research on India and its history of crimes against women to get a better understanding of the data.

Difficulty in finding socioeconomic factors by state level

  • Look to different data sources to find more socioeconomic factors for consideration.

The socioeconomic factors identified may not be indicative of the crime rate against women in India, as there may not be a relationship between the two.

  • Do EDA to discover any correlation between each socioeconomic factor and the crime rate, then select the relevant ones from there


TIMELINE



COMMENTS