Difference between revisions of "IS428-AY2019-20T1 Group09-Proposal-v1"

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Revision as of 16:02, 24 November 2019

G9TeamLogo.png


Team

 

Proposal

 

Poster

 

Application

 

Research Paper


Version 1

 

Version 2




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
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.
Chapter_29_Education-Table_29.2-A_All_India_Enst

(2000 - 2015)

(Click to View Data)
  • Year
  • Education enrolment by Education Level
This data set provides data of the number of enrolment into different education levels by year.
NCRB_Table_17.5

(2001 - 2011)

(Click to View Data)
  • STATE/UT
  • Year
  • Estimated Mid-Year Population (In 000)
  • Area (In Sqr. Kms)
  • Actual Police Strength
This data set provides data of police strength by state level.
13TABLE02EF44EED7234EAF9D1E3ED6417A5D25

(1993 - 2011)

(Click to View Data)

14TABLE6CAC6CF8DA7244B7BD572FE1DF53FE71

(1993 - 2011)

(Click to View Data)
  • STATE/UT
  • Year
  • Unemployment rate by gender
  • Total unemployment rate
This data set provides data of unemployment rates by state level in India.
14TABLE9AA846828C16454182612FF9B7172C0F

(1993 - 2011)

(Click to View Data)
  • STATE/UT
  • Year
  • Number of persons
  • Percentage of poverty
This data set provides data of poverty rates by States in India.
15TABLE60DE5A74134F432D9F2B437AB73348AF

(1990 - 2014)

(Click to View Data)
  • STATE/UT
  • Year
  • Per Capita Net State Domestic Product at Factor Cost
This data set provides data of per capita GDP by States in India.


LITERATURE REVIEW


Reference of Other Interactive Visualization Learning Point

Title: Crime Map of India

Example1.png

Source:https://tvganesh.shinyapps.io/crimesAgainstWomenInIndia/

  • The use of a chloropleth map allows us to compare the magnitude of crimes against women in different states.
  • However, it does not account for the difference in the population size within each state, and merely takes the absolute number of crimes in each state as analysis.

Title: RAPE IN INDIA: A visual exploration of systemic rape culture

Example2.png

Source:https://adityajain15.github.io/Rape_In_India/

  • This treemap displays the relationship of the rape offenders to their victims.
  • It also shows the treemap for each state, allowing for comparison across states.
  • The colour scheme of the treemap could be adjusted to be clearer to the viewer as it is hard to compare.

Title: RAPE IN INDIA: A visual exploration of systemic rape culture

Example3.png

Source:https://adityajain15.github.io/Rape_In_India/

  • This visualisation shows the efficacy of the justice system in India in handling rape cases.
  • Each dot represents a single rape case in India, and the dots will travel to show the final outcome of the case - whether it ends in conviction or acquittal, or is dropped in the middle of the process.


CONSIDERATION & VISUAL SELECTION


Below are a few visualizations and charts we considered making for our projects.

Visual Considerations Insights / Comments

Title: Chromosome-based Circos Plot

Circos Plot.png

Source:https://jokergoo.github.io/circlize_book/book/

  • Pros:
    • Useful in showing data with multiple tracks in a single plot
    • Demonstrates relationships between variables
    • Could be used to show how different socioeconomic factors affect different crime categories over time
  • Cons:
    • Difficult to formulate a Circos Plot
    • Overloading plot with information may lead to difficulty in interpreting it

Title: Sunburst Diagram

Sunburst.png

Source:https://www.data-to-viz.com/graph/sunburst.html

  • Pros:
    • Shows hierarchy of multivariate data
    • Visually appealing and easy to distinguish between node and leaf nodes
    • Could be used to show the prominence of different crime types by district level


  • Cons:
    • Difficult to label sunburst diagrams, which makes interactivity important
    • Tree maps will be more effective in displaying information at first glance

Title: Funnel Plot

Funnelplot.png

Source:https://community.jmp.com/t5/JMP-Blog/Graph-Makeover-Where-same-sex-couples-live-in-the-US/ba-p/30616

  • Pros:
    • Able to simultaneously display sample statistics and the corresponding sample size for multiple cases
    • Shows us what lies outside of the upper and lower limits
    • Useful in helping us determine outliers and abnormalities


  • Cons:
    • Depending on variables used, funnel plot may result in publication bias if the magnitude of effect is different for population considered

Title: Geofacet Plot

Geofacet.png

Source:https://hafen.github.io/geofacet/

  • Pros:
    • Generates a plot for each of the different geographical regions
    • Could be used to show us the different rates of crimes across the districts and states of India
    • Easy to interpret as plots are organised according to India's geography


  • Cons:
    • Complex plots cannot be used in geofacet plots
    • Plots limited to bar and line charts


PROPOSED STORYBOARDS

Storyboard 1 - Overview - Introduction to Crimes Against Women in India

Visualisation 1: Bar Graph with Treemap

Storyboard1-1.png
  • Aims to show the yearly increasing trend of number of crimes against women
  • Upon hovering over a particular year, the treemap showing the breakdown of crime types will be shown for that year.


Visualisation 2: Line Graph
Storyboard1-2.png
  • Shows the trend for the individual crime type across the years

Storyboard 2 - State-Level Comparison of Crime Rates


Visualisation 1: Chloropleth Map
Storyboard2-1.png
  • Shows comparison of crime rates across different states
  • Usage of colour scale for easy identification of crimes with higher crime rates
  • Allows user to filter by year in order to visualise changes in crime rates over the years.
  • Clicking on the button at the bottom would direct the user to Visualisation 3 to view the crime breakdown per state.


Visualisation 2: Funnel Plot
Storyboard2-2.png
  • Would be displayed side by side with Visualisation 1
  • If the user clicks on a particular state on the chloropleth map, its relative position on the funnel plot will be highlighted
  • Usage of the funnel plot allows us to account for differences in population size within each state, as we would be able to plot Population size vs. Number of Crimes against Women for each State.
  • Allows us to identify states with abnormally low/high crime rates for further analysis to be done.


Visualisation 3: Geo Facet
Storyboard2-31.png
  • Bar Graph in each state shows occurrence of each crime type in each state
  • Allows easy comparison across states for which crime type is most common
  • Each cell in the grid shows the distribution of crime type versus just a single value, unlike the chloropleth map

Storyboard 3 - Analysis of Socioeconomic Factors in contributing to Crime Rate Against Women

Visualisation 1: Correlation Matrix

Storyboard 3-1.png
  • Shows the correlation between each socioeconomic factor and the different type of crime against women.
  • Color scale highlights if correlation is positive or negative
  • Individual values show how strong the correlation is
  • Clicking on a individual value will highlight the corresponding point on the Correlation heatmap as shown in Visualisation 3-2


Visualisation 2: Correlation Heat Map
Storyboard 3-2.png
  • Shows the correlation between each socioeconomic factor in a more visually appealing manner
  • Color scale highlights how correlated each variables are to one another
  • Clicking on a individual box will highlight the corresponding point on the Correlation matrix as shown in Visualisation 3-1


Visualisation 3: Parallel Coordinate Plot
Storyboard 3-3.png
  • Compares the socio-economic factors with one another and helps visualise the relationship they have with one another
  • Shows how respective socio-economic factors affect each other with respect to the crime rates against women
  • Boxplot helps determine if the different factors are in the lower or upper range

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


Timeline.png


COMMENTS


Feel free to leave us any comments!

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