Difference between revisions of "Group08 Proposal"
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| − | < | + | == <big>Background</big> == |
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| − | < | + | == <big>Data Sources</big> == |
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The World Bank is a global partnership between 189 countries across the world that seeks to "reduce poverty and build shared prosperity in developing countries". While it regularly collects information and publishes reports toward its work, it also hosts its data on [https://data.worldbank.org/ data.worldbank.org] in a bid to provide free and open access to development data across the globe. | The World Bank is a global partnership between 189 countries across the world that seeks to "reduce poverty and build shared prosperity in developing countries". While it regularly collects information and publishes reports toward its work, it also hosts its data on [https://data.worldbank.org/ data.worldbank.org] in a bid to provide free and open access to development data across the globe. | ||
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| − | < | + | == <big>Approach</big> == |
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We will be exploring the following few approaches to the analysis and visualisation of the data: | We will be exploring the following few approaches to the analysis and visualisation of the data: | ||
| − | <div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Exploration</font></div> | + | ===<div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Exploration</font></div>=== |
* Illustrate every independent variable’s changing pattern by countries | * Illustrate every independent variable’s changing pattern by countries | ||
* Illustrate changing patterns in indicators of women empowerment for various countries or groups of countries | * Illustrate changing patterns in indicators of women empowerment for various countries or groups of countries | ||
* Detect any exceptional variations or trends for further analysis | * Detect any exceptional variations or trends for further analysis | ||
| − | + | ===<div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Multi-variate linear regression</font></div>=== | |
| − | <div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Multi-variate linear regression</font></div> | ||
With some reorganisation, the data can be structured into 3 dimensions of country (region), time (year), and indicators for the purpose of multi-variate linear regression. | With some reorganisation, the data can be structured into 3 dimensions of country (region), time (year), and indicators for the purpose of multi-variate linear regression. | ||
<blockquote><p>Y<sub>i</sub> = β<sub>0</sub> + β<sub>1</sub>X<sub>i</sub> + β<sub>2</sub>X<sub>2i</sub> + ... + β<sub>n</sub>X<sub>ni</sub></p></blockquote> | <blockquote><p>Y<sub>i</sub> = β<sub>0</sub> + β<sub>1</sub>X<sub>i</sub> + β<sub>2</sub>X<sub>2i</sub> + ... + β<sub>n</sub>X<sub>ni</sub></p></blockquote> | ||
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* With the model, we will perform cross-comparison between countries and regions, and identify potential segregation factors such as rate of development, geographical, environmental etc. | * With the model, we will perform cross-comparison between countries and regions, and identify potential segregation factors such as rate of development, geographical, environmental etc. | ||
| − | + | ===<div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Time series forecasting</font></div>=== | |
| − | <div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Time series forecasting</font></div> | ||
* Capture important features (those which significantly impact Y) of each year for every country in each group | * Capture important features (those which significantly impact Y) of each year for every country in each group | ||
* Illustrate how the impact of different factors change over time goes for different countries | * Illustrate how the impact of different factors change over time goes for different countries | ||
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| − | < | + | == <big>Interface Design</big> == |
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The approach to the design principles are outlined under each area: | The approach to the design principles are outlined under each area: | ||
| − | <div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Data visualisation</font></div> | + | ===<div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Data visualisation</font></div>=== |
* Animations to show the impact factors change with time | * Animations to show the impact factors change with time | ||
* Respective graphical breakdowns of interactions between input variables and key indicators for the user-defined model | * Respective graphical breakdowns of interactions between input variables and key indicators for the user-defined model | ||
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* Static and interactive graphs that highlight interesting patterns, and interesting similarities and differences among countries | * Static and interactive graphs that highlight interesting patterns, and interesting similarities and differences among countries | ||
| − | <div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Application Interface</font></div> | + | ===<div style="font-family:Segoe UI Semibold;"><font size = 3; color="#176585">Application Interface</font></div>=== |
* Allow users to select their tolerance for forecasting women empowerment measures in the near future | * Allow users to select their tolerance for forecasting women empowerment measures in the near future | ||
* Allow users to select time and country for indicators they want to explore under the filters and embed controls to avoid 'overcrowding' of parameters in any visualization | * Allow users to select time and country for indicators they want to explore under the filters and embed controls to avoid 'overcrowding' of parameters in any visualization | ||
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| − | < | + | == <big>References</big> == |
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Revision as of 13:37, 17 June 2018
Understanding gender equality from a visual perspective
Contents
Background
With the improvement of quality of life across the world, the argument for various freedoms and rights surface and turn to hotly debated topics especially in highly developed countries. One such agenda is the push for the empowerment of women, amongst the fight against other forms of discrimination.
Moral and ethical concerns aside, there appears to be a strong economic case for the empowerment of women. Intuitively, this makes sense since it increases human capital and potential contribution of half the population towards a nation's economy. This is further argued by the International Institute for Environment and Development on "Why women's empowerment is essential for sustainable development" - which highlights factors on the direct contribution of women, the value of diverse opinions, as well as accounting for the huge untapped potential economic resource.
Given the growing importance on this area, our project seeks to encapsulate existing data and present it visually in a simple, understandable manner, to help further efforts in driving the agenda. Using graphical techniques complemented with some level of statistical modelling in R, our dashboard will provide a view on changing patterns in key indicators relating to gender equality and women empowerment with time, as well as attempt to highlight important variables which aid the growth of such indicators.
Data Sources
The World Bank is a global partnership between 189 countries across the world that seeks to "reduce poverty and build shared prosperity in developing countries". While it regularly collects information and publishes reports toward its work, it also hosts its data on data.worldbank.org in a bid to provide free and open access to development data across the globe.
As a significant area of our topic covers economic indicators as proxies of women empowerment, the World Bank database serves as a primary source of data for us to work with. We will be exploring data mainly from 1980 onwards, where majority of data collection related to this area commenced.
Approach
We will be exploring the following few approaches to the analysis and visualisation of the data:
Exploration
- Illustrate every independent variable’s changing pattern by countries
- Illustrate changing patterns in indicators of women empowerment for various countries or groups of countries
- Detect any exceptional variations or trends for further analysis
Multi-variate linear regression
With some reorganisation, the data can be structured into 3 dimensions of country (region), time (year), and indicators for the purpose of multi-variate linear regression.
Yi = β0 + β1Xi + β2X2i + ... + βnXni
- Y represents the dependent variable which is an indicator of women empowerment, while X denotes dependent variables influence Y values. We have identified female labor force participation rate (or calculated Non-agricultural female employment rate) as one such potential indicator
- The model will be used to evaluate various X variables and generate a list of featured variables of high importance. Statistical significance and adjusted R-squared values will be key deciding factors to help us exclude irrelevant variables
- Set assumptions: Classical Hypothesis, significance level, OLSE (ordinary least squares estimation); set applicable testing methods for model updating: goodness of fit, F-test, T-test, multicollinearity, etc.
- With the model, we will perform cross-comparison between countries and regions, and identify potential segregation factors such as rate of development, geographical, environmental etc.
Time series forecasting
- Capture important features (those which significantly impact Y) of each year for every country in each group
- Illustrate how the impact of different factors change over time goes for different countries
- Provide visualisations of women empowerment indicators with economic growth over time
- Set customized tolerance level (low, medium, high)
- Forecast Y value(s) towards the near future for each selected country based on input variables using ARIMA (Auto-Regressive Integrated Moving Average) algorithm or Xgboost
Interface Design
The approach to the design principles are outlined under each area:
Data visualisation
- Animations to show the impact factors change with time
- Respective graphical breakdowns of interactions between input variables and key indicators for the user-defined model
- Drill-downs based on selected variables or outputs from the user-defined model
- Complementary interactive charts to aid discovery of further insights based on outputs from user-defined model
- Static and interactive graphs that highlight interesting patterns, and interesting similarities and differences among countries
Application Interface
- Allow users to select their tolerance for forecasting women empowerment measures in the near future
- Allow users to select time and country for indicators they want to explore under the filters and embed controls to avoid 'overcrowding' of parameters in any visualization
- Allow users to customize variables to be input in the model so that they may directly see the difference of target results between their input and important features input
- Creative visuals to complement key insights or commentaries generated in the dashboard
- Snapshots of 'profiles' of typical woman for each country with accompanying information on key variables, economic data, outliers etc
References
| Essay on Women Empowerment: Its Meaning and Importance - published by Important India | https://www.importantindia.com/19050/essay-on-women-empowerment/ |
| Ready to Measure: Twenty Indicators for Monitoring SDG Gender Targets - published by Data2X | http://data2x.org/wp-content/uploads/2017/11/Ready-to-Measure.pdf |
| Ready to Measure: Phase II - published by Data2X | http://data2x.org/wp-content/uploads/2017/09/Ready-to-Measure-Phase-II_Report.pdf |
| Indicators for Gender Equality and Women's Empowerment - published by OECD | http://www.oecd.org/dac/gender-development/43041409.pdf |
| Indicators of Gender Equality - published by UNECE | https://www.unece.org/fileadmin/DAM/stats/publications/2015/ECE_CES_37_WEB.pdf |
