Rowdy_Rooster_Project 2: Exploring and visualizing data related to key world views

Website link: https://hej6853.github.io/Rowdy_Rooster_Project-2/index.html

Motivation

The motivation comes from this book called ‘Factfulness’ by Hans Rosling. The book says when asked simple 13 questions about global trends, much of our worldview is skewed across certain fronts. Among 13 questions,87% of respondents picked the right answer to the last question (Q13- global warming). We can tell this is the ideal situation how people should recognize and accept the data. However, the average score for the humans except for the last question was much lower which was only 2.2 out of 12! Therefore, we picked the top 3 wrong answer rates questions conducted by the 14 richest countries.

Interestingly, most people in the richest countries or even the scientists, ppl work in NGO or governments are absolutely wrong about the state of the world. Overall, all the groups asked considered the world a scarier, more violent, and more hopeless place than it really is. Then how those decision-makers who work in the government sector, NGO or leader can make a decision wisely if they don't see the thing based on the data ?

Question to Answer

• Why things are better than you think and �Why knowing the fact based on the data is important?
• Is there any correlation or discrepancy between the statistics and US Foreign Aid?

Dataset Summary

Key Coding Approach

Data Visualization

1) Javascript Interactive Quiz

(https://hej6853.github.io/Rowdy_Rooster_Project-2/FLASK/templates/popupquiz.html)

2) Low-Income Countries Primary School Completion Rate

(https://hej6853.github.io/Rowdy_Rooster_Project-2/FLASK/templates/primary_school.html)

3) Worldwide Primay School Completion Rate

(https://hej6853.github.io/Rowdy_Rooster_Project-2/FLASK/templates/primary_school.html)

4) Worldwide 'Extreme Poverty' Rate (0-14 years) Over Time

(https://hej6853.github.io/Rowdy_Rooster_Project-2/FLASK/templates/poverty_rate.html)

5) World Children Population Forecast (by 2100)

(https://hej6853.github.io/Rowdy_Rooster_Project-2/FLASK/templates/birth_rate.html)

6) Worldwide Children Population Rate (0-14 years) Over Time

(https://hej6853.github.io/Rowdy_Rooster_Project-2/FLASK/templates/birth_rate.html)

7) US Foreign Aid Disbursements Over Time (Bar Chart Race)

(https://hej6853.github.io/Rowdy_Rooster_Project-2/FLASK/templates/birth_rate.html)

Built With

• Python - API, Flask, Pandas, json, jsonify, CORS, pymongo
• MongoDB
• JavaScript - D3, Plotly, HighCharts, Surveyjs.io
• HTML
• CSS, Bootstrap

Key Skills Learned

• ETL process
• Data Visualization
• flask

Conclusion

● Integrated 50 years of 165 countries’ government data to JSON file, loaded on MongoDB, and connected to Python flask to build up a data visualization web application using JavaScript, HTML, CSS, and Bootstrap.

● Visualized 6 interactive graphs for the user-end using JavaScript – D3, Plotly, and Highcharts, analyzed 8 sectors of US Foreign Aid trend correlation and discrepancy, and increased access to information by 30%.

=> It’s always best to read multiple sources, not just one. It’s the equivalent of traveling around the world. Only when we surf the globe can we learn to see it as it really is, so that we may form our opinions based on facts, not feelings.