Interactive Global Map for filtering the data by regions. The selected regions get highlighted in pink.

Scatter Plot for visualizing the correlation between 'Depression Prevalence'(X-axis) and 'Air Particle Pollution'(Y-axis). Users can filter the plot by Land Area or Population.

Stacked Bar Chart to visualize the strength of the relationship between global Depression Prevalence and Air Particle Pollution. The X-axis represents different countries, while the yellow dots on the Y-axis represent the correlation coefficient ranging from -1 to 1. The user can order the visualization from low to high or high to low, either by Depression Prevalence or Particle Pollution.

Why this topic?

Depression affects millions worldwide, with 264 million sufferers globally, as reported by the World Health Organization (WHO). While genetics, environment, and social factors are known risk factors, the potential link between air pollution and depression is a recent focus. Our mission is to explore this connection and enhance mental health understanding.

What's my role here?

Throughout this project, I actively participated in every stage, taking a leading role from design and data scrubbing to the development of the entire website. In fact, I was the sole developed in the team responsible for developing the entire web-page prototype.

Now, let's delve into each of these steps in detail!

RESEARCH QUESTION

What questions do we address with this visualization?

Our research started with a broad topic of weather and depression, and we narrowed it down to five key questions:

1. Does age correlate with seasonal depression?
2. Is there a gender difference in seasonal depression rates?
3. How do social class variations affect seasonal depression rates?4. Does the length of daylight hours impact the occurrence of depression?
5. Is there a correlation between air quality and depression?

While researching these questions, we encountered challenges in finding adequate datasets for analysis. To overcome this, we merged two datasets on global pollution and depression rates to examine any correlations between these factors. Thus, our final research question is:

‍"Is there a correlation between air pollution and depression?"

Identifying Stakeholders

Who cares about this visualization webpage?

The visualizations are designed to appeal to a diverse range of stakeholders:
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1. Individuals diagnosed with depression: by providing insights into the relationship between air quality and mood

2. Raising awareness in the general public: about the risks of air pollution on mental health and motivate them to take action.

3. Policymakers: for creating policies and regulations related to pollution and mental health, and help them identify specific communities or regions that are most at risk.
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By providing insights into the potential connection between air pollution and mental health, this visualization aims to raise public awareness about this crucial global health issue and promote evidence-based policy making to address it.

Finding Datasets and Data Cleaning

What data do we visualize, and how do we ensure its quality?

- Our project utilizes 2 datasets, one on global depression rates and the other on air pollution, to explore the relationship between these factors.

- We obtained both datasets from the World Population Review website, which sources its data from reliable organizations such as the World Health Organization and IQ Air.

- We combined the two datasets into a tidy datatable using a common “id” column and removing unwanted and duplicate columns. We used a combination of VLookup in Microsoft Excel and OpenRefine to accomplish this.

Design Brainstorming

What design ideas and alternatives drove our final design?

We explored various data visualizations such as scatter plots, line graphs, and bar charts, and identified which ones would work best for our project. We then generated ideas on how to present the data clearly and concisely, focusing on the variables we wanted to highlight.