Comparing Material Collections: 2019 vs. 2021 - Changes by Category

✍️What Has Been Done and What Lies Ahead?

Welcome to the captivating realm of data visualization and exploration within the Blue Box Program! In the previous section, "Data Model and Data Manipulation," I undertook a journey of meticulous data preparation and data modeling, leveraging Power BI and Python.

In this section, "Data Visualization with Power BI," I invite you to join me as we leverage this finely tuned data to craft compelling visual narratives. Power BI serves as our canvas, and Data Analysis Expressions (DAX) our artistic medium, enabling us to paint a vivid picture of waste management and recyclable materials data.

As we delve into this visual journey, we'll address specific questions designed to extract valuable insights and guide decision-making. Explore with me the intricate patterns and meaningful stories concealed within the data. Together, we'll embark on a voyage toward a more sustainable future, powered by the art of visualization.

❓Questions of the study

In this study, I've adopted a strategic approach that encompasses three key phases:

1. 🤏Understanding the Program's Volume and Scope:

Delving into the depth and breadth of the programs, I aim to gain a comprehensive understanding of their volume and scope. This entails questions related to program size, scale, and reach.

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1. What are the top 5 materials, ranked by tonnage marketed under single-stream? Let's employ effective ranking charts and explore diverse visualization options.
2. Can we pinpoint materials that experienced the most significant tonnage growth and decline from 2019 to 2021, making use of multi-variate charts for clarity?
3. What are the average annual growth rates of tonnes by material in each municipal group, and how can we showcase these rates using informative charts? </aside>

2. 💵Exploring the Program's Economy:

To unravel the economic aspects of the program, I delve into questions centered on its financial dimensions. This involves examining aspects like costs, revenues, and cost-effectiveness.

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1. Can we visualize total net costs by municipal group and year, revealing year-over-year variances through captivating charts?
2. Who are the municipalities boasting the highest revenue per household? Let's represent this data with visually appealing graphics.
3. Which municipal group demonstrates the lowest administration costs? Let's illustrate this with a suitable chart.
4. How do cost-effective programs within each municipal group compare, and which program stands out as the most cost-effective overall? Let's dive into a multi-chart display to find out. </aside>

3. 💡Analyzing the Relationship between Scope and Economy, and Policy Impact: