Data update 1

 1. What dataset will you use for your final report? (Title of your dataset, include a link to it) 

2. Describe the dataset. What kind of data does it contain?

3. Is there anything about your data that you don’t understand? (I.e. what a column heading means) how will you find this out?

4. What are some questions you hope to answer with your data? List at least three. (You don’t need the answers at this point)

1. Animal Control Inventory (Lost and Found)  https://opendata.vancouver.ca/explore/dataset/animal-control-inventory-lost-and-found/export/?disjunctive.breed&disjunctive.color&sort=date

2. This dataset contains information about lost and found animals, including their breed, colour, date of entry, name, sex, and current status (e.g., lost). Each entry seems to represent an individual animal.

3. One column that might need further clarification is the "Sex" column, which includes entries like "F/S" and "M/N." These abbreviations likely refer to whether the animal is spayed/neutered. To confirm this, I would refer to any accompanying documentation for the dataset or consult with someone familiar with animal control records.

4.

   - What are the most common breeds of animals reported as lost?

   - Are there specific times of the year when more animals go missing?

   - What are the most common colours or patterns of animals that go missing or are found?

Tracking global data on electric vehicles

 

The data visualizations on the Our World in Data page, “Tracking global data on electric vehicles” for electric car sales are generally well done, providing a clear and interactive look at global trends. They use simple line graphs to show how sales have grown over time, making it easy to understand the overall pattern. The world map gives a useful visual context, showing which regions are leading in electric car adoption and which are behind. You can hover over different countries on the map to see specific numbers, which adds an engaging and personalized way to explore the data. The colours used in the charts help differentiate between countries without being overwhelming, making it easier to compare trends. Alongside these visuals, there are notes and explanations that help explain what's happening in the data, like how policy changes have impacted sales.

However, this simplicity also has some drawbacks. The line charts and maps give a broad view but don't go into deeper details, such as how economic factors or technology developments have influenced sales in different areas. Including other types of charts, like scatter plots, could provide more insights into these relationships. The map shows the big picture well, but it would be helpful if users could zoom in on specific regions or countries for more detailed information. By relying mainly on line graphs and a single map, the page misses the chance to show other important comparisons, like how electric car sales stack up against total car sales in each country. Also, with so much data and interactivity available, some people might find it overwhelming, especially if they're not used to analyzing data. Offering simpler guides or breaking down some of the information into smaller sections could make it easier to understand.

Overall, these visualizations do a good job of showing the global state of electric vehicle adoption. They follow many good practices in data visualization, such as being clear, using colour effectively, and providing context. But they could be improved by offering more detail, a wider variety of charts, and making the information more accessible for all viewers.