http://www.visualcomplexity.com/vc/project_details.cfm?id=980&index=980&domain=
This diagram displays the areas most walked in downtown Boston, it uses lines to demonstrate a path of travel, the darker and denser the line, the more popular that street is walked. The map is much darker in the center and diffuses out, streets near the water are also more populated. We can learn from this that the city center and water ways must have something attracting people likely food, jobs, and activities that cause more people to want to be there. We can also tell from this that the city was likely designed to have these things closer together so people could easily go from place to place. The areas by water are likely attracting people because it’s more scenic for walking and running, for those living in the city it’s better to walk or run away from the center of the city where there is a lot of traffic. This is useful for people in Boston to see where most activity is so they can find places, or avoid highly trafficked areas. This is interesting because it differs from a normal map which only shows the streets, instead it actually shows the areas on the streets that are populated. The data here is interesting because this also demonstrates a pattern, if we were to look at other city maps showing places of travel we would likely find similar results, a dense city center and coast.
https://www.reddit.com/r/dataisbeautiful/comments/9ddzey/oc_amount_of_music_listened_to_vs_time_of_day/?utm_content=title&utm_medium=hot&utm_source=reddit&utm_name=dataisbeautiful
The bar graph demonstrates the number of songs listened to per given time of day. There are large peaks at 3pm and 9pm, we can use these trends to see why people are listening to more music at these points in the day. We can assume the 3pm peak is from students getting out of school and listening to music and the 9pm peak is from people listening before bed or partying. There is also a smaller peak at noon, we can assume this is because most people are at their lunch break around this time. The graph is in no way symmetrical, the left side is empty from 3am-8am while the rest of the day is packed with music, this is likely because most people are asleep from 3-8am and aren’t listening to music. This data can be used by companies to know when they should put in ads to make sure they reach a larger audience. The trends in this data is interesting to understand not only when people like to listen to music, but also to understand society. People often listen to music independently today on their phones with headphones, this data shows us when people are more independent during the day. It also demonstrates when people usually wake up and go to bed. Data can represent more than just what the variables show nbecause data trends demonstrate other trends in society.
The bar graph displaying the number of songs listened to by time of day is very fascinating, and it was surprising to see how drastic the changes in amount of songs listened to was when comparing the period between 3:00 and 8:00 AM to any other time. It can be assumed from this data that people start listening to music during the usual hours for morning commuting, most likely due to the audience that listens to music on their car radio. I find the significant divots in number of songs listened to during lunch and dinner to be quite interesting. I hadn’t realized that people are more likely to stop listening to music during a meal until I saw this bar graph. I did find a graph that was quite similar to the bar graph you posted in terms of measurement of data. This graph is a scatter plot that shows the number of university students that take the bus at any given time during the day. The data from both graphs shows a similar obvious trend that people don’t tend to listen to music or ride the bus from 3:00 to 8:00 AM. There are several large peaks in this graph that indicate the times that lecture blocks start.
https://www.reddit.com/r/dataisbeautiful/comments/7dtb94/bus_use_of_university_students_employees/
The city map of Boston is really interesting to me for a number of reasons. I used to work on a food delivery app, and I would look at a map similar to this, showing me where the busiest areas were by coloring them in on a scale of clear to orange to red. If I wanted to get a lot of orders, I would drive to these areas so the algorithm could see I was looking for work. But I faced a problem with the app that this map also forgets to address. When I would go towards these areas, sometimes they would die down by the time I got there, since the algorithm would only update these areas every so often. I also couldn’t tell if a slightly orange spot was just from one order 30 minutes ago, or was a steady stream, or if a red spot represented a bunch of people who are just coming back from lunch, and had all been picked up by other drivers. Both of these maps would benefit from time data being added, which is hard to display on a map- but you can use something other than colors, such as patterns or symbols for different times of day or time amounts. It’s still an awesome map of Boston to look at, and shows data in an efficient way, but it’s hard to tell if some areas are truly thriving, city centers during the day or are just residential areas where everyone is at night. Here’s a link to the closest thing I could find to the map I used.
https://image.winudf.com/v2/image/Y29tLnBvc3RtYXRlcy5hbmRyb2lkLmNvdXJpZXJfc2NyZWVuXzBfMTUzNTc4MjMxM18wMTA/screen-0.jpg?h=800&fakeurl=1&type=.jpg