Visualization critique — Manhattan Population

Manhattan Population Explorer visualization screenshot

The purpose

Justin Fung, a Software Engineer, produced the Manhattan Population Explorer graphic. The primary goal of this work was to demonstrate the various dynamics of New York City’s most populous boroughs. According to the author of the visualization, approximately 1.6 million individuals call ‘Manhattan’ home. And while 1.6 million is the official population, consider how many more people live there unregistered, or how many tourists can add up to the final figure. Because there are a lot of people in the area, there should be a lot of data and a lot of stakeholders. Justin used Mapbox GL JS was utilized as the map engine, and D3.js was used as the graphing engine.

The data

MTA data website screenshot

The data for the visualization came from the MTA’s official website. You may be wondering, “Why MTA if we’re talking about congested regions of the city?” To begin with, the MTA operates 24 hours a day, seven days a week. Second, the usage of accurate MTA statistics in this presentation is due to the fact that the majority of individuals who live in Manhattan use the subway as their primary mode of transportation and movement throughout the area. As a result, the author of the graphic proposed that congested neighborhoods are equivalent to congested MTA stations.

The data manipulation

Justing Fung used forecasting model, geo-spatial model and assigned blocks to specific stations based on that. There are different statistics techniques which will not be discussed here but it can be found in visualization presentation.

The users

Image Source: https://www.nytimes.com/2019/12/14/opinion/sunday/new-york-subway.html

The visualization’s key stakeholders are, first and foremost, Manhattan residents. They can plainly observe the most congested districts of Manhattan on any given day and time of week by using this map. It is useful for people to identify which places are less congested because some may want to go for a nice and relaxing walk or ride on their bikes. Because of COVID-19 or other diseases, some people are terrified of crowds. Some people may be agoraphobic and wish to relocate to a less congested region.

The government may also use this if they want to develop a new public transportation system or add new subway stations or lines in the city. It could assist to relieve congestion in several Manhattan neighborhoods and train stations. In congested locations, new public transportation infrastructure such as bike lanes, pedestrian bridges/walk paths, and so on could be implemented.

The visualization

When you initially visit the website, you will see a screen with the project name on the top left, followed by top bar menu options on the right that include ‘Story’ mode, ‘Visualization,’ ‘Statistics,’ and ‘About.’ The visualization’s legend and certain controls are on the right side, and the user can alter the day and time at the bottom of the screen.

Let us begin our analysis with the Story mode (default one). I also want to emphasize that the time and day are automatically displayed based on your current time and day, which is quite useful for folks who want to quickly observe what is going on in the streets right now if they plan to leave soon.

The story mode differs from others in that it contains a text box in the upper left corner. The text contains 15 slides on the visualization, some fascinating facts about Manhattan and its people, insights from the visualization that the author discovered himself, and basic tips on how to utilize the visualization. When you navigate through the text slides, the graphic adapts to the context of the content.

For instance, above you can read the caption “The City That Never Sleeps…” and the visualization automatically indicates 2AM on Monday, which logically should be the least populated period of the week.

In addition, the author evaluates other Manhattan neighborhoods, such as financial districts, and demonstrates how congested they are during lunchtime in the middle of the week. The visualization also automatically altered the controls on the right to highlight the places the author was writing about in the text field.

The user has a second option, which is Visualization mode. The mode is similar to the ‘Story’ mode, except it lacks the text box in the top left corner and allows the viewer to explore the visualization on their own. All of the controls and the legend are accessible.

Another intriguing feature is that by holding the Shift key and using the mouse, the user can select an area of interest and the visualization will continuously zoom in to that area.

The statistics mode is entirely distinct from the other modes. The options on the right are no longer available, and the legend now displays the difference between the overnight population and the estimated population.
This mode is great for observing the dynamics of people’s movements from one region of the island to another. The default view displays the overall statistics of Manhattan data on a specific day and hour that the user specifies. When you hover over a specific location of Manhattan, the top left box changes to reveal statistics exclusively for that area:

In my opinion, this visualization was done well since when you hover over a specific location, it begins to glow slightly, indicating that even minor elements were taken into account while producing the visualization.

The data for the Upper West Side are now displayed in the top left corner, along with a graph of dynamics through time. To be honest, this graph is a little perplexing because it has the year and time on the x axis. Finally it determines whether the area is an exporter or importer (depending on the rides), and then it calculates the estimated population for a given day and hour (which user picks).

When you click on the area, the map will zoom in and you will be able to see the exact block division.

This mode is useful for studying different sections of Manhattan, such as which areas are used for living and which are used for working. If the area is exporter on weekdays when work generally begins (around 9AM or 10 AM), it is most likely a dwelling area with primarily residential complexes. Aside than that, it’s mostly offices.

The about page is simply text overlaid on top of the visualization. It contains information about Manhattan, the visualization, the creator, and the source of the data.

The possible improvements

I believe it’s great that the creator simply included two time controls: weekday and time of day. However, I believe it would be beneficial to examine different periods of the year. Justin stated in the ‘About’ part that the visualization depicts a hypothetical week during Spring, but I believe that the dynamics should shift throughout the year dependent on the season. For example, New Year’s Eve is an extremely congested season, as is summer, when there are typically more tourists than at other times.

A table may be included to simply determine which days and times are the least and most crowded.

It would also be interesting to have an overall estimate of the number of people in a given area on a given day. It may, for example, be added to the Shift functionality. It would be difficult to see the overall number of individuals by selecting a specific region.

The graphs in Statistics mode are a little difficult to grasp. The x axis displays both the year and the time.

Conclusion

Overall, I believe that this visualization was a fantastic project that can benefit many people in Manhattan and can be used as a foundation for similar projects in other major cities such as Chicago or Los Angeles.

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