Group members: Kalli Retzepi, Sofia Reinach, Olivia Brode-Roger, Alicia Ouyang
The datasets we focused on are the tree census and traffic volume of New York City. As the initiative to increase nature in cities pushes forward and population of cities grow, the number and health of trees are growing as the number and duration of cars in New York, and are sometimes are odds with each other, as more residents means the need to find space for buildings.
We decided to tell the story of the tree health and traffic volume of two neighborhoods in Manhattan, Midtown and Upper West Side, because we wanted to compare the growth of two areas that are less than a mile apart, but have very different vibes and values. Midtown is the location of many offices and tourist attractions while the Upper West Side is more of a residential and cultural location. We choose to make a scrolling visualization because we wanted the viewer to focus the numbers and relate to the story of the individual feature. At the end, we created a bar chart so the viewers can see the overall comparison, as well as bring the two neighborhoods back together as part of one city.
The map above would serve as the background of all the components, and provide context. We also intend to include more visualization types with the numbers, such as stacking bars or pie charts, as show in the our handwritten sketch below:
We hope that in the end, the visualization will provide more context of the health of the city and perhaps inspire improvement.
NYC’s 2015 Tree Census: https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/pi5s-9p35
Marc Esposito Gomez
The data say that the number of trees, as well as the most common species of trees, differ across NYC’s five boroughs. These differences create very different landscapes that can impact the experience of each neighborhood. We want to tell this story because the more people know about the trees that make up their neighborhood landscape, the more invested they might be in appreciating or even becoming actively involved in taking care of the trees that surround them.
Our data source was the 2015 NYC tree census, which provided information about the number and species of each tree, as well as additional information about their health. We think that by showing the proportion of trees that are within each borough as well as dividing them by their most common species, people will be able to compare the landscapes of each borough. While this example infographic focuses on the borough of Manhattan, there will be a series of posters produced each with a focus on a single borough. The poster focusing on a certain borough will be displayed on bus stops and buildings around that borough, providing a point of comparison to the borough where the person is reading the poster with others around the city. The inclusion of images of leaves from the most common tree species in each borough invites interactivity of a sort of scavenger hunt to identify each species as they explore the borough. The additional facts about trees found in the highlighted borough also makes it fun to see the poster when you visit different areas, because each contains unique information relevant to your current location.
To illustrate the proportions of trees in each borough as well as the divisions of those trees into each borough’s top species, we utilized a tree map (pun only half-intended). This allows for both levels of analysis (between as well as within boroughs) to be observed in one visualization. In addition to this plot, rather than just listing the top species in each borough, an image of each of the top three tree’s leaves are shown extending from each branch. This visual representation adds additional information about the trees and how one might identify them while exploring each borough’s landscape. Over the course of the project we also identified the top issues that the trees in each borough face, but ultimately decided that the message and intended interaction with the infographic would be clearer if we limited it to a comparison story of tree landscape across the five boroughs.
Team Member Names: Haley Meisenholder, Rikhav Shah, Mitchel Myers
Summary: The data says that trees take up a small fraction of land area in Manhattan. We want to tell this story because trees should have an equitable share of space. Further, the addition of more trees to Manhattan could greatly enhance its livability.
For our data, we compare the land area of trees in Manhattan to several other groups: people, cars, parkland, and all of Manhattan. For information on trees, we utilized the NYC tree data set from 2015 available on the City of New York website. This data set contains the chest height diameter of every tree and the borough which allowed us to calculate the land area of all trees in Manhattan. For information on people, we used census data to determine the population of Manhattan. From there, the land area of people was calculated using the assumption that each person takes up roughly 2 square feet. For information on cars, we used data from the New York City Economic Development corporation to determine the approximate number of cars and taxis in Manhattan. From there, we used the dimensions of a standard four-door sedan to estimate the land area of a single car. With this information, we calculated the total land area of cars and taxis in Manhattan. For information on parkland in Manhattan, we used data from the New York City Department of Parks and Recreation which provided a precise measure of park land area in Manhattan. For information on the total land area of Manhattan, we used data from the City of New York website.
We believe that our presentation is effective at communicating the un-equitable share of land area that trees possess in Manhattan. This is because the visual presentation: 1) is simple and elegant, 2) is effective for quick comparisons, 3) utilizes recognizable images that allow for rapid recognition of key parties and data sets, 4) utilizes scale to portray the magnitude of the discrepancy between the land area of trees and other groups, 5) is consistent with the symbolism of “tree rings”, 6) uses a slideshow to progress through the data visually. Beyond this, the text and narrative component of the presentation orients the viewer around the specific location in question (Manhattan) and provides the viewer with specific quantitative values for comparisons being made in the visual.
The data say that most Hubway riders keep riding through adverse weather conditions like rain and heat. We want to tell this story because we want to celebrate Hubway riders’ resilience and illustrate how important biking and Hubway is to life in Boston.
Our data sources included all Hubway trips taken between 2015 and 2017 (excluding December 2016), and the weather (high temperature and precipitation events) for every day in that time period. Our presentation starts with a hook: an appeal to Boston’s hometown pride through giving several examples of the city’s tough character and the map of Boston in the background. We then present a surprising fact that illustrates this – there are more Hubway trips taken on average when it’s over 90 degrees, and then compare this with behavior in other kinds of weather. Then, we look at a similar story through a different lens – average trip duration. Through these sections, we use easily recognizable icons to tie the graphs into the story – bike icons for the average number of trip pictographs, and stopwatches as pie charts to illustrate average trip duration. This fits into our framing of the fact that trip duration doesn’t change as a result of weather as “We don’t cut corners”. Using the Hubway color palette is also a visual language that our intended audience will associate with biking.
Furthermore, using the first-person plural and a casual tone in our narrative fosters the sense of community that we are trying to convey. Our final chart, which compares the viewer’s average ride duration in different weather with the average Hubway users, also encourages viewers to think of themselves as part of the Boston biking community and makes the data that we present more relatable by providing a personal point of comparison.
There are several stories that we considered but decided to leave out. For example, we looked at ridership of snowy days, which is lower than ridership on rainy or hot days but not all that different from the winter normal. However, we didn’t want to introduce another baseline of comparison which might make interpreting the charts and narrative more difficult. Telling the story of biking in the snow would be best suited for its own presentation. We also did not tell the story of gender in different weather. Similarly to age, the gender composition of riders did not change across different weather types, but we found this less surprising than the fact that the age distribution did not change. Finally, we also did an analysis of how origin and destination stations change in different weather but found (aside from those closed in winter) that they did not change all that much, which suggests riders are still going where they need to go. Ultimately, though, we decided that the duration piece told that story in a more comprehensible and relatable way.
The data says that some of the largest economies are far from meeting their Paris Agreement CO2 reduction targets. We wanted to tell this story because the ability to credibly commit to sustainable practices will be essential to preventing catastrophic climate changes. Just as many individuals commit to lose weight but fail to follow through, many national regimes to reduce carbon emissions are off track. To make this issue more relatable to our audience, we have created a collection of health reports, resembling the report one would receive at a yearly checkup. This format naturally lends itself to sharing several indicator variables for healthy CO2 reduction along with a set of interventions that the patient (or nation) can take to alter the current trend.
Our group was drawn to study the CO2 data because of its relevance to the future of society. The World Bank’s statistics about CO2 emissions over time speak volumes about many aspects of life around of the world. However, often the scale of the numbers and units of measure are so large and the corresponding forecasts so technical, decision makers around the world struggle to internalize and act upon this information. We saw this as an exciting challenge for data visualization, so we have incorporated creative charts to display CO2 emissions trajectories overlaid with national commitments from the Paris Agreement. Our goal is for the viewer to find the information eye-catching and precise but also relatable.
At first, we were overwhelmed by the span of the data, both in terms of time and number of nations. We decided to reduced our scope to the countries producing a large share of global CO2 emissions. We used Tableau to sort and slice the data to find patterns over the past 10-15 years. With the coverage of the Paris Agreement controversy in the news, we were interested to explore how CO2 levels corresponded to promised reduction.
We found many data visualizations on CO2 emissions, but few were able to relate large numbers into a digestible form that had personal meaning. Many graphics used simple bar and line charts that failed to clearly express the story we were interested to find. We believe that the health report format is an appropriate way to organize statistics to tell a story about the credibility of Paris Agreement commitments to CO2 reduction. If we had more time, we would expand our analysis to more nations and segment CO2 by industry sector in each country in order to tell a more comprehensive story of the global CO2 reduction effort.
By Caroline Liu, Kunyi Li, Yihang Sui, and Arturo Chavez