Cut a Tree — Make a Difference

Cut a Tree, Make a Difference

Divya Goel, Meghan Kokoski and Zachary Collins

Link to Flyer: https://www.dropbox.com/s/lw01r50y6vglqre/Cut%20a%20Tree%2C%20Make%20a%20Difference.pdf?dl=0

 

When looking through the data displayed by the World Bank, we saw an undeniable increase in carbon dioxide emissions and a drastic decrease in global forest size. We saw these two developments as very related issues, even if not necessarily correlated. The natural recycling process that plant life does with carbon dioxide is very important for reducing our global footprint. The damage we are doing at both ends of this process is concerning, and so we wanted to make a visual display that would connect these two problems in a different yet still very effective manner.

 

We displayed our data with a very satirical approach. It took the form of a flyer produced by a fictional company whose platform advocates deforestation and increased carbon dioxide emissions for the purposes of eliminating fresh air and increasing climate change. Quite immediately it becomes clear that this advertisement is satirical, however this perspective adds much more weight to the arguments we use our data to make.

 

We first propose the current issue — that trees are one of the largest reducers of carbon dioxide emissions. We highlight to the audience the effect they have on clearing the air and then turn attention toward global deforestation. We highlight the massive reduction in forest area describing it as “a great step toward increasing net emission.” Taking it in from a satirical perspective hits the audience in a much stronger manner as something that clearly shouldn’t be having much success is proving to be quite effective. We then turn it into a call to action, encouraging readers to “take the fight to their own backyards” and chopping down local trees. Highlighting the damage they can do actually highlights the positive impact they can have (i.e. planting and protecting local trees).

 

The major chart that we implement is an area graph displaying increased carbon emissions coupled with a tree infographic that displays the damage done to tree populations. The most flawed chart we critiqued in class was the flipped area graph displaying “Gun Deaths in Florida” created by Christine Chan. It’s deceptiveness and confusion caused it to be a very ineffective way of telling the story the author intended, however, we believe that the major fault of this chart was a lack of context and a confusing background. If those were mended, the interesting features this layout contains could be effectively used to convey a story. We decided to take a page from Nigel Holmes and inject humourous and contextual images that would make the graph’s intentions clear and give the reader the motivation to correctly understand what the chart was displaying.

 

We labeled our upside down area as carbon dioxide and gave it a distinctive coloring. Moreover, we inserted iconography related to forest area reduction to the bottom portion of this graph. Having movement and action occurring in this section makes it very clear that this isn’t what is being plotted in the graph. The gaseous nature of carbon dioxide makes it very clear why it may be situated in this inverted form as every object in our display has some relation to the physical item it represents. The interaction between the trees and carbon dioxide creates a clear metaphor that forests are protecting us from it, and provides meaning to the trends within the graph. Using these tools, we were able to capture the reader’s attention and display our data in a way that highlights and provides immediate meaning to both the problem at hand and the information being showcased in our graph.

 

Our target audience is the general individual who may consistently hear about these problems but has become desensitized to the usual and common arguments. Millennials would resonate well with the satirical nature of the flyer. Choosing to tell the story in this type of context focuses on what has went wrong, providing negative reinforcement rather than just the potential to do good. Because millennials are more likely to change their attitudes and habits moving forward, this visualization will have a more persuasive aura among them.

 

CO2 Emission Data and Forest Area Data: http://data.worldbank.org/topic/climate-change

Tree Carbon Consumption Info: http://www.americanforests.org/explore-forests/forest-facts/

Planet Tree Total: http://news.yale.edu/2015/09/02/seeing-forest-and-trees-all-3-trillion-them

 

Trees: Saving Lives in NYC

See our presentation here!

Sharlene Chiu, Margaret Tian, Kevin Zhang

According to a 1994 study of air pollution removal by trees in urban areas, trees only remove 0.09% of fine particulate matter. This amounts to every tree absorbing about 8 lbs annually, based on the 1995 New York City Tree Census. At first glance, 8 lbs may seem negligible, but we were excited to discover that 7.6 lives are saved each year, thanks to the removal of particulate matter by trees!

Our data sources are listed below:

  • NYC Environment & Health Data Portal, http://a816-dohbesp.nyc.gov/IndicatorPublic/BuildATable.aspx#
  • Urban Tree Effects on Fine Particulate Matter and Human Health, https://www.fs.fed.us/nrs/pubs/jrnl/2014/nrs_2014_nowak_002.pdf
  • Air pollution removal by urban trees and shrubs in the United States, https://www.fs.fed.us/ne/newtown_square/publications/other_publishers/OCR/ne_2006_nowak001.pdf

We decided to represent our data with a stacked bar chart and highlight the different layers of information with the zoom features on Prezi. The stacked bar chart predisposes the audience to expect that the percentage of pollution removal by trees is great enough to be easily spotted on the bars. However, we headed in the opposite direction by zooming into a minuscule portion of the bar. The most striking part of our narrative is that such a small percent reduction (0.09%) can have such a substantial effect (saving $60 million and around 8 lives).

We begin by showing the entire bar, which represents all the PM2.5 produced. Then we emphasize how seemingly insignificant the amount absorbed by trees is by slowly zooming into a tiny piece of the bar.  At this point in the presentation, we expect the audience to feel that trees have a trivial effect, but we then demonstrate that trees are indeed important by zooming out and showing relevant (and big) benefits. This theme of zooming in and out to showcase scale is repeated to the end of our creative chart presentation.

Architecture and Related Services Occupation Share

-what data is being shown

The data show the occupations by share in the architecture and related services.

-who you think the audience is

The audience are architects, people who have the related jobs, people who are interested in the architecture and related services, people who study the structure of the architecture related occupations, and people who want to start business related to architecture etc.

-what you think the goals of the data presentation are

The goal of this map is to show there are many related services of architecture, and what are the Percentage of those services in the industry chain. Secondly, the map creator also categories the occupations by management, business, science and art; Sales and office occupations; Service occupations; production, transportation and material moving occupations; natural resources, construction and maintenance occupations; Military specific occupations. In addition, the complicated map indicate the innate association of one occupation with another.

-whether you think it is effective or not and why

It is very effective in terms of showing the percentages of each occupation take in the whole industry. And the category is very clear. It is very helpful to understand the relationships and thus better refine this architecture related industry. It is also good that the audience could click on each occupation to see the population and the salaries. However, I find some of the information are too small to read. In addition, I could not understand the logic of how the map creator associate one occupation with another and why the order of the occupations are like these.

https://datausa.io/profile/cip/0406/

Siyang Jing’s Data Log

8:00_wake up and check Facebook, WeChat, news on the phone and chatting with my friends on line for a while.

8:30_turn on a Reading book App while doing the exercise. 4

9:00_checking the email while having breakfast

10:00_Checking out the book in the library

12:00_Watch the TV show while having lunch at home

14:00_Use the Harvard App to Check the M2 shuttle to go to MIT

15:00_From a discussion group by using google PPT, google Doc and other group chatting softwares.

18: 00_go to the Gym running while listening to music

19:00_Using an food making App to teach me prepare for the food for tomorrow

20:00_Chatting with my team through wechat and sometimes facetime with them

21:00_Doing homework and searching the materials on line

23:00_make a phone call to my mother or friends.

00:00_Check Facebook, WeChat and News online

Most often used: Wechat, google chrome, office 365

Niki Waghani’s Data Log

9am – Added an event on my Google Calendar. Was surprised when I realized someone else’s travel calendar and schedule was showing up on my phone, just because we had been traveling together earlier.

10am – Looked up all sorts of information on movies about the Oscars on Safari. Google stores my last few searches.

11am – Tapping in to the EECS lounge with my card. They probably keep track of how many people come in and out. It’s possible they could even keep track of who.

12pm – Listened to music on Youtube. It now knows what kinds of songs I like and makes suggestions based off of that.

1pm – Facebook: Tracks where I like to online shop and the times of day I’m most active on the Internet

2pm – Allowed Google Maps to use my location.

6pm – Uber: Tracks where I am and where I’m going.

7pm – Uploaded video for UAT to the class website. Have also uploaded other psets to class websites earlier in the day.

9pm – Netflix knows the last show I was watching and kept track of where it stopped. It also still remembers the names and viewing information of a few friends who shared my account nearly two years ago.

Girl Scout Cookies

I chose to review a data infographic about the very popular and delicious Girl Scout cookies. Anyone who is a fan of these cookies would enjoy this infographic. It doesn’t have an agenda or stance but rather just provides lots of fun facts and history. The end goal is to build interest. The infographic is long, so I’ve clipped out the best parts.

The image above appears at the top of the infographic. I like the way the green ties together the number and the banner. It would be even more impactful if the number was directly related to the data in the green box. Otherwise, the actual layout of the top is busy and not very visually appealing.

The picture above shows how much each girl scout cookie contributes to their total sales and includes how many calories worth was sold. The bar graph below shows how many boxes of each cookie were sold. It uses two colors but doesn’t identify what the different colors mean. Plus, there is another row of cookies below it that have no discernable purpose other than to add clutter. Also it would’ve been nice to know how many calories each cookie was because without knowing that, quantifying them in calories is impossible to interpret. This can technically can be calculated from the provided information, but it just makes me think that there must’ve been a better way to coordinate the information in the two main graphs. I also really don’t like the colors. They all clash with each other and it makes me eyes hurt to look at the infographic for the amount of time it took to write this.

Later on in the infographic, it has pictures of old, discontinued cookies as well as a recipe for the original cookie at the very bottom. The information is really fun to know. It is worth noting, however, that the graphic has an odd jumble of information. The focus on calories in the first half made me think it was going to have some sort of message about health. Then, by the end, it was telling me to make cookies. Thus while the infographic is interesting, it has no overall purpose and the parts don’t work together effectively.

http://www.dailyinfographic.com/the-business-nutrition-of-girl-scout-cookies-infographic

Exploring Sustainable Energy Policies Around the World

The internet is littered with poor data presentations on climate change. The storytelling in the charts is often ineffective, and the connection to human activity is often absent. In many cases, the “so what?” and the “call to action” are missing from these data presentations.

One example of the effective presentation of data is a new tool by the World Bank to rate sustainable energy policies in more than 100 countries. Referred to as “RISE” (Regulatory Indicators for Sustainable Energy), the tool is a scorecard that grades countries in three areas: energy access, energy efficiency, and renewable energy.

As shown below, the map enables users to explore a country’s policies and regulations in the energy sector, and also compare scores (more than 25 indicators are tracked) across countries. Users can also download the underlying data.

Source: http://rise.worldbank.org/

Source: http://rise.worldbank.org/

The intended audience for this tool is policymakers, since it can help them identify policies and regulations to expand and improve sustainable energy. However, anyone who is interested in sustainability or climate change will find the tool valuable.

The RISE tool is effective because it is interactive and communicates the data visually. It also displays relevant information and filters it down to the most essential bits. Most importantly, it provides actionable information, enabling the user to make data-driven decisions.

It can be improved in a few ways, however. For example, enabling the user to change key parameters easily and run simulations would provide greater transparency and insight into the specific actions that would be required to reach specific goals. The tool could also be improved by adding functionality to recommend policies or actions to take based on a country’s profile (geography, resources, demographics, regulatory/legal framework, politics). Still, RISE is a remarkable tool that can serve as an example of effective data presentation on climate change.

Data Log 2/21: A Day Observing My Data

In this day and age, it is very difficult to go through one’s daily routine in a networked economy without generating digital data in some way. Indeed, you almost have to go out of your way to consciously avoid certain activities, tasks, and behaviors that are routinely tracked and captured in digital form.

The following activity log illustrates both the frequency and breadth of the data that I generated in a single day.

  • 7AM: Wake up. My smartwatch senses that I am moving and provides a summary of my sleep.
  • 7:30AM: Turn on cable TV while eating breakfast, and select and save programs to watch later.
  • 9AM: Go on the internet and read news articles, check and send email, and update my calendar.
  • 11AM: Walk to MIT campus. My smartwatch provides a summary of my walk.
  • 1PM: Listen to music on my smartphone. I receive recommendations based on my listening habits.
  • 2PM: Order grocery delivery online and receive automatic email notification.
  • 2:15PM: Purchase books on Amazon and receive automatic email notification.
  • 2:30PM: Play guitar and record my playing on an iPad app, which sends the information to the Cloud.
  • 6:30PM: Exercise on treadmill, which tracks my workout (along with my smartwatch).
  • 7PM: Use CharlieCard to board the T and travel from Cambridge to Boston.
  • 7:15PM: Visit store and use smartwatch to purchase items.
  • 7:30PM: Visit convenience store and use credit card to purchase items.
  • 8PM Use Uber app on my smartphone to request a pickup to go home.
  • 10:30PM: Change thermostat setting in my apartment.
  • 11PM: Watch TV and update my saved programs.

The resulting activity log is interesting in several regards. First, since it captures only the activity that can be tracked digitally, it can result in an inaccurate portrayal of how one’s time is spent (or more generally, how a system functions or behaves). In my case, for example, I spent more than five hours during the day reading and studying, yet that activity was not captured digitally.

Second, the data that is generated can be categorized in many different ways: location tracking, motion detection, and transactional, for example. Data can also be captured for different purposes: health, entertainment, operational efficiency, convenience, surveillance.

A less obvious attribute of the data log above, however, is the degree of awareness associated with the capturing of data in each activity. Some behaviors (such as requesting an Uber ride) are active and require more consciousness and explicit consent about the data that is being tracked. Other activities (such as walking around in areas that have IP surveillance cameras) are more passive and subconscious with regard to data, and consent is usually implicit. In all cases, however, vast amounts of data are being captured digitally.

 

Paul’s Data Log – Living a live with Google

Android smartphone, Chrome browser, Google Maps, and Gmail. Regardless you recognize it or not, using an iPhone or Galaxy; Google is near to us more than we are expecting. Even Google is playing a crucial role to make our lives much easier, many people are unsure how much and how often they are using it and which kind of data are provided for. A simple way to check is the Google Dashboard where we can see our data and account activities. I want to illustrate how my typical day looks like with Google services.

9 AM – Managed schedule (Google relation: Google Calendar, Android phone)

10 AM – Ordered food visiting Instacart (Chrome browser)

11 AM – Watched YouTube videos (YouTube, Android phone)

11:30 AM – Visited Dell.com to check my order status (Chrome)

12:00 PM – Checked email (Gmail, Android phone)

1:00 PM – Visited Accounting-simplified.com to learn Finance languages and added the site to the bookmark bar (Chrome)

2:00 PM – Searched the route to a Japanese restaurant in Alston (Google maps, Android phone)

3:00 PM – Made a call to confirm dinner (Contacts, Android phone)

4:00 PM – Ordered battery for smoke alarm replacement (Chrome)

5:00 PM – Read Study.net articles (Chrome, Android tablet)

6:00 PM – Went to Japanese restaurant using Uber (Google maps, Android phone)

9:00 PM – Visited Stellar (Chrome)

9:45 PM – Updated apps in my phone (Play Store, Android phone)

10:30 PM – Updated course folders downloading new materials (Chrome, Google Drive)

Willie Zhu Data Log

iPhone

My iPhone is constantly on, and it’s probably both a) the device I use the most, and b) the device I have on my person the most. It tracks:

  • My sleep—when I sleep (based on when I stop using it)
  • Location—even though I’ve turned as many permissions off as I can, it’s still tracking my location. Good when I need Find My iPhone, but otherwise…
  • My contacts—who I call/text/message, and when
  • App usage—it knows all the time I’ve wasted on Reddit
  • Cell network—every cell tower I connect to
  • Apps—the apps I use definitely track my actions. My most used: Inbox, Spotify, Reddit, Messenger
  • Wifi—my phone is a walking data beacon that spews probe requests, uniquely identifying myself to anyone willing to listen

Computers

My Macbook, Windows desktop, Athena workstations, and so on

  • Operating systems—Windows 10 collects all kinds of data without my permission, and macOS probably does too
  • Intel ME—pretty much all of my computers have Intel processors
  • SSH logs—connecting to remote computers leaves logs behind
  • Athena—MIT probably tracks just about everything I do on Athena

General

  • Browsing history—this goes without saying. I’m pretty unique, according to https://panopticlick.eff.org/
  • Security cameras—MIT has cameras everywhere
  • Card taps—Similarly, there’s all kinds of places I need to tap to get in. It goes without saying that MIT records all of them
  • Purchase history—every time I buy something with a credit card or TechCash