Code Name: Deforest-MCG

Team Members: Molly R. Kappes, Jun Song, Zachary F. Shanshory, Drishti Vidyarthi

Affiliation:
Info-201: Technical Foundations of Informatics
The Information School
University of Washington
Autumn 2019

1.1 Problem Situation
Stakeholders: Organisms that are native to the forests (plants and animals), indigenous people that live nearby, loggers, miners, farmers/cattle ranchers, and the government (Bradford, 2018).
Setting: Forests across 189 different countries (Bradford, 2018).
Policy and Ethics: Deforestation is leading to other environmental issues such as climate change by increasing the amount of carbon dioxide in the atmosphere. Also, as more animals are forced out of their habitats, it is creating a larger risk for species extinction. There have been policies issued by the UN to address this issue, such as the Paris Agreement created to address climate change by focusing on greenhouse gas emissions (Heller, N., Stolle F., 2016).

Envisioning Card Use: Card 1: The Long Cycles in Nature card explains that, “relative to human lifespan, some natural processes occur quickly. Other natural processes take hundreds or thousands of years. For the latter, interacting with the nonhuman world requires us to think of systems that will extend far beyond a single human lifespan.” This card delves into topics regarding some of the social and ethical concerns within our problem. Firstly, deforestation can cause dramatic changes to our climate and surrounding environments by removing carbon storage pools that would otherwise be controlling those carbon dioxide levels. This proves to be harmful to humans and the rest of society, as we already emit ridiculously high levels of carbon dioxide. If the rate of emissions aren’t slowed down, deforestation has the potential to harm the current generation, as well as the following generations of humans that come after us. In regards to ethics, deforestation is highly destructive to plant/animal habitats everywhere on earth. Since these organisms don’t get to have a say in the destruction of their homes, we need to consider their safety when carrying out these actions.

1.2 What is the Problem?
The problem our group is focusing on concerns deforestation throughout the world and the rate at which it has occurred over the past several decades. As deforestation destroys the lives of organisms and their surrounding ecosystems, it also makes the world more dangerous for humans. With forests and rainforests being logged/removed at alarming rates, it’s important for humans to understand the ecological and societal repercussions of their actions (Schwartz, 2014).

1.3 Why Does it Matter?
Our planet thrives because of ecological factors such as biodiversity, and since such a large scale of wildlife lives within forest biomes, we need to make sure that their lives are being considered. We cannot continue to allow money and other economic factors cloud the fact that the world’s forests are a necessary part of our planet’s wellbeing, and they need protection (Schwartz, 2014).

1.4 How Will it be Addressed?
We will address this problem by understanding how the rates of deforestation have been changing overtime and in which places deforestation has had the largest negative effects. We will create some interactive visualizations to show the importance of this data, and so that those viewing the project can easily understand the problem. Additionally, we will be using large data sets that have information on 189 of the world’s countries to reveal any trends based on region.

RQ #1: What effect does logging and anthropogenic causes have on forests worldwide annually?
RQ #2: How have deforestation rates changed overtime and how effective are current measures taken to reduce deforestation?

Both of our datasets were created by developers and employees working at the Gapminder Foundation in Stockholm, Sweden (“About Gapminder”).

Dataset 1: The first dataset was constructed to describe how much land area across the globe is covered with forests from 1990-2015. The dataset was accessed through the Gapminder website (https://www.gapminder.org/data/). Gapminder retrieved the data from the Food and Agriculture Organization (FAO). Each observation in the first dataset lists a particular country, and each variable lists the specific year to be looked at. In terms of size, the dataset contains 189 different countries across 25 years. Some of the data points are not included in the Gapminder datasets, and will not be considered (as “NAs”) when performing data wrangling and making visualizations.

Dataset 2: The second dataset was created to demonstrate the amount of wood removed (in cubic meters) for production of goods and services other than energy/fuel from 1990-2011. Similar to the first dataset, it spans across 189 different countries across 21 years. This data set was also accessed via the Gapminder website, which had processed the data from the FAO like in dataset 1. Each observation lists the country and each variable lists the specific year. Some of the data points are not included in the Gapminder datasets, and will not be considered (as “NAs”) when performing data wrangling and making visualizations. In addition, we had to use the gather function from the tidyr package to rearrange the data to make the scatter plot easier to make.

Data Wrangling: We organized the data by creating an Excel spreadsheet and formatted the data from Gapminder to fit within each cell. Blank values from the datasets were not included when doing data wrangling/analysis. Then, we were able to convert the spreadsheet into a comma-separated values (csv) file which could be used in Rstudio.

Envisioning Card Use:
Card 2: The Crossing National Boundaries card states that, “nations have different rules, customs, and infrastructure that affect the use of a technology.” This can pose as a weakness within our dataset, as we don’t know what each country’s laws or customs allow/prohibit when it comes to deforestation and logging. In other words, Australia may have more strict deforestation laws than we do here in the U.S. It would have been useful if Gapminder included information on what limits these different countries or regions have when it comes to cutting down trees and forests. This information could have alluded to reasons why certain countries had lower forest coverage or higher wood removal rates than others.

Forest Coverage Visualization: A choropleth world map was chosen to visualize the forest coverage dataset in order to allow the user to easily see the trends in forest coverage in different countries over time. The user toggles which year’s data they want to see on the map, which has each country change shade on a continuous scale depending on the percentage of forest coverage for that year. This way, the user is able to visually see the differences in forest coverage over any year interval between 1990-2015. This is intended to address the research question regarding deforestation rates over time, and the effectiveness of current measures to reduce deforestation throughout the world.

Wood Removal Visualization: For our wood removal visualization, we wanted to see the trend of the wood removal over the course of the years from 1990 - 2011. Thus, I chose a scatter plot to easily show the trend. The user is able to decide which country they want to look at. By selecting a country, it displays a unique scatter plot. Our original plan was to have the user select a year, and show a world map of the wood removal, but we thought using a scatter plot to display the information was better to visualize the trend. This visualization will allow us to answer how much wood removal has been increasing over the years and why that is a problem.

Page 1: Research Question/Background

Page 2: Forest Coverage Visualization

Page 3: Wood Removal Visualization

Page 4: Conclusion

Page 5: How to Help

Page 6: About Us

Our shiny application allows the user to understand deforestation rates overtime in different countries. We loaded our pages by downloading the csv files from the gapminder website. We loaded it to the application using the read function. When creating our application, we used dyplr, ggplot 2, shiny, tidyr, maps, and mapproj to organize our data and create visualizations. In our repository for the application, we have our "app.R”, "app_ui.R", "app_server.R", a directory with the data, and a README.md file. We created one main R studio file, titled “app.R” that references variables in "app_ui.R" and "app_server.R" to display the app. Our ui code was organized by page and our server code was organized by our two visualizations, the map and the scatter plot. In our ui code, we split up our six pages and organized how our content would be displayed to the user. In our server code, we made our visualizations interactive so the user can see how forest coverage changed overtime in different countries and how wood removal occurred in different countries over the years. From our analysis of the rate of deforestation, we will answer questions regarding how effective current measures are at reducing deforestation and the effect of wood removal in the forest area.

Our application analyzes rates of deforestation in different parts of the world and how those rates have changed overtime. A weakness in our application would be that the data we used was from the 90s to 2011 which is not very recent. We were also not able to find much data on certain countries and some of the data contained NA values. A strength of our report would be the way we scoped the project and the way we visualized the data. We were able to find accurate data to help us scope our problem to rates of deforestation around the world. We also included different visualizations such as maps and plots to show the user how the rates were changed overtime. The content on our application is well-tailored to our problem situation. Some future research could be what we can do if current measures are not very effective. Another factor would be to look into how deforestation is affecting and being affected by other climate change factors. Overall, our application provides a strong foundation to future research studies of deforestation rates.

1. Figure 1 - The World Bank. “Forest Area (% of Land Area).” Actualitix World Data and Statistics, https://en.actualitix.com/country/wld/forest-area-by-country.php.
   
2. Figure 2 - Verisk Maplecroft. "Deforestation Index 2019." https://www.maplecroft.com/risk-indices/deforestation-index/
3. “Deforestation: Facts, Causes & Effects.” LiveScience, Purch, 4 Apr. 2018, www.livescience.com/27692-deforestation.html.
4. Harris, Nancy, and Fred Stolle. “Forests Are in the Paris Agreement! Now What?” World Resources Institute, 26 Sept. 2018, www.wri.org/blog/2016/01/forests-are-paris-agreement-now-what.
5. Nunez, Christina. “Deforestation and Its Effect on the Planet.” Deforestation Facts and Information, 25 Feb. 2019, www.nationalgeographic.com/environment/global-warming/deforestation/.
6. Schwartz, Jason. “6 Reasons Stopping Deforestation (Still) Matters.” Greenpeace USA, 16 Nov. 2015, www.greenpeace.org/usa/6-reasons-stopping-deforestation-still-matters/.
7. “Data.” Gapminder, www.gapminder.org/data/.
8. “Ten Reasons to Reduce Tropical Deforestation.” Union of Concerned Scientists, 6 Aug. 2009, www.ucsusa.org/resources/ten-reasons-reduce-tropical-deforestation.
9. “How Can We Stop Deforestation?” The World Counts, 14 May 2014, www.theworldcounts.com/stories/How-Can-We-Stop-Deforestation.

Variable Name	Description	Data Type	Measurement Type
country	Name of the country	string	nominal
year	The year in which the data was collected	numeric	interval
forest_coverage_table	Forest coverage in a particular country/year	data frame	percentage
wood_removal_table	Wood removed in a particular country/year	data frame	volume
background	Displays the background tab in our shiny application	tabPanel	N/A
forest_coverage_visualization	Displays the forest coverage map in our shiny application	tabPanel	N/A
wood_removal_visualization	Displays the wood removal scatter plot in our shiny application	tabPanel	N/A
conclusion	Displays the conclusion tab in our shiny application	tabPanel	N/A
how_to_help	Displays ways people can help fight against deforestation	tabPanel	N/A
about_page	Displays the about page tab in our shiny application	tabPanel	N/A
my_ui	Holds the ui components of our shiny application	navbarPage	N/A
my_server	A function of our server side of the shiny application	function	N/A

• Jun Song: The project mainly helped me learn about the trends of deforestation across the world. It was intriguing to see the changes in the amount of wood removal over the years, and it has lead me to come up with insightful conclusions about deforestation. Working with this group also helped me develop my communication skills as we needed to communicate well in order to avoid merge conflicts. In addition, I also learned the main aspects of shiny and how I can use the data sets to concisely display the information. The most frustrating part was creating the UI because I am not good at designing and knowing what the best option for the user is. With my other courses, I mainly worked on the back-end/server-side of the application so working on the server was fairly easy. In the future, I probably won't use R to create a web application. Though it has the functionality, creating a web application through other languages/frameworks such as React is a lot more applicable. In terms of myself as a coder, I did not really expand my ability to problem solve because I have taken other courses that already covered the same concepts. However, I did learn how to code in R. Overall, for data science, I would not use R. There are some syntax that I personally don't like. Python is becoming more popular in terms of data science, so that's probably what I would use if I were to do a data science project.

• Molly Kappes: Throughout this project, I learned a lot about how to visualize and organize data in a way that can be interpreted meaningfully and make some impact on the world. The most frustrating part of this project for me was wrangling the data sets into formats that I could actually use for the visualization (forest coverage). This was difficult for me as I don’t have a lot of prior coding experience, but in the end all of my difficulties ended up making it so much more satisfying when I got the map up and working correctly. Another important skill I learned through this process was working in a team on a collective piece of code. Pushing and pulling was a little bit tedious at first, considering how easy it is for merge conflicts to occur, but by the end of it my team and I were working together and integrating our code without hitches. The most interesting and possibly most valuable experience I gained from this project overall was learning how the people on the other side of the visualizations and analyses actually come up with a product from raw data, which gives me a different perspective when consuming the analyses of others.

• Drishti Vidyarthi: This project helped me learn a lot about organizing, displaying, and understanding information. I developed skills for learning how to analyze data and patterns to make practical and viable conclusions. This project also helped me understand how to display information in a way that was the most beneficial for the user. I personally have a fairly strong art and design background and I always assumed the best way to display information would be the way that was the most appealing or aesthetic, however, this project helped me understand that is not always the case and it has more to do with overall clarity and simplicity. I also grew my skills in programming in R and I learned how applications in Shiny work from the server and UI perspectives. Other than these technical skills, I also gained experience in project development and management. I was fortunate enough to have a team that worked very well together and was very efficient at dividing tasks to meet deadlines. In the future, I would try to spend more time researching the problem space to gain a better understanding of what we may be looking for in the data. Given our time constraint, I think we rushed this part of the project a little bit and spending more time on research would help improve our analysis. Overall this project made me feel much more comfortable coding and analyzing data and I know these skills will be very beneficial regardless of the career I end up pursuing.

• Zachary Shansory: Working on this project has proved to be a great learning experience for myself. Not only have I been learning more about how Shiny applications work (between the UI and the server), but I’ve also bettered my understanding of how working on larger programming projects can happen in everyday life. Pulling previous versions and pushing new ones to GitHub correctly is essential to completing a project like this in a timely manner. Working and communicating together with my teammates has been an awesome time, as everyone is fairly easy-going and we work well when it comes to dividing up tasks and making sure they are completed on time. Something that I would probably do differently in the future would be to pick a topic that I was unfamiliar with. I’ve done a lot of previous research on deforestation and climate change, so it isn’t new to me. A topic regarding how much data (maybe emails, texts, etc.) is sent out everyday or year would be an interesting one, or maybe something to do with medicine.

We used the envisioning cards to highlight factors such as strengths, weaknesses, and the social/ethical considerations regarding our datasets. For the problem situation, we wanted to make sure we could think about our chosen datasets thoroughly, and identify aspects that may have been overlooked by Gapminder. Specifically, societal and ethical considerations aren’t included in the data. The needs and values of humans and other organisms should be recognized and thought about so that generalizations and biases aren’t made when conducting analysis. In regards to the datasets themselves, the envisioning cards were used to identify strengths and weaknesses. Something that was potentially overlooked when examining forest coverage % and wood removal data was the laws and rules governing the allowance of deforestation. It’s crucial to think about this, as larger contributors of deforestation may have laws in place that are more lenient than in other countries. The envisioning cards allowed our group to approach the problem situation and data with a “humanistic” lens which is vital when looking at environmental issues like deforestation.