For this lab, imagine you are the mapping specialist and a member of a new “Happier World 2020” team, tasked with creating a report of maps and supporting text (500+ words) to present to a group of leaders at the United Nations. The total length of the report should be no longer than 7 pages (this page count is inclusive of all text and maps).
Use data from the 2018 World Happiness Report [1] to visualize multiple variables related to world happiness and well-being; these relate to the UN’s Sustainable Development Goals for 2030 [2]. In creating this document, you will gain experience both with creating maps from a multivariate data set and in visualizing data uncertainty. In compiling these maps as a document rather than in a map layout, you will explore a new way to present your maps - one that is common when designing maps to present to policy-makers, or when illustrating a scientific paper.
For Lab 7, your only deliverable will be a single compiled PDF with text and images of your four maps.
Export your main map and three smaller maps as images (or use the Snipping tool) and include them as figures in a report with accompanying text. As when creating a map layout, attend to aesthetics, visual hierarchy, and negative space.
A rubric is posted for your review.
Please refer to the Lesson 7 Lab Visual Guide.
You can learn more about these indicators in the World Happiness Report [1], but this will help you get started as you decide what indicators to map and what ideas you might propose in your report.
The Life (Cantril) Ladder asks people to imagine a ladder with steps from 0 to 10 (the top), where the top of the ladder is their best possible life, and the bottom is the worst—and to place their life at the present at some point on the ladder.
GDP per capita is calculated in terms of Purchasing Power Parity (PPP) in 2011 international dollars as indicated by the World Bank.
Healthy life expectancy is calculated based on data from the World Health Organization (WHO) and WDI. WHO data is based on estimates from 2012.
Social support is the national average of responses (0 or 1) to the question “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?” This question is part of the Gallup World Poll (GWP).
Freedom is calculated as the national average of responses to the question “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?” This question is part of the Gallup World Poll (GWP).
Generosity is calculated as the residual of regressing the national average of answers to: “Have you donated money to a charity in the past month?” on GDP per capita. This question is part of the Gallup World Poll (GWP).
Perceptions of corruption is calculated as the average of answers to two questions: “Is corruption widespread throughout the government or not?” and “Is corruption widespread within businesses or not?” Perception of business corruption is used as a proxy for a total corruption measure in countries where responses to questions of government corruption are not available. These questions are part of the Gallup World Poll (GWP).
Helliwell, J., Layard, R., & Sachs, J. (2018). World Happiness Report 2018, New York: Sustainable Development Solutions Network.
Credit for all screenshots is to Cary Anderson, Penn State University; Data Source: World Happiness Report (Helliwell et al. 2018), Natural Earth.
This is your starting file in ArcGIS Pro: It contains boundary data from Natural Earth, as well as thematic data from the World Happiness Report [5].
As suggested above, the purpose of this lab is to visualize data from the World Happiness Report. You should explore this data in ArcGIS Pro (Figure 7.2), and view the World Happiness Report [5] online to get a sense of what the data means. Definitions for each happiness indicator variable are also listed at the bottom of the Lab 7 requirements page.
Your first task is to choose a happiness variable of interest and to map this as your primary map for the lab. For this map, you must select one of the variables with an associated "Standard Error" column. We will be using this as a proxy for uncertainty (more on that later).
You may choose from among several thematic mapping options (choropleth; graduated symbol; proportional symbol, etc.) to map your happiness data. This survey data is a bit more abstract than the data we have become accustomed to working with, so for this lab, you have a bit more freedom than usual in selecting a mapping method. Shown below are some examples of symbolization methods that you might use for your primary map.
The data provided to you for this lab has already been standardized for you, so you will not need to choose a "normalization" field.
When designing your map symbols, recall previous labs and focus on creating a useful and aesthetically-pleasing design. If using proportional symbols, for example, you will likely want them to be semi-transparent so that both symbols can be seen in the case of overlap.
As you might notice, not every country is included in the World Happiness Report. Due to this, an important element of your map design will be deciding how to visualize null values. You want it to be clear to the map reader which countries are not included in the report, but you do not want these to be too prominent in your map's visual hierarchy - they should not distract from your map's main purpose. As shown below, symbolizing null or "out of range" values is a slightly different process depending on which symbolization method you choose.
Your primary map should visualize not only the happiness indicator you have selected, but also its associated uncertainty. For this lab, you will use your chosen variable's associated "standard error" field as a proxy for uncertainty. Assume that higher standard error = higher uncertainty. Though the statistics involved are slightly more complicated than this, the focus of this lab is on visualization and thus this generous assumption is suitable for our purposes. Additionally, as we are interested in where the data is more or less certain, rather than in the actual standard error values, you should classify this uncertainty data into general groupings (e.g., "low," "medium," "high").
You may choose to visualize uncertainty either extrinsically or intrinsically. When selecting a method, consider how you will represent this uncertainty in your map's legend, as well as how your design might be interpreted by your map's intended audience. This guide presents two popular methods for visualizing uncertainty, though there may be others.
Option #1: Extrinsic uncertainty visualization
The easiest way to create an extrinsic uncertainty visualization layer is to copy-and-paste your country-boundary layer (which includes all the happiness data), and to symbolize uncertainty with the duplicate layer.
Two examples of extrinsic uncertainty visualization are shown in Figure 7.9. Recall from the lesson content the visual variables most effective for visualizing uncertainty. Your goal should be to create an intuitive design.
Once you've finished creating your map and adding it to a layout, you'll need to design an import part of this lab - your map legend. Figure 7.10 below contains some examples of legend designs. More so than with previous labs, you will likely want to make significant edits to your legends via the "convert to graphics" function in ArcGIS Pro.
Option #2: Intrinsic uncertainty visualization.
The other primary option for visualizing uncertainty in your map is intrinsically, via the "vary symbology by attribute" option in ArcGIS Pro. Think carefully about how you apply elements like transparency—the progression of your data should match the visual progression of your design. To accurately depict your data and its uncertainty, you may have to manually edit or reverse a color scheme or transparency range.
Once you've finished your primary map, you will create three smaller maps. These maps should visualize three additional variables from the World Happiness Report. You do not need to visualize uncertainty in these smaller maps. For this lab's final deliverable, you will include your four maps in a report that focuses on answering the following questions:
You may start with this template: Report Template: Lab 7 [6] and customize it as you wish. You do not need to include additional research from sources outside of the World Happiness Report, though you may if you wish.
Remember design ideas from previous labs - you may want to add elements such as a grid, explanatory text, and data credits to your layouts. It's up to you how you balance your final document with such elements - for example, instead of listing a data source on your map images, you can simply include this source in the text you write. Also, note that due to the scale of these maps you do not need a north arrow or scale bar - focus on creating a useful and cohesive document, as well as smartly-designed legends.
Links
[1] https://s3.amazonaws.com/happiness-report/2018/WHR_web.pdf
[2] https://www.un.org/sustainabledevelopment/sustainable-development-goals/
[3] https://pennstateoffice365.sharepoint.com/:u:/s/GEOG486FIles/ER6wQkPrAY5MosaUH_eX6wwBSGvr2rknl-72xfB8B2qZUw
[4] https://pennstateoffice365.sharepoint.com/:w:/s/GEOG486FIles/EVII8Kc9EeBMmxl5d9INvUgBS0ukJpHAV-Su0H6Zp0n_Tg
[5] http://worldhappiness.report/ed/2018/
[6] https://pennstateoffice365.sharepoint.com/:w:/s/GEOG486FIles/EVII8Kc9EeBMmxl5d9INvUgBS0ukJpHAV-Su0H6Zp0n_Tg?e=Shdz1u