The document discusses redesigning infographics from a blog post about the global food challenge. It summarizes the original graphics, which showed growing global population and shifts to meat-heavy diets straining food resources. The redesign transforms a stacked bar chart of population growth into a slope graph to more clearly show dramatic growth in Asia, Africa. Tables of meat consumption are redesigned with heat maps to highlight areas of highest growth. Multiple bar graphs of resource use are replaced with Cleveland dot plots to easily compare plant and animal foods' land, water and emissions impacts. The redesign aims to concisely visualize key trends exacerbating the looming global food crisis.

1. THE GLOBAL FOOD CHALLENGE
GRAPHIC REDESIGN PROJECT
STAT 515 – APPLIED STATISTICS AND VISUALIZATION FOR ANALYTICS, FALL 2016
MELISSA A. JOHNSON

2. 2
ORIGINAL GRAPHICS & CONTENT
The original graphics are from a blog post that is part of the World Resource Institute blog series, Creating a Sustainable Food
Future. The series explore strategies to sustainably feed the explosive growth in global population by 2050 with varying
infographics. I’ve chosen to omit some of these diagrams to focus particular attention to the world’s population growth and shifting
diet trends that are reshaping the way our food is sustainably produced. The world’s population is projected to grow from 7 billion
to 9.6 billion people by 2050; in addition to the 37% increase in population, the world’s per capita consumption of meat and dairy
products especially in emerging markets (China and India) are on the rise. These foods are more resource intensive and
environmentally impactful to produce than plant based products. A “food gap” is characterized by nutritional disparities between
rich and poor populations and it is intensified with overconsumption of protein that widens the gap.
Diagram 1: Growing Population: United Nations Department of Economic and Social Affairs, Population Division (UNDESA). 2013.
World Population Prospects: The 2012 Revision. New York: United Nations.
Total population by major area, region, and country. Medium fertility scenario.
Diagram 2: Global Consumption of Meat and Milk Products: Bunderson, W. T. 2012.
“Faidherbia albida: the Malawi experience.” Lilongwe, Malawi: Total LandCare.

3. 3
Diagram 3: Animal Based Foods are More Resource Intensive
based on data from the Working Paper by, J. Ranganathan. 2016. “Shifting Diets for a Sustainable Food Future”
REDESIGN DIAGRAM 1
The most important piece of information to convey about the food crisis is perhaps the explosive global population rising at a
staggering rate of 7 billion to 9.6 billion by 2050. The stacked bar chart in diagram 1 is not the worst graph but it can be visually
overwhelming without doing this important statistic enough justice. As an audience, we are required to do some computing if we
want to make sense of the information. There are no labeled data points, thus we need to continuously look at the vertical and
horizontal axes to compare and compute numbers. Since the two-colored stacked bar chart shows “Population in 2o12” in yellow
and “Population growth from 2012-2050” in red, we have to compute the estimated population in 2050 by adding the yellow and
red areas together for all seven regions. We may also be inclined to know the rate of growth or direction of growth (increase or
decrease) in population but since it is not intuitive from the bar chart, further computing is required. In addition, bar charts tend to
slightly skew the magnitude of the data, leaving the audience to confuse Sub-Saharan Africa as half the population of Asia, although
the numbers would argue otherwise.
I chose to redesign the stacked bar chart as a slope graph because they are useful when we have two time periods or points of
comparison (population in 2012 and estimated population in 2050) and want to quickly show relative increases and decreases across
categorical variables (regions) between the two data points. I added absolute value data points (2012 population, 2050 population)
and connected the points to show visual increase or decrease in rate of change (via the slope and direction) so that it instantly jumps
out at the reader. I used color, a pre-attentive attribute to code Asia, Sub-Saharan Africa, and North Africa to draw attention to
densely populated regions where population increases exponentially over time while the rest of the data is preserved for context
without competing for attention. Additionally, I computed the percentage change between population in 2012 and estimated
population in 2050 to add more context adjacent to the slope graph. We can quickly discern that the majority of the 9.6 billion
population growth will occur in Asia, Sub-Saharan Africa, and North Africa where there are many developing countries currently
undernourished.
I created the slope graph by extracting data points (estimated) from the stacked bar chart in diagram 1. I ordered the data into a
table with three columns (region, population in 2012, and population growth 2012-2015) to prepare the data for graphics. I
followed a slope graph tutorial to transform a line graph to a slope graph (instructions in appendix).

4. 4
REDESIGNED SLOPEGRAPH FOR DIAGRAM 1
REDESIGN DIAGRAM 2
In order to create my tables, I had to first record all the data from the original table in a .csv file and use Excel table editing features
to restructure and redesign the tables. The table in diagram 2 is attempting to convey a lot of numerical information with poor
design. A quick glance at the table and the audience may be instantly overwhelmed by the distracting (black and yellow) heavy
borders and shading which only competes for attention without any indicators as to which piece(s) of information should stand out
and grab our attention.
Since the table is attempting to communicate multiple different units of measure such as time (2006 vs. 2050), kilocalories per
person, per day (1 kcal = 1000 calories), and percentage change between current and future consumption, it is typically easier to
read all of that information in a table than a graph. I felt that combining livestock and beef/mutton numbers into one table is
crowded and produces greater mental load for readers who are trying to observe all the numerical values, which is why I separated
the data into two tables. Although I kept the table format, I redesigned the layout so that the design fades into the background and
the data does all the talking.
Tables interacts with our verbal system, which means we read them to compare values and deduce any particular points of interest.
My goal was to use a heat map to target these potential points of interest. This approach is appropriate for mixing details I still
want to include in the table while also making use of visual cues to direct the reader to the most important aspect of the data. A
heat map aids in visualizing data in tabular form, which in addition to the numbers I also leveraged the filter feature to sort from
descending to ascending to convey the relative magnitude of the percentages. To reduce the mental processing, I used an
orange/blue color saturation to provide visual cues, the higher saturation of blue, the higher the number. This makes the process of
picking out the tails of the spectrum—the lowest number (-2%, -19%) and highest number (94%, 138%) an easier and faster process
than in the original table where we didn’t have any visual cues to help direct our attention. By utilizing light borders and white
spaces to set apart elements of the table, I ensured my design was not distracting from the important details I want the audience to
discern.

5. 5
We can now see that the world’s per capita meat and dairy consumption is growing, especially in China and India, and is projected to
remain high in Latin America and Asia. These foods are more resource-intensive to produce than plant-based diets, which further
exacerbates the imminent food crisis.
REDESIGNED TABLES FOR DIAGRAM 2
Asia excluding China and India
Latin America excluding Brazil
Asia excluding China and India
Latin America excluding Brazil

6. 6
REDESIGN DIAGRAM 3
In order to plot these graphics, I recorded all the data (estimations) from the original bar chart in a .csv file where I used R to read in
the file. The Cleveland dot plots are created using the ggplot2 graphics package. R codes script can be found in the appendix section
of this report. The stacked bar chart in diagram 3 is trying to plot across many categories (land, water, greenhouse emissions) that
are measured on different scales and units while simultaneously showing the subcomponent pieces. This graph can quickly become
visually overwhelming because we are constantly comparing differing unit scales and summing up subcomponent pieces to discern
the effects of each category. The use of color is somewhat helpful since they naturally adhere to their categorical variables (i.e.,
water is blue). This chart further splits the categories into plants versus animals to try to convey the main purpose of the graph:
animal based foods are more resource intensive than plant based foods. Since the stacked bar chart is structures as absolute
numbers with the three-unit scales on the y-axis, it is harder to deduce these numerical values. I had difficulty in switching back and
forth with different units/scales when trying to record the data, as some bars measure equivalently to another bar but because of
the scale/unit difference, I would then need to deduce a different value.
I chose to enhance diagram 3 using several Cleveland dot plots instead of a bar graph because it reduces the visual clutter and is
easier to read. I wanted to separate the three-units/scales into six different plots to show the magnitude and clear resource
consumption patterns between animal and plant foods.
The original graphic emphasizes the resource intensity of animal-based foods as well as greater environmental impacts to produce
these foods. Since many animal-based foods rely on crops for feed, increased demand for animal-based foods widens the food gap
relative to increased demand for plant-based foods. Therefore, overconsumption of protein leads to a wider food gap. The
redesigned plot graphs clearly show that our primary sources of protein (beef, chicken, pork) are the most resource intensive and
environmentally impactful foods to produce across all categories.
REDESIGNED PLOTS FOR DIAGRAM 3
ha = one hectare contains about 2.47 acres. primarily used in the measurement of land

7. 7
ha = one hectare contains about 2.47 acres. primarily used in the measurement of land
1 meter
3
= 1000 liters

8. 8
1 meter
3
= 1000 liters
Tonnes of carbon dioxide equivalent (t CO2e) is a measure that allows you to compare the emissions of other greenhouse gases
relative to one unit of CO2.

9. 9
Tonnes of carbon dioxide equivalent (t CO2e) is a measure that allows you to compare the emissions of other greenhouse gases
relative to one unit of CO2.

10. 10
APPENDIX
Slope Graph Tutorial on Excel - http://peltiertech.com/slope-graphs-in-excel/
1) Converted data from diagram 1 to an Excel table (data may be estimates based on information from the bar chart)
a. Formatted data into a slope graph to redesign diagram 1
2) Redesigned Table Data (.csv file) for diagram 2

11. 11
3) R script for Cleveland Dot Plots
mydata <- read.csv("highresourcefood.csv", header=TRUE)
head(mydata)
require(ggplot2)
require(plyr)
ce <- ddply(mydata, "Resources", transform,Percent_Landuse.Cropland = Landuse.Cropland / sum(Landuse.Cropland)
* 100)
ggplot(ce, aes(x=Resources, y=Percent_Landuse.Cropland, fill=Landuse.Cropland)) + geom_bar(stat="identity")
ggplot(mydata, aes(x=Landuse.Cropland, y=reorder(Resources,Type))) + geom_point(size=3) +
theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank(), panel.grid.major.y =
element_line(colour="grey60", linetype="dashed"))
mydata[, c("Resources", "Type", "Landuse.Cropland")]
Resourceorder <- mydata$Type [order(mydata$Resources, mydata$Landuse.Cropland)]
mydata$Type <- factor(mydata$Type, levels=Resourceorder)
p<- ggplot(mydata, aes(x=Landuse.Cropland, y=Resources)) +
geom_segment(aes(yend=Resources), xend=0, colour="grey50") +
geom_point(size=3, aes(colour=Resources)) +
scale_colour_brewer (palette="Set1", limits=c("R","T"), guide=FALSE) + theme_bw() +
theme(panel.grid.major.y = element_blank()) +
facet_grid(Type ~ ., scales="free_y", space="free_y")
p + ggtitle("Cropland Use (in Hectare)") + theme(plot.title=element_text(size=rel(1.3), lineheight=.9,
family="Arial",face="bold", colour="darkgreen"))
mydata[, c("Resources", "Type", "Landuse.Pasture")]
Resourceorder <- mydata$Type [order(mydata$Resources, mydata$Landuse.Pasture)]
mydata$Type <- factor(mydata$Type, levels=Resourceorder)
p<- ggplot(mydata, aes(x=Landuse.Pasture, y=Resources)) +
geom_segment(aes(yend=Resources), xend=0, colour="grey50") +
geom_point(size=3, aes(colour=Resources)) +
scale_colour_brewer (palette="Set1", limits=c("R","T"), guide=FALSE) + theme_bw() +
theme(panel.grid.major.y = element_blank()) +
facet_grid(Type ~ ., scales="free_y", space="free_y")
p + ggtitle("Pasture Land Use (in Hectare)") + theme(plot.title=element_text(size=rel(1.3), lineheight=.9,
family="Arial",face="bold", colour="aquamarine4"))
mydata[, c("Resources", "Type", "Water.Consumption.Rain")]
Resourceorder <- mydata$Type [order(mydata$Resources, mydata$Water.Consumption.Rain)]
mydata$Type <- factor(mydata$Type, levels=Resourceorder)
p<- ggplot(mydata, aes(x=Water.Consumption.Rain, y=Resources)) +
geom_segment(aes(yend=Resources), xend=0, colour="grey50") +
geom_point(size=3, aes(colour=Resources)) +
scale_colour_brewer (palette="Set1", limits=c("R","T"), guide=FALSE) + theme_bw() +
theme(panel.grid.major.y = element_blank()) +
facet_grid(Type ~ ., scales="free_y", space="free_y")
p + ggtitle("Rainwater Use (in Meter Cube)") + theme(plot.title=element_text(size=rel(1.3), lineheight=.9,
family="Arial",face="bold", colour="blue4"))
mydata[, c("Resources", "Type", "Water.Consumption.Irrigation")]
Resourceorder <- mydata$Type [order(mydata$Resources, mydata$Water.Consumption.Irrigation)]
mydata$Type <- factor(mydata$Type, levels=Resourceorder)
p<- ggplot(mydata, aes(x=Water.Consumption.Irrigation, y=Resources)) +
geom_segment(aes(yend=Resources), xend=0, colour="grey50") +
geom_point(size=3, aes(colour=Resources)) +
scale_colour_brewer (palette="Set1", limits=c("R","T"), guide=FALSE) + theme_bw() +
theme(panel.grid.major.y = element_blank()) +
facet_grid(Type ~ ., scales="free_y", space="free_y")