Photos

Humans evolved keen eyesight for survival, and although we no longer need to spot predators, over 90% of our communication is visual. What’s more, humans process images 60,000 times faster than text! So, if you want to get your point across quickly, images are the way to go. But don’t be fooled! Choosing the best image for the job isn’t always easy.

ASSIGNMENT:

For this discussion, examine the following 6 pairs of images along with their figure captions. For each pair, select the image (top or bottom) that you think is more effective at conveying the intended information. Explain your reasoning. You may also include whether the image you chose still needs some improvement or whether the image you did not select could be used with some changes. For maximum effect, view only the image to see what information it provides, then read to caption for additional information.

REMINDER: Make sure to include questions at the end of (or elsewhere within) your post for others to discuss (and to receive maximum points).

IMAGE SET #1

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Example Mica in India.jpg

Figure 1. The map represents the states in India that produce mica.
The region of Bihar and Jharkhand in India exported the most mica
at 100,600 tonnes as of 2015 (Vivek Muthuramalingam and Nita
Bhalla, 2016).

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Example Mica in India 2.jpg

Figure 1: Location of natural Mica in India.

IMAGE SET #2

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Example Mica in India 2 kid.jpg

Figure: 3: How Mica is mined with children being largely
the workforce without any safety equipment.

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Example Mica in India kid.jpg

Figure 4. The dangers of mica
mining are presented as this child
digs deep through the dirt in search
for the mineral. Risk factors include
collapsing of the mine, an accumulation
of dust in the lungs, and death
(Gonzaga, 2018).

IMAGE SET #3

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Example Mining in Amazon.jpg

Figure 3.This figure depicts the annual imports of mercury to Peru, the gold prices per oz biweekly, and the mining area per hectare. Mercury imports were recorded until September 2009, while the rest of the data was a projection of the line equation (Swenson, Carter, Domec, & Delgado, 2011).

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Example Mining in Amazon 2.jpg

Figure 4. The value of
gold in Brazil is shown
here. The x-axis
represents the amount of
gold per ounce and the
y-axis represents the
value in U.S dollars (Gold
Price, 2020).

IMAGE SET #4

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Example Mining in Amazon 2 map.jpg

Figure 2. Topographic image of Brazil
including the Amazon Rainforest. The
purple color is representing the tree
cover loss from 2001-2018 (Gibbens, S.,
& Champine, R. D., 2019).

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Example Mining in Amazon map.jpg

Figure 1.At the bottom left corner, the base map highlights the forest loss concentration at 3 different levels. Medium ( yellow) being the lowest loss; while high (red) indicates the highest loss. The area covered with the white shade represents the protected area. Lastly, the letters in the map correspond to the following locations:
A.La Pampa(Madre de Dios)B.Bahuaja Sonene National Park (Madre de Dios, Puno)C.Iberia(Madre de Dios)D.OrganizedDeforestation(Ucayali, Loreto)E.CentralAmazon(Ucayali, Huánuco) (Amazon Conservation, 2019)

IMAGE SET #5

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Example MTR mining 2.jpg

Figure 3
Poverty Rates in Appalachia
Note. Nearly all counties with a Mountaintop Removal Coal
Mining site have higher-than-average rates of death due to
cancer, respiratory disease, and cardiovascular disease.
(Appalachia Voices)

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Example MTR mining.jpg

Figure 4. In the distributed
data representing the
concentration of deaths in the
Appalachian region, it is seen
that areas with mountaintop
removal coal mines, marked
in red, correlate with the
most deaths from respiratory
diseases, including lung
cancer (Center for Disease
Control and Prevention for
Health statistics, 2011).

IMAGE SET #6

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Example MTR mining 2 mine.jpg

Figure 1
Sheep Knob, W.Va
Note. Mountaintop Removal
Coal Mining Site in Sheep
Knob, West Virginia. Photo by
Kent Kessinger. (Kessinger,
n.d.) .

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Example MTR mining mine.jpg

Figure 1. Mountaintop mining extracts coal from high elevations by destroying large amounts of
mountain summits using explosives, to access coal seams underneath (Mason, n.d.).