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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="description" content="Relatable Impact!" />
<meta name="author" content="Your Name" />
<title>Relatable Impact!</title>
<link rel="stylesheet" href="./styles.css" />
</head>
<body>
<div class="container">
<div class="back-to-blog">
<a href="https://www.bhargavkantheti.com/" class="arrow-link"
>← Back to Blogs</a
>
</div>
<h1>Relatable Impact!</h1>
<p>
The outcomes of a Data Science project
<strong>require</strong> impactful communication to the
audience. For this project, I considered global crises that
effected large populations.
</p>
<p>You can watch this 5min YouTube video for a short summary.</p>
<iframe
width="560"
height="315"
src="https://www.youtube.com/embed/UT_-BeZl2o4"
frameborder="0"
allowfullscreen
></iframe>
<hr />
<h1>Principles</h1>
<p>
Global crises can be made more relatable by understanding the
<strong>Psychological Distance</strong> inducing lack of
relatability.
<br>
<br>
Psychological distance refers to how far away something feels in your mind. It's not about physical distance, but more about how close or far things seem in time, space, or even emotionally. For example, something happening far away might feel less important to you than something happening nearby. It's all about how we see things in our minds, whether they feel close or far, even if they're not physically distant.
<br>
<br>
For the case studies I take, I reduce psychological distance using
<ul>
<li>Space</li>
<li>Time</li>
<li>Reality</li>
</ul>
I visually showcase this with high-contrast maps.
<br>
<br>
I also use the concept of the "extra mile", which uses two principles:
<ul>
<li><strong>Contrasting Clarity:</strong> When you want to show how bad something is, you emphasize its impact with something even worse.</li>
<li><strong>Relative Deprivation:</strong> When you feel like things are really bad because you compare them to how they used to be.</li>
</ul>
</p>
<hr />
<h1>Case Studies</h1>
<p>
It can seem daunting to visualize so many datapoints, or to even find the landcover for
your required population count. I personally went an extra mile, and
created two libraries which you can call to
<strong>optimally</strong> find desired
<code>region-of-interest</code>.
</p>
<p>
The added advantage of using this library is that you can play
around with the widget in your jupyter notebook, all you have to
do is grant Google Earth Engine permissions to your google cloud
platform so that it can render your desired results.
</p>
<h2>SPACE</h2>
<h3>What if Australian Bushfires occurred in New York?</h3>
Making the Bushfires relatable to citizens of New York.
<pre><code>
bands = [3]
nyc = (-73.968285, 40.785091)
aus_cover = 59000
USLandCover().simulate(bands, aus_cover, nyc)
</code></pre>
<img src="./assets/meta/nyc-bf.jpeg" alt="NYC Bushfire" />
<p>
Extra MILE: Showing the number of trees the
<a href="https://teamtrees.org/">Team Trees</a> planted.
</p>
<pre><code>
bands = [3]
nyc = (-73.968285, 40.785091)
tt_cover = 244.95
USLandCover('008b00').simulate(bands, tt_cover, nyc)
</code></pre>
<img src="assets/meta/nyc-tt.jpeg" alt="NYC TT" />
<hr>
<h2>TIME</h2>
<h3>What if Spanish Plague re-emerged?</h3>
<pre><code>
USPopulations().sim_plague()
</code></pre>
<img src="./assets/meta/us-sp.jpeg" alt="US Spanish Plague" />
<hr>
<h2>REALITY</h2>
<h3>What if Genghis Khan sailed into the US?</h3>
<pre><code>
nyc = (-73.968285, 40.785091)
gg_dt = 40000000
USPopulations().simulate(gg_dt, nyc)
</code></pre>
<img src="./assets/meta/nyc-gk.jpeg" alt="NYC Genghis Khan" />
<hr>
<h2>GOOD STUFF</h2>
We can also showcase good things.
<h3>What if Diwali was celebrated in the US?</h3>
<pre><code>
up = USPopulations('FFFF00')
up.mep.add_basemap('CartoDB.DarkMatter')
up.sim_plague()
</code></pre>
<img src="assets/meta/us-diwali.png" alt="US DIWALI" />
<hr />
<h1>Credits</h1>
<p>
All the data are as latest as possible. The population data was
released on Jul 2023, corresponding to 2022 closing populations.
</p>
<p>
Population Data:
<a
href="https://www.census.gov/data/tables/time-series/demo/popest/2020s-total-cities-and-towns.html"
>US Gov</a
>
</p>
<p>Geemap Library: <a href="https://geemap.org/">geemap</a></p>
</div>
</body>
</html>