Visualizing Street Tree Population Variance in NYC Using GeoPandas, Plotly, and JavaScript
<h2>And a comparison of the performance of the server and client-side rendering using Plotly Dash and JavaScript</h2>
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<p>17 min read</p>
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<p>Jan 27</p>
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<p>Photo by <a href="https://unsplash.com/@miteneva?utm_source=medium&utm_medium=referral" rel="noopener ugc nofollow" target="_blank">Maria Teneva</a> on <a href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="noopener ugc nofollow" target="_blank">Unsplash</a></p>
<p>In this article, I will:</p>
<ul>
<li>Use data from the US Census Bureau and the NYC Parks Department to map the change in street tree cover in NYC from 1995 to 2015.</li>
<li>Walk you through cleaning, aggregating, analyzing, and visualizing the data using Pandas/GeoPandas and Plotly and then presenting the data interactively using a web app.</li>
<li>Discuss how tree and population density varied spatially from 1995–2020 and the social implications of these changes.</li>
<li>Find that the median number of trees per block increased substantially citywide over this period, but that the magnitude of change varied greatly from borough to borough and neighborhood to neighborhood, with some neighborhoods losing trees.</li>
<li>Find that targeted tree planting efforts from 1995 onwards ameliorated urban heat island effects in under-forested, historically redlined areas, but also contributed to gentrification.</li>
</ul>
<p>Finally, I’ll compare the performance of the server and client-side rendering (using Plotly Dash and JavaScript) for this web app and find that for simple interactive data visualizations, client-side rendering performs best.</p>
<p><a href="https://betterprogramming.pub/visualizing-street-tree-population-variance-in-nyc-using-geopandas-plotly-and-javascript-f12251186342">Visit Now</a></p>