Analyzing Geospatial Data with Python (Part 2 — Hypothesis Test)
<h1>Introduction</h1>
<p>In the first post, linked below, we worked with an introduction to Geospatial Data Analysis, where we downloaded the listings from <em>AirBnb </em>for the city of Asheville, in North Carolina (USA) and went through some steps to extract insights from geospatial data.</p>
<h2><a href="https://towardsdatascience.com/analyzing-geospatial-data-with-python-7244c1b9e302?source=post_page-----fe3f3f18fc82--------------------------------" rel="noopener follow" target="_blank">Analyzing Geospatial Data with Python</a></h2>
<h3><a href="https://towardsdatascience.com/analyzing-geospatial-data-with-python-7244c1b9e302?source=post_page-----fe3f3f18fc82--------------------------------" rel="noopener follow" target="_blank">A practical data analysis post with Python code.</a></h3>
<p><a href="https://towardsdatascience.com/analyzing-geospatial-data-with-python-7244c1b9e302?source=post_page-----fe3f3f18fc82--------------------------------" rel="noopener follow" target="_blank">towardsdatascience.com</a></p>
<p>In that post, we focused more on where the rental properties were concentrated and in their prices. Therefore, we concluded that Asheville’s listings are concentrated on the downtown area and the highest prices can be seen along the Blue Ridge Parkway road, given the beautiful view, and country environment probably.</p>
<p><a href="https://towardsdatascience.com/analyzing-geospatial-data-with-python-part-2-hypothesis-test-fe3f3f18fc82"><strong>Click Here</strong></a></p>