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&nbsp;<em>AirBnb&nbsp;</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&rsquo;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>