Analyzing Geospatial Data with Python (Part 2 — Hypothesis Test)

<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>Good. I recommend that you read the first post, so you can get the initial code and thoughts together and then move on with the knowledge made available in this second part.</p> <h2>Dataset</h2> <p>AirBnb, if you don&rsquo;t know it, is a peer-to-peer platform for people to list their houses, rooms or bedrooms for renting. Their rental listings data are gathered by this community project in the website&nbsp;<a href="http://insideairbnb.com/" rel="noopener ugc nofollow" target="_blank">http://insideairbnb.com/</a>, where anyone can go and download the datasets for analysis. So we will keep using the same data for this part. The data is open under the&nbsp;<a href="http://creativecommons.org/licenses/by/4.0/" rel="noopener ugc nofollow" target="_blank">Creative Commons Attribution 4.0 International License</a>.</p> <h1>In this post</h1> <p>In this post, we will learn about the components to create a geospatial hypothesis test. Here they are</p> <p><a href="https://towardsdatascience.com/analyzing-geospatial-data-with-python-part-2-hypothesis-test-fe3f3f18fc82">Read More</a></p>