In the first post, linked below, we worked with an introduction to Geospatial Data Analysis, where we downloaded the listings from AirBnb for the city of Asheville, in North Carolina (USA) and went through some steps to extract insights from geospatial data.
Analyzing Geospatial Data with Python
A practical data analysis post with Python code.
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.
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.
Dataset
AirBnb, if you don’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 http://insideairbnb.com/, 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 Creative Commons Attribution 4.0 International License.
In this post
In this post, we will learn about the components to create a geospatial hypothesis test. Here they are