A practical data evaluation post with Python code.

Geospatial Data Science is considered one of my areas of interest. I find it fascinating how we are able to visualize data on a map and the way — again and again — the relationships between the info points present great insights real quickly.

I think the applicability of this sub area of information science is pretty useful for any business, namely grocery stores, automobile rentals, logistics, real estate etc. On this post, we are going to go over a dataset from AirBnb for the town of Asheville, NC, in USA.

Side note: In that city lies one of the crucial amazing real estates in America, — and I might dare to say on this planet. The property pertains to the Vanderbilt family and, during an extended time, it was the biggest private property within the country. Well, it’s so value a visit, but that’s not the core subject here.

The datasets to be utilized in this exercise are the AirBnb rentals for the town of Asheville. They might be downloaded directly from their web page in http://insideairbnb.com/get-the-data, under the Creative Commons Attribution 4.0 International License.

Let’s get to work.

The knowledge from this post is usually from the book referred below (Applied Geospatial Data Science with Python, by David S. JORDAN). So let’s begin importing some modules to our session.

import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import pysal
import splot
import re
import seaborn as sns
import folium
# For points map
import geoplot.crs as gcrs
import geoplot as gplt

Now notice that a few of them could be recent for you, as they’re for me as well. If needed, use pip install module_name to put in any package needed. In my case, pysal and geoplot are recent to me, so that they needed to be installed.

Next, we are going to read the info from AirBnb.

# Open listings file
listings = pd.read_csv('/content/listings.csv',
usecols=['id', 'property_type', 'neighbourhood_cleansed',
'bedrooms', 'beds', 'bathrooms_text', 'price'…