A practical data evaluation post with Python code.

Geospatial Data Science is one in all my areas of interest. I find it fascinating how we will visualize data on a map and the way — over and over — the relationships between the information points present great insights real quickly.

I consider the applicability of this sub area of information science is pretty useful for any business, namely grocery stores, automotive rentals, logistics, real estate etc. On this post, we are going to go over a dataset from AirBnb for 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 the planet. The property pertains to the Vanderbilt family and, during a protracted 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 town of Asheville. They could be downloaded directly from their site 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 is perhaps 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, in order that they needed to be installed.

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

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