Is there a relationship between the number of murals and home values in a neighborhood? This was the central question behind our 2022 LEADING Fellows research project, directed by Dr. Synatra Smith (Philadelphia Museum of Art) in collaboration with Dr. Alex Wermer-Colan at the Temple University Scholars Studio.

At first, it seemed straightforward: wealthier neighborhoods have more murals because residents can better advocate for beautification projects. But the relationship might work the other way – murals could contribute to rising home values, either directly through curb appeal or indirectly through gentrification. We set out to examine this in two ways: statistical analysis and interactive mapping.

Hugo is a useful tool for creating a website. In these notes, I will refer to using Hugo alongside Github. Github is used here both as a place to store the code for your site so that you can collaborate with others and access it from any computer, and as a way of hosting your site (putting it online so others can see it).

To begin with, make a github account here. Then create a new repository. This will be a folder where your code will live. Next you need to clone this repository locally so that you can open it on your computer and edit it. In your web browser, navigate to Github.com and open the main page of your repository. Click “code” and copy the repository URL.

For this project, we used Python to run SPARQL queries against Wikidata and turn the results into interactive visualizations. The main tools are SPARQLWrapper for querying and Plotly for visualization.

Setup

The notebooks were built in Google Colab, so setup is minimal:

from SPARQLWrapper import SPARQLWrapper, JSON
import pandas as pd

sparql = SPARQLWrapper("https://query.wikidata.org/sparql")

Running a Query

After defining a SPARQL query string (see the SPARQL post for examples), you send it to Wikidata and convert the JSON response into a pandas DataFrame:

SPARQL is a query language for retrieving data from knowledge graphs like Wikidata. If you’ve used SQL to query databases, SPARQL follows a similar logic – but instead of rows and columns, you’re working with linked triples: subject, predicate, object.

For this project, we used SPARQL to pull structured data about Black artists in Philadelphia from Wikidata’s public endpoint at query.wikidata.org.

A Basic Query

Here’s a minimal query that retrieves artists tagged with the PMA’s African American artist entity (Q94124522):

About

Wikidata is a secondary collaborative database that is curated by individuals to ultimately retrieve and store information. As of now, the database contains nearly 100 million data items, all of which can be edited by Wikidata users, at any given time.

Wikidata Items

For instance, below is an image of the item Philadelphia Musuem of Art. As the image shows, the museum is classified as an instance of another item, the art museum.