Visualizing Religious Diversity
Faith not only plays an important role in an individual’s life but also contributes a great deal in shaping societies. The increased movement of people across borders in search of better opportunities often leads to the peaceful co-existence of people of different faiths, religions, and value systems. In fact, the debate on religious pluralism and tolerance is ever more important in today’s discourse. In particular, the major urban areas, especially in the United States have become the melting pot of diverse ideas, values, and faiths. However, is there a way we can visualize this diversity of faiths beyond plain numbers and charts? Is there an underlying story that big urban centers of the US tell about their religious diversity?
In this visualization, I have used the places of worship as a proxy to represent religious diversity in the top 30 most populated cities of the US. The places of worship not only provide a safe space for the members of that faith but also allow them to exercise their religious freedom and preserve their value system in a foreign land.
The visualizations presented here are designed in an abstract form of a networked mesh of the places of worship of different dominant faiths in the country. The purpose was not only to look at the places worship as quantities but also as a network of interconnected places and how they share space with other religious places of worship in large significant urban centers. As a result, each urban region explored here gets its own portrait of religious diversity that accounts for the geographical spread of the places of worship, their numbers/quantity, and their relative position sharing the space with the places of worship of other faiths.
Awards and accolades:
Longlist, the Kantar Information is Beautiful awards 2019
Honorable mention in the Pudding cup 2019, annual dataviz challenge organized by Pudding.cool.
Process & Insights
Each dot in the visualization represents a place of worship positioned based on its longitude and latitude values. It transforms these geolocations into Mercator projections and then connects them using a Delaunay triangulation. Religions are color-coded with major faiths included in the data being Christian, Hindu, Buddhist, Jewish, Islamic. The resulting “mesh” creates a unique portrait of each city depicting the spread, quantity, and co-existence of different places of worship belonging to different faiths.
Even though the list of cities is not exhaustive, it does give us some insight into how these shared spaces get formed in the cities creating these unique portraits:
Undeniably, Christianity is a major religion with a significant number and spread of its places of worship. Visually, this appears like an underlying mesh in the background of all the other places of worship of different faiths.
The overall spread of the religious places of worship seems proportional to the size of the city. Instead of creating small pockets, the places of worship, overall, tend to take the shape of how big the city’s spread is.
Smaller cities tend to have higher shared spaces of different faiths compared to larger cities, where oftentimes, isolated pockets of places of worship of different faiths can be seen.
Population density also seems to be a driving factor of establishing places of worship, irrespective of the size of the city. It is possible that the size of the city only influences the spread of places of worship but the population density might influence the number of places of worship of different faiths.
The final visualization is shown below with a basic 'how to read' guide.
View the city crystals in the slide show and small multiples below.
The dataset contained places of worship data in the US in 2009 with the following attributes:
I first explored the dataset in tableau to understand the various visualization possibilities. I first started with the visualization of places of worships for various states in the US. However, after some discussions decided to explore diversity in cities for greater granularity.
Another decision that came after looking at the data was that the footprint of Christian places of worship was overpowering the other minority religions. In order to highlight the minorities and yet show Christian places of worships as a big mesh that engulfs all, I used a subdued yellow to depict them.
In addition to that, I sorted the data for each city such that the religion with the largest number of points is at the bottom and it decreases as they stack on top of each other.
When I started exploring various ways in which I can show religions in cities, I was intrigued by the 'mesh' like forms they were taking with just scattered dots. A mesh or network has a similar dichotomy that it can keep things together or keep things separate as we choose to use it. Just like how religions can keep humans together or can cause rifts between them.
I started exploring various forms of mesh representations and the feasibility of implementing them.
This is when I came across the Delaunay triangulation in D3 which looked promising to explore this design further.
One I had my code running, I started exploring different colours and filters which will make the visualization more interesting.
This project allowed me to experiment with creative visualizations. Here are some of my key learnings-
Ideas are cheap, it is what you build from it that really matters.
You are as good as your tools. Sometimes your skill set or your knowledge of tools can be your weakness. Be smart and find workarounds.
Be patient when coding visualizations from scratch and learn to make use of existing libraries to expedite your process. Follow the documentation carefully.
Work really hard but ask for help when you are stuck. Shout out to Steven Braun Data Visualization expert at Northeastern University for helping me troubleshoot my code.
When coding, assign yourself micro goals to accomplish. Completing those will give you tiny joys even if your actual visualization is a long way to do. It also keeps the code relatively cleaner.
Meddle around with your data before taking off with an idea. See if you see any stories emerging.Use alternative tools such as Tableau or even MS excel to observe early trends.
Code more often. It keeps you grounded as a designer. Also because it is the easiest skill to forget!
US Places of worship data:
https://hifld-geoplatform.opendata.arcgis.com/datasets/all-places-of-worship, accessed on April 14, 2019, at 8.00PM.
US city by population:
https://www.biggestuscities.com/, accessed on April 14, 2019, at 8.00PM.
US city by area:
Area: https://en.wikipedia.org/wiki/List_of_United_States_cities_by_area, accessed on April 14, 2019, at 8.00PM.
Data Visualizations, Data Wrangling in R, Infographics, Data Portraits
R Studio, D3.js, Tableau, ggplot2, dplyr, Excel, Adobe Illustrator
This project was done as a part of the Information Design Studio II class taught by Prof. Pedro Cruz.