Data visualization is a core piece of our daily lives, whether in art, entertainment, advertising, or technology. Take Spotify Wrapped, for example, a viral marketing campaign where Spotify users get a design-heavy analysis of their listening patterns over the course of a year. Everything we encounter has been designed by someone, and data is no different.
The use and importance of data visualization
There are two important use cases when it comes to data visualization:
- Provide context to data and datasets, specifically for communication purposes
- Spot and illustrate trends represented in data, which can supplement decision making, performance tracking, or various forms of data analysis.
Most people don’t understand data in raw formats. This is where data visualization comes in. Creating graphs, maps, charts, or any form of data visualization brings much needed context to your data.
This allows you to tell a story, and even convey emotion, with your data. Elements like color and scale in a map or other form of visual data can alter someone’s mindset when they’re ingesting information. Financial graphs, for example, utilize the color black to portray positive gains and revenue, while red is associated with negative performances or losses.
In addition to providing context to your data, data visualization also affords the opportunity to spot trends present in your data. For example, transforming a population data table into a map allows viewers to easily see where people live and which areas are more populated, thus amplifying the data set.
The 30 Day Map Challenge
The 30 Day Map Challenge was created by Topi Tjukanov in 2019 with the goal of challenging the cartography and GIS communities to share maps, exchange ideas, and start conversations about mapping and spatial data. More than 1200 mappers participated this year, creating more than 9000 maps for the challenge.
In Cuebiq’s inaugural year of participation, our data science team created maps with our privacy-first data. The maps are represented below, with brief details about the datasets sourced and the processes used to create our submissions.
Technology used to make the maps
Our team used several different programs to construct these maps. The datasets were sourced from our privacy-first data, and the stylized maps were created using Kepler.gl, Deck.gl, and Apache Superset.
Points: Popular stops in NY for tourist vs. residents
This first map uses Cuebiq’s Stops data, comparing popular locations between residents and tourists around New York City on a specific date. We categorized tourists as individuals who live at least 500 miles – or 800 km – away from the city, and visited New York on the specified date. Establishing this difference allowed us to divide the two groups and their stops, represented by red and blue points on the map.
Lines: Brooklyn Bridge travelers
Cuebiq Trajectories data was used to map the travelers’ journeys after they crossed the Brooklyn Bridge on a single date. The lines were created as we considered the trajectories of all devices which passed over the bridge.
Evacuation: Hurricane Elsa
Using Cuebiq’s Evacuation Rate data, our team produced a heat map of the nighttime evacuation rates for each county during Hurricane Elsa.
Heat Maps: LA Gentrification
Next we created a gentrification heat map of Los Angeles by using our Home and Work tables in Workbench. Using these data tables, we located all the residential areas throughout LA. Then we layered in census block group data, which allowed us to identify the patterns of individuals who moved from areas with higher average incomes to areas with lower average incomes.
Hexagons: Stops in Chicago
Using Cuebiq’s Stops data, we are able to present a time-varying distribution map, accounting for all the stops in Chicago over a 24-hour period.
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