All posts in Trawling - a blog by Big Lake Data tagged with data visualization

Because how many people actually read the report?

When the San Francisco County Transportation Agency released its in-depth report on transportation network companies (TNCs), it also launched an interactive data map. Well-designed custom apps, like this one, are excellent ways to engage policy makers and the broader public.

Take a moment to explore the TNCs Today map.

The map strikes a fine balance between offering the user many ways to interact with the data, while still providing an understandable interface. You can tell that the San Francisco County Transportation Agency put quality work into this – clearly considering the map to be an important piece of its research and policy effort. Nicely done.

Interested in learning more about custom data maps and how they can support your work? Drop us a line, we excel at making tools like this.

Often the most critical part of visualizing data is knowing where to start


When Dollars & Sense Magazine hired us to create custom data graphics for its December 2015 cover story, our first task was choosing the right approach.

Author Dan Schneider had argued his main point quite clearly in the cover article, “The Worst Place in the U.S. to be Black is Wisconsin,” using supporting evidence from a broad survey of social-economic studies. So, before doing anything else, we needed to determine which class of data graphic would visually drive home the cover story’s thesis.

We resisted the inclination to create a visually extravagant graphic with datasets plotted from each source – knowing that it could overwhelm the main point with a sea of disparate statistics. Instead, we sought a design that would emphasize Wisconsin’s miserable standing compared to other states. This seemed like a job for a special class of data visualization: the slopegraph.

In our case, the key feature to display was not the actual value of each state across various statistical measures – it was simply the relative rank of each state to its peers. Therefore, unlike most slopegraphs which plot continuous data on one or two y-axes, we chose an ordinal ranking of states across all statistical measures with a uniform scale on the y-axis:


Then, to clearly show how poorly Wisconsin ranked, we emboldened the lines and type for the three states that came in worst place on any one of the measures (New York, Minnesota, and Wisconsin) and used a spare amount of color to highlight Wisconsin’s particularly sad place among its peers.

The trade-off with this design is a loss of information on each measure (what is Wisconsin’s African-American incarceration rate?) for a clear comparison of states across multiple measures.

For more on slopegraph variants and design considerations, check out Charlie Park’s fine essay on slopegraph typology.

December 9, 2015 update: My good friend and toughest critic, Micheal Tofias, takes exception to calling this a slopegraph:

I am pretty sure that the point of the slopegraph is that the slopes are meaningful. That’s why they are often used to connect the same variable measured at two or more time points (across some number of subjects). The slope is the rate of change.

In your work for Dollars & Sense, the slopes are not directly or easily interpretable. At best they represent something like consistency across the various rankings.

I am sure this kind of plot has a name, but I am not sure what it is.

A fair point. As noted, this graph plots the rank order of the states across various measures, not the actual values of the measure for each state. That, plus the fact that the measures are not comparable by time, certainly means that the lines convey much less information than most slopecharts. In fact, what the lines are actually doing here are simply serving as extended labels that connect the rank values of each state across all measures.

I could call it a “bumpchart,” which Parker considers a particular subset of slopecharts. But again, it does lack the temporal component. So, maybe I’ll just call it “custom” and leave it at that.

In need of a custom data visualization? Let us help. Get in touch.

Unknown Pleasures

Sometimes beauty is inherent. Sometimes it’s familiar. And sometimes it’s both.


For me, John Cheshire’s visualization of the world’s population is both. First, there’s an elegance and economy by which the data is encoded. Then, there’s the striking similarity between his map and Joy Division’s Unknown Pleasures iconic album cover, which featured a plot of pulsar radio signals.


If you’re a fan of British post-punk rock and Edward Tufte – you’ll probably find Cheshire’s work beautiful in both ways. Heck, you can even order a print of his world map and hang it on the wall right next to your stacks of vinyl.

(As a bonus for R users, Ryan Brideau has a neat post on how he reverse-engineered the visualization and applied it to New Brunswick.)

breathing city

Joey Cherdarchuk at DarkHorse Analytics created this visualization of home and work population patterns in Manhattan.


His blog post on the viz development is a fun read and familiar for those of us who have been down similar roads.

See, it is super easy and takes almost no time at all to create something like this, as long as your definitions of “super easy” and “no time” are flexible enough to include difficult and time-consuming.

So true.

Vizual Statistix

Seth Kadish, blogging at Vizual Statistix, has a great data visualization comparing the orientation and congruity of various U.S. metropolitan street grids.

The plots reveal some stark trends. Most of the counties considered do conform to a grid pattern. This is particularly pronounced with Chicago, even though much of Cook County is suburban. Denver, Jacksonville, Houston, and Washington, D.C., also have dominant grid patterns that are oriented in the cardinal directions. … Downtown Boston has some gridded streets, but the suburban grids are differently aligned, dampening the expression of a single grid on the rose diagram. Finally, the minimal geographic extents of the grids in Charlotte and Honolulu are completely overwhelmed by the winding roads of the suburbs, resulting in plots that show only slight favoritism for certain street orientations.


And then he does the same for some European metros:



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