Is Starbucks really always two blocks away?

This one started on Reddit.

Someone on r/NYC complained (naturally) about the poor quality of an overpriced burger they bought at a restaurant chain, and u/seancurry1 took the time to share a life story relevant to the topic:

Leaving aside the usual heckling-of-the-tourists—although I admit, having lived here all of my life, I do enjoy a good heckling—one wonders whether or not this statement really is true. Starbucks baristas always (or almost always) ask for your name when serving your order, but I have long since given up on my real name, going by the pseudonym Al whenever I'm on location. Well, the last two times I've been in a Starbucks I've gotten back "Ale". Not the good kind. Come on guys. Two letters.

It's pretty well established that no one actually really likes Starbucks (except maybe this guy), but we all tolerate it well enough, and you do have to respect their business acumen, because they really do seem to be literally everywhere. But is it really true that every two blocks, no matter which direction, another Starbucks calls? This is a hypothesis worth putting under the microscope.

Mapping coordinates

First and foremost I needed some sort of way of actually getting every instance of a chain in Manhattan. There are two major APIs available for this sort of job that I am aware of, Yelp! and Google Maps (and maybe Foursquare), and Yelp! turned out to be the better of the two. Yelp! lists its businesses in ascending order: so "dunkin-donuts-3" accedes to "dunkin-donuts-4", then "dunkin-donuts-5", and so on down the line of infamy. Using their Python driver and taking the time to check for stores which have closed, it's a simple enough matter to gather a list of every instance of their "biz" (Yelp! URLs are in the form http://www.yelp.com/biz/[...] ).

If you're the sort that likes reading code (I know I am), here's the core of it:

def fetch_businesses(name, area='New York', manual_override=0): area = area.lower().replace(' ', '-') name = name.lower().replace(' ', '-') i = 2 # Run the first one through by hand. try: responses = [client.get_business("{0}-{1}".format(name, area))] # This can happen, and did, in the Dunkin' Donuts case. except BusinessUnavailable: responses = [] pass # The rest are handled by a loop. while True: bus_id = "{0}-{1}-{2}".format(name, area, i) try: response = client.get_business(bus_id) except BusinessUnavailable: # We manually check trouble spots. # But see the TODO. if requests.get('http://www.yelp.com/biz/' + bus_id).status_code != requests.codes.ok: break else: # Increment the counter but don't include the troubled ID. i += 1 continue responses += [response] i += 1 print("Ended `fetch_businesses()` on:", "http://www.yelp.com/biz/" + bus_id) return responses

I used folium (the Python driver for leaflet.js) to get a sense of the data. Here's the coordinate map for Gregory's Coffee, a locally-grown coffee chain that some of my friends know that I love to hate (their logo is literally their founder's well-coiffed head—tacky) but go to anyway:

No word yet on the Williamsburg location. Playing around with these maps you can even find some weird discrepancies in Yelp's data. The following coordinate map for Dunkin Donuts, for instance, reveals a franchise nominally in "New York" but actually way out north in Rockaway County:

And here's the Starbucks map. Your analysis is only as good as your data, of course, and a large number of comments on the corresponding Reddit post for this thread point out that the paucity of outer borough locations on this map is incorrect, as many locations are actually instead ID'd either by borough or, amazingly, by neighborhood! The Yelp! API documentation, annoyingly, is actually mum on this fact. It's not practical to try to scrape data by borough and by neighborhood, however, but after digging around for a bit in their data it appears that this never happens in Manhattan—only in the outer boroughs. Since we're focusing on Manhattan in this post, net-net this doesn't impact our analysis later on—but the data on locations outside of Manhattan displayed here should be taken with a grain of salt, as it is extremely partial.

If you scroll in and look around for a bit, it becomes immediately obvious that, ok, there are a lot of areas in Manhattan which are absolutely not always in a two-block radius of a pumkin spice latte. But amazingly, there are also a decent number of areas that always are. Try navigating through the Financial District or through Midtown without hitting one location or other, for instance—you won't get far. And Columbus Circle, the precise point of interest in our story? Awash in them.

Calculating average distance

This still doesn't answer the question on my mind, though: on average, how far away are you from a Starbucks? To figure that out we have to go a little deeper. We start with the following point cloud of 2000 random locations in Manhattan:

This point cloud uses an underlying GeoJSON layer data (hand-drawn using the wonderful geojson.io web-app). I then constructed uniformly random variables within the "Manhattanite" bounding box, and us ed a point-in-polygon algorithm to verify interior points until I got 2000 of them.

By taking a measure of the smallest distance between a uniformally distributed random sample of points in Manhattan and a list of chain locations, and then taking the average of the resulting distances, we get an accurate measure of nearness. It's possible to use an even larger sample, but 2000 points turned out to be more than enough once I threw them at my distance measurement algorithms: you don't really gain much in the way of accuracy using more, and you lose a lot of speed.

And with that done, one data crunch later, we get our answer. If you were randomly dropped anywhere in Manhattan, anywhere at all, the nearest Starbucks would be, on average, a mere 1335 feet away.

Let's put this figure in context. Most blocks in Manhattan consist of a short street and a long avenue. The ballpark figure for the average length of a Manhattan avenue is 750 feet, so the nearest Starbucks is a little under two avenues away; or, using the old ballpark figure of 20 city streets to a mile (which is 264 feet per block), five streets.

Furthermore, analysis done by another Redditor a little while ago (titled "Half of Manhattan is Within 4 Blocks of a Starbucks") bore out the fact the 20% of Manhattan is within 0.1 miles (520 feet) of a Starbucks, while a whopping 50% of it is within 0.2 miles (1040 feet) of it. So not quite two blocks—at least, not always—but it's still hella close.

Visualizing the data

But why stop there? Starbucks isn't the only game in town; in fact, as the Center for an Urban Future reports in its "State of the Chain 2015" report, Starbucks is only the fourth largest chain in Manhattan, trailing the likes of Dunkin Donuts, Subways, and, um, Metro PCS (wait, seriously?).

I ran the numbers on what I felt was a somewhat representative sample of my favorite chains in the State of the Chain 2015's dataset and compiled the results into the d3.js visualization below. Data is provided in both feet and in the number of streets (which is, unscientifically, feet / 264 ). The small blue bars demarkate the standard deviation of the data: if you don't know what those are, ignore them, as I will have more to say on that topic in a future post.

How do competing chains compare against one another? What chains are particularly good at distributing themselves throughout Manhattan? What chains are bad at it (e.g. why are Apple Stores only where the rich people live)? If Whole Foods is still so hard to find, why do people feel endangered by it? What are the confidence intervals at play here? This is a broad dataset, we've only scratched the surface on what we can do with it, and I definitely plan to revisit this topic in a future continuation. In the meantime you can sleep soundly at night, if you're lucky enough (or foolish enough, or both) to live in Manhattan, knowing that the nearest Starbucks is probably just a few minutes' walk away.

To play with the code yourself see the corresponding repository on GitHub. You may have also seen this post in the media—it was republished in Gothamist and elsewhere.