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Comparing City Street Orientations


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We say the cows laid out Boston. Well, there are worse surveyors. –Ralph Waldo Emerson, 1860


In 1960, one hundred years after Emerson’s quote, Kevin Lynch published The Image of the City, his treatise on the legibility of urban patterns. How coherent is a city’s spatial organization? How do these patterns help or hinder urban navigation? I recently wrote about visualizing street orientations with Python and OSMnx. That is, how is a city’s street network oriented in terms of the streets’ compass bearings? How well does it adhere to a straightforward north-south-east-west layout? I wanted to revisit this by comparing 25 major US cities’ orientations:

City street network grid orientations, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib. Atlanta, Boston, Buffalo, Charlotte, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Las Vegas, Los Angeles, Manhattan, New York, Miami, Minneapolis, Orlando, Philadelphia, Phoenix, Portland, Sacramento, San Francisco, Seattle, St Louis, Tampa, Washington DC.


Each of the cities above is represented by a polar histogram (aka rose diagram) depicting how its streets orient. Each bar’s direction represents the compass bearings of the streets (in that histogram bin) and its length represents the relative frequency of streets with those bearings. For example, in Manhattan we can clearly see the angled, primarily orthogonal street grid in its polar histogram:

Manhattan, New York City, New York street network, bearing, orientation from OpenStreetMap mapped with OSMnx and Python

Similar orthogonal grids can be seen in the histograms of Chicago, Denver, Tampa, etc. Detroit is an interesting case, as it primarily comprises two separate orthogonal grids, one a slight rotation of the other:

Detroit, Michigan city street network, bearing, orientation from OpenStreetMap mapped with OSMnx and Python

Most cities’ polar histograms similarly tend to cluster in at least a rough, approximate way. But then there are Boston and Charlotte. Unlike most American cities that have one or two primary street grids organizing city circulation, their streets are more evenly distributed in every direction. For example, here’s Boston:

Boston, Massachusetts city street network, bearing, orientation from OpenStreetMap mapped with OSMnx and Python

Although it features a grid in some neighborhoods like the Back Bay and South Boston, these grids tend to not be aligned with one another, resulting in a mish-mash of competing orientations. Furthermore, these grids are not ubiquitous and Boston’s other streets wind in many directions. If you’re going north and then take a right turn, you might know that you are immediately heading east, but it’s hard to know where you’re eventually really heading in the long run.


This makes it harder for unfamiliar visitors to navigate Boston than many other US cities. It does not adhere to a straightforward north-south-east-west pattern (or any other consistent, predictable pattern) that our brains adjust to in most places – not because Boston apocryphally paved over its cow paths, but because of its age, terrain, and annexation of various independent towns.


60 years ago, Kevin Lynch defined “legible” cities as those whose patterns lend themselves to coherent, organized, recognizable, and comprehensible mental images. These help us organize city space into cognitive maps for wayfinding and a sense of place. But what Boston lacks in legible circulation patterns, it makes up for in other Lynchian elements (paths, edges, districts, nodes, landmarks) that help make it an “imageable” city for locals and visitors. From Lynch:

A highly imageable city in this peculiar sense would seem well formed, distinct, remarkable; it would invite the eye and the ear to greater attention and participation… The concept of imageability does not necessarily connote something fixed, limited, precise, unified, or regularly ordered, although it may sometimes have these qualities. Nor does it mean apparent at a glance, obvious, patent, or plain. The total environment to be patterned is highly complex, while the obvious image is soon boring and can point to only a few features of the living world.

I find Boston’s street patterns illegible and difficult to navigate. But as a newcomer I can settle for the concomitant sense of wonder, bafflement, and inexplicable adventure that accompanies every simple right turn.


For more on OSMnx, see this post or this notebook to recreate the visualization.


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