The attached map is from Christopher Barrington-Leigh and Adam Millard-Ball’s 2015 paper in the Proceedings of the National Academy of Sciences, A Century of Sprawl in the United States. Because of the good parcel file data in the Twin Cities, we got analyzed in depth. The base of their argument is measurement of nodal degree (how many links meet at an intersection). Nodal degree 4 (a 4-way intersection) indicates less sprawl (more connectivity) than a nodal degree of 3 (3-way) or 1 (cul-de-sac) (nodal degree of 2 doesn’t make a lot of sense topologically, since that would be one continuous link, though it can happen depending on how the data is mapped and roads are named and laid out, and nodes are often used to indicate curvature). In short, the network used to be very gridlike, went through decades of non-gridlike additions, though more recent additions have been more gridlike again.
Spatial and temporal patterns of sprawl in the Minneapolis–St. Paul re- gion. Individual edges—that is, road segments bounded by two intersections— are shown at three time points. Edges are colored in five categories according to their connectivity, ranging from highly connected (gridded) in blue to cul-de-sacs in red. Connectivity is measured by the mean degree of an edge’s two terminal intersections, explained in the text. Because nodes can be cul-de-sacs, degree three, or degree four-plus, there are five possible values of edge degree, ranging from 2.0 to 4.0. In 1950, the developed area is largely gridded, but growth by 1980 and by 2013 is largely of the low-connectivity kind. Rural roads also tend to be gridded. The Lower Right panel shows the fraction, indicated by the vertical extent of a color, of each edge type built each year. The black line shows the pace of construction, defined as the number of edges dated to each year. Dramatic drops are evident during the Depression, World War II, oil shocks, a recession in the 1970s and 1980s, and the recent Global Financial Crisis. We focus on Minneapolis–St. Paul because all seven central counties are included in our parcel-based data and because the region closely tracks national trends (SI Appendix, Fig. S6).