The primary aims were to test how the variables behaved in relation to each other and if these patterns were consistent across scales. The variables were mostly identified from the literature and limited to the kind of data that was readily accessible.

We used GIS to sample the variables and then principal component analyses to search for patterns among them, repeating the sampling and analysis at four different scales (250 × 250, 750 × 750, 1,250 × 1,250 and 1,750 × 1,750, all in meters). At the smallest scale most variables seemed to be roughly structured along two axes, one with landscape indices and one mainly with demographic factors but also impervious surface and coniferous forest.

The other land-cover types did not align very well with these two axes. When increasing the scale this pattern was not as obvious, instead the variables separated into several smaller bundles of highly correlated variables. Some pairs or bundles of variables were correlated on all scales and thus interchangeable while other associations changed with scale. This is important to keep in mind when one chooses measures of urbanization, especially if the measures are indices based on several variables.

Comparing our results with the findings from other cities, we argue that universal gradients will be difficult to find since city shape and size, as well as available information, differ greatly.

We also believe that a multivariate gradient is needed if you wish not only to compare cities but also ask questions about how urbanization influences the ecological character in different parts of a city.