Housing prices have declined less significantly in greater D.C., Chicago (hub of the great Chi-Pitts mega-region and a magnet for regional talent), Seattle (a high-tech, high human capital center), Atlanta (a talent hub for the southeast), New York, Portland, Boston, Denver - (talent hub for the Rockies), Dallas (a mega-region hub), and Charlotte (which along with Atlanta hubs the great Char-lanta mega-region). Cleveland breaks the pattern, but like Detroit its absolute housing values have fallen. Prices in greater D.C., along with Denver, Dallas, and Cleveland, were actually up in April.Rich uses "talent hub", but the idea is the same. There exists a delineable region for talent migration. However, I haven't encountered any agreed upon methodology for making such a map. I mention this because Chicago isn't the talent hub for Pittsburgh. DC holds that distinction, somewhat complicating the coherence of Chi-Pitts as a mega-region.
Defining the migrant is easy enough. We're already tracking where people with a college degree are heading. I also think we have an approach that could work for measuring talent connectivity. If you haven't viewed the Atlas of Hinterworlds, check it out:
- We have selected the top 123 cities in terms of global network connectivity and measure the connectivity between each city and the other 122 cities.
- These individual city inter-linkages closely reflect the overall pattern of global network connectivity (i.e. every city is most connected to either London or New York).
- For each city we regress its connectivities with other cities against their global network connectivity.
- We compute the residuals from the regression line and these are interpreted as a city being either over-linked (positive residuals) to another city (relative to its global network connectivity) or under-linked (negative residuals). It is these residuals that are mapped to show a city’s hinterworld.
- The standard error of estimate indicates how close the scatter of points is to the line: it is interpreted in this context of indicating the specificity of a city’s hinterworld (i.e. zero would indicate a hinterworld exactly the same as the global network connectivity pattern and therefore the higher the standard error the more unlike a city’s hinterworld is from the general pattern of global connectivities. In the analyses, standard errors range from 0.013 (Madrid) to 0.067 (Indianapolis)).
- The same ordinal scale is used for all maps to facilitate simple comparisons between hinterwords. An initial discussion of comparisons can be found in chapter 5 of P.J. Taylor (2004) World City Network: a Global Urban Analysis (London: Routledge).
- The maps are in cartogram form to show each city in equally. The cartogram places cities in their approximate relative geographical positions.