Preferential centrality - a new measure unifying urban activity, attraction and accessibility

The fact that accessibility shapes the geographic distribution of activity needs to be addressed in any long-term policy and planning for urban systems. One major problem is that current accessibility measures rely on the identification and quantification of attractions in the system. We propose that it is possible to devise a network centrality measure that bypasses this reliance and predicts the distribution of urban activity directly from the structure of the infrastructure networks over which interactions take place. From a basis of spatial interaction modelling and eigenvector centrality measures we develop what we call a preferential centrality measure that recursively and self-consistently integrates activity, attraction and accessibility. Derived from the same logic as Google’s PageRank algorithm, we may describe its operation by drawing a parallel: Google’s PageRank algorithm ranks the importance of networked documents without the need to perform any analysis of their contents. Instead it considers the topological structure of the network and piggybacks thereby on contextualized and deep evaluation of documents by the myriad distributed agents that constructed the network. We do the same thing with regard to networked geographical zones. Our approach opens up new applications of modelling and promises to alleviate a host of recalcitrant problems, associated with integrated modelling, and the need for large volumes of socioeconomic data. We present an initial validation of our proposed measure by using land taxation values in the Gothenburg municipality as an empirical proxy of urban activity. The resulting measure shows a promising correlation with the taxation values