Urban CA models use sets of rules that are applied to each cell in the geographical array to change the state of the cell usually according to attributes that exist in the neighbourhood of the cell in question. As there are usually many thousands of cells and therefore large quantities of data that map one time slice of the system into the next one, then in CA models there is the opportunity to search for pattern in these changes of state, thus deriving transition rules from this data. Ten years or more ago, when Claudia Maria de Almeida from INPE (National Institute for Space Research, Brazil) visited us in CASA, I worked with her on her CA models of development change in Brazilian cities and she developed a number of multivariate methods for extracting the rules from the dynamics of cellular change. The 2003 paper can be downloaded here. Recently I have worked with Yan Liu from Brisbane (U Queensland) and Yongjiu Feng from Shanghai (College of Marine Sciences) on developing a machine learning approach to extracting nonlinear transition rules based on least squares support vector machines which essentially define the patterns needed get appropriate rules. It is all quite tricky stuff in detail but rather generic in terms of what these methods are designed to do. We published a paper recently on this in the journal Stochastic Environmental Research Risk Assessment (Volume 29, 2015, online) and if you click here you can see a copy of the paper and its source. Enjoy.
Roberto Murcio led our work on applying ideas of information theory across scales so that mutual information can be transmitted one way, rather than symmetrically. The paper has just appeared in PLOS One. And you can Download the PDF from here. The abstract follows:
“The morphology of urban agglomeration is studied here in the context of information exchange between different spatio-temporal scales. Urban migration to and from cities is characterised as non-random and following non-random pathways. Cities are multidimensional non-linear phenomena, so understanding the relationships and connectivity between scales is important in determining how the interplay of local/regional urban policies may affect the distribution of urban settlements. In order to quantify these relationships, we follow an information theoretic approach using the concept of Transfer Entropy. Our analysis is based on a stochastic urban fractal model, which mimics urban growing settlements and migration waves. The results indicate how different policies could affect urban morphology in terms of the information generated across geographical scales.”
An interesting report funded by the MacArthur and Knight Foundations by Anthony Townsend of Smart Cities fame about the rapid emergence of urban science and the flurry of centres that have grown up in the wake of these new ideas as well as the development of smart cities and their technologies. We in CASA are earmarked as beginning the trend initially as a GIS-spatial analysis centre with a focus on cities – but way ahead of the curve – with some morphing of our mission towards new goals pertaining to modelling and visualisation using computers and cities within the last decade. The report also refers to my ESRC report Urban Informatics and Big Data which you can also download here alongside Anthony’s report.