[b]UrbanPulse: Instruments for monitoring and analysis of urban change[/b]
UrbanPulse is an exchange project between RIKS and the Urban Planning group of the Technical University of Eindhoven. The project is supported by the Netherlands Organisation for Scientific Research (NWO), who awarded RIKS researcher Alex Hagen-Zanker a CASIMIR grant to perform this research at, and together with, the Urban Planning group.
The purpose of the UrbanPulse project is to develop tools and methods to analyse urban dynamics. The developed tools and methods seek to offer an advance over the current state of the art by explicitly addressing the changes in the urban landscape.
This focus on change is motivated both by scientific considerations and practical applications for policy evaluation and preparation.
Urban planners are confronted by a constantly changing reality. Demographic, social and economic changes are constantly affecting the urban landscape. Planners need to facilitate or influence these trends through spatial planning policies. The tools developed in UrbanPulse will promote the insight of planners in the trends they are facing. The idea of early warning is important. The analysis of changes allows identifying problematic trends already before their consequences pose severe problems. Likewise, it is possible to recognize opportunities before they have passed.
Another application is the classification of urban dynamics, which identifies cities, regions or countries that are undergoing similar trends or have done so in the past. This understanding can be instrumental in pooling knowledge and formulating ‘best practices’.
Besides understanding of current trends, the tools will be helpful for monitoring the effects of policies. The temporal aspect of the methods is crucial, because policies are only valid for a limited period in time. Quantitative methods for spatial policy evaluation can be the basis for evidence based policy development.
[b]Bridging a scientific gap[/b]
Descriptive models of urban pattern, by large, describe single moments in time. These models present us with surprising regularities in space and time, such as the rank size distribution of city populations, the cluster size distributions and fractal relations in urban form. Furthermore there are many metrics of spatial (auto)correlation. Patterns are recognized, but due to the static nature the relation with causal processes remains obscure.
Explanatory models of urban landscapes, by contrast are typically process based, they represent the dynamic interactions between actors and the urban patterns unfold from these interactions. Modern computing makes it possible to simulate virtual cities which are composed of many small elements and relatively simple interaction rules, for instance the by means of Cellular Automata and Agent Based Modelling. Although the results of these explanatory models are promising and find applications in urban planning practice, their empirical base is limited.
[b]Supporting theoretical progress[/b]
By developing descriptive models of transitions in the urban landscape, the UrbanPulse project intents to bridge the gap between exploratory and descriptive models. Assumptions underlying exploratory models may be empirically rejected or confirmed. Results from the descriptive models may also give rise to new hypotheses on the nature of urban processes. Thus, ultimately the project should contribute to the theory of urban dynamics.
[b]Technical approach [/b]
The analysis of urban change is based on the analysis of raster maps. These maps directly or indirectly assign attributes to all locations on the map. For instance, the population density or the distance to recreation areas can be represented in raster maps. Of particular importance are land use maps, from which many characteristics may be derived.
Our main strategy is to summarize the changes that occur in a region in transition matrices. Such transition matrices are the source material for further analysis. Summary metrics can cover a wide range of indicators, for instance the compactness of urban development, the formation and protection of networks, segregation, specialization/diversification, regional similarity, etc.
Besides the high resolution spatial data, the project will also integrate coarser scale data that is available at regional scales. In particular socio-economic data, such as employment records and demographic statistics are typically available in this form.