Urban Planning and Big Data What The Connection? #urban #bigdata #data #USA #Europe #UK #cities


Cities are growing very rapidly worldwide. This growth entails many challenges which cut across different city layers. In terms of demography, we are facing many issues to do with migration and aging of the population. In terms of land use, a big challenge involves how we deal with congestion in terms of high densities and sprawl in cities and also how we can tackle segregation so that we might decrease inequality and deprivation. The availability of resources is of concern in terms of how efficiently and sustainably we use energy. The transport sector faces big tests with respect to congestion in infrastructure across all travel modes, growing levels of pollution and noise, and accidents. To add to the complexity of the challenges just mentioned, they span different spatial and temporal scales as well.

Urban planners need to somehow juggle these issues through the use of a variety of tools. One of these tools is the so called Land Use Transport interaction (LUTi) model. This is really a family of models that aim to estimate how cities will develop on a long term basis (typically over a period of 30 to 50 years) through the interaction of three main factors: population, land use and transport services. Among the many processes addressed by LUTi models, the main one is perhaps the interplay and feedback of information from the land use system to the transport system and vice versa. This reflects the influence of land use patterns on mobility patterns and the evolution of transport infrastructure in one direction, and in the other direction, how transport systems have an impact on how urban form evolves and how people engage in various land use activities. Typical plans evaluated using this family of models include the estimation of the impacts around a change in transport infrastructure, e.g. a new railway line between two areas, or the building of a new development in the region, e.g. a new industrial estate. This would include economic impacts (regional and/or national), often disaggregated by industrial sectors; and the prediction of diverse data on households, population (by type) and the number of additional jobs for each of the modelled areas.

Urban models have become a useful tool for planners to tackle many of the problems around the growth of cities. These models are now over 40 years old which means they have gone through many re-evaluations to improve their accuracy. Having said that, urban models still face many challenges ahead. We will emphasise four of them. First, they require a lot of computer processing power, especially regarding transport modelling. Fast and yet realistic implementations need to be sought to enable various model runs using shorter times. Second, the visual interface showing the results of LUTi models still has much room for improvement. More interactive and comprehensive tools to understand the results need to be implemented to help practitioners and other stakeholders. Third, there has been some discussion in the modelling community around the concept of dynamic versus static model implementations. As discussed by Ying and Wegener, this is a very challenging topic as it points to the core of the model design in order to better capture a world whose equilibrium is most likely dynamic. Finally, and perhaps most relevant in the context of our current research in EUNOIA, we need to find out whether more representative samples such as the ones from big data lead to potential advances in urban modelling. We believe breakthroughs in any of these areas will allow urban planners to be in a better position to tackle many of the challenges that cities are currently facing.


By Joan Serras, Melanie Bosredon, Ricardo Herranz & Michael Batty



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