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Geosimulation of urban growth and demographic decline in the Ruhr: a case study for 2025 using the artificial intelligence of cells and agents

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Abstract

The Ruhr is an “old acquaintance” in the discourse of urban decline in old industrialized cities. The agglomeration has to struggle with archetypical problems of former monofunctional manufacturing cities. Surprisingly, the image of a shrinking city has to be refuted if you shift the focus from socioeconomic wealth to its morphological extension. Thus, it is the objective of this study to meet the challenge of modeling urban sprawl and demographic decline by combining two artificial intelligent solutions: The popular urban cellular automaton SLEUTH simulates urban growth using four simple but effective growth rules. In order to improve its performance, SLEUTH has been modified among others by combining it with a robust probability map based on support vector machines. Additionally, a complex multi-agent system is developed to simulate residential mobility in a shrinking city agglomeration: residential mobility and the housing market of shrinking city systems focuses on the dynamic of interregional housing markets implying the development of potential dwelling areas. The multi-agent system comprises the simulation of population patterns, housing prices, and housing demand in shrinking city agglomerations. Both models are calibrated and validated regarding their localization and quantification performance. Subsequently, the urban landscape configuration and composition of the Ruhr 2025 are simulated. A simple spatial join is used to combine the results serving as valuable inputs for future regional planning in the context of multifarious demographic change and preceding urban growth.

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Abbreviations

AI:

Artificial intelligence

CA:

Cellular automaton

CWD:

Cost-weighted distance

IDW:

Inverse distance-weighted

MAS:

Multi-agent system

MC:

Monte Carlo iterations

MRV:

Multiple resolution validation

NRW:

North Rhine-Westphalia

OOP:

Object-oriented programming

ReHoSh:

Residential mobility and the housing market of shrinking city systems

ROC:

Receiver operating characteristic

SLEUTH:

Slope, land-use, exclusion, urban, transport, hillshade

SVM:

Support vector machines

UGM:

Urban growth model

UGMr:

Urban growth model with reduced input data sets

UGMr–SVM:

Urban growth model with reduced input data sets and a probability map derived from support vector machines as exclusion layer

XULU:

eXtendable Unified Land Use Modelling Platform

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Acknowledgments

This study was carried out in the Remote Sensing Research Group (RSRG, Department of Geography, University of Bonn) with the support of Gunter Menz (RSRG) and Kerstin Voß (University of Education Heidelberg, Department of Geography). For the provision of the land-use data, we would like to thank the project “Visualisierung der Landnutzung und des Flächenverbrauchs in Nordrhein-Westfalen auf der Basis von Satellitenbildern” funded by the Ministry for Climate Protection, Environment, Agriculture, Nature Conservation and Consumer Protection of the State of North Rhine-Westphalia. Last but not least, we are grateful to Miriam Halfmann (University of Bonn), and Annette Ortwein (University of Bonn) who gave us competent linguistic assistance.

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  1. Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115, Bonn, Germany

    Andreas Rienow & Dirk Stenger

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  1. Andreas Rienow
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  2. Dirk Stenger
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Correspondence to Andreas Rienow.

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Rienow, A., Stenger, D. Geosimulation of urban growth and demographic decline in the Ruhr: a case study for 2025 using the artificial intelligence of cells and agents. J Geogr Syst 16, 311–342 (2014). https://doi.org/10.1007/s10109-014-0196-9

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  • DOI: https://doi.org/10.1007/s10109-014-0196-9

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