Abstract
Cities in the ‘New Urban World’ display an enormous diversity in appearance, growth and performance. The awareness is growing that the urban development potential (‘magnetism’) of cities is closely related to safety and security conditions in these cities. This paper develops a new analytical framework based on a wealth of empirical data on both safety/security and socio-economic magnetism achievements of many world cities, by combining two comprehensive relevant global urban data bases. The aim of the study is to offer a comparative analysis of the combined safety/security data and socio-economic performance data of 30 global cities, through the use of an advanced sequential cluster dynamics analysis that is (partly) inspired by a novel machine learning approach (using Python software). In this way, cities can be categorized according to their quantitative characteristic features represented by the relevant clusters. It appears that city safety/security features are an important predictor of the variability in overall urban performance regarding magnetism. This study allows also for drawing relevant policy lessons.
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