Functionality between the size and indicators of smart cities: A research challenge with policy implications

• Published in: Cities Vol. 78; pp. 17 - 26
• Main Authors: Borsekova, Kamila, Koróny, Samuel, Vaňová, Anna, Vitálišová, Katarína
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2018; Elsevier Science Ltd
• Subjects: Academic disciplines; Classification; Classification and regression tree; Decision makers; Decision tree; Decision trees; Development policy; Ecological awareness; Larger cities; Medium-sized cities; Planners; Policy making; Presumption; Size; Smart cities; Smart city; Statistics; Technology; Urban development; Urban planning; Medium-sized cities; Classification and regression tree; Smart city; Decision tree; Larger cities; Ecological awareness
• ISSN: 0264-2751; 1873-6084
• DOI: 10.1016/j.cities.2018.03.010

The paper focusses on the concept of a smart city and its specific components in relation to size of the city. Smart cities are a topic whose key importance is being increasingly recognised across both academic disciplines and urban planning. The idea of a smart city is a dream of urban planners all over the world, and a subject of many research and business initiatives as well as policy debates. As cities vary considerably in size, it is important to ask if the size influences the level of selected indicators of smart cities. Our main presumption is that the development level of indicators of smart cities varies in cities of different size. Our scientific objective is to find a simple understandable model linking the categorical variable “city size” to a group of smart city indicators. Our data set contains 26 smart city indicators for 158 European smart cities, divided into two sizes: medium-sized cities and larger cities. We draw from the methodology of “European Smart Cities” elaborated by the Vienna University of Technology (Project ID: 314704) that classify European smart cities and smart city indicators by considering their size. Analysing the statistics by using decision tree modelling, we identify the most significant indicators of smart cities that can divide smart cities into size categories with impressive 96.2% correct classification. Besides excellent classification result based on real empirical data, several research results overturn common assumptions about smart cities. Based on the research results the paper also highlights intriguing future challenges in smart city research and policy development. Several research results have policy implications and might be useful for urban planners, policy representatives and decision makers.