Understanding Smart City—A Data-Driven Literature Review

Johannes Stübinger and Lucas Schneider
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Johannes Stübinger: Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany
Lucas Schneider: Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany

Sustainability, 2020, vol. 12, issue 20, 1-23

Abstract: This paper systematically reviews the top 200 Google Scholar publications in the area of smart city with the aid of data-driven methods from the fields natural language processing and time series forecasting. Specifically, our algorithm crawls the textual information of the considered articles and uses the created ad-hoc database to identify the most relevant streams “smart infrastructure”, “smart economy & policy”, “smart technology”, “smart sustainability”, and “smart health”. Next, we automatically assign each manuscript into these subject areas by dint of several interdisciplinary scientific methods. Each stream is evaluated in a deep-dive analysis by (i) creating a word cloud to find the most important keywords, (ii) examining the main contributions, and (iii) applying time series methodologies to determine the past and future relevance. Due to our large-scaled literature, an in-depth evaluation of each stream is possible, which ultimately reveals strengths and weaknesses. We hereby acknowledge that smart sustainability will come to the fore in the next years—this fact confirms the current trend, as minimizing the required input of energy, water, food, waste, heat output and air pollution is becoming increasingly important.

Keywords: smart city; Google Scholar; data-driven review; natural language processing; time series analysis; smart infrastructure; smart economy & policy; smart technology; smart sustainability; smart health (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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