| Title | Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis |
|---|---|
| ID_Doc | 45531 |
| Authors | Kousis, A; Tjortjis, C; Esposito, A |
| Title | Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis |
| Year | 2024 |
| Published | Future Internet, 16, 1 |
| Abstract | In recent years, the emergence of the smart city concept has garnered attention as a promising innovation aimed at addressing the multifactorial challenges arising from the concurrent trends of urban population growth and the climate crisis. In this study, we delve into the multifaceted dimensions of the smart city paradigm to unveil its underlying structure, employing a combination of quantitative and qualitative techniques. To achieve this, we collected textual data from three sources: scientific publication abstracts, news blog posts, and social media entries. For the analysis of this textual data, we introduce an innovative semi-automated methodology that integrates topic modeling and thematic analysis. Our findings highlight the intricate nature of the smart city domain, which necessitates examination from three perspectives: applications, technology, and socio-economic perspective. Through our analysis, we identified ten distinct aspects of the smart city paradigm, encompassing mobility, energy, infrastructure, environment, IoT, data, business, planning and administration, security, and people. When comparing the outcomes across the three diverse datasets, we noted a relative lack of attention within the scientific community towards certain aspects, notably in the realm of business, as well as themes relevant to citizens' everyday lives, such as food, shopping, and green spaces. This work reveals the underlying thematic structure of the smart city concept to help researchers, practitioners, and public administrators participate effectively in smart city transformation initiatives. Furthermore, it introduces a novel data-driven method for conducting thematic analysis on large text datasets. |
| https://www.mdpi.com/1999-5903/16/1/3/pdf?version=1703255657 |
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