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Abstract

Rapid urbanization challenges traditional urban analysis methods, prompting increased reliance on data and text mining techniques. This systematic review evaluates 75 peer-reviewed articles (2015–2024), highlighting applications in governance, transportation, sustainability, and public engagement. Studies demonstrate benefits like improved policymaking through machine learning techniques (e.g., topic modeling, sentiment analysis) utilizing diverse datasets from social media and sensors. However, challenges remain, including methodological standardization, ethical issues, and spatial integration. Future research addressing these barriers will enhance the transformative potential of data-driven approaches, promoting sustainable, efficient, and equitable urban development.

Keywords

data mining text mining natural language processing urban studies urban planning machine learning big data

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Applications of Data Mining and Text Mining in Urban Studies and Planning Research: A Systematic Review

Abstract

Keywords

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