A multivariate hierarchical regionalization method to discovering spatiotemporal patterns

Wang, Haoran; Zhang, Haiping; Zhu, Hui; Zhao, Fei; Jiang, Shangjing; Tang, Guoan; Xiong, Liyang

doi:10.1080/15481603.2023.2176704

Cited by 5 publications

(2 citation statements)

References 59 publications

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“…Ecosystem services, including water yield, soil conservation, carbon storage, and crop production, were evaluated using meteorological and soil property data [63,66]. Points of Interest (POI) analysis [37,39,47] facilitated diverse assessments such as street safety, urban functional region identification, and street space quality evaluation. Spatial distribution data aids urban planning, environmental conservation, and resource allocation.…”

Section: Methodological Approachesmentioning

confidence: 99%

“…Burini et al [35] proposed the J-CO framework based on JSON format. At the same time, Bareche and Xia [36] developed the VeST indexing technique, and Wang et al [37] implemented the STR method, all contributing to more precise and dynamic analysis of urban spatial data. These techniques allow for more accurate and efficient processing of larger-scale and complex urban spatial data in geo-design.…”

Section: Data-driven Approachmentioning

confidence: 99%

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Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning

He,

Chen

2024

Buildings

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The advancement of cutting-edge technologies significantly transforms urban lifestyles and is indispensable in sustainable urban design and planning. This systematic review focuses on the critical role of innovative technologies and digitalization, particularly artificial intelligence (AI), in urban planning through geo-design, aiming to enhance urban life. It begins with exploring the importance of AI and digital tools in revolutionizing contemporary urban planning practices. Through the methodology based on the Systematic Reviews and Meta-Analyses (PRISMA) protocol, this review sifts through relevant literature over the past two decades by categorizing artificial intelligence technologies based on their functionalities. These technologies are examined for their utility in urban planning, environmental modeling, and infrastructure development, highlighting how they contribute to creating smarter and more livable cities. For instance, machine learning techniques like supervised learning excel in forecasting urban trends, whereas artificial neural networks and deep learning are superior in pattern recognition and vital for environmental modeling. This analysis, which refers to the comprehensive evaluation conducted in this Systematic Review, encompasses studies based on diverse data inputs and domains of application, revealing a trend toward leveraging AI for predictive analytics, decision-making improvements, and the automation of complex geospatial tasks in urban areas. The paper also addresses the challenges encountered, including data privacy, ethical issues, and the demand for cross-disciplinary knowledge. The concluding remarks emphasize the transformative potential of innovative technologies and digitalization in urban planning, advocating for their role in fostering better urban life. It also identifies future research avenues and development opportunities. In light of our review findings, this study concludes that AI technologies indeed hold transformative promise for the field of geo-design and urban planning. They have proven instrumental in advancing predictive analytics, refining decision-making, and streamlining complex geospatial tasks. The AI’s capacity to process expansive datasets and improve urban planning accuracy has facilitated more sustainable urban development and enhanced the resilience of urban environments.

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