Sustainability · 2019 · 76 citations · 36 references
Social SciencesGeographyUrban PlanningSpatial AnalysisUrban DevelopmentUrban GeographyDistribution AnalysisUrban DensityUrban SpaceUrban Spatial AnalysisUrban Spatial DistributionUrban Spatial StructureGeospatial Big DataUrban ClusteringUrban Feature ExtractionUrban Land Use AnalysisUrban Land Use DistributionUrban Land Use PatternsUrban Pattern RecognitionUrban Vitality
Urban vitality provides an important basis for evaluating urban development and spatial balance. In the era of big data, the quantitative analysis of urban vitality has become a research hotspot in the field of urban sustainability and planning research. However, time variation characteristics are often neglected, which leads to one-sidedness in the pattern analysis of urban vitality. In this paper, a method for extracting vitality areas and integrating spatiotemporal features clustering is proposed. The method is used to divide urban space into multiple vitality areas scientifically. The spatial and temporal distribution patterns of urban vitality areas are found, and the driving factors of various vitality patterns are analyzed by combining points of interest (POI)-based land use characteristics. To illustrate this method, this paper takes Nanjing city as an example. One week’s worth of mobile phone data indicated that Nanjing has 10 and 8 vitality areas on weekdays and weekends, respectively. The spatial and temporal distribution patterns of the vitality areas and their correlation with land use were analyzed, which proved that POI density and entropy have strong correlations with urban vitality.
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Teuvo Kohonen · Proceedings of the IEEE · 1990 · 8.2K citations
Clustering of the self-organizing map
Juha Vesanto, Esa Alhoniemi · IEEE Transactions on Neural Networks · 2000 · 2.7K citations
ST-DBSCAN: An algorithm for clustering spatial–temporal data
Derya Birant, Alp Kut · Data & Knowledge Engineering · 2006 · 1.4K citations