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A probabilistic optimal estimation method for detecting spatial fuzzy communities
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DOI：10.1080/13658816.2025.2483850.png)
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Urban areas comprise numerous spatial communities due to the frequent and limited range of human movements. Due to the partial spatial stochasticity of human movements, urban spatial communities are fuzzy and spatially heterogeneous. Existing spatial community detection strategies based on deterministic and globally uniform criteria fail to account for these characteristics. Therefore, this study presents a framework for detecting spatial fuzzy communities by transforming spatial fuzzy community detection into trip estimations between spatial units. We developed a probabilistic optimal estimation (ProOE) method to estimate trip volumes between spatial units by adjusting the probability of the membership of each unit in a spatial community. A trip intensity parameter was introduced for each community to adjust the estimated trip volumes. The distance decay effect (DDE) of human movement was then incorporated into the model, further improving the accuracy of community delineation for specific cities. Finally, spatial continuity guidance was incorporated into the solution algorithm, minimizing unnecessary community fragmentation. The experimental results demonstrate that ProOE outperforms existing methods, achieving an average improvement of 31.57% in accuracy while effectively capturing the ambiguity in the interplay between spatial units and communities. This study contributes to a more precise understanding of the spatial structures of cities.
Keyword:
Spatial fuzzy community
trip volume estimation
spatial heterogeneity
distance decay effect
human movement dynamics
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5.1
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2.7K
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9.3K
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