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Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing

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References

2017Extracting urban functional regions from points of interest and human activities on location‐based social networks470 cited
2015Mining point-of-interest data from social networks for urban land use classification and disaggregation337 cited
2018Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs258 cited
2018Using Spatial Semantics and Interactions to Identify Urban Functional Regions84 cited
2018Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data82 cited
2011Extracting hierarchical landmarks from urban POI data53 cited
2011Extracting hierarchical landmarks from urban POI data47 cited
2018CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery32 cited

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2022Investigating Human Travel Patterns from an Activity Semantic Flow Perspective: A Case Study within the Fifth Ring Road in Beijing Using Taxi Trajectory Data15 cited
2021Functional Area Recognition and Use-Intensity Analysis Based on Multi-Source Data: A Case Study of Jinan, China12 cited
2021Research on the Method of Urban Jobs-Housing Space Recognition Combining Trajectory and POI Data10 cited
2025Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai9 cited
2022A GloVe Model for Urban Functional Area Identification Considering Nonlinear Spatial Relationships between Points of Interest

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9 cited

2024Classifying Urban Functional Zones by Integrating the Homogeneity and Structural Similarity of POIs7 cited

2021Using POI Data to Identify the Demand for Pedestrian Crossing Facilities at Mid-Block7 cited

2025Exploring the Urban Structure of Recreational Spaces through Residents’ Mobility Behavior Using Mobile Phone Signaling Data4 cited

2022Exploring the spatiotemporal characteristics of intracity travels by time series clustering: A case study of Beijing3 cited