Recognizing urban functional zones by a hierarchical fusion method considering landscape features and human activities

Hui Li · Jianbo Tang · Min Deng · Jincai Huang · Wentao Yang · Fang Wu

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Human Mobility and Location-Based Analysis Urban Design and Spatial Analysis Remote Sensing and Land Use

Abstract

Abstract Functional zones are basic spatial units of urban management and urban planning and they provide the basic space where human activities occur. The concept of urban functional zones is closely related to two basic factors: urban landscape environment and human activities. The former provides the basic space for human activity and influences urban land use at a coarse scale, and the latter indicates the differentiation of functions in local urban areas. In previous studies, the hierarchical correspondence and interaction between urban landscape and human activities have not been given full consideration in the cognition of urban functional zones, which would influence the accuracy and interpretability of the results. Therefore, a hierarchical fusion method considering urban landscape and human activity patterns based on multi‐source data is proposed in this article. We introduce a new cognition framework. First, a land use classification based on urban landscape features from remote sensing images is extracted and used as the fundamental results of urban land uses. Then, fine‐grained functional semantics of local urban areas are recognized based on human activity patterns extracted from crowdsourced data (i.e., points of interest and taxi trajectories). Finally, the above results at different scales are fused with hierarchical constraints, which means that the information from both urban landscape and human activities can be fully utilized to obtain a refined and fine‐grained map of urban functional zones. Wuhan, China was chosen as an example to evaluate the performance of the proposed method. Results show that the overall accuracy of the proposed method is 82.51% (accuracies of the mixed functional zones and single functional zones are 77.93 and 87.96%, respectively) and compared with state‐of‐the‐art methods, the proposed method performed better for the recognition of mixed functional zones.

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References

2012Discovering regions of different functions in a city using human mobility and POIs 2015Social Sensing: A New Approach to Understanding Our Socioeconomic Environments 2017Extracting urban functional regions from points of interest and human activities on location‐based social networks 2014A new insight into land use classification based on aggregated mobile phone data 2012Inferring land use from mobile phone activity 2012Land-Use Classification Using Taxi GPS Traces 2018Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs 2017Integrating multi-source big data to infer building functions

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2023A Novel Graph-Based Framework for Classifying Urban Functional Zones with Multisource Data and Human Mobility Patterns 2022Exploring spatiotemporal patterns of geosocial media data for urban functional zone identification 2023Exploring Crowd Travel Demands Based on the Characteristics of Spatiotemporal Interaction between Urban Functional Zones 2024A novel dynamic quantification model for diurnal urban land use intensity

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