Social functional mapping of urban green space using remote sensing and social sensing data

Abstract Urban green space (UGS) is an indispensable component of urban environmental systems and is important to urban residents. Both physical features (e.g., shrubs, trees) and social functions (e.g., public parks, green buffers) are important in UGS mapping. Most UGS studies rely solely on remote sensing data to conduct UGS mapping of physical features, and few studies have focused on UGS mapping from a social function perspective. Due to the limitations of remote sensing in identifying social features; social sensing, which can reflect socioeconomic characteristics, is needed. As a result, a novel methodological framework for integrating these two different data sources to conduct the social functional mapping of UGS has been required. Consequently, we first extracted vegetation patches from an area in Beijing, via the Hyperplanes for Plant Extraction Methodology (HPEM) and considered the parcels segmented by the OpenStreetMap (OSM) road networks as the basic analytical units. Then, near-convex-hull analysis (NCHA) and text-concave-hull analysis (TCHA) were performed to integrate the multi-source data. The results show that the Level I and Level II (refer to Table 3) social function types of UGS had overall accuracies of 92.48% and 88.76%, respectively. Our study provides an improved understanding of UGS and can assist government departments in urban planning. It can also help researchers broaden their research scope by acting as a freely available data source for their work.

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