Abstract
Urban parks have important impacts on urban ecosystems and in disaster prevention. They also have diverse social functions that are important to the living conditions and spatial structures of cities. Identifying and classifying the different types of urban parks are important for analyzing the sustainable development and the greening progress in cities. Existing studies have predominantly focused on the data extraction of urban green spaces as a whole, while there have been relatively few studies that have considered different categories of urban parks and their impact, which makes it difficult to characterize or predict the spatial distribution and structures of urban parks and limits further refinement of urban research. At present, the classification of urban parks relies on the physical features observed in remote sensing images, but these methods are limited when mapping the diverse functions and attributes of urban parks. Crowd-sourced geographic data may more accurately express the social functions of points of interest (POIs) in cities, and, therefore, employing open data sources may assist in data extraction and the classification of different types of urban parks. This paper proposed a multi-source data fusion approach for urban park classification including POI and urban functional zone (UFZ) data. First, the POI data were automatically reclassified using improved natural language processing (NLP) (i.e., text similarity measurements and topic modeling) to establish the links between urban park green-space types and POIs. The reclassified POI data as well as the UFZ data were then subjected to scene-based data fusion, and various types of urban parks were extracted using data attribute analysis and social attribute recognition for urban park mapping. Experimental analysis was conducted across Beijing and Hangzhou to verify the effectiveness of the proposed method, which had an overall classification accuracy of 82.8%. Finally, the urban park types of the two cities were compared and analyzed to obtain the characteristics of urban park types and structures in the two cities, which have different climates and urban structures.
Highlights
Urban green spaces play an important role in urban ecosystems [1,2], and they can include many types of spaces, such as protective green spaces, educational and scientific green spaces, and residential land used as accessory green spaces
The rapid urbanization process has created large areas of artificial, impervious surfaces that have replaced the native natural areas such as grasslands, wetlands, and forests [5]. This has led to a year-over-year reduction in urban green spaces [6] that has caused a series of problems, such as changes in the urban environment [7], a decline in the quality of life for urban residents [8], and the destruction of urban ecosystems [4]
natural language processing (NLP) method to maximize the retention of social attributes in the points of interest (POIs) data; the overall accuracy was 84.3% and the kappa coefficient was 0.79 (Table 7), indicating that the method performed well in the classification of various types of urban park POIs and effectively improved the accuracy of urban park classification
Summary
Urban green spaces play an important role in urban ecosystems [1,2], and they can include many types of spaces, such as protective green spaces, educational and scientific green spaces, and residential land used as accessory green spaces. The rapid urbanization process has created large areas of artificial, impervious surfaces that have replaced the native natural areas such as grasslands, wetlands, and forests [5]. This has led to a year-over-year reduction in urban green spaces [6] that has caused a series of problems, such as changes in the urban environment [7], a decline in the quality of life for urban residents [8], and the destruction of urban ecosystems [4]
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