关键词: 多源数据, 主体功能区, POI, 广州

Abstract:

Major Function Oriented Zoning (MFOZ) is a part of the national land space development and protection system and is the main basis for other special space development and layout plans; further, it plays a role in strategic planning and target transmission in the spatial planning system. The study area is the city of Guangzhou, China, and based on a 96.6% matching rate for the interest point (POI) data, the village-level administrative unit is selected as the basic calculation unit of the study. Functional land type identification is performed using a deep network convolutional neural network, and semantic extraction of the remote sensing images is conducted. The extraction results and the POI data function land obtained by the sample density method undergo data reclassification and are assigned to an assignment model. The result is supplemented and verified. In accordance with the relevant policies and planning documents, an association is established between the functional land and the MFOZ, and the rules of interpretation for the functional area and MFOZ are developed. As the POI data is peculiar, an information entropy method with quantitative metric elements is used to analyze the degree of mixing of functional land use in Guangzhou, and the road network data is used to determine the regional traffic development to assist in judging the MFOZ. A cluster analysis of the functional land of the village-level administrative unit from the geographical space is performed by dividing Guangzhou City into five functional areas: core promotion zone, adjustment optimization zone, key expansion zone, moderate development zone, and prohibited development zone. The resulting zone map is compared with the draft of the Guangzhou Major Function Oriented Zoning Planning (2008-2020) document and the Guangzhou City Master Plan (2017-2035). We observe that the proposed method has a high accuracy and appropriately reflects the Major Function Oriented Zoning. The actual distribution of the functional area reflects the spatial pattern of Guangzhou: a core upgrading area dominated by the main urban area, an adjustment and optimization area mainly in the Panyu and Baiyun Districts, a large-scale expansion area dominated by the Huadu District and Huangpu, a moderate development zone dominated by the Conghua and Zengcheng Districts, and a prohibited construction zone.

Key words: multi-source data, Major Function Oriented Zoning, POI, Guangzhou