Classifying Urban Functional Zones Based on Modeling POIs by Deepwalk

Abstract

Developing urban functional zone classification method to study urban spatial structure is a hotspot in current research. Using the word embedding model to excavate spatial relationship of the geographic elements in urban functional zones is an important way to develop urban functional zone classification method. However, in these studies, the spatial relationship of geographic elements was regarded as their homogeneity, while the structural similarity of geographical elements was ignored, which inevitably reduces the classification accuracy of urban functional zone classification method. This paper proposes to develop an urban functional zone classification method based on Deepwalk model, which could extract homogeneity and structural similarity of nodes in graph. The proposed method uses POI data to represent geographical elements, organizes POIs into graphs, and uses Deepwalk to embedding POIs for urban functional zone classification. It was applied to classify the urban functional zones of Chaoyang district in Beijing; and the classification results were compared with those of two baseline method based on Word2vec model and Place2vec model. The experimental results show that considering both the homogeneity and structural similarity of geographical elements, the proposed model has higher accuracy than the models only considering the homogeneity of geographical elements.