Building-Level Urban Functional Area Identification Based on Multi-Attribute Aggregated Data from Cell Phones—A Method Combining Multidimensional Time Series with a SOM Neural Network

Abstract

Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation.

Function2vec: A Geographic Knowledge Graph Model of Urban Function Evolution and Its Application

“…(3) The system composed of function units represents a subsystem that expresses the internal structure of the urban system. These function units similarly exhibit interconnections and variations in terms of function scales [32,33]. Thus, by considering basic units that express urban functions (such as grids, blocks, and clusters) as the fundamental elements of a city, the hierarchical tree-like structure between cities can be mapped onto the UFE.…”

A Progressive Simplification Method for Buildings Based on Structural Subdivision

“…As B 1 1 transforms to B 7 1 , the minimal and unimportant details are deleted preferentially. The complex-shaped buildings are simplified to a simple one, which also preserves the main shape characteristics, e.g., B 7 1 . Since the orthogonal characteristic of buildings is preserved well in continuous scale transformation, the results show that the proposed method simplifies rectangular buildings commendably, such as B 1 2 and B 1 6 in Figure 14.…”

“…In addition to its application in traditional cartography, the simplification of buildings also helps to simplify the outline of buildings extracted from high-resolution remote sensing images, making the shape of buildings more regular [5]. In the identification of urban functional areas, the shapes of buildings at different scales are the important basis for mining spatial information [6,7]. Building simplification is usually conducted on a single building, largely independent of contextual information.…”

A Multi-Feature Fusion Method for Urban Functional Regions Identification: A Case Study of Xi’an, China

“…Current research focuses on two main aspects: firstly, improving data feature extraction and fusion methods. To this end, scholars have proposed methods such as multimodal deep learning approaches with attention mechanisms [33], self-organizing map (SOM) neural network models based on improved dynamic time warping (Ndim-DTW) distances [34], context-coupled matrix factorization (CCMF) considering contextual relationships [35], and the adoption of Synthetic Minority Over-sampling Technique (SMOTE) to mitigate the impact of data imbalance [36]. Secondly, addressing the spatial heterogeneity of functional region units, also known as the scale effect problem.…”