Urban Function as a New Perspective for Adaptive Street Quality Assessment

Hanfa Xing,Junyu Lu,Chengpeng Song,Feng Hu,Yuan Meng,Jun Wang,Wei Liu

doi:10.3390/su12041296

Abstract

Street networks are considered to be one significant component of urban structures that serve various urban functions. Assessing the quality of each street is important for managing natural and public resources, organizing urban morphologies and improving city vitality. While current research focuses on particular street assessment indices, such as accessibility and connectivity, they ignore biases in street assessment caused by differences in urban functions. To address this issue, an adaptive approach to assessing street quality from the perspective of the variation in urban functions is proposed. First, an adaptive urban function detection model is established, with street-level element segmenting using PSPNet and semantic urban function extraction using LDA topic modelling. On this basis, an urban function-driven street quality assessment is proposed to adaptively evaluate multilevel urban streets. Taking Tianhe District in Guangzhou, Guangdong Province, as the study area, experiments using street view images and points of interest (POIs) are applied to validate the proposed approach. The experiment results in a model for adaptive urban function detection with an overall accuracy of 64.3%, showing that streets with different urban functions, including traffic, commercial, and residential functions, can be assessed. The experimental results can facilitate urban function organization and urban land-use planning.

Highlights

Street networks are considered to be one significant component of urban structures and serve different urban functions for sustainable development, including commercial, traffic, industry and landscape-based functions [1,2,3]
By proposing pyramid scene parsing network (PSPNet) for image segmentation, the open space, building enclosure degree, and greenery enclosure degree were extracted from street view images
By proposing PSPNet for image segmentation, the open space, building enclosure degree, and Sgursetaeinnaebriylitye2n0c2l0o, s1u2,r1e29d6egree were extracted from street view images

Summary

Introduction

Street networks are considered to be one significant component of urban structures and serve different urban functions for sustainable development, including commercial, traffic, industry and landscape-based functions [1,2,3]. Assessing the quality of each street is important for managing natural and public resources, organizing urban morphologies and improving city vitality [4,5,6]. Traditional approaches to assessing street quality are by means of analyzing walking behaviors using questionnaire surveys, which ask pedestrians for detailed respondents [7,8]. The rise of geographic information science and big geodata has brought new opportunities for understanding spatial configurations along streets [9]. Compared with conventional data sources, big geodata presents the advantages of being freely obtainable and continuously updated, and it contains sufficient physical and socioeconomic information describing urban environments [10]. Street view images, referring to the photographs captured along streets, show the potential to describe urban environments from the perspective of human vision [11,12]. Point of interest (POI) data are locations posted on the Internet for a specific place and directly represent its functions and services for citizens [13,14,15]

Results

Discussion

Conclusion