A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception

Haohao Ji,Yongqiang Cheng,Zhengyong Wang,Longmei Han,Linbo Qing,Yonghong Peng

doi:10.3390/ijgi10060400

Abstract

The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with a better urban environment. Existing studies for assessing urban perception have primarily relied on low efficiency methods, which also result in low evaluation accuracy. Furthermore, there lacks a sophisticated understanding on how to correlate the urban perception with the built environment and other socio-economic data, which limits their applications in supporting urban planning. In this study, a new data-enabled intelligence framework for evaluating human perceptions of urban space is proposed. Specifically, a novel classification-then-regression strategy based on a deep convolutional neural network and a random-forest algorithm is proposed. The proposed approach has been applied to evaluate the perceptions of Beijing and Chengdu against six perceptual criteria. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators for the built environment and socio-economic data including visual elements, facility attributes and socio-economic indicators. Experimental results show that the proposed framework can effectively evaluate urban perceptions. The associations between urban perceptions and the visual elements, facility attributes and a socio-economic dimension have also been identified, which can provide substantial inputs to guide the urban planning for a better urban space.

Highlights

The experiment of linking multi-dimensional factors to perceptual criteria was implemented to explore the associations between human perceptions and urban physical environment
Most of the POI elements are positive perceptions in urban space; for the six perceptual attributes in this paper, most elements contribute to one side
We proposed a new data-enabled intelligence framework and approach to automatically evaluate human perceptions of the urban region in respect to six perceptual dimensions, namely, beautiful, boring, depressing, lively, safe and wealthy

Summary

Introduction

The wellbeing of humans, a sense of community and a sense of place are important attributes that influence social sustainability [1,2]. Human wellbeing and behaviors are affected by the built environment and sense of place [3,4,5]. By gathering holistic knowledge about the people’s real experience of the urban environment, is of great significance for researchers, urban planners and decision makers to improve the built environment, so as to further enhance the wellbeing of citizens. Research on urban space perception provides a promising way to uncover the psychological feelings perceived by humans towards the urban built environment and gives more scientific clues for urban planners by answering what and how to improve the built environment for urban planning or renewal

Methods

Results

Discussion

Conclusion