Evaluating Urban Community Sustainability by Integrating Housing, Ecosystem Services, and Landscape Configuration

Liang Zhou,Bao Wang,Bo Yuan,Xuewei Dang,Haowei Mu

doi:10.1155/2020/3460962

Abstract

Community is the core spatial unit for evaluating sustainable development. However, single data and method seem inadequate for conducting a scientific, effective, and innovative sustainable evaluation of complex community units. In this study, we perform a sustainable-oriented land use scheme using multisource remote sensing, machine learning, and object-based postclassification refinement. Furthermore, we assess the sustainability of the traffic community by data-driven and combined housing, ecosystem services, and landscape configuration. The results indicated that (1) the relationship between housing, ecosystem services, and landscape pattern has obvious synergistic effects, although with dissimilar importance in different sustainability levels. High sustainability level is intensely coordinated with landscape configuration, medium sustainability level is more affected by ecosystem services, and low sustainability level is more related to housing. (2) Community sustainability presents a significant spatial distribution. The communities of high sustainability level are mainly located in both sides of the Pearl River and emerging urban areas, while those of medium sustainability level are distributed sporadically in the study area and those of low sustainability level are concentrated in old towns. (3) Community transformation cannot be accomplished at one step. Along with the continuous optimization of landscape configuration, the priority should be given to housing reconstruction and improvement of ecosystem services further. We provide scientific and effective data-based evidence for urban decision-makers by integrating the advantages of the Earth Observation System and multifactor analysis.

Highlights

Cities are hubs for ideas, science, culture, commerce, productivity, social development, etc. [1, 2], which were deemed as sustainability problems rather than solutions in the last few decades [3, 4]
E classification results are shown in Table 3, and the accuracy of Support vector machine (SVM) and Random forest (RF) on eight land use types improved significantly after using OBPR
For OBPR-RF results, the accuracy of I and W increases to 100%, while T accuracy reduces and Storied building (SB) and Bare land (BL) remain unchanged. e OBPR-RF achieves a higher classification accuracy (OA is 92.79%, and k is 0.92) than RF (OA is 90.17%, and k is 0.88)

Summary

Introduction

Cities are hubs for ideas, science, culture, commerce, productivity, social development, etc. [1, 2], which were deemed as sustainability problems rather than solutions in the last few decades [3, 4]. [1, 2], which were deemed as sustainability problems rather than solutions in the last few decades [3, 4]. Some of the largest economies in the world, such as the US, the UK, and Japan, have already developed their sustainable community rating framework while lacking in China [7, 8]. Rapid urbanization has caused increasing social-environmental problems, such as uneven income, unfair housing cities poverty, and environmental deterioration, which exhibit a substantial negative impact on urban and regional sustainable development [9, 10]. Erefore, it urgently needs to launch a sustainable community evaluation system in China. As building sustainable cities and communities was defined as one of the sustainable development goals (SDGs) by the United Nations in the 2030

Results

Discussion

Conclusion