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Bulletin of Chinese Academy of Sciences (Chinese Version)

Jun GAO, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, ChinaFollow
Zhonghao ZHANG, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
Weiyue LI, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
Fengyuan SUN, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
Yina HU, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
Liangxu WANG, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
Jing FU, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
Xin LI, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100039, ChinaFollow
Guodong CHENG, School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, ChinaFollow

Keywords

urban sustainability, Big Earth Data, Sustainable Development Goals (SDGs), indicators

Document Type

Strategy & Practice

Abstract

The sustainable development of cities is the key to achieving the United Nations 2030 Sustainable Development Goals (SDGs). There have been numbers of studies on urban sustainable development assessment, but the existing indicators are usually based on traditional statistical data, which makes urban sustainable development assessment limited in global city assessment and comparison. Big Earth Data can overcome the problems of traditional data statistics such as inconsistent statistical caliber, incomplete statistical information, and difficulty in obtaining data. With its macroscopic, dynamic, and diverse advantages, it can provide new assistance for urban sustainable development assessment. Based on the research on sustainable development of cities worldwide, this study sorts out the SDG 11 and other goals related to urban sustainability, and discusses the possibility of Big Earth Data, such as remote sensing data and the Internet big data, in the urban sustainable development evaluation. This study hopes to provide a scientific reference for the sustainable development of cities. The sustainability indicators using the Big Earth Data has realized the integration and utilization of multi-source information, which will help to achieve more quantitative, real-time, and detailed evaluation for urban sustainability.

First page

940

Last Page

949

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1 Huang S L, Wong J H, Chen T C. A framework of indicator system for measuring Taipei's urban sustainability. Landscape and Urban Planning, 1998, 42(1):15-27.

2 Yigitcanlar T, Dur F, Dizdaroglu D. Towards prosperous sustainable cities:A multiscalar urban sustainability assessment approach. Habitat International, 2015, 45:36-46.

3 Kaur H, Garg P. Urban sustainability assessment tools:A review. Journal of Cleaner Production, 2019, 210:146-158.

4 Ameen R, Mourshed M. Urban sustainability assessment framework development:The ranking and weighting of sustainability indicators using analytic hierarchy process. Sustainable Cities and Society, 2019, 44:356-366.

5 Michael F L, Noor Z Z, Figueroa M J. Review of urban sustainability indicators assessment-Case study between Asian countries. Habitat International, 2014, 44:491-500.

6 周成虎, 王华, 王成善, 等. 大数据时代的地学知识图谱研究展望. 中国科学:地球科学, 2021, 51, doi:10.1360/SSTe-2020-0337.

7 Guo H D, Wang L, Liang D, et al. Big Earth Data from space:A new engine for Earth science. Science Bulletin, 2016, 61(7):505-513.

8 张盛, 吕永龙, 苑晶晶, 等. 持续城镇化对中国推进实施联合国可持续发展目标的作用. 生态学报, 2019, 39(4):1135-1143.

9 王鹏龙, 高峰, 黄春林, 等. 面向SDGs的城市可持续发展评价指标体系进展研究. 遥感技术与应用, 2018, 33(5):784-792.

10 宋晓谕, 高峻, 李新, 等. 遥感与网络数据支撑的城市可持续性评价:进展与前瞻. 地球科学进展, 2018, 33(10):1075-1083.

11 肖禾, 王馨怡, 陈甲全, 等. 联合国可持续发展目标监测的数据供应需求探讨. 中国人口·资源与环境, 2018, 28(7):6-9.

12 王卷乐, 程凯, 边玲玲, 等. 面向SDGs和美丽中国评价的地球大数据集成框架与关键技术. 遥感技术与应用, 2018, 33(5):3-11.

13 中国科学院地球大数据科学工程. 地球大数据支撑可持续发展目标报告(2020). (2020-09-01)[2021-08-09]. https://www.fmprc.gov.cn/web/ziliao_674904/zt_674979/dnzt_674981/qtzt/2030kcxfzyc_686343/P020200927634068540177.pdf.

14 董少春, 齐浩, 胡欢. 地球科学大数据的现状与发展. 科学技术与工程, 2019, 19(20):1-11.

15 Jean N, Burke M, Xie M, et al. Combining satellite imagery and machine learning to predict poverty. Science, 2016, 353:790-794.

16 Simon D, Arfvidsson H, Anand G, et al. Developing and testing the Urban Sustainable Development Goal's targets and indicators-A five-city study. Environment and Urbanization, 2016, 28(1):49-63.

17 Gong P, Liu H, Zhang M, et al. Stable classification with limited sample:Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 2019, 64(3):370-373.

18 Liu X, Hu G, Chen Y, et al. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 2018, 209:227-239.

19 Kuang W, Zhang S, Li X, et al. A 30 m resolution dataset of China's urban impervious surface area and green space, 2000-2018. Earth System Science Data, 2021, 13(1):63-82.

20 孟庆伟. 城市公共交通覆盖度空间统计分析研究. 测绘与空间地理信息, 2020, 43(3):141-145.

21 张一帆, 景海涛. OpenStreetMap数据质量评估及适用性分析——以河南省铁路网为例. 地理信息世界, 2019, 26(4):112-116.

22 沈安南, 王亮绪, 高峻. 基于深度学习的公民参与城市治理的时空格局研究——以苏州市为例. 地理信息世界, 2020, 27(2):44-48.

23 伍亿真, 施开放, 余柏蒗, 等. 利用NPP-VIIRS夜间灯光遥感数据分析城市蔓延对雾霾污染的影响. 武汉大学学报 (信息科学版), 2021, 46(5):777-789.

24 王娇娇, 李中元. 基于Landsat OLI影像及NPP-VIIRS灯光数据的武汉市人口密度估算. 地域研究与开发, 2019, 38(6):146-151.

25 Sun F, Zhao H, Deng L, et al. Characterizing the warming effect of increasing temperatures on land surface:Temperature change, heat pattern dynamics and thermal sensitivity. Sustainable Cities and Society, 2021, 70(1):102904.

26 孙凤云, 邓灵稚, 刘垚燚, 等. 基于增强回归树与区域增温敏感性指数的城市升温效应空间分异研究. 生态学报, 2021, 41(15):1-11

27 疏小舟, 尹球, 匡定波. 内陆水体藻类叶绿素浓度与反射光谱特征的关系. 遥感学报, 2000, 4(1):41-45.

28 薛冰, 李京忠, 肖骁, 等. 基于兴趣点(POI)大数据的人地关系研究综述:理论、方法与应用. 地理与地理信息科学, 2019, 35(6):51-60.

29 余柏蒗, 王丛笑, 宫文康, 等. 夜间灯光遥感与城市问题研究:数据、方法、应用和展望. 遥感学报, 2021, 25(1):342-364.

30 Huang X, Yang J, Li J, et al. Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 175:403-415.

31 张平, 崔琪楣, 侯延昭, 等. 移动大数据时代:无线网络的挑战与机遇. 科学通报, 2015, 60:433-438.

32 Bian J, Li A, Lei G, et al. Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 162:63-76.

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