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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Measuring and evaluating SDG indicators with Big Earth Data.

Huadong Guo1, Dong Liang1, Zhongchang Sun2

  • 1International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.

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|December 22, 2022
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Summary
This summary is machine-generated.

Big Earth Data, leveraging multi-source information, aids in monitoring Sustainable Development Goals (SDGs). China

Keywords:
Big Earth DataBig dataCASEarthDecision supportDigital EarthSustainable Development Goals (SDGs)

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Area of Science:

  • Sustainability Science
  • Geospatial Analysis
  • Big Data Analytics

Background:

  • The United Nations 2030 Agenda for Sustainable Development (SDGs) requires robust monitoring systems.
  • Data limitations hinder comprehensive SDG implementation and progress tracking in many nations.
  • Big Earth Data offers innovative solutions to bridge data gaps for SDG monitoring.

Purpose of the Study:

  • To demonstrate the utility of Big Earth Data in evaluating and monitoring Sustainable Development Goals (SDGs).
  • To showcase advancements in algorithms, indicator development, and data products for sustainability science.
  • To analyze progress on specific SDGs in China from 2010 to 2020.

Main Methods:

  • Development of novel algorithms for SDG indicator evaluation.
  • Expansion and extension of indicators for SDG 11.4.1 and SDG 11.3.1.
  • Introduction of a biodiversity risk index for SDG 15.5.1 analysis.
  • Creation of high-quality Big Earth Data products (e.g., global net ecosystem productivity).

Main Results:

  • Successful implementation of Big Earth Data for evaluating SDGs 2, 6, 11, 13, 14, and 15 in China.
  • Demonstrated effectiveness of new algorithms and data products in sustainability science.
  • Analysis indicates all six monitored SDGs are on track for achievement by 2030.

Conclusions:

  • Big Earth Data is a powerful tool for overcoming data limitations in SDG monitoring.
  • Innovative methodologies and data products derived from Big Earth Data enhance sustainability science.
  • China's progress suggests feasibility of achieving key SDGs through strategic data utilization.