Journal of Geographical Sciences >
Geographical big data and data mining: A new opportunity for “water-energy-food” nexus analysis
Yang Jie (1991-), Assistant Researcher, specialized in water-energy-food nexus and water resources management. E-mail: yangjie0201@snnu.edu.cn |
Received date: 2023-05-05
Accepted date: 2023-11-03
Online published: 2024-02-06
Supported by
National Natural Science Foundation of China(52209030)
Fundamental Research Funds for the Central Universities(GK202207005)
China Postdoctoral Science Foundation(2023M732163)
Shaanxi Province Postdoctoral Science Foundation(2023BSHYDZZ110)
Since the Bonn 2011 conference, the “water-energy-food” (WEF) nexus has aroused global concern to promote sustainable development. The WEF nexus is a complex, dynamic, and open system containing interrelated and interdependent elements. However, the nexus studies have mainly focused on natural elements based on massive earth observation data. Human elements (e.g., society, economy, politics, culture) are described insufficiently, because traditional earth observation technologies cannot effectively perceive socioeconomic characteristics, especially human feelings, emotions, and experiences. Thus, it is difficult to simulate the complex WEF nexus. With the development of earth observation sensor technologies and human activity perception methods, geographical big data covering both human activities and natural elements offers a new opportunity for in-depth WEF nexus analysis. This study proposes a five-step framework by leveraging geographical big data mining to dig for the hidden value in the data of various natural and human elements. This framework can enable a thorough and comprehensive analysis of the WEF nexus. Some application examples of the framework, major challenges, and possible solutions are discussed. Geographical big data mining is a promising approach to enhance the analysis of the WEF nexus, strengthen the coordinated management of resources and sectors, and facilitate the progress toward sustainable development.
YANG Jie , CAO Xiaoshu , YAO Jun , KANG Zhewen , CHANG Jianxia , WANG Yimin . Geographical big data and data mining: A new opportunity for “water-energy-food” nexus analysis[J]. Journal of Geographical Sciences, 2024 , 34(2) : 203 -228 . DOI: 10.1007/s11442-024-2202-6
Figure 1 The interlinkages among water, energy, and food |
Figure 2 Challenges of the “water-energy-food” (WEF) nexus |
Figure 3 Classification of geographical big data |
Figure 4 Framework of geographical big data mining in WEF nexus analysis |
Figure 5 Challenges and possible solutions of geographical big data mining in WEF nexus |
[1] |
|
[2] |
|
[3] |
|
[4] |
|
[5] |
|
[6] |
|
[7] |
|
[8] |
|
[9] |
|
[10] |
|
[11] |
|
[12] |
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
|
[21] |
|
[22] |
|
[23] |
|
[24] |
|
[25] |
|
[26] |
|
[27] |
|
[28] |
|
[29] |
|
[30] |
|
[31] |
|
[32] |
|
[33] |
|
[34] |
|
[35] |
|
[36] |
|
[37] |
|
[38] |
|
[39] |
|
[40] |
|
[41] |
|
[42] |
|
[43] |
|
[44] |
|
[45] |
|
[46] |
|
[47] |
|
[48] |
|
[49] |
|
[50] |
|
[51] |
|
[52] |
|
[53] |
|
[54] |
|
[55] |
|
[56] |
|
[57] |
|
[58] |
|
[59] |
|
[60] |
|
[61] |
|
[62] |
|
[63] |
|
[64] |
|
[65] |
|
[66] |
|
[67] |
|
[68] |
|
[69] |
|
[70] |
|
[71] |
|
[72] |
|
[73] |
|
[74] |
|
[75] |
|
[76] |
|
[77] |
|
[78] |
|
[79] |
|
[80] |
|
[81] |
|
[82] |
|
[83] |
UNESCAP, 2013. ESCAP Status Report on the Water-Energy-Food Security Nexus in the Asia Pacific Region. https://www.unescap.org/sites/default/files/UNESCAP-WEF-Nexus-AP-Bangkok-Hezri.pdf.
|
[84] |
United Nations (UN),2015. Transforming Our World:The 2030 Agenda for Sustainable Development. Outcome Document for the UN Summit to Adopt the Post- 2015 Development Agenda: Draft for Adoption. New York.
|
[85] |
United States National Intelligence Council, 2012. Global Trends 2030:Alternative Worlds. US NIC, Washington DC, USA, pp. 137.
|
[86] |
|
[87] |
|
[88] |
|
[89] |
|
[90] |
|
[91] |
World Economic Forum, 2011. Global Risks Report 2011. 6th ed. Cologne: World Economic Forum.
|
[92] |
|
[93] |
|
[94] |
|
[95] |
|
[96] |
|
[97] |
|
[98] |
|
[99] |
|
/
〈 | 〉 |