The UN’s 2030 Agenda defined 17 Sustainable Development Goals (SDGs). In order to ensure the implementation of this global agenda, the UN proposed a systematic follow-up and review through indicator-based tracking and reporting of the progress with statistical and geospatial information toward SDGs at national, regional, and global levels. This has posed many technical and institutional challenges. Although international communities have devoted great attention to this hot topic, most of their work has focused on the conceptual design and preliminary testing. There are very few good practices for a comprehensive measurement and assessment of progress toward SDGs with the integration of statistical and geospatial information at national or local levels. This paper presents the methodology and results of a pioneer project which measured the progress toward SDGs at a local level in China (i.e., Deqing County) by integrating statistical and geospatial information. In this study, a number of technical/institutional issues have been tackled, such as the adoption of appropriate indicators at a local level, availability and acquisition of reliable data sets, and spatiotemporal analysis with a geographical perspective, interaction between SDGs and cross-sector coordination. The major conclusions are (a) the comprehensive progress toward SDGs in Deqing can be most appropriately measured and assessed by integrating geospatial and statistical information; (b) Deqing has made significant economic and social advances while maintaining a good ecological environment over the past few years. The results were released at the first United Nations World Geospatial Information Congress as a good practice and a live example to stimulate discussions.

Social networking and network socialization provide abundant text information and social relationships into our daily lives. Making full use of these data in the big data era is of great significance for us to better understand the changing world and the information-based society. Though politics have been integrally involved in the hyperlinked world issues since the 1990s, the text analysis and data visualization of geo-events faced the bottleneck of traditional manual analysis. Though automatic assembly of different geospatial web and distributed geospatial information systems utilizing service chaining have been explored and built recently, the data mining and information collection are not comprehensive enough because of the sensibility, complexity, relativity, timeliness, and unexpected characteristics of political events. Based on the framework of Heritrix and the analysis of web-based text, word frequency, sentiment tendency, and dissemination path of the Huangyan Island incident were studied by using web crawler technology and the text analysis. The results indicate that tag cloud, frequency map, attitudes pie, individual mention ratios, and dissemination flow graph, based on the crawled information and data processing not only highlight the characteristics of geo-event itself, but also implicate many interesting phenomenon and deep-seated problems behind it, such as related topics, theme vocabularies, subject contents, hot countries, event bodies, opinion leaders, high-frequency vocabularies, information sources, semantic structure, propagation paths, distribution of different attitudes, and regional difference of net citizens’ response in the Huangyan Island incident. Furthermore, the text analysis of network information with the help of focused web crawler is able to express the time-space relationship of crawled information and the information characteristic of semantic network to the geo-events. Therefore, it is a useful tool to collect information for understanding the formation and diffusion of web-based public opinions in political events.

Societal safety is the result of the coordinated development of several subsystems; the coupling–coordination relationship among economic development (ED), social stability (SS), and ecological environment (EE) is the premise of realizing sustainable societal safety (SSS). Taking Qinghai Province as an example, this paper quantitatively evaluates the development index of each subsystem by constructing ED-SS-EE evaluation index system, then analyzes the spatio-temporal evolution of ED, SS, and EE coupling coordination levels based on the coupling coordination model, and finally predicts the coupling coordination level of ED, SS, and EE by using the GM(1.1) model. The findings are as follows: (1) The economy is developing rapidly, but the regional development is extremely uneven; ED is driving SS, and SS lags behind ED and slowly improves; EE is improving continuously, and the spatial pattern is relatively stable. (2) The ED, SS, and EE systems have a high degree of coupling, showing a fluctuating upward trend. Coordination level is low, showing a slow upward trend, and the regional differences are obvious. (3) In the future, the coordination level of ED, SS, and EE systems in different regions will be upgraded from the level of near imbalance and barely coordination to the level of intermediate coordination, good coordination, and even high-quality coordination. The spatio-temporal description of ED, SS, and EE coupling coordination level not only reveals the development trend and problems of SSS in Qinghai, it also proves the feasibility of evaluating societal safety level based on the coupling coordination level of sustainable social structure system.

With the continuous advancement of the Belt and Road Initiative, countries along the Belt and Road are becoming the most important geo-economic space for China’s peaceful rise. This paper constructs a geo-economic model to measure the geo-economic relations between China and countries along the Belt and Road, analyzes the spatial-temporal patterns of geo-economic flow between China and these countries, and discusses the spatial agglomeration of geo-economic flow between China and these countries. The results show that the geo-economic flow between China and the countries along the Belt and Road is increasing and the geo-economic relations between China and these countries are continuing to improve. Trade flow is far greater than investment flow, and China’s geo-economic relations with these countries have mainly relied on trade flow. The spatial differentiation of geo-economic flow between China and countries along the Belt and Road is significant. The development of geo-economic relations is unbalanced, and regional powers play an important role. The geo-economic center of gravity of China and countries along the Belt and Road was moving in the northeastern part of India and it first moved to the northwest and then to the southeast. China’s geo-economic space has obvious core-peripheral structure characteristics, and Southeast Asia is the core area of China’s geo-economic space. China’s geo-economic space presents a northwest-southeast pattern in the direction. There has been a significant, positive spatial autocorrelation in geo-economic flow between China and the countries along the Belt and Road, but the overall spatial agglomeration has been weak. The geo-economic flow agglomerations of China and these countries have mainly occurred in Southeast Asia, and the main agglomeration type has been high–high. The geo-economic activities of China and Southeast Asia had certain spatial spillover effects. To promote the sustainable development of China’s geo-economic relations with the countries along the Belt and Road, China should upgrade investment networks with these countries, optimize geo-economic relations with regional powers, and promote the full development of trade flow, investment flow and other geo-economic element flow.

With the advances of the Belt and Road Initiative (BRI), the geo-economic interactions between China and countries along the 21st-Century Maritime Silk Road counties (MSRCs) continue to increase, and analyzing the geo-economic relations between China and the MSRCs is extremely important for a scientific understanding of bilateral geo-economic cooperation. Differently from the traditional logic of geo-economic competition and cooperation, we constructed a new framework based on the dominant factors of geo-economic relations and used an extreme random forest regression model to classify the geo-economic relation types between China and MSRCs from 2006 to 2017. The results show that the unbalanced development of investment and trade between China and MSRCs hindered the enhancement of the intensity of bilateral geo-economic linkage from 2006 to 2017. The “Matthew effect” of China’s geo-economic flow linkage with MSRCs is significant. There are obvious differences in the dominant factors affecting the types of geo-economic relations between China and MSRCs, and the distribution of the importance of the indices of the types of geo-economic relations in each country is disordered. Geopolitics, markets, and resources have played important roles in the geo-economic linkages between China and MSRCs. There are five types of geo-economic relations between China and the MSRCs: market-oriented type, resource-oriented type, market-resource-oriented type, market-geopolitics-oriented type, and resource-geopolitics-oriented type, of which the market-oriented type is the most important type of geo-economic relations. In the future, China should focus on regional powers along the Maritime Silk Road for bilateral geo-economic cooperation, actively promote the balanced development of bilateral geo-economic elements flows, strengthen geopolitical cooperation with MSRCs, and formulate cooperation plans according to the types of geo-economic relations.

With the rise of China and the implementation of the “21st Century Maritime Silk Road” strategy, research on geo-economics between China and ASEAN (Association of Southeast Asian Nations) countries has become increasingly important. Current studies mainly focus on influencing factors, while there is little consideration about how these influencing factors act on geo-economic relationships. Therefore, this paper explores the key driving forces for geo-economic relationships between China and ASEAN countries by use of the structural equation modeling based on Partial Lease Squares. There are three main findings: (1) Economic factors have the greatest impact on geo-economic relationships and the total path effect is 0.778. Geo-location, geopolitics and geo-culture act on geo-economic relationships directly and indirectly. Their total path effects are 0.731, 0.645 and 0.513, respectively. (2) Indirect effects of geo-location, geopolitics and geo-culture impacting geo-economic relationships are far greater than direct effects. Geo-culture, in particular, has a vital mediating effect on geo-economic relationships. (3) Economic drivers promote geo-economic relationships through market, industrial policy, technical, network and benefit-sharing mechanisms. Political drivers improve geo-economic relationships through cooperation, negotiation, coordination and institutional mechanisms. Cultural drivers enhance geo-economic relationships through transmission mechanism. Location drivers facilitate geo-economic relationships through selection mechanism. We provide new insights on the geo-economic relationships through quantitative analysis and enrich the existing literature by revealing the key driving forces and mechanisms for geo-economic relationships.