Food security is the primary prerequisite for achieving other Millennium Development Goals (MDGs). Given that the MDG of “halving the proportion of hungers by 2015” was not realized as scheduled, it will be more pressing and challenging to reach the goal of zero hunger by 2030. So there is high urgency to find the pattern and mechanism of global food security from the perspective of spatio-temporal evolution. In this paper, based on the analysis of database by using a multi-index evaluation method and radar map area model, the global food security level for 172 countries from 2000 to 2014 were assessed; and then spatial autocorrelation analysis was conducted to depict the spatial patterns and changing characteristics of global food security; then, multi-nonlinear regression methods were employed to identify the factors affecting the food security patterns. The results show: 1) The global food security pattern can be summarized as “high-high aggregation, low-low aggregation”. The most secure countries are mainly distributed in Western Europe, North America, Oceania and parts of East Asia. The least secure countries are mainly distributed in sub-Saharan Africa, South Asia and West Asia, and parts of Southeast Asia. 2) Europe and sub-Saharan Africa are hot and cold spots of the global food security pattern respectively, while in non-aggregation areas, Haiti, North Korea, Tajikistan and Afghanistan have long-historical food insecurity problems. 3) The pattern of global food security is generally stable, but the internal fluctuations in the extremely insecure groups were significant. The countries with the highest food insecurity are also the countries with the most fluctuated levels of food security. 4) The annual average temperature, per capita GDP, proportion of people accessible to clean water, political stability and non-violence levels are the main factors influencing the global food security pattern. Research shows that the status of global food security has improved since the year 2000, yet there are still many challenges such as unstable global food security and acute regional food security issues. It will be difficult to understand these differences from a single factor, especially the annual average temperature and annual precipitation. The abnormal performance of the above factors indicates that appropriate natural conditions alone do not absolutely guarantee food security,while the levels of agricultural development, the purchasing power of residents, regional accessibility, as well as political and economic stability have more direct influence.
The spatial structures of China’s Major Function Zoning are important constraining indicators in all types of spatial planning and key parameters for accurately downscaling major functions. Taking the proportion of urbanization zones, agricultural development zones and ecological security zones as the basic parameter, this paper explores the spatial structures of major function zoning at different scales using spatial statistics, spatial modeling and landscape metrics methods. The results show: First, major function zones have spatial gradient structures, which are prominently represented by latitudinal and longitudinal gradients, a coastal distance gradient, and an eastern-central-western gradient. Second, the pole-axis system structure and core-periphery structure exist at provincial scales. The general principle of the pole-axis structure is that as one moves along the distance axis, the proportion of urbanization zones decreases and the proportion of ecological security zones increases. This also means that the proportion of different function zones has a ring-shaped spatial differentiation principle with distance from the core. Third, there is a spatial mosaic structure at the city and county scale. This spatial mosaic structure has features of both spatial heterogeneity, such as agglomeration and dispersion, as well as of mutual, adjacent topological correlation and spatial proximity. The results of this study contribute to scientific knowledge on major function zones and the principles of spatial organization, and it acts as an important reference for China’s integrated geographical zoning.
Geomorphological regionalization (geomor-region) and geomorphological type (geomor-type) classification are two core components in the geomorphologic research. Although remarkable achievements have been made in the study of geomor-region, many deficiencies still exist, such as the inconsistency of landform indicators, the small quantity of division orders, disparities of geomorphological characteristics, differences of mapping results, and the small scale of zoning maps. Requirements for improved national geomor-regions are therefore needed for the purpose of an enhanced national geo-information system. Based on theories of geomor-region in China including plate tectonics, crustal features, endogenic and exogenic forced geomorphological features, and regional differentiations of geomor-type, a three-order (major-region, sub-region, and small-region) research program on China’s geomor-regions is proposed on the basis of previous 2013 geomor-region system. The major contents of the new geomor-region scheme are: (1) principles of the national multi-order geomor-regions; (2) hierarchical indicator systems of geomor-regions including characteristics of the terrain ladder under the control of tectonic setting, combinations of regional macro-form types, combinations of endogenic and exogenic forces and basic types of morphology, combinations of regional morphological types, and combinations of regional micro-morphological types; (3) naming rules and coding methods of geomor-regions; and (4) precise positioning techniques and methods of multi-order geomor-region divisions based on multi-source data. Using the new geomor-region theory and division methodology, the partition of national three-order geomor-regions of China was successfully constructed. The geomor-region system divided China into six first-order major-regions, 36 second-order sub-regions, and 136 third-order small-regions. In addition, a database and management information system of the national geomor-regions were established. This research has an important guiding significance for promoting the development of China’s regional geomorphology and for practical applications based on geomor-regions.
It is possible to obtain vast amounts of spatiotemporal data related to human activities to support the study of human behavior and social evolution. In this context, geography, with the human-nature relationship as its core, is undergoing a transition from strictly earth observations to the observation of human activities. Geocomputation for social science is one manifestation thereof. Geocomputation for social science is an interdisciplinary approach combining remote sensing techniques, social science, and big data computation. Driven by the availability of spatially and temporally expansive big data, geocomputation for social science uses spatiotemporal statistical analyses to detect and analyze the interactions between human behavior, the natural environment, and social activities; Remote sensing (RS) observations are used as primary data. Geocomputation for social science can be used to investigate major social issues and to assess the impact of major natural and societal events, and will surely be an area of focused development in geography in the near future. We briefly review the background of geocomputation in the social sciences, discuss its definition and disciplinary characteristics, and highlight the main research foci. Several key technologies and applications are also illustrated with relevant case studies of the Syrian Civil War, typhoon transits, and traffic patterns.
The objective, connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper. Big geodata may be categorized into two domains: big earth observation data and big human behavior data. A description of big geodata includes, in addition to the “5Vs” (volume, velocity, value, variety and veracity), a further five features, that is, granularity, scope, density, skewness and precision. Based on this approach, the essence of mining big geodata includes four aspects. First, flow space, where flow replaces points in traditional space, will become the new presentation form for big human behavior data. Second, the objectives for mining big geodata are the spatial patterns and the spatial relationships. Third, the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data, namely heterogeneity and homogeneity, may change with scale. Fourth, data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships. The big geodata mining methods may be categorized into two types in view of the mining objective, i.e., classification mining and relationship mining. Future research will be faced by a number of issues, including the aggregation and connection of big geodata, the effective evaluation of the mining results and the challenge for mining to reveal “non-trivial” knowledge.
The compilation of 1:250,000 vegetation type map in the North-South transitional zone and 1:50,000 vegetation type maps in typical mountainous areas is one of the main tasks of Integrated Scientific Investigation of the North-South Transitional Zone of China. In the past, vegetation type maps were compiled by a large number of ground field surveys. Although the field survey method is accurate, it is not only time-consuming, but also only covers a small area due to the limitations of physical environment conditions. Remote sensing data can make up for the limitation of field survey because of its full coverage. However, there are still some difficulties and bottlenecks in the extraction of remote sensing information of vegetation types, especially in the automatic extraction. As an example of the compilation of 1:50,000 vegetation type map, this paper explores and studies the remote sensing extraction and mapping methods of vegetation type with medium and large scales based on mountain altitudinal belts of Taibai Mountain, using multi-temporal high resolution remote sensing data, ground survey data, previous vegetation type map and forest survey data. The results show that: 1) mountain altitudinal belts can effectively support remote sensing classification and mapping of 1:50,000 vegetation type map in mountain areas. Terrain constraint factors with mountain altitudinal belt information can be generated by mountain altitudinal belts and 1:10,000 Digital Surface Model (DSM) data of Taibai Mountain. Combining the terrain constraint factors with multi-temporal and high-resolution remote sensing data, ground survey data and previous small-scale vegetation type map data, the vegetation types at all levels can be extracted effectively. 2) The basic remote sensing interpretation and mapping process for typical mountains is interpretation of vegetation type-groups→interpretation of vegetation formation groups, formations and subformations→interpretation and classification of vegetation types & subtypes, which is a combination method of top-down method and bottom-up method, not the top-down or the bottom-up classification according to the level of mapping units. The results of this study provide a demonstration and scientific basis for the compilation of large and medium scale vegetation type maps.
The Three Gorges Reservoir Area (TGRA) is typical of an ecologically vulnerable area, comprised of rural and mountainous areas, and with high immigration. Because of its economic and ecologic importance, studying the traditional agroecosystem changes in the TGRA is key to rural development and revitalization. In this study, we apply a framework of theoretical analysis, empirical study, and trend prediction to the Caotangxi River watershed within the TGRA. Using QuickBird high-resolution remote sensing images from 2012 to 2017 to evaluate natural resources and farmers’ behavior, we analyze the transition and trends in the traditional agroecosystem in mountainous areas of the TGRA at spatial scale of the man-land relationship. We find that the agroecosystem in the TGRA can be divided into four modes using 100 m interval buffer rings: high-low-low, high-low-high, low-high-low and low-low-high mode where the different modes represent the agricultural development stages in the TGRA. Furthermore, the traditional agroecosystem in TGRA, represented by system elements such as farmers and sloping farmland, is transforming to accommodate the diversification of farmer livelihoods. For example, sloping farmland, which was dominated by a production function, now has equal emphasis on ecological and economic functions. Spatially, the range of the agroecosystem transition has migrated beyond high mountain areas to flat valley areas. Generally, this study provides an overview of land use in rural areas, controls on soil and water loss in mountainous areas, and better rural living environments in the TGRA.
Research into urban expansion patterns and their driving forces is of great significance for urban agglomeration development planning and decision-making. In this paper, we reveal the multi-dimensional characteristics of urban expansion patterns, based on the intensity index of the urban expansion, the differentiation index of the urban expansion, the fractal dimension index, the land urbanization rate, and the center of gravity model, by taking the Beijing-Tianjin-Hebei (Jing-Jin-Ji) urban agglomeration as an example. We then build the center of gravity-geographically and temporally weighted regression (GTWR) model by coupling the center of gravity model with the GTWR model. Through the analysis of the temporal and spatial patterns and by using the center of gravity-GTWR model, we analyze the driving forces of the urban land expansion and summarize the dominant development modes and core driving forces of the Jing-Jin-Ji urban agglomeration. The results show that: 1) Between 1990 and 2015, the expansion intensity of the Jing-Jin-Ji urban agglomeration showed a down-up-down trend, and the peak period was in 2005-2010. Before 2005, high-speed development took place in Beijing, Tianjin, Baoding, and Langfang; after 2005, rapid development was seen in Xingtai and Handan. 2) Although the barycenter of cities in the Jing-Jin-Ji urban agglomeration has shown a divergent trend, the local interaction between cities has been enhanced, and the driving forces of urban land expansion have shown a characteristic of spatial spillover. 3) The spatial development mode of the Jing-Jin-Ji urban agglomeration has changed from a dual-core development mode to a multi-core development mode, which is made up of three functional cores: the transportation core in the northern part, the economic development core in the central part, and the investment core in the southern part. The synergistic development between each functional core has led to the multi-core development mode. 4) The center of gravity-GTWR model combines the analysis of spatial and temporal nonstationarity with urban spatial interaction, and analyzes the urban land expansion as a space-time dynamic system. The results of this study show that the model is a feasible approach in the analysis of the driving forces of urban land expansion.
With the degradation of natural resources and environment caused by industrial development in some developing countries, the requirement of implementing a “social ecological” approach to development is imminent. Resource and environment carrying capacity provides a means of assessing regional development potential by measuring regional sustainable development in terms of economy, population and resources & environment. This study develops a conceptual framework for resource and environment carrying capacity estimation to support the co-development planning of industries, population and resources & environment. First, the framework constructs an index system for evaluating importance of industry or influence based on the role of industry played in the local socio-economic system. Then, the framework computes the quantitative relations through the importance of local industry, population size and resource utilization and environment effects, and subsequently estimates the resource and environment carrying capacity of the study area. With a particular attention to its land resources, water resources and environment, the Tibet case study shows that: the non-ferrous metal mining, tourism, liquor and refined tea industries play a pillar role in the Tibet’s socio-economic system; under each industrial structure, land resource carrying capacity is the weakest, and water resources carrying capacity is the strongest; to focus on tourism will improve local resource and environment carrying capacity. The research results provide a solid guide for Tibet government’s co-actions in industrial restructuring, ecological protection, and the pursuit of economic development. This study will contribute to bridge the gap between theoretical research and practical applications of resource and environment carrying capacity, and help local governments plan the regional “socio-ecological” sustainable development.
Urbanization and eco-environment coupling is a research hotspot. Dynamic simulation of urbanization and eco-environment coupling needs to be improved because the processes of coupling are complex and statistical methods are limited. Systems science and cross-scale coupling allow us to define the coupled urbanization and eco-environment system as an open complex giant system with multiple feedback loops. We review the current state of dynamic simulation of urbanization and eco-environment coupling and find that: (1) The use of dynamic simulation is an increasing trend, the relevant theory is being developed, and modeling processes are being improved; (2) Dynamic simulation technology has become diversified, refined, intelligent and integrated; (3) Simulation is mainly performed for three aspects of the coupling, multiple regions and multiple elements, local coupling and telecoupling, and regional synergy. However, we also found some shortcomings: (1) Basic theories are inadequately developed and insufficiently integrated; (2) The methods of unifying systems and sharing data are behind the times; (3) Coupling relations and the dynamic characteristics of the main driving elements are not fully understood or completely identified. Additionally, simulation of telecoupling does not quantify parameters and is not systemically unified, and therefore cannot be used to represent spatial synergy. In the future, we must promote communication between research networks, technology integration and data sharing to identify the processes governing change in coupled relations and in the main driving elements in urban agglomerations. Finally, we must build decision support systems to plan and ensure regional sustainable urbanization.
