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. 相似文献
Mineral potential prediction is a process of establishing a statistical model that describes the relationship between evidence variables and mineral occurrences. In this study, evidence variables were constructed from geological, remote sensing, and geochemical data collected from the Lalingzaohuo district, Qinghai Province, China. Based on these evidence variables, a conjugate gradient logistic regression (CG-LR) model was established to predict exploration targets in the study area. The receiver operating characteristic (ROC) and prediction–area (P-A) curves were used to evaluate the effectiveness of the CG-LR model in mineral potential mapping. The difference between the vertical and horizontal coordinates of each point on the ROC curve was used to determine the optimal threshold for classifying the exploration targets. The optimal threshold corresponds to the point on the ROC curve where the difference between the vertical coordinate and the horizontal coordinate is the largest. In exploration target prediction in the study area, the CG algorithm was used to optimize iteratively the LR coefficients, and the prediction effectiveness was tested for different epochs. With increasing iterations, the prediction performance of the model becomes increasingly better. After 60 iterations, the LR model becomes stable and has the best performance in exploration target prediction. At this point, the exploration targets predicted by the CG-LR model occupy 14.39% of the study area and contain 93% of the known mineral deposits. The exploration targets predicted by the model are consistent with the metallogenic geological characteristics of the study area. Therefore, the CG-LR model can effectively integrate geological, remote sensing, and geochemical data for the study area to predict targets for mineral exploration.
