No abstract

With the recent advances in information networks, the problem of community detection has attracted much attention in the last decade. While network community detection has been ubiquitous, the task of collecting complete network data remains challenging in many real-world applications. Usually the collected network is incomplete with most of the edges missing. Commonly, in such networks, all nodes with attributes are available while only the edges within a few local regions of the network can be observed. In this paper, we study the problem of detecting communities in incomplete information networks with missing edges. We first learn a distance metric to reproduce the link-based distance between nodes from the observed edges in the local information regions. We then use the learned distance metric to estimate the distance between any pair of nodes in the network. A hierarchical clustering approach is proposed to detect communities within the incomplete information networks. Empirical studies on real-world information networks demonstrate that our proposed method can effectively detect community structures within incomplete information networks.

Coal mining subsidence is a common human geological disaster that was particularly conspicuous in China. It seriously restricts the sustainable development of mining areas, and it not only damages land resources but also triggers a series of ecological and environmental problems that may result in social and economic issues. This report studied the coal mining subsidence area of Longkou in Shandong province and uses digital elevation data (DEM) of the mining area before subsidence in 1978 as the baseline elevation. Through image algorithms, we obtained coal mining subsidence region data for 1984, 1996, 2000, and 2004. And with spatial data sources of the same period of TM/ETM ? and SPOT5 remote sensing images, BP artificial neural network (BPNN) classification is used to extract surface landscape information in the subsidence area. With the support of GIS technology, superimposing subsidence area on the surface landscapeusing the largest landscape ecology patch index, landscape shape index, landscape condensation index, and the index of landscape distribution-report analyzes the mining landscape changes before and after subsidence. This study also carries on exploratory research with the landscape changes, thereby providing a scientific basis for integrated prevention and treatment.

Ambient air pollution is correlated with AECOPD hospitalizations spatially. A 10 μg/m(3) increase of PM10 at workplace was associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD in Jinan, 2009. As a spatial data processing tool, GIS has novel and great potential on air pollutants exposure assessment and spatial analysis in AECOPD research.

Food security is an important issue affecting people’s lives and social stability. Clarifying levels of food security and the factors affecting it (social, economic, agricultural, climatic) can help improve regional food security. The spatiotemporal patterns and driving factors of food security vary at different scales. There is, however, a lack of research that considers the various factors affecting food security at multiple scales. This study, therefore, analyzed dynamic spatiotemporal changes in food security at small (city), medium (province), and large (country) scales; identified hot and cold areas of food security; and revealed the main factors affecting food security at different scales. A food security index (FSI) was built based on the coupling of grain yield, population, and GDP, and spatial analysis was used to evaluate dynamic spatiotemporal changes in China’s food security from 1980 to 2017. Further, the relationship between food security and its driving factors was quantitatively analyzed using stepwise regression. The results showed greater heterogeneity in food security at the smaller scale than at the larger scale. The key factors affecting food security varied substantially at different scales: the added value of tertiary industry dominated the prefecture level, and gross agricultural output value was the main factor at the provincial and national levels. Multiple-scale research can reveal the status and primary factors of food security and provide a decision-making basis for improving regional food security.