Does geographic distance have a significant impact on enterprise financing costs?

Journal of Geographical Sciences - As information technology has been applied more broadly and transportation infrastructure has improved, persistent debate has existed as to the question of...     

Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices

Understanding scale effects is important and indispensable for geography studies. However, spatial and spatiotemporal statistical tools for measuring the operational scales of different processes are rather limited. This article extends the popular geographically and temporally weighted regression (GTWR) model to consider operational scale effects by proposing multiscale GTWR (MGTWR), which offers a flexible and scalable framework for identifying and analysing multiscale processes by specifying flexible bandwidths for various covariates. Then, MGTWR is employed to explore spatiotemporal variations and how influential factors are associated with housing prices in Shenzhen. This article attempts to extend GTWR to MGTWR in consideration of scale effects, thereby highlighting the importance of different levels of spatiotemporal heterogeneity. Furthermore, the empirical results of this study can provide valuable policy implications for real estate development in areas where urban planning should address multiscale effects in both temporal and spatial dimensions.     

A density-based approach for detecting network-constrained clusters in spatial point events

Existing spatial clustering methods primarily focus on points distributed in planar space. However, occurrence locations and background processes of most human mobility events within cities are constrained by the road network space. Here we describe a density-based clustering approach for objectively detecting clusters in network-constrained point events. First, the network-constrained Delaunay triangulation is constructed to facilitate the measurement of network distances between points. Then, a combination of network kernel density estimation and potential entropy is executed to determine the optimal neighbourhood size. Furthermore, all network-constrained events are tested under a null hypothesis to statistically identify core points with significantly high densities. Finally, spatial clusters can be formed by expanding from the identified core points. Experimental comparisons performed on the origin and destination points of taxis in Beijing demonstrate that the proposed method can ascertain network-constrained clusters precisely and significantly. The resulting time-dependent patterns of clusters will be informative for taxi route selections in the future.     

Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis

Natural Resources Research - Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of...     

Two-stage permutation tests for determining homogeneity within a spatial cluster

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.     

Species Abundance Distribution Patterns of a Toona ciliata Community in Xingdoushan Nature Reserve

With the goal of model fitting species abundance distribution patterns of the tree, shrub and herb layers of the natural Toona ciliata community in Xingdoushan Nature Reserve, Enshi Autonomous Prefecture, Hubei Province, we used the data collected from the field survey and employed different ecological niche models. The models tested were the broken stick model (BSM), the overlapping niche model (ONM) and the niche preemption model (NPM), as well as three statistic models, the log-series distribution model (LSD), the log-normal distribution model (LND) and the Weibull distribution model (WDM). To determine the fitted model most suitable to each layer, the fitting effects were judged by criteria of the lowest value of Akaike Information Criterion (AIC), Chi-square and the K-S values with no significant difference (P>0.05) between the theoretical predictions and observed species abundance distribution values. The result showed: (1) The fitting suitability and goodness of fit of the tree, shrub and herb layers by using the three ecological niche models were ranked as: NPM>BSM>ONM. Of the three statistical models, by accepting the fitting results of the three layers, WDM was the best fitting model, followed by LND. By rejecting the fitting tests of the herb layer, LSD had the worst fitting effect. The goodness of the statistical models was ranked as: WDM>LND>LSD. In general, the statistical models had better fitting results than the ecological models. (2) T. ciliata was the dominant species of the tree layer. The species richness and diversity of the herb layer were much higher than those of either the tree layer or the shrub layer. The species richness and diversity of the shrub layer were slightly higher than those of the tree layer. The community evenness accorded to the following order: herb>shrub>tree. Considering the fitting results of the different layers, different ecological niche models or statistical models with optimal goodness of fit and ecological significance can be given priority to in studying the species abundance distribution patterns of T. ciliata communities.     

Research on the Optimization of Capital for the Governance of Bulk Coal in Beijing-Tianjin-Hebei Region

Beijing, Tianjin and Hebei each contributed to the comprehensive governance of bulk coal to treat bulk coal pollution in a mutually beneficial way in 2017. The cooperative game theory is used in this paper to study the environmental benefits and cost effectiveness brought about by this comprehensive governance strategy, primarily focusing on the issue of how to maximize the environmental benefits by choosing an appropriate strategy since the benefits to Beijing, Tianjin and Hebei are closely related. Therefore, the linear optimization, game theory and Shapley value method in the cooperative game model are used to find the ways to minimize the total governance cost of bulk coal in the three areas. In addition, the issues of how to carry out rational distribution and transfer of governance capital among the three places are explored according to the actual amounts of consumption of bulk coal, the influence of the coal burning on the PM_2.5 and the actual cost of coal governance in Beijing, Tianjin and Hebei in 2017. The results show that the governance task in Hebei Province is the most onerous, and requires more investment than the other two cities. Thus, it requires the support from other two cities, with the amount of increased capital required of about 600 million Yuan. At the same time, the cost saved after optimization in Tianjin is calculated to be the largest, which thus can be adjusted appropriately and allocated to Hebei for the governance of bulk coal. The model constructed in this paper can not only be used to solve the issues related to bulk coal consumption in Beijing, Tianjin and Hebei, but also to carry out the effective distribution of capital, by which a win-win scenario among the three places can be achieved.     

中国半干旱草原施氮梯度下的土壤激发效应（英文）

为了探究施氮对土壤有机质(SOM)的激发效应,本研究在施氮梯度样地(0、4和16 g N m^–2 yr^–1)上进行了¹³C标记葡萄糖的原位添加实验,并对土壤CO_2排放量和磷脂脂肪酸(PLFA)含量进行了测定。研究发现施氮降低了土壤CO_2排放、土壤PLFA含量以及土壤真菌细菌比。在0 g N m^–2 yr^–1样地上葡萄糖添加导致的正向激发效应最强,同时4 g N m^–2 yr^–1样地释放的葡萄糖来源的碳最多。因此,施氮减少了土壤中SOM转化产生的CO_2,微生物碳的来源由SOM转变为添加的易分解碳。本研究采样早期土壤微生物生物量和群落结构稳定,表明该草原存在"表观激发效应",因此未来研究应着重对微生物功能的多样性进行探讨。     

Response of Microbial Communities in Soil to Multi-level Warming in a Highland Barley System of the Lhasa River

No studies have examined the effect of experimental warming on the microbial biomass and community composition of soil in agricultural ecosystem on the Qinghai-Tibet Plateau. Thus it is unclear whether the influences of experimental warming on microbial communities in soil are related to warming magnitude in croplands on this Plateau. This study performed warming experiment (control, low- and high-level) in a highland barley system of the Lhasa River in May 2014 to examine the correlation between the response of microbial communities in soil to warming and warming magnitude. Topsoil samples (0-10 and 10-20 cm) were collected on September 14, 2014. Experimental warming at both low and high levels significantly increased soil temperature by 1.02 ℃ and 1.59 ℃, respectively at the depth of 15 cm. Phospho lipid fatty acid (PLFA) method was used to determine the microbial community in soil. The low-level experimental warming did not significantly affect the soil’s total PLFA, fungi, bacteria, arbuscular mycorrhizal fungi (AMF), actinomycetes, gram-positive bacteria (G⁺), gram-negative bacteria (G^-), protozoa, the ratio of fungi to bacteria (F/B ratio), and ratio of G⁺ to G^- (G⁺/G^- ratio) at the 0-10 and 10-20 cm depth. The low-level experimental warming also did not significantly alter the composition of microbial community in soil at the 0-10 and 10-20 cm depth. The high-level experimental warming significantly increased total PLFA by 74.4%, fungi by 78.0%, bacteria by 74.0%, AMF by 66.9%, actinomycetes by 81.4%, G⁺ by 67.0% and G^- by 74.4% at the 0-10 cm depth rather than at 10-20 cm depth. The high-level experimental warming significantly altered microbial community composition in soil at the 0-10 cm depth rather than at 10-20 cm depth. Our findings suggest that the response of microbial communities in soil to warming varied with warming magnitudes in the highland barley system of the Lhasa River.     

Farmland Abandonment Research Progress: Influencing Factors and Simulation Model

Farmland abandonment is a global problem and considered one of the most important areas in land use change research. Farmland abandonment research currently focuses on understanding the factors that affect farmland abandonment and developing scientific models to simulate farmland abandonment. The study reviewed the natural and political factors driving farmland abandonment and summarized the main models for farmland abandonment simulation together with their advantages and disadvantages. We discuss the main ecological effects of farmland abandonment and propose farmland abandonment research directions. The study found that: (1) the influence of labor cost change and ageing labor force on farmland abandonment needs further investigation, (2) simulation models for farmland abandonment must include the decision-making mechanism of individual farmers and focus on macro large-scale abandonment prediction models, and (3) the influence of farmland abandonment on landscape culture must be investigated in detail.