CALCIUM,MAGNESIUM AND POTASSIUM USE EFFICIENCY OF TROPICAL RAINFORESTS

A useful index of nutrient use efficiency (NUE) is the relationship between litterfall mass and the quantity of a nutrient returned to the ground by litterfall and by aqueous leaching from aboveground vegetative surfaces. Using previously published data, Ca, Mg and K use by tropical rainforests in widely separated geographic localities were considered in relation to the availability of these nutrient elements in the soil. Significant inverse relationships were found between Ca use efficiency and soil Ca availability (P < 0.001), and between Mg use efficiency and soil Mg availability (P < 0.01). The relationship was not found to be statistically significant for K (P > 0.1). The predicted inverse relationship between tropical rainforest NUE and soil fertility is evidently not consistent for all nutrient elements.     

Comparison of dew point temperature estimation methods in Southwestern Georgia

Recent upward trends in acres irrigated have been linked to increasing near-surface moisture. Unfortunately, stations with dew point data for monitoring near-surface moisture are sparse. Thus, models that estimate dew points from more readily observed data sources are useful. Daily average dew temperatures were estimated and evaluated at 14 stations in Southwest Georgia using linear regression models and artificial neural networks (ANN). Estimation methods were drawn from simple and readily available meteorological observations, therefore only temperature and precipitation were considered as input variables. In total, three linear regression models and 27 ANN were analyzed. The two methods were evaluated using root mean square error (RMSE), mean absolute error (MAE), and other model evaluation techniques to assess the skill of the estimation methods. Both methods produced adequate estimates of daily averaged dew point temperatures, with the ANN displaying the best overall skill. The optimal performance of both models was during the warm season. Both methods had higher error associated with colder dew points, potentially due to the lack of observed values at those ranges. On average, the ANN reduced RMSE by 6.86% and MAE by 8.30% when compared to the best performing linear regression model.     

大城市内部连锁超市空间分布格局及其区位选择——以南京市苏果超市为例

连锁超市已成为中国大城市内部零售商业的主要业态。针对目前研究中对城市内部尺度连锁超市空间布局问题关注不足,本文以南京市苏果超市为例,从空间格局演化、集聚分布状况、不同类型超市的区位选择等方面进行分析。研究方法包括点模式分析、空间密度分析和空间自相关分析等。结果发现:南京市苏果超市的空间总体分布呈中心到外围递减的倒"U"型集聚特征;不同类型苏果超市的分布特征不同,华润卖场的空间分布相对分散,社区店、便利店的分布相对集聚;空间分布热点呈蔓延扩散与等级扩散混合模式。运用Binary Logistic二分逻辑回归对影响超市区位选择的因素进行计量分析,结果表明经济发展情况、消费者规模、交通通达性、居民购买力、城乡差别、公司战略等多种因素,通过综合作用机制,共同推动了南京市苏果超市的空间分布格局。     

Driving forces analysis of reservoir wetland evolution in Beijing during 1984–2010

The reservoir wetland, which is the largest artificial wetland in Beijing, constitutes one of the important urban ecological infrastructures. Considering two elements of natural environment and socio-economy, this paper established the driving factor indexing system of Beijing reservoir wetland evolution. Natural environment driving factors include precipitation, temperature, entry water and groundwater depth; social economic driving factors include resident population, urbanization rate and per capita GDP. Using multi-temporal Landsat TM images from 1984 to 2010 in Beijing, the spatial extent and the distribution of Beijing reservoir wetlands were extracted, and the change of the wetland area about the three decade years were analyzed. Logistic regression model was used to explore for each of the three periods： from 1984 to 1998, from 1998 to 2004 and from 2004 to 2010. The results showed that the leading driving factors and their influences on reservoir wetland evolution were different for each period. During 1984-1998, two natural environment indices： average annual precipitation and entry water index were the major factors driving the increase in wetland area with the contribution rate of Logistic regression being 5.78 and 3.50, respectively, and caused the wetland growth from total area of 104.93 km 2 to 219.96 km 2 . From 1998 to 2004, as the impact of human activities intensified the main driving factors were the number of residents, groundwater depth and urbanization rate with the contribution rate of Logistic regression 9.41, 9.18, and 7.77, respectively, and caused the wetland shrinkage rapidly from the total area of 219.96 km 2 to 95.71 km 2 . During 2004-2010, reservoir wetland evolution was impacted by both natural and socio-economic factors, and the dominant driving factors were urbanization rate and precipitation with the contribution rate of 6.62 and 4.22, respectively, and caused the wetland total area growth slightly to 109.73 km 2 .     

Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices

By incorporating temporal effects into the geographically weighted regression (GWR) model, an extended GWR model, geographically and temporally weighted regression (GTWR), has been developed to deal with both spatial and temporal nonstationarity simultaneously in real estate market data. Unlike the standard GWR model, GTWR integrates both temporal and spatial information in the weighting matrices to capture spatial and temporal heterogeneity. The GTWR design embodies a local weighting scheme wherein GWR and temporally weighted regression (TWR) become special cases of GTWR. In order to test its improved performance, GTWR was compared with global ordinary least squares, TWR, and GWR in terms of goodness-of-fit and other statistical measures using a case study of residential housing sales in the city of Calgary, Canada, from 2002 to 2004. The results showed that there were substantial benefits in modeling both spatial and temporal nonstationarity simultaneously. In the test sample, the TWR, GWR, and GTWR models, respectively, reduced absolute errors by 3.5%, 31.5%, and 46.4% relative to a global ordinary least squares model. More impressively, the GTWR model demonstrated a better goodness-of-fit (0.9282) than the TWR model (0.7794) and the GWR model (0.8897). McNamara's test supported the hypothesis that the improvements made by GTWR over the TWR and GWR models are statistically significant for the sample data.     

GIS techniques for regional-scale landslide susceptibility assessment: the Sicily (Italy) case study

This study describes the assessment of landslide susceptibility in Sicily (Italy) at a 1:100,000 scale using a multivariate logistic regression model. The model was implemented in a GIS environment by using the ArcSDM (Arc Spatial Data Modeller) module, modified to develop spatial prediction through regional data sets. A newly developed algorithm was used to automatically extract the detachment area from mapped landslide polygons. The following factors were selected as independent variables of the logistic regression model: slope gradient, lithology, land cover, a curve number derived index and a pluviometric anomaly index. The above-described configuration has been verified to be the best one among others employing from three to eight factors. All the regression coefficients and parameters were calculated using selected landslide training data sets. The results of the analysis were validated using an independent landslide data set. On an average, 82% of the area affected by instability and 79% of the not affected area were correctly classified by the model, which proved to be a useful tool for planners and decision-makers.     

Late-Stage Breast Cancer Diagnosis and Health Care Access in Illinois∗

The variations of breast cancer mortality rates from place to place reflect both underlying differences in breast cancer prevalence and differences in diagnosis and treatment that affect the risk of death. This article examines the role of access to health care in explaining the variation of late-stage diagnosis of breast cancer. We use cancer registry data for the state of Illinois by zip code to investigate spatial variation in late diagnosis. Geographic information systems and spatial analysis methods are used to create detailed measures of spatial access to health care such as convenience of visiting primary care physicians and travel time from the nearest mammography facility. The effects of spatial access, in combination with the influences of socioeconomic factors, on late-stage breast cancer diagnosis are assessed using statistical methods. The results suggest that for breast cancer, poor geographical access to primary health care significantly increases the risk of late diagnosis for persons living outside the city of Chicago. Disadvantaged population groups including those with low income and racial and ethnic minorities tend to experience high rates of late diagnosis. In Illinois, poor spatial access to primary health care is more strongly associated with late diagnosis than is spatial access to mammography. This suggests the importance of primary care physicians as gatekeepers in early breast cancer detection.     

对氨基二甲基苯胺比色法测定的地下水中硫化物 不确定度的评定

采用不确定度连续传递模型对对氨基二甲基苯胺分光光度法测定的地下水中硫化物的不确定度进行了评定,在标准曲线拟合这一关键步骤上采用双误差曲线拟合,并与最小二乘法的拟合结果进行比较。结果表明,对于含较高浓度硫化物的水样,由双误差拟合出来的结果比最小二乘法拟合的结果更为准确。在采用双误差曲线拟合对实际样品进行定量时,应选择合适浓度范围内的标准曲线,使测定浓度落在标准曲线的较高浓度部分,使结果更为准确、合理。     

Determination of ultimate capacity of driven piles in cohesionless soil: A Multivariate Adaptive Regression Spline approach

The determination of ultimate capacity (Q) of driven piles in cohesionless soil is an important task in geotechnical engineering. This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction Q of driven piles in cohesionless soil. MARS uses length (L), angle of shear resistance of the soil around the shaft (?_shaft), angle of shear resistance of the soil at the tip of the pile (?_tip), area (A), and effective vertical stress at the tip of the pile as input variables. Q is the output of MARS. The results of MARS are compared with that of the Generalized Regression Neural Network model. An equation has been also presented based on the developed MARS. The results show the strong potential of MARS to be applied to geotechnical engineering as a regression tool. Copyright © 2011 John Wiley & Sons, Ltd.     

无线气象站数据与人工观测蒸发量的资料同化——以白鹤滩电站坝址区数据为例

在MATLAB中使用最小二乘法拟合Goff-Gratch公式,得到关于气温的饱和水汽压函数。无线气象站观测得到的气象资料结合人工观测蒸发量,在MATLAB中进行回归分析,得到基于彭曼公式的日水面蒸发量计算公式。算例表明,与无线气象站的蒸发量计算值相比,回归分析得到的日水面蒸发量公式计算值与人工观测值能够同步对比。无线气象站资料和人工观测资料能够资料同化,适合在工程中推广。