With the development of the social economy, Chinese tourism will surely change from medium scale to massive scale. As people are more and more focusing on the inner values of life style, new types of tourist demands w...With the development of the social economy, Chinese tourism will surely change from medium scale to massive scale. As people are more and more focusing on the inner values of life style, new types of tourist demands will emerge at anytime, and the spatial structure of tourist demands will show new characteristics as well Until now there is few study on tourist demand, especially on its spatial pattern. Study on spatial characteristics of tourist demand will contribute to spatial optimization and adjustments of tourist flow and supply. So this article has put forward the spatial pattern of tourist demand in China and measures of spatial adjustments and grade system of the spatial adjustment of tourists demand.展开更多
Spatial variation is often encountered when large scale field trials are conducted which can result in biased estimation or prediction of treatment (i.e. genotype) values. An effective removal of spatial variation is ...Spatial variation is often encountered when large scale field trials are conducted which can result in biased estimation or prediction of treatment (i.e. genotype) values. An effective removal of spatial variation is needed to ensure unbiased estimation or prediction and thus increase the accuracy of field data evaluation. A moving grid adjustment (MGA) method, which was proposed by Technow, was evaluated through Monte Carlo simulation for its statistical properties regarding field spatial variation control. Our simulation results showed that the MGA method can effectively account for field spatial variation if it does exist;however, this method will not change phenotype results if field spatial variation does not exist. The MGA method was applied to a large-scale cotton field trial data set with two representative agronomic traits: lint yield (strong field spatial pattern) and lint percentage (no field spatial pattern). The results suggested that the MGA method was able to effectively separate the spatial variation including blocking effects from random error variation for lint yield while the adjusted data remained almost identical to the original phenotypic data. With application of the MGA method, the estimated variance for residuals was significantly reduced (62.2% decrease) for lint yield while more genetic variation (29.7% increase) was detected compared to the original data analysis subject to the conventional randomized complete block design analysis. On the other hand, the results were almost identical for lint percentage with and without the application of the MGA method. Therefore, the MGA method can be a useful addition to enhance data analysis when field spatial pattern exists.展开更多
Since the 1970s, the studies on the geography of enterprise have become increasingly concerned with the spatial evolution of an enterprises in the West developed countries. However, little attention has so far been g...Since the 1970s, the studies on the geography of enterprise have become increasingly concerned with the spatial evolution of an enterprises in the West developed countries. However, little attention has so far been given to the spatial evolution of an enterprise in China. With China's ongoing economy and political reforms, a number of fundamental changes of enterprise behavior have occurred. These changes have certainly important influences on the evolution of industrial location. Therefore, there is a need for examining the spatial evolution of an enterprise in China. The purpose of the paper is to review the mapjor models of both the growth,and associated spatial evolution of an enterprise, with an illustrative case study of the HeaVy Automobile Enterprise Group of China. The paper is organized by three parts. The first examines the spoilal groWth and location adjustment of multi-plant enterprise. The second reviews mapjor models of the spatial evolution of an enterprise. And the third analyzes the spatial evolution, over a period of time, of a representative sample of the Heavy Automobile Enterprise Group of China, and illustrates the findings with case studies. It is suggested that the models of spatial evolution of an enterprise would provide more evidence about micro-mechanism for evolution of micro- regional economic systems.展开更多
Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such ho...Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such hotspots, but also their spatial patterns. We used spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters and areas in which nine malignant neoplasms are situated in Taiwan. In addition, we used a logistic regression model to test the characteristics of similarity and dissimilarity between males and females and to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis was used to identify spatial cluster patterns. We found a significant relationship between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, the geographic distribution of clusters where oral cavity cancer in males is prevalent was closely correspond to the locations in central Taiwan with serious metal pollution. In females, clusters of oral cavity cancer were closely related with aboriginal townships in eastern Taiwan, where cigarette smoking, alcohol drinking, and betel nut chewing are commonplace. The difference between males and females in the spatial distributions was stark. Furthermore, areas with a high morbidity of gastric cancer were clustered in aboriginal townships where the occurrence of Helicobacter pylori is frequent. Our results revealed a similarity between both males and females in spatial pattern. Cluster mapping clarified the spatial aspects of both internal and external correlations for the nine malignant neoplasms. In addition, using a method of logistic regression also enabled us to find differentiation between gender-specific spatial patterns.展开更多
文摘With the development of the social economy, Chinese tourism will surely change from medium scale to massive scale. As people are more and more focusing on the inner values of life style, new types of tourist demands will emerge at anytime, and the spatial structure of tourist demands will show new characteristics as well Until now there is few study on tourist demand, especially on its spatial pattern. Study on spatial characteristics of tourist demand will contribute to spatial optimization and adjustments of tourist flow and supply. So this article has put forward the spatial pattern of tourist demand in China and measures of spatial adjustments and grade system of the spatial adjustment of tourists demand.
文摘Spatial variation is often encountered when large scale field trials are conducted which can result in biased estimation or prediction of treatment (i.e. genotype) values. An effective removal of spatial variation is needed to ensure unbiased estimation or prediction and thus increase the accuracy of field data evaluation. A moving grid adjustment (MGA) method, which was proposed by Technow, was evaluated through Monte Carlo simulation for its statistical properties regarding field spatial variation control. Our simulation results showed that the MGA method can effectively account for field spatial variation if it does exist;however, this method will not change phenotype results if field spatial variation does not exist. The MGA method was applied to a large-scale cotton field trial data set with two representative agronomic traits: lint yield (strong field spatial pattern) and lint percentage (no field spatial pattern). The results suggested that the MGA method was able to effectively separate the spatial variation including blocking effects from random error variation for lint yield while the adjusted data remained almost identical to the original phenotypic data. With application of the MGA method, the estimated variance for residuals was significantly reduced (62.2% decrease) for lint yield while more genetic variation (29.7% increase) was detected compared to the original data analysis subject to the conventional randomized complete block design analysis. On the other hand, the results were almost identical for lint percentage with and without the application of the MGA method. Therefore, the MGA method can be a useful addition to enhance data analysis when field spatial pattern exists.
文摘Since the 1970s, the studies on the geography of enterprise have become increasingly concerned with the spatial evolution of an enterprises in the West developed countries. However, little attention has so far been given to the spatial evolution of an enterprise in China. With China's ongoing economy and political reforms, a number of fundamental changes of enterprise behavior have occurred. These changes have certainly important influences on the evolution of industrial location. Therefore, there is a need for examining the spatial evolution of an enterprise in China. The purpose of the paper is to review the mapjor models of both the growth,and associated spatial evolution of an enterprise, with an illustrative case study of the HeaVy Automobile Enterprise Group of China. The paper is organized by three parts. The first examines the spoilal groWth and location adjustment of multi-plant enterprise. The second reviews mapjor models of the spatial evolution of an enterprise. And the third analyzes the spatial evolution, over a period of time, of a representative sample of the Heavy Automobile Enterprise Group of China, and illustrates the findings with case studies. It is suggested that the models of spatial evolution of an enterprise would provide more evidence about micro-mechanism for evolution of micro- regional economic systems.
文摘Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such hotspots, but also their spatial patterns. We used spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters and areas in which nine malignant neoplasms are situated in Taiwan. In addition, we used a logistic regression model to test the characteristics of similarity and dissimilarity between males and females and to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis was used to identify spatial cluster patterns. We found a significant relationship between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, the geographic distribution of clusters where oral cavity cancer in males is prevalent was closely correspond to the locations in central Taiwan with serious metal pollution. In females, clusters of oral cavity cancer were closely related with aboriginal townships in eastern Taiwan, where cigarette smoking, alcohol drinking, and betel nut chewing are commonplace. The difference between males and females in the spatial distributions was stark. Furthermore, areas with a high morbidity of gastric cancer were clustered in aboriginal townships where the occurrence of Helicobacter pylori is frequent. Our results revealed a similarity between both males and females in spatial pattern. Cluster mapping clarified the spatial aspects of both internal and external correlations for the nine malignant neoplasms. In addition, using a method of logistic regression also enabled us to find differentiation between gender-specific spatial patterns.
