Based on registered individual floating population data from 2005 to 2008 of Yiwu, the phenomena that population floating to Yiwu City from 34 province and 91 counties in Jiangxi provinces is analyzed. The study aims ...
详细信息
Based on registered individual floating population data from 2005 to 2008 of Yiwu, the phenomena that population floating to Yiwu City from 34 province and 91 counties in Jiangxi provinces is analyzed. The study aims at analyzing the "pull" forces of Yiwu City and developing migration models for understanding determinants factors of population migration/floating into Yiwu City from other areas in China. The spatial layout of Yiwu's pull forces is proved as a V-shaped pattern consisting of the two axes by using explorative spatial data analysis and map visualization method. The migration models with (model 3) or without (model 2) migration stock are presented and estimated using standard linear regressionmodel, spatial error model as well as spatial lag model at the county scale in Jiangxi province. Based on the likelihood statistics, the AIC and the Moran's I statistics of residuals, the model with migration stock provides an improved fit over the model without migration stock. The correlation between migration ratio and man land ratio is significant at the 0.5 level according to estimates of model 3 and spatial version of model 2. All the three estimates of model 2 and the OLS results of model 3 confirm the distance-decay effect while results from the spatial version of model 3 failed to support the distance rule in population floating. Contrary to the previous studies at the provincial level, the correlation between per capital net income of rural labor forces and migration ratio is not significant according to the three versions of the two models due to the small disparities of income within the counties in Jiangxi. Examination of specification tests in spatial version of model 3 indicates that there is less significant spatial error dependence in the spatial lag models than spatial lag dependence in the error models, further suggesting a preference for the lag model. model 2 does not suggest any preference for choosing spatial error model and spatial lag model.
Based on registered individual floating population data from 2005 to 2008 of Yiwu, the phenomena that population floating to Yiwu City from 34 province and 91 counties in Jiangxi provinces is analyzed. The study aims ...
详细信息
Based on registered individual floating population data from 2005 to 2008 of Yiwu, the phenomena that population floating to Yiwu City from 34 province and 91 counties in Jiangxi provinces is analyzed. The study aims at analyzing the “pull” forces of Yiwu City and developing migration models for understanding determinants factors of population migration/floating into Yiwu City from other areas in China. The spatial layout of Yiwu's pull forces is proved as a V-shaped pattern consisting of the two axes by using explorative spatial data analysis and map visualization method. The migration models with (model 3) or without (model 2) migration stock are presented and estimated using standard linear regressionmodel, spatial error model as well as spatial lag model at the county scale in Jiangxi province. Based on the likelihood statistics, the AIC and the Moran's I statistics of residuals, the model with migration stock provides an improved fit over the model without migration stock. The correlation between migration ratio and man land ratio is significant at the 0.5 level according to estimates of model 3 and spatial version of model 2. All the three estimates of model 2 and the OLS results of model 3 confirm the distance-decay effect while results from the spatial version of model 3 failed to support the distance rule in population floating. Contrary to the previous studies at the provincial level, the correlation between per capital net income of rural labor forces and migration ratio is not significant according to the three versions of the two models due to the small disparities of income within the counties in Jiangxi. Examination of specification tests in spatial version of model 3 indicates that there is less significant spatial error dependence in the spatial lag models than spatial lag dependence in the error models, further suggesting a preference for the lag model. model 2 does not suggest any preference for choosing spatial error model and spatial lag model.
Malaria is a leading cause of infectious disease and death worldwide. As a common example of a vector-borne disease, malaria could be greatly affected by the influence of climate change. Climate impacts the transmissi...
详细信息
Malaria is a leading cause of infectious disease and death worldwide. As a common example of a vector-borne disease, malaria could be greatly affected by the influence of climate change. Climate impacts the transmission of malaria in several ways, affecting all stages of the disease's development. Using various weather-related factors that influence climate change, this study utilizes statistical analysis to determine the effect of climate change on reported malaria rates in an African region with endemic malaria. It examines the relationship between malaria prevalence and climate in western Africa using spatial regression modeling and tests for correlation. Our analysis suggests that minimal correlation exists between reported malaria rates and climate in western Africa. This analysis further contradicts the prevailing theory that climate and malaria prevalence are closely linked and negates the idea that climate change will increase malaria transmission in this region.
Increasing evidence suggests that the environment is related to many publichealth challenges. Unequal distributions of services and resources needed for healthylifestyles may contribute to increasing levels of health ...
详细信息
Increasing evidence suggests that the environment is related to many public
health challenges. Unequal distributions of services and resources needed for healthy
lifestyles may contribute to increasing levels of health disparity. However, empirical
studies are not sufficient to understand the relationship between health disparity and the
built environment.
This dissertation examines how health disparity are associated with the built
environment and if the environmental conditions that support physical activity and
healthy diet are associated with lower health disparity. This research uses a multidisciplinary
approach, drawing from urban planning, regional economics and public
health.
The data came from the Behavioral Risk Factor Surveillance System, and the
GIS derived environmental data and the 608-respondent survey data from a larger study
conducted in urbanized King County, Washington. Health disparity was measured with
the Gini-coefficient, and health status and obesity were used as indicators of health. Hot spot analysis was used to identify the spatial aggregations of high health disparity, and
multiple regressionmodels identified the environmental correlates of health disparity.
The overall trend showed that disparity has increased in most states in the US
over the past decade and the southern states showed the highest disparity levels. Strong
spatial autocorrelations were found for disparities, indicating that disparity levels are not
equally distributed across different geographic areas. From the multivariate analyses
estimating disparity levels, spatial regression models significantly improved the overall
model fit compared to the ordinary least-square models. Areas with more supportive
built environments for physical activity had lower health disparities, including proximity
to downtown (+) and access to parks (+), day care centers (+), offices (+), schools (+),
theaters (+), big box shopping centers (-), and libraries (-). Overall results showed that
th
作者：
Wang, HongWang, JianghaoLiu, GaohuanHohai Univ
Coll Hydrol & Water Resources State Key Lab Hydrol Water Resources & Hydraul En 1 Xikang Rd Nanjing 210098 Peoples R China Chinese Acad Sci
State Key Lab Resources & Environm Beijing 100101 Peoples R China
In this paper, spatial autocorrelation analysis, ordinary least square (OLS) and spatial regression models were applied to explore spatial variation of soil salinity based on samples collected from the Yellow River De...
详细信息
ISBN:
(纸本)9780819469137
In this paper, spatial autocorrelation analysis, ordinary least square (OLS) and spatial regression models were applied to explore spatial variation of soil salinity based on samples collected from the Yellow River Delta. Generally, spatial data, like soil salinity, elevation height etc., are characterized by spatial effects such as spatial dependence and spatial structure. Inasmuch as these effects exist, the utilization of OLS model may lead to inaccurate inference about predictor variable. Moreover, the traditional regressionmodels used to analyze spatial data often have autocorrelated residuals which violate the assumption of Guess-Markov Theorem. This indicates that conventional regressionmodels cannot be used in analyzing variability of soil salinity directly. To overcome this limitation, spatial regression model was introduced to explore the relationship between soil salinity and environmental factors (including elevation height, pH value and organic matter concentration). By verifying Moran's I scatterplot of residuals, we found no autocorrelation in spatial regression model compared with high significant (p < 0.001) positive autocorrelation in the OLS model;besides, the spatial regression model had a significant (p < 0.01) estimations and good-fit-it in our study. Finally, an approach of specifying optimal spatial weight matrix was also put forward.
Conventional statistical methods are often ineffective to evaluate spatial regression models. One reason is that spatial regression models usually have more parameters or smaller sample sizes than a simple model, so t...
详细信息
Conventional statistical methods are often ineffective to evaluate spatial regression models. One reason is that spatial regression models usually have more parameters or smaller sample sizes than a simple model, so their degree of freedom is reduced. Thus, it is often unlikely to evaluate them based on traditional tests. Another reason, which is theoretically associated with statistical methods, is that statistical criteria are crucially dependent on such assumptions as normality, independence, and homogeneity. This may create problems because the assumptions are open for testing. In view of these problems, this paper proposes an alternative empirical evaluation method. To illustrate the idea, a few hedonic regressionmodels for a house and land price data set are evaluated, including a simple, ordinary linear regressionmodel and three spatialmodels. Their performance as to how well the price of the house and land can be predicted is examined. With a cross-validation technique, the prices at each sample point are predicted with a model estimated with the samples excluding the one being concerned. Then, empirical criteria are established whereby the predicted prices are compared with the real, observed prices. The proposed method provides an objective guidance for the selection of a suitable model specification for a data set. Moreover, the method is seen as an alternative way to test the significance of the spatial relationships being concerned in spatial regression models. (c) 2006 Published by Elsevier Ltd.
暂无评论