Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

An understanding of the factors that affect the spread of endemic bovine tuberculosis (bTB) is critical for the development of measures to stop and reverse this spread. Analyses of spatial data need to account for the inherent spatial heterogeneity within the data, or else spatial autocorrelation can lead to an overestimate of the significance of variables. This study used three methods of analysis—least-squares linear regression with a spatial autocorrelation term, geographically weighted regression (GWR) and boosted regression tree (BRT) analysis—to identify the factors that influence the spread of endemic bTB at a local level in England and Wales. The linear regression and GWR methods demonstrated the importance of accounting for spatial differences in risk factors for bTB, and showed some consistency in the identification of certain factors related to flooding, disease history and the presence of multiple genotypes of bTB. This is the first attempt to explore the factors associated with the spread of endemic bTB in England and Wales using GWR. This technique improves on least-squares linear regression approaches by identifying regional differences in the factors associated with bTB spread. However, interpretation of these complex regional differences is difficult and the approach does not lend itself to predictive models which are likely to be of more value to policy makers. Methods such as BRT may be more suited to such a task. Here we have demonstrated that GWR and BRT can produce comparable outputs

Production and R&D networks of foreign ventures in China: Implications for technological dynamism and regional development

This paper analyzes the nature of FDI local networks in production and R&D activities in China and discusses their implications for technological dynamism and regional development. We investigate foreign ventures (or foreign-invested enterprises, FIEs) in the information and communication technology (ICT) industry, based on a large-scale survey of ICT firms conducted in three mega-city regions of China: Beijing, Shanghai-Suzhou, and Shenzhen-Dongguan. Our data show that FIEs in China are gradually localizing their production, but the extent of local embeddedness is contingent upon home country effects, local specific contexts and FDI-host region relationships. We have also found significant influence of industrial agglomeration on FDI location and network decisions. Beijing tends to have broader FDI sources and better integrated global-local networks, while in those regions dominated by FDI such as Suzhou and Dongguan, FIEs are thinly embedded with local economies and tend to establish global-local networks among themselves; local embeddedness is limited by a series of technological, institutional, spatial, and structural mismatches. Shanghai and Shenzhen are in between. More efforts are still needed to better integrate FDI with local economies and strengthen China's local innovative capacities. © 2010 Elsevier Ltd.link_to_subscribed_fulltex

Spatial data analysis of regional development in Greater Beijing, China, in a GIS environment

This study investigates spatial dependence and mechanisms of regional development in Greater Beijing, China by employing spatial statistical techniques. We have detected positive, strengthening global spatial autocorrelation from 1978 to 2001, and found such strengthening is the result of newly formed/extended clusters in the area. The local analysis recognizes local regimes of two-tier urban-rural spatial structure at the beginning of the reform period. While the urban-rural divide was lessening due to the reform, a north-south divide has emerged because of local natural conditions and development trajectories. Regarding mechanisms of regional development, ordinary least squares analysis is constrained by the existence of significant spatial autocorrelation among spatial units. Analytical results reveal that an error spatial regression model is a more appropriate alternative due to possible mismatch between boundaries of the underlying spatial process and the spatial units where data are organised. In 1995 and 2001, the signs of all the regression coefficients remained the same for both OLS and spatial models. However, their magnitude and significance change. Specifically, foreign direct investment and fixed-asset investment became less influential in the spatial model, while local government spending emerged as more influential. Copyright (c) 2007 the author(s).