The Effect Of Omitted Spatial Effects And Social Dependence In The Modelling Of Household Expenditure For Fruits And Vegetables

We find the omitted elements of the external environment affect negatively the estimates for other spatial (social) effect parameters. Especially, we notice the overestimation of the random effect variance when the social dependence is omitted and the overestimation of the social interaction effect when the spatial heterogeneity is ignored

Governing China with the news television & national development in China since 1958

This thesis is an historical study of the interplay between television news and politics in China since 1958. It examines the evolution of television news and political development in China, as well as the continuing innovations to theca’s ideological system. Television in China has become the major source of social and political information and knowledge to facilitate development, at a time when China is experiencing its fastest development in the national economy. The Chinese television institutions now are far from a monolithic and unified propaganda machine, and have begun to play the role of a multifaceted creature undergoing a process of rapid transformation, with different parts of the body straining in different directions. Meanwhile, however, CCTV (China Central Television), which serves as the central hub of official information and social and political knowledge provision in the process of Chinese national development, is still mainly running according to the aims and agenda of the Chinese Communist Party and central government. Among China’s journalists, the debate on the democratisation of journalism arises once again at a time when the social market economic system has been well established throughout the country. This thesis has involved the examination of a diverse range of sources, dispersed in several archives and libraries in the UK, US and China. Bibliographical research has been combined with fieldwork carried out among Chinese television journalists and senior managers in television stations. This work aims to contribute to a better understanding of the media and politics of modern China, adding a new perspective to the study of televised democracy and mediated politics in China

Reconsidering the Relationship between Air Pollution and Deprivation

This paper critically examines the relationship between air pollution and deprivation. We argue that focusing on a particular economic or social model of urban development might lead one to erroneously expect all cities to converge towards a particular universal norm. A naive market sorting model, for example, would predict that poor households will eventually be sorted into high pollution areas, leading to a positive relationship between air pollution and deprivation. If, however, one considers a wider set of theoretical perspectives, the anticipated relationship between air pollution and deprivation becomes more complex and idiosyncratic. Specifically, we argue the relationship between pollution and deprivation can only be made sense of by considering processes of risk perception, path dependency, gentrification and urbanization. Rather than expecting all areas to eventually converge to some universal norm, we should expect the differences in the relationship between air pollution and deprivation across localities to persist. Mindful of these insights, we propose an approach to modeling which does not impose a geographically fixed relationship. Results for Scotland reveal substantial variations in the observed relationships over space and time, supporting our argument

Multi-level temporal autoregressive modelling of daily activity satisfaction using GPS-integrated activity diary data

In this research, we match web-based activity diary data with daily mobility information recorded by GPS trackers for a sample of 709 residents in a 7-day survey in Beijing in 2012 to investigate activity satisfaction. Given the complications arising from the irregular time intervals of GPS-integrated diary data and the associated complex dependency structure, a direct application of standard (spatial) panel data econometric approaches is inappropriate. This study develops a multi-level temporal autoregressive modelling approach to analyse such data, which conceptualises time as continuous and examines sequential correlations via a time or space-time weights matrix. Moreover, we manage to simultaneously model individual heterogeneity through the inclusion of individual random effects, which can be treated flexibly either as independent or dependent. Bayesian Markov chain Monte Carlo (MCMC) algorithms are developed for model implementation. Positive sequential correlations and individual heterogeneity effects are both found to be statistically significant. Geographical contextual characteristics of sites where activities take place are significantly associated with daily activity satisfaction, controlling for a range of situational characteristics and individual socio-demographic attributes. Apart from the conceivable urban planning and development implications of our study, we demonstrate a novel statistical methodology for analysing semantic GPS trajectory data in general

Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China

This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure—for example, census zones into regions) but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts ‘top down’ upon members of a group) with proximity effects (some sort of joint dependency that emerges between neighbours). To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the performance of the proposed method and the classic multilevel model, a series of Monte Carlo simulations are conducted. The results show that the proposed method performs well in retrieving the true model parameters whereas the classic multilevel model provides biased and inefficient parameter estimation in the presence of spatial interactions. An important implication of the study is to be cautious of an apparent neighbourhood effect in terms of both its magnitude and statistical significance if spatial interaction effects at a lower level are suspected. Applying the new approach to a two-level land price data set for Beijing, China, we find significant spatial interactions at both the land parcel and district levels