There has been a rapidly growing number of studies of the geographical aspects of happiness and well-being. Many of these studies have been highlighting the role of space and place and of individual and spatial contextual determinants of happiness. However, most of the studies to date do not explicitly consider spatial clustering and possible spatial spillover effects of happiness and well-being. The few studies that do consider spatial clustering and spillovers conduct the analysis at a relatively coarse geographical scale of country or region. This article analyses such effects at a much smaller geographical unit: community areas. These are small area level geographies at the intra-urban level. In particular, the article presents a spatial econometric approach to the analysis of life satisfaction data aggregated to 1,215 communities in Canada and examines spatial clustering and spatial spillovers. Communities are suitable given that they form a small geographical reference point for households. We find that communities’ life satisfaction is spatially clustered while regression results show that it is associated to the life satisfaction of neighbouring communities as well as to the latter's average household income and unemployment rate. We consider the role of shared cultural traits and institutions that may explain such spillovers of life satisfaction. The findings highlight the importance of neighbouring characteristics when discussing policies to improve the well-being of a (small area) place.
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Introduction: There are good reasons to study urban innovation from a systemic perspective. A key finding in innovation research is that organizations rarely innovate in isolation, but in interaction with clients, competitors, suppliers, and other organizations. A system perspective is useful in understanding and analyzing these interactions. Cities and urban regions are increasingly recognized as key milieus in which these interactions occur. The urban innovation system approach conceptualizes the city or urban region as a context in which innovations emerge from complex interactions between urban actors—firms, citizens, governments, knowledge institutes— in a particular institutional setting. The systemic view of innovation departs from traditional linear models that depict innovation as a staged process that starts with (basic) scientific research and ends with commercialization by companies. Innovation processes are much more complex and diverse, influenced by multiple actors that interact in networks with feedback loops, and involving many types of knowledge beyond scientific knowledge. Urban innovation systems are nested in innovation systems on other spatial levels—regional, national, international. Studies on urban innovation systems seek to explain how innovations emerge in an urban context, why urban regions differ in their innovative performance, and also address questions on the governance and management of such systems. Studies in this field draw from a variety of disciplines including economic geography, urban and regional economics, political sciences, innovation studies, social sciences, and urban planning.
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Several studies show that logistics facilities have spread spatially from relatively concentrated clusters in the 1970s to geographically more decentralized patterns away from urban areas. The literature indicates that logistics costs are one of the major influences on changes in distribution structures, or locations and usage of logistics facilities. Quantitative modelling studies that aim to describe or predict these phenomena in relation to logistics costs are lacking, however. This is relevant to design more effective policies concerning spatial development, transport and infrastructure investments as well as for understanding environmental consequences of freight transport. The objective of this paper is to gain an understanding of the responsiveness of spatial logistics patterns to changes in these costs, using a quantitative model that links production and consumption points via distribution centers. The model is estimated to reproduce observed use of logistics facilities as well as related transport flows, for the case of the Netherlands. We apply the model to estimate the impacts of a number of scenarios on the spatial spreading of regional distribution activity, interregional vehicle movements and commodity flows. We estimate new cost elasticities, of the demand for trade and transport together, as well as specifically for the demand for the distribution facility services. The relatively low cost elasticity of transport services and high cost elasticity for the distribution services provide new insights for policy makers, relevant to understand the possible impacts of their policies on land use and freight flows.
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