AGGREGATION ERROR EFFECTS ON THE DISCRETE-SPACE p-MEDIAN MODEL: THE CASE OF EDMONTON, CANADA

The spatial analysis literature recognizes three sources of aggregation error, termed Source A, Source B, and Source C, which affect models relying on distance measurements between populations and facilities. We consider these effects with respect to aggregating from census enumeration areas to census tracts, on a popular location model. We identify a further source of aggregation error, which we dub Source D error, arising from the representation of facility sites by discrete points. Source D effects are of the same magnitude as Source A and B combined, much greater than Source C effects. Source D error is further significant, because, unlike Source A and B error, it can be eliminated only by disaggregating .
La littérature sur Vanalyse spatiale reconnaît trois sources d'erreur d'agrégation appelées erreurs de source A, B, et C. Ces erreurs influencent les modèles qui reposent sur des mesures de distance entre des populations et des installations. On considère les effets d'agrégation des secteurs de dénombrement en secteurs de recensement sur un modèle de localisation courant. On identifie une quatrième source d'erreur d'agrégation appelée erreur de source D. Cette erreur découle de la représentation des sites des installations par des points discrets. Ses effets sont du même ordre de grandeur que ceux des sources A et B combinées et beaucoup plus grands que les effets de source G. L'erreur de source D est d'autant plus significative à cause du fait que, contrairement aux erreurs de source A et B, elle ne peut être éliminée que par la désagrégation .     

Peripheralization: Theoretical Concepts Explaining Socio-Spatial Inequalities

The current increase in socio-spatial inequalities in Europe has led to a revival of the terms “peripheralization” and “marginalization” in spatial research. In contrast to the geographical notion of a “periphery”, which is synonymous with distance to a centre and being situated on the fringes of a city, region or nation, research on “peripheralization” describes the production of peripheries through social relations and their spatial implications. The main part of the article provides a critical review of theoretical concepts which attempt to explain socio-spatial disparities between centralization and peripheralization processes. This includes theories of economic polarization, social inequality and political power. Building on this, a multidimensional concept of socio-spatial polarization is outlined, one which comprises processes of centralization and peripheralization in economic, social and political dimensions. Finally, implications are drawn for spatial planning regarding the polarization between metropolitan and non-metropolitan regions and areas for further research are highlighted.     

Sequential, Bayesian Geostatistics: A Principled Method for Large Data Sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of " basis vectors " that best represent the " true " posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.     

Geoarchaeological studies on Roman time harbour sediments in Cologne — comparison of different OSL dating techniques

Due to the construction of a new North-South subway in Cologne, Roman time harbour sediments were exposed and were sampled for luminescence dating. A very good independent age control was given by the precise knowledge of the chronology of Roman activity and by radiocarbon ages of charcoal samples. Hence, different methodological approaches within luminescence dating were applied for Holocene heterogeneously bleached fluvial samples and were compared to the known ages. For one sample, optically stimulated luminescence (OSL) dating was applied to coarse-grained quartz using a single aliquot regenerative-dose (SAR) protocol. After D_e-measurements, different statistical approaches were tested (i.e. arithmetic mean, median, minimum age model, finite mixture model, leading edge method and the Fuchs and Lang approach). It is demonstrated that the Fuchs and Lang approach along with the leading edge method yielded the best matching OSL ages with respect to the known ages.     

Theory of Comparative Advantage: Do Transportation Costs Matter?

In this paper we present a formal analysis that incorporates returns to transportation into a Ricardian framework to predict trade patterns. The important point gained from this analysis is that increasing returns to transportation, coupled with appropriate distances between trading partners, can be shown to reverse Ricardian predictions even when there are no international differences in tastes, technology, or factor endowments. Additional gains from trade may emerge from reductions in aggregate delivery costs owing to scale economies.