On proximity and hierarchy : exploring and modelling space using multilevel modelling and spatial econometrics

Spatial econometrics and also multilevel modelling techniques are increasingly part of the regional scientists‟ toolbox. Both approaches are used to model spatial autocorrelation in a wide variety of applications. However, it is not always clear on which basis researchers make a choice between spatial econometrics and spatial multilevel modelling. Therefore it is useful to compare both techniques. Spatial econometrics incorporates neighbouring areas into the model design; and thus interprets spatial proximity as defined in Tobler‟s first law of geography. On the other hand, multilevel modelling using geographical units takes a more hierarchical approach. In this case the first law of geography can be rephrased as „everything is related to everything else, but things in the same region are more related than things in different regions‟. The hierarchy (multilevel) and the proximity (spatial econometrics) approach are illustrated using Belgian mobility data and productivity data of European regions. One of the advantages of a multilevel model is that it can incorporate more than two levels (spatial scales). Another advantage is that a multilevel structure can easily reflect an administrative structure with different government levels. Spatial econometrics on the other hand works with a unique set of neighbours which has the advantage that there still is a relation between neighbouring municipalities separated by a regional boundary. The concept of distance can also more easily be incorporated in a spatial econometrics setting. Both spatial econometrics and spatial multilevel modelling proved to be valuable techniques in spatial research but more attention should go to the rationale why one of the two approaches is chosen. We conclude with some comments on models which make a combination of both techniques

Modelling Australian Domestic and International Inbound Travel: a Spatial-Temporal Approach

In this paper Australian domestic and international inbound travel are modelled by an anisotropic dynamic spatial lag panel Origin-Destination (OD) travel flow model. Spatial OD travel flow models have traditionally been applied in a single cross-sectional context, where the spatial structure is assumed to have reached its long run equilibrium and temporal dynamics are not explicitly considered. On the other hand, spatial effects are rarely accounted for in traditional tourism demand modelling. We attempt to address this dichotomy between spatial modelling and time series modelling in tourism research by using a spatial-temporal model. In particular, tourism behaviour is modelled as travel flows between regions. Temporal dependencies are accounted for via the inclusion of autoregressive components, while spatial autocorrelations are explicitly accounted for at both the origin and the destination. We allow the strength of spatial autocorrelation to exhibit seasonal variations, and we allow for the possibility of asymmetry between capital-city neighbours and non-capital-city neighbours. Significant spatial dynamics have been uncovered, which lead to some interesting policy implications.Tourism demand, Dynamic panel models, Travel flow model.

Modelling spatial recall, mental imagery and neglect

We present a computational model of the neural mechanisms in the pari-etal and temporal lobes that support spatial navigation, recall of scenes and imagery of the products of recall. Long term representations are stored in the hippocampus, and are associated with local spatial and object-related features in the parahippocampal region. Viewer-centered representations are dynamically generated from long term memory in the parietal part of the model. The model thereby simulates recall and im-agery of locations and objects in complex environments. After parietal damage, the model exhibits hemispatial neglect in mental imagery that rotates with the imagined perspective of the observer, as in the famous Milan Square experiment [1]. Our model makes novel predictions for the neural representations in the parahippocampal and parietal regions and for behavior in healthy volunteers and neuropsychological patients.

Spatial modelling for mixed-state observations

In several application fields like daily pluviometry data modelling, or motion analysis from image sequences, observations contain two components of different nature. A first part is made with discrete values accounting for some symbolic information and a second part records a continuous (real-valued) measurement. We call such type of observations "mixed-state observations". This paper introduces spatial models suited for the analysis of these kinds of data. We consider multi-parameter auto-models whose local conditional distributions belong to a mixed state exponential family. Specific examples with exponential distributions are detailed, and we present some experimental results for modelling motion measurements from video sequences.Comment: Published in at http://dx.doi.org/10.1214/08-EJS173 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Modelling of Dynamic Spatial Processes

The paper is concerned with econometric modeling of the dynamic spatial processes on the example of the GDP per capita in selected European countries. The considerations of the paper are focused on investigations of the structure of components of the spatio-temporal process. As a result of the analysis some specifications of the dynamic spatial models have been obtained. Next the issues of the estimation and verification of the models are presented. The main conclusion from the analysis is that the econometric models of the spatio-temporal processes ought to be of the dynamic character, e.g. considering the spatial and spatio-temporal trends and spatial, temporal and spatio-temporal autodependence as well.spatio-temporal trend, autocorrelation, spatial lag model, dynamic spatial model.

Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

Investigating the use of semantic technologies in spatial mapping applications

Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend

Dundee Discussion Papers in Economics 253:Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales

The Integration of Coastal Flooding into an ArcFLOOD Data Model

With the impact of global climate change, the speedy, intelligent and accessible dissemination of coastal flood predictions from a number of modelling tools at a range of temporal and spatial scales becomes increasingly important for policy decision makers. This thesis provides a novel approach to integrate the coastal flood data into an ArcFLOOD data model to improve the analysis, assessment and mitigation of the potential flood risk in coastal zones. This novel methodology has improved the accessibility, dissemination and visualisation of coastal flood risk. The results were condensed into spatial information flows, data model schematic diagrams and XML schema for end-user extension, customisation and spatial analysis. More importantly, software developers with these applications can now develop rich internet applications with little knowledge of numerical flood modelling systems. Specifically, this work has developed a coastal flooding geodatabase based upon the amalgamation, reconditioning and analysis of numerical flood modelling. In this research, a distinct lack of Geographic Information Systems (GIS) data modelling for coastal flooding prediction was identified in the literature. A schema was developed to provide the linkage between numerical flood modelling, flood risk assessment and information technology (IT) by extending the ESRI ArcGIS Marine Data Model (MDM) to include coastal flooding. The results of a linked hybrid hydrodynamic-morphological numerical flood model were used to define the time-series representation of a coastal flood in the schema. The results generated from GIS spatial analyses have improved the interpretation of numerical flood modelling output by effectively mapping the flood risk in the study site, with an improved definition according to the time-series duration of a flood. The improved results include flood water depth at a point and flood water increase which equates to the difference in significant wave height for each time step of coastal flooding. The flood risk mapping provided has indicated the potential risk to infrastructure and property and depicted the failure of flood defence structures. In the wider context, the results have been provided to allow knowledge transfer to a range of coastal flooding end-users.Natural Environment Research Counci