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

Recent Advances in Spatial Data Analysis

This article views spatial analysis as a research paradigm that provides a unique set of specialised techniques and models for a wide range of research questions in which the prime variables of interest vary significantly over space. The heart of spatial analysis is concerned with the analysis and modeling of spatial data. Spatial point patterns and area referenced data represent the most appropriate perspectives for applications in the social sciences. The researcher analysing and modeling spatial data tends to be confronted with a series of problems such as the data quality problem, the ecological fallacy problem, the modifiable areal unit problem, boundary and frame effects, and the spatial dependence problem. The problem of spatial dependence is at the core of modern spatial analysis and requires the use of specialised techniques and models in the data analysis. The discussion focuses on exploratory techniques and model-driven [confirmatory] modes of analysing spatial point patterns and area data. In closing, prospects are given towards a new style of data-driven spatial analysis characterized by computational intelligence techniques such as evolutionary computation and neural network modeling to meet the challenges of huge quantities of spatial data characteristic in remote sensing, geodemographics and marketing. (author's abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Economics of Conflict and Terrorism

This book contributes to the literature on conflict and terrorism through a selection of articles that deal with theoretical, methodological and empirical issues related to the topic. The papers study important problems, are original in their approach and innovative in the techniques used. This will be useful for researchers in the fields of game theory, economics and political sciences

A review of the role of spatial resolution in energy systems modelling:Lessons learned and applicability to the North Sea region

The importance of spatial resolution for energy modelling has increased in the last years. Incorporating more spatial resolution in energy models presents wide benefits, but it is not straightforward, as it might compromise their computational performance. This paper aims to provide a comprehensive review of spatial resolution in energy models, including benefits, challenges and future research avenues. The paper is divided in four parts: first, it reviews and analyses the applications of geographic information systems (GIS) for energy modelling in the literature. GIS analyses are found to be relevant to analyse how meteorology affects renewable production, to assess infrastructure needs, design and routing, and to analyse resource allocation, among others. Second, it analyses a selection of large scale energy modelling tools, in terms of how they can include spatial data, which resolution they have and to what extent this resolution can be modified. Out of the 34 energy models reviewed, 16 permit to include regional coverage, while 13 of them permit to include a tailor-made spatial resolution, showing that current available modelling tools permit regional analysis in large scale frameworks. The third part presents a collection of practices used in the literature to include spatial resolution in energy models, ranging from aggregated methods where the spatial granularity is non-existent to sophisticated clustering methods. Out of the spatial data clustering methods available in the literature, k-means and max-p have been successfully used in energy related applications showing promising results. K-means permits to cluster large amounts of spatial data at a low computational cost, while max-p ensures contiguity and homogeneity in the resulting clusters. The fourth part aims to apply the findings and lessons learned throughout the paper to the North Sea region. This region combines large amounts of planned deployment of variable renewable energy sources with multiple spatial claims and geographical constraints, and therefore it is ideal as a case study. We propose a complete modelling framework for the region in order to fill two knowledge gaps identified in the literature: the lack of offshore integrated system modelling, and the lack of spatial analysis while defining the offshore regions of the modelling framework

Image inpainting based on self-organizing maps by using multi-agent implementation

AbstractThe image inpainting is a well-known task of visual editing. However, the efficiency strongly depends on sizes and textural neighborhood of “missing” area. Various methods of image inpainting exist, among which the Kohonen Self-Organizing Map (SOM) network as a mean of unsupervised learning is widely used. The weaknesses of the Kohonen SOM network such as the necessity for tuning of algorithm parameters and the low computational speed caused the application of multi- agent system with a multi-mapping possibility and a parallel processing by the identical agents. During experiments, it was shown that the preliminary image segmentation and the creation of the SOMs for each type of homogeneous textures provide better results in comparison with the classical SOM application. Also the optimal number of inpainting agents was determined. The quality of inpainting was estimated by several metrics, and good results were obtained in complex images