Local spatiotemporal modeling of house prices: a mixed model approach

The real estate market has long provided an active application area for spatial–temporal modeling and analysis and it is well known that house prices tend to be not only spatially but also temporally correlated. In the spatial dimension, nearby properties tend to have similar values because they share similar characteristics, but house prices tend to vary over space due to differences in these characteristics. In the temporal dimension, current house prices tend to be based on property values from previous years and in the spatial–temporal dimension, the properties on which current prices are based tend to be in close spatial proximity. To date, however, most research on house prices has adopted either a spatial perspective or a temporal one; relatively little effort has been devoted to situations where both spatial and temporal effects coexist. Using ten years of house price data in Fife, Scotland (2003–2012), this research applies a mixed model approach, semiparametric geographically weighted regression (GWR), to explore, model, and analyze the spatiotemporal variations in the relationships between house prices and associated determinants. The study demonstrates that the mixed modeling technique provides better results than standard approaches to predicting house prices by accounting for spatiotemporal relationships at both global and local scales

Local modelling: one size does not fit all

This editorial piece considers what happens when we abandon the concept that models of social processes have global application in favor of a local approach in which context or the influence of \u27place\u27 has an important role. A brief history of this local approach to statistical modelling is given, followed by a consideration of its ramifications for understanding societal issues. The piece concludes with futures challenges and prospects in this area

Calibrating spatial interaction models from GPS tracking data: an example of retail behaviour

Global Positioning System (GPS) technology has changed the world. We now depend on it for navigating vehicles, for route finding and we use it in our everyday lives to extract information about our locations and to track our movements. The latter use offers a potential alternative to more traditional sources of movement data through the construction of trip trajectories and, ultimately, the construction of origin-destination flow matrices. The advantage of being able to use GPS-derived movement data is that such data are potentially much richer than traditional sources of movement data both temporally and spatially. GPS-derived movement data potentially allow the calibration of spatial interaction models specific to very short time intervals, such as daily or even hourly, and for user-specified origins and destinations. Ultimately, it should be possible to calibrate continuously updated models in near real-time. However, the processing of GPS data into trajectories and then origin-destination flow matrices is not straightforward and is not well understood. This paper describes the process of transferring GPS tracking data into matrices that can be used to calibrate spatial interaction models. An example is given using retail behaviour in two towns in Scotland with an origin-constrained spatial interaction model calibrated for each day of the week and under different weather conditions (normal, rainy, windy). Although the study is small in terms of individuals and spatial context, it serves to demonstrate a future for spatial interaction modelling free from the tyranny of temporally static and spatially predefined data sets

Gravity and Spatial Interaction Models

One of the major intellectual achievements and, at the same time, perhaps the most useful contribution by spatial analysts to social science literature is the development of gravity and spatial interaction models. This book provides an excellent and lucid introduction to the evolution of the gravity and spatial interaction models and their specification. These models are placed within the historical context of the development of the general spatial interaction literature. Haynes and Fotheringham outline the characteristics that have contributed to making these models among the most widely applied in forecasting and in general studies of migration, communications, transportation, and retailing, among other topics in urban and regional analysis. SCIENTIFIC GEOGRAPHY SERIES, Grant Ian Thrall, editor.https://researchrepository.wvu.edu/rri-web-book/1010/thumbnail.jp

A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices

Abstract Land price studies typically employ hedonic analysis to identify the impact of land characteristics on price. Owing to the spatial fixity of land, however, the question of possible spatial dependence in agricultural land prices arises. The presence of spatial dependence in agricultural land prices can have serious consequences for the hedonic model analysis. Ignoring spatial autocorrelation can lead to biased estimates in land price hedonic models. We propose using a flexible quantile regression-based estimation of the spatial lag hedonic model allowing for varying effects of the characteristics and, more importantly, varying degrees of spatial autocorrelation. In applying this approach to a sample of agricultural land sales in Northern Ireland we find that the market effectively consists of two relatively separate segments. The larger of these two segments conforms to the conventional hedonic model with no spatial lag dependence, while the smaller, much thinner market segment exhibits considerable spatial lag dependence. Un mod�le h�donique � r�gression quantile spatiale des prix des terrains agricoles R�sum� Les �tudes sur le prix des terrains font g�n�ralement usage d'une analyse h�donique pour identifier l'impact des caract�ristiques des terrains sur le prix. Toutefois, du fait de la fixit� spatiale des terrains, la question d'une �ventuelle d�pendance spatiale sur la valeur des terrains agricoles se pose. L'existence d'une d�pendance spatiale dans le prix des terrains agricoles peut avoir des cons�quences importantes sur l'analyse du mod�le h�donique. En ignorant cette corr�lation s�rielle, on s'expose au risque d'�valuations biais�es des mod�les h�doniques du prix des terrains. Nous proposons l'emploi d'une estimation � base de r�gression flexible du mod�le h�donique � d�calage spatial, tenant compte de diff�rents effets des caract�ristiques, et surtout de diff�rents degr�s de corr�lations s�rielles spatiales. En appliquant ce principe � un �chantillon de ventes de terrains agricoles en Irlande du Nord, nous d�couvrons que le march� se compose de deux segments relativement distincts. Le plus important de ces deux segments est conforme au mod�le h�donique traditionnel, sans d�pendance du d�calage spatial, tandis que le deuxi�me segment du march�, plus petit et beaucoup plus �troit, pr�sente une d�pendance consid�rable du d�calage spatial. Un modelo hed�nico de regresi�n cuantil espacial de los precios del terreno agr�cola Resumen T�picamente, los estudios del precio de la tierra emplean un an�lisis hed�nico para identificar el impacto de las caracter�sticas de la tierra sobre el precio. No obstante, debido a la fijeza espacial de la tierra, surge la cuesti�n de una posible dependencia espacial en los precios del terreno agr�cola. La presencia de dependencia espacial en los precios del terreno agr�cola puede tener consecuencias graves para el modelo de an�lisis hed�nico. Ignorar la autocorrelaci�n espacial puede conducir a estimados parciales en los modelos hed�nicos del precio de la tierra. Proponemos el uso de una valoraci�n basada en una regresi�n cuantil flexible del modelo hed�nico del lapso espacial que tenga en cuenta los diversos efectos de las caracter�sticas y, particularmente, los diversos grados de autocorrelaci�n espacial. Al aplicar este planteamiento a una muestra de ventas de terreno agr�cola en Irlanda del Norte, descubrimos que el mercado consiste efectivamente de dos segmento relativamente separados. El m�s grande de estos dos segmentos se ajusta al modelo hed�nico convencional sin dependencia del lapso espacial, mientras que el segmento m�s peque�o, y mucho m�s fino, muestra una dependencia considerable del lapso espacial.Spatial lag, quantile regression, hedonic model, C13, C14, C21, Q24,

Associate Students in transition from college to university: a sociocultural study

It is important to understand students’ experience of transition from college to university if institutional and national targets to widen participation in higher education are to be achieved. The Associate Student Project (ASP) funded by Scottish Funding Council, supports dual matriculation for Associate Students. This study explores the sociocultural experience of 23 Associate Students from four Scottish colleges who transitioned as direct entrants into the third year of engineering degree programmes at a post-92 university. Further it illuminates these students’ participation in the communities that they encountered during their first year at university. First the analysis draws from Cultural Historical Activity Theory (CHAT) to offer a system-wide perspective of the ASP enacted in the colleges. This illustrates how the subjects (students) and the sociocultural context co-evolve to meet the object and outcomes of the system. Next, findings highlight the contradiction of being matriculated in both institutions yet the university was largely absent during the college years. Skills workshops position the Associate Students as in deficit and needing support despite being viewed by college lecturers as amongst the most academically able. The micro-perspective of direct entry students’ participation at university is framed by Wenger-Trayner and Wenger-Trayner’s (2015) landscapes of practice. Student participants preferred to remain at the periphery, engaging with former college peers at the expense of engaging with new social networks or academic support provided by the university. Findings further suggest that Associate Students’ transition to university is mediated as much by spatial mobility and an individual’s personal circumstances as it is by transition support. This study contributes to knowledge about the transition of students from one educational sector to another, and about their engagement as they gain access to university through flexible routes. Without these, some of them would have been unable to go to university at all

Catheter insertion techniques for improving catheter function and clinical outcomes in peritoneal dialysis patients (Protocol)

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: This review aims to look at the benefits and harms of different PD catheter insertion techniques. To establish whether a specific technique used to place catheters in adults and children, who are new to PD, result in any significant differences in clinical outcomes. Insertion techniques will be further defined as peritoneoscopic, percutaneous, fluoroscopic, laparoscopic insertion or open surgery. To identify which technique offers optimal clinical outcomes and minimises post-procedure complications including postoperative haemorrhage, PD catheter dysfunction, exit site infection/peritonitis and bowel perforation

Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE