Dual-frequency GPS survey for validation of a regional DTM and for the generation of local DTM data for sea-level rise modelling in an estuarine salt marsh

Global average temperatures have risen by an average of 0.07°C per decade over the last 100 years, with a warming trend of 0.13°C per decade over the last 50 years. Temperatures are predicted to rise by 2°C - 4.4°C by 2100 leading to global average sealevel rise (SLR) of 2 – 6mm per year (20 – 60cms in total) up to 2100 (IPCC 2007) with impacts for protected coastal habitats in Ireland. Estuaries are predominantly sedimentary environments, and are characterised by shallow coastal slope gradients, making them sensitive to even modest changes in sea-level. The Shannon estuary is the largest river estuary in Ireland and is designated as a Special Area of Conservation (SAC) under the EU Habitats Directive (EU 1992) providing protection for listed habitats within it, including estuarine salt marsh. Trends in Shannon estuary tidal data from 1877 – 2004 suggest an average upward SLR trend of 4 - 5mm/yr over this period. A simple linear extension of this historical trend would imply that local SLR will be in the region of 40 - 45cm by 2100. However, this may underestimate actual SLR for the estuary by 2100, since it takes no account of predicted climate-driven global SLR acceleration (IPCC 2007) up to 2100

Terrestrial laser scan error in the presence of dense ground vegetation

Terrestrial Laser Scan (TLS) data are seeing increasing use in geology, geomorphology, forestry and urban mapping. The ease of use, affordability and operational flexibility of TLS suggests that demand for it is likely to increase in large scale mapping studies. However, its advantages may remain restricted to specific environments, due to difficulties in defining bare-ground level in the presence of ground level vegetation. This paper seeks to clarify the component contributions to TLS elevation error deriving from vegetation occlusion, scan co-registration error, point-cloud georeferencing error and target position-definition in TLS point-cloud data. A very high-resolution (c.250 points/m2) multi-scan single-returns TLS point-cloud data-set is acquired for an 11-hectare area of open, substantially flat and 100% vegetated coastal saltmarsh, providing data for the empirical quantification of TLS error. Errors deriving from the sources discussed are quantified, clarifying the potential proportional contribution of vegetation to other error sources. Initial data validation is applied to the TLS point-cloud data after application of a local-lowest-point selection process, and repeat validation tests are applied to the resulting filtered point-cloud after application of a kriging-based error-adjustment using a data fusion with GPS. The final results highlight the problem of representing bare-ground effectively within TLS data captured in the presence of dense ground vegetation and clarify the component contributions of elevation error deriving from surveying and data processing

Plutarch and Dio on Cicero at the trial of Milo

The accounts of the trial of Milo given by Plutarch and Dio provide a valuable insight into the working methods of the two writers. A close examination of the incident in relation to the structure of Plutarch’s Life of Cicero confirms that the Life is a carefully structured unit which assumes audience knowledge of events and presents a particular picture of Cicero. A similar exploration of Dio’s account of the failing years of the Republic reveals the historian both playing with annalistic structure and emphasizing this incident in order to highlight the contemporary political breakdown; he too is interested in constructing a picture of Cicero in relation to his context, and hostility to the orator cannot explain all his choices. Granting literary skill and purpose to both of these writers allows us greater insight into the imperial reception of Cicero

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,

Persuasive Language in Cicero’s Pro Milone: A Close Reading and Commentary

This innovative approach to Cicero’s persuasive language analyses the style and structure of one of his important speeches in more details than has ever been done before. It applies ideas from modern linguistics (sentential topic, lexical patterning, interactional discourse), and explores the possibilities and limitations of quantitative analysis, made easier by modern computing power, in the areas of syntax and vocabulary. The result is a reading of the Pro Milone as a unified text, whether aimed at persuading the jury to acquit Milo or at persuading a wider audience that Milo should have been acquitted. This reading not only contributes to our understanding of late republican discourse, but also suggests a new methodology for using the study of language and style to illuminate literary/historical aspects of texts

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

Estimation and inference in spatially varying coefficient models

Spatially varying coefficient models are a classical tool to explore the spatial nonstationarity of a regression relationship for spatial data. In this paper, we study the estimation and inference in spatially varying coefficient models for data distributed over complex domains. We use bivariate splines over triangulations to represent the coefficient functions. The estimators of the coefficient functions are consistent, and rates of convergence of the proposed estimators are established. A penalized bivariate spline estimation method is also introduced, in which a roughness penalty is incorporated to balance the goodness of fit and smoothness. In addition, we propose hypothesis tests to examine if the coefficient function is really varying over space or admits a certain parametric form. The proposed method is much more computationally efficient than the well-known geographically weighted regression technique and thus usable for analyzing massive data sets. The performances of the estimators and the proposed tests are evaluated by simulation experiments. An environmental data example is used to illustrate the application of the proposed method