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,

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

Overturning circulation, nutrient limitation, and warming in the Glacial North Pacific

This work was funded by NERC grant NE/N011716/1 to J.W.B.R., a NERC studentship to B.T., and NSF grant OPP 1643445 to I.E. A.R. acknowledges support from NSF grant 1736771.Although the Pacific Ocean is a major reservoir of heat and CO2, and thus an important component of the global climate system, its circulation under different climatic conditions is poorly understood. Here, we present evidence that during the Last Glacial Maximum (LGM), the North Pacific was better ventilated at intermediate depths and had surface waters with lower nutrients, higher salinity, and warmer temperatures compared to today. Modeling shows that this pattern is well explained by enhanced Pacific meridional overturning circulation (PMOC), which brings warm, salty, and nutrient-poor subtropical waters to high latitudes. Enhanced PMOC at the LGM would have lowered atmospheric CO2—in part through synergy with the Southern Ocean—and supported an equable regional climate, which may have aided human habitability in Beringia, and migration from Asia to North America.Publisher PDFPeer reviewe

Comets, historical records and vedic literature

A verse in book I of Rigveda mentions a cosmic tree with rope-like aerial roots held up in the sky. Such an imagery might have ensued from the appearance of a comet having `tree stem' like tail, with branched out portions resembling aerial roots. Interestingly enough, a comet referred to as `heavenly tree' was seen in 162 BC, as reported by old Chinese records. Because of weak surface gravity, cometary appendages may possibly assume strange shapes depending on factors like rotation, structure and composition of the comet as well as solar wind pattern. Varahamihira and Ballala Sena listed several comets having strange forms as reported originally by ancient seers such as Parashara, Vriddha Garga, Narada and Garga. Mahabharata speaks of a mortal king Nahusha who ruled the heavens when Indra, king of gods, went into hiding. Nahusha became luminous and egoistic after absorbing radiance from gods and seers. When he kicked Agastya (southern star Canopus), the latter cursed him to become a serpent and fall from the sky. We posit arguments to surmise that this Mahabharata lore is a mythical recounting of a cometary event wherein a comet crossed Ursa Major, moved southwards with an elongated tail in the direction of Canopus and eventually went out of sight. In order to check whether such a conjecture is feasible, a preliminary list of comets (that could have or did come close to Canopus) drawn from various historical records is presented and discussed.Comment: This work was presented in the International Conference on Oriental Astronomy held at IISER, Pune (India) during November, 201

Exploration of ecological factors related to the spatial heterogeneity of tuberculosis prevalence in P. R. China

Background: The current prevalence of tuberculosis (TB) in the People's Republic of China (P. R. China) demonstrates geographical heterogeneities, which show that the TB prevalence in the remote areas of Western China is more serious than that in the coastal plain of Eastern China. Although a lot of ecological studies have been applied in the exploration on the regional difference of disease risks, there is still a paucity of ecological studies on TB prevalence in P. R. China. Objective: To understand the underlying factors contributing to the regional inequity of TB burden in P. R. China by using an ecological approach and, thus, aiming to provide a basis to eliminate the TB spatial heterogeneity in the near future. Design: Latent ecological variables were identified by using exploratory factor analysis from data obtained from four sources, i.e. the databases of the National TB Control Programme (2001–2010) in P. R. China, the China Health Statistical Yearbook during 2002–2011, the China Statistical Yearbook during 2002–2011, and the provincial government websites in 2013. Partial least squares path modelling was chosen to construct the structural equation model to evaluate the relationship between TB prevalence and ecological variables. Furthermore, a geographically weighted regression model was used to explore the local spatial heterogeneity in the relationships. Results: The latent ecological variables in terms of ‘TB prevalence’, ‘TB investment’, ‘TB service’, ‘health investment’, ‘health level’, ‘economic level’, ‘air quality’, ‘climatic factor’ and ‘geographic factor’ were identified. With the exception of TB service and health levels, other ecological factors had explicit and significant impacts on TB prevalence to varying degrees. Additionally, each ecological factor had different impacts on TB prevalence in different regions significantly. Conclusion: Ecological factors that were found predictive of TB prevalence in P. R. China are essential to take into account in the formulation of locally comprehensive strategies and interventions aiming to tailor the TB control and prevention programme into local settings in each ecozone

Understanding cost of care for patients on renal replacement therapy: looking beyond fixed tariffs.

BACKGROUND: In a number of countries, reimbursement to hospitals providing renal dialysis services is set according to a fixed tariff. While the cost of maintenance dialysis and transplant surgery are amenable to a system of fixed tariffs, patients with established renal failure commonly present with comorbid conditions that can lead to variations in the need for hospitalization beyond the provision of renal replacement therapy. METHODS: Patient-level cost data for incident renal replacement therapy patients in England were obtained as a result of linkage of the Hospital Episodes Statistics dataset to UK Renal Registry data. Regression models were developed to explore variations in hospital costs in relation to treatment modality, number of years on treatment and factors such as age and comorbidities. The final models were then used to predict annual costs for patients with different sets of characteristics. RESULTS: Excluding the cost of renal replacement therapy itself, inpatient costs generally decreased with number of years on treatment for haemodialysis and transplant patients, whereas costs for patients receiving peritoneal dialysis remained constant. Diabetes was associated with higher mean annual costs for all patients irrespective of treatment modality and hospital setting. Age did not have a consistent effect on costs. CONCLUSIONS: Combining predicted hospital costs with the fixed costs of renal replacement therapy showed that the total cost differential for a patient continuing on dialysis rather than receiving a transplant is considerable following the first year of renal replacement therapy, thus reinforcing the longer-term economic advantage of transplantation over dialysis for the health service.<br/

Airbnb and crime in Barcelona (Spain): testing the relationship using a geographically weighted regression

The existence of works proving the possible relationship empirically that Airbnb lodgings could have with crime in Spain is not known. This research analyzes the relationship between Airbnb lodgings and crimes against the properties and people in Barcelona’s neighbourhoods. To achieve this, we use an ordinary least squares regression model and a geographically weighted regression model. The results show a significant and positive relationship between the higher density of Airbnb lodgings and the higher crime rates in the neighbourhoods, especially of patrimonial nature. Divided by type of leased space, the Airbnb homes, in which the guest shares a room with other guests, show a higher relationship with crimes against property and people. The results of the local model show a spatial heterogeneity in all variables used, indicating the need to address non-stationary spatial processes that reveal hidden patterns. However, the only variable that shows statistically significant local variability is the total Airbnb lodgings variable. Finally, we discussed some unexpected results, proposing some future lines of research. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Nanjing Normal University

Local niche differences predict genotype associations in sister taxa of desert tortoise

Aims: To investigate spatial congruence between ecological niches and genotype in two allopatric species of desert tortoise that are species of conservation concern. Location: Mojave and Sonoran Desert ecoregions; California, Nevada, Arizona, Utah, USA. Methods: We compare ecological niches of Gopherus agassizii and Gopherus morafkai using species distribution modelling (SDM) and then calibrate a pooled-taxa distribution model to explore local differences in species-environment relationships based on the spatial residuals of the pooled-taxa model. We use multiscale geographically weighted regression (MGWR) applied to those residuals to estimate local species-environment relationships that can vary across the landscape. We identify multivariate clusters in these local species-environment relationships and compare them against models of (a) a geographically based taxonomic designation for two sister species and (b) an environmental ecoregion designation, with respect to their ability to predict a genotype association index for these two species. Results: We find non-identical niches for these species, with differences that span physiographic and vegetation niche dimensions. We find evidence for two distinct clusters of local species-environment relationships that when mapped, predict an index of genotype association for the two sister taxa better than did either the geographically based taxonomic designation or an environmental ecoregion designation. Main conclusions: Exploring local species-environment relationships by coupling SDM and MGWR can benefit studies of biogeography and conservation. We find that niche separation in habitat selection conforms to genotypic differences between sister taxa of tortoise in a recent secondary contact zone. This result may inform decision making by agencies with regulatory or land management authority for the two sister taxa addressed here.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The Geography of Diabetes in London, Canada: The Need for Local Level Policy for Prevention and Management

Recent reports aimed at improving diabetes care in socially disadvantaged populations suggest that interventions must be tailored to meet the unique needs of the local community—specifically, the community’s geography. We have examined the spatial distribution of diabetes in the context of socioeconomic determinants of health in London (Ontario, Canada) to characterize neighbourhoods in an effort to target these neighbourhoods for local level community-based program planning and intervention. Multivariate spatial-statistical techniques and geographic information systems were used to examine diabetes rates and socioeconomic variables aggregated at the census tract level. Creation of a deprivation index facilitated investigation across multiple determinants of health. Findings from our research identified ‘at risk’ neighbourhoods in London with socioeconomic disadvantage and high diabetes. Future endeavours must continue to identify local level trends in order to support policy development, resource planning and care for improved health outcomes and improved equity in access to care across geographic regions