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Point Data (point + data)

Distribution by Scientific Domains

Mathematics and Statistics	50%
Earth and Environmental Science	33%

Selected Abstracts

A Class of Multiplicity Adjusted Tests for Spatial Clustering Based on Case,Control Point Data

BIOMETRICS, Issue 1 2007
Toshiro Tango
Summary A class of tests with quadratic forms for detecting spatial clustering of health events based on case,control point data is proposed. It includes Cuzick and Edwards's test statistic (1990, Journal of theRoyal Statistical Society, Series B52, 73,104). Although they used the property of asymptotic normality of the test statistic, we show that such an approximation is generally poor for moderately large sample sizes. Instead, we suggest a central chi-square distribution as a better approximation to the asymptotic distribution of the test statistic. Furthermore, not only to estimate the optimal value of the unknown parameter on the scale of cluster but also to adjust for multiple testing due to repeating the procedure by changing the parameter value, we propose the minimum of the profile p-value of the test statistic for the parameter as an integrated test statistic. We also provide a statistic to estimate the areas or cases which make large contributions to significant clustering. The proposed methods are illustrated with a data set concerning the locations of cases of childhood leukemia and lymphoma and another on early medieval grave site locations consisting of affected and nonaffected grave sites. [source]

Risk assessment for nonindigenous pests: 1.

DIVERSITY AND DISTRIBUTIONS, Issue 5 2001
Mapping the outputs of phenology models to assess the likelihood of establishment
Abstract This paper demonstrates the use of phenology models mapped over the landscape as a tool in support of risk assessments for nonindigenous plant pests. Drawing on the relationship between pest development and temperature, the approach uses gridded sequential interpolated temperatures at a resolution of 1 km, linked with phenology models, to predict the potential for a pest to develop throughout the landscape. The potential for establishment of Colorado beetle (Leptinotarsa decemlineata) in England and Wales was used as an illustration. The likelihood of the pest completing a single generation during a 30-year period (1961,90) was computed. Summaries of phenology, based firstly upon point temperature series from weather stations and secondly upon temperatures interpolated across the landscape, were compared. The results revealed that the use of point data led to a 70% likelihood of over-estimating the area at risk from year to year. In the case of average long-term risk however, the point-based and landscape-wide distributions of establishment potential were similar. We demonstrate how the use of phenology models running on a daily time scale provides date based results, so allowing outputs to be tied in with periods in the cropping cycle. The application of daily data in computing the phenological results, unlike the main body of published work on pest risk assessment which uses averaged monthly data, reflects more fully the underlying variability and degrees of sensitivity of the pest to changes in weather. [source]

Wind erosion characteristics of Sahelian surface types

EARTH SURFACE PROCESSES AND LANDFORMS, Issue 12 2010
Thomas Maurer
Abstract The assessment of wind erosion magnitudes for a given area requires knowledge of wind erosion susceptibilities of the dominant local surface types. Relative wind erosion potentials of surfaces can hardly be compared under field conditions, as each erosion event is unique in terms of duration, intensity and extent. The objective of this study was to determine and compare relative wind erosion potentials of the most representative surface types over a transect comprising most parts of southwestern Niger. For this purpose, mobile wind tunnel experiments were run on 26 dominant surface types. The effects of surface disturbance were additionally determined for 13 of these surfaces. The results, namely measurements of wind fields and mass fluxes, can be classified according to specific surface characteristics. Three basic surface groups with similar emission behaviour and aerodynamic characteristics were identified: (1) sand surfaces, (2) rough stone surfaces and (3) flat crusted surfaces. Sand surfaces feature a turbulent zone close to the surface due to the development of a saltation layer. Their surface roughness is medium to high, as a consequence of the loss of kinetic energy of the wind field to saltating particles. Sand surfaces show the highest mass fluxes due to the abundance of loose particles, but also fairly high PM10 fluxes, as potential dust particles are not contained in stable crusts or aggregates. Rough stone surfaces, due to their fragmented and irregular surface, feature the highest surface roughness and the most intense turbulence. They are among the weakest emitters but, due to their relatively high share of potential dust particles, PM10 emissions are still average. Flat crusted surfaces, in contrast, show low turbulence and the lowest surface roughness. This group of surfaces shows rather heterogeneous mass fluxes, which range from moderate to almost zero, although the share of PM10 particles is always relatively high. Topsoil disturbance always results in higher total and PM10 emissions on sand surfaces and also on flat crusted surfaces. Stone surfaces regularly exhibit a decrease in emission after disturbance, which can possibly be attributed to a reorganization which protects finer particles from entrainment. The results are comparable with field studies of natural erosion events and similar wind tunnel field campaigns. The broad range of tested surfaces and the standardized methodology are a precondition for the future regionalization of the experimental point data. Copyright © 2010 John Wiley & Sons, Ltd. [source]

Mapping snow characteristics based on snow observation probability

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 10 2007
Bahram Saghafian
Abstract Measurement/estimation of snow water equivalent (SWE) is a difficult task in water resources studies of snowy regions. SWE point data is measured at snow courses that are normally operated with low density owing to high costs and great difficulty in reaching the stations in cold seasons. Moreover, snow is known to exhibit high spatial variability, which makes SWE studies based solely on sparse station data more uncertain. Ever-increasing availability of satellite images is a promising tool to overcome some of the difficulties associated with analyzing spatial variability of snow. Although National Oceanic and Atmospheric Administration (NOAA) satellite images have low spatial resolution with approximately 1.1-km pixel size, they are adequate for mapping snow cover at regional scales and enjoy a moderate length of record period. In this paper, rain and snow records of synoptic stations and the time series of NOAA-based snow cover maps were used to map average SWE of a vast area in southwestern Iran. First, monthly and annual snow coefficient (SC) at synoptic stations were determined on the basis of analysis of hourly observation of type and amount of precipitation. Then, two new spatially distributed snow characteristics were introduced, namely, average frequency of snow observation (FSO) and monthly frequency of maximum snow observation (FMSO), on the basis of existing satellite snow observations. FSO and monthly FMSO maps were prepared by a geographic information system on the basis of snow map time series. Correlation of these two parameters with SC was studied and spatial distribution of SC was estimated on the basis of the best correlation. Moreover, the distribution of mean annual precipitation was derived by comparing a number of interpolation methods. SWE map was generated by multiplying SC and precipitation maps and its spatial variability in the region was analyzed. Copyright © 2007 Royal Meteorological Society [source]

PATTERN RECOGNITION VIA ROBUST SMOOTHING WITH APPLICATION TO LASER DATA

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2007
Carlo Grillenzoni
Summary Nowadays airborne laser scanning is used in many territorial studies, providing point data which may contain strong discontinuities. Motivated by the need to interpolate such data and preserve their edges, this paper considers robust nonparametric smoothers. These estimators, when implemented with bounded loss functions, have suitable jump-preserving properties. Iterative algorithms are developed here, and are equivalent to nonlinear M-smoothers, but have the advantage of resembling the linear Kernel regression. The selection of their coefficients is carried out by combining cross-validation and robust-tuning techniques. Two real case studies and a simulation experiment confirm the validity of the method; in particular, the performance in building recognition is excellent. [source]