Pierre Goovaerts

Productivity is very dependent on the environmental and biotic factors present at the site where the forest species of interest is present. Forest site productivity is usually assessed using empirical models applied to inventory data providing discrete predictions. While the use of GIS-based models enables building a site productivity distribution map. Therefore, the aim of this study was to derive a productivity index using multivariate statistics and coupled GIS-geostatistics to obtain a forest productivity map. To that end, a study area vastly covered by naturally regenerated forests of maritime pine in central Portugal was used. First, a productivity index (PI) was built based on Factorial Correspondence Analysis (FCA) by incorporating a classical site index for the species and region (Sh25 - height index model) and GIS-derived environmental variables (slope and aspect). After, the PI map was obtained by multi-Gaussian kriging and used as a GIS layer to evaluate maritime pine ar...

Dolines or sinkholes are earth depressions that develop in soluble rocks complexes such as limestone, dolomite, gypsum, anhydrite, and halite; dolines appear in a variety of shapes from nearly circular to complex structures with highly curved perimeters. The occurrence of dolines in the studied karst area is not random; they are the results of geomorphic, hydrologic, and chemical processes that have caused partial subsidence, even the total collapse of the land surface when voids and caves are present in the bedrock and the regolith arch overbridging these voids is unstable. In the study area, the majority of collapses occur in the regolith (bedrock cover) that bridges voids in the bedrock. Because these collapsing dolines may result in property damage and even cause the loss of lives, there is a need to develop methods for evaluating karst hazards. These methods can then be used by planners and practitioners for urban and economic development, especially in regions with a growing p...

Nonlinear constrained optimization algorithms are widely utilized in artifact design. Certain algorithms also lend themselves well to design of experiments (DOE). Adaptive design refers to experimental design where determining where to sample next is influenced by information from previous experiments. We present a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch and Jones) that is able to create adaptive designs effectively. Its ability to allow easily for a variety of sampling criteria and to incorporate constraint information accurately makes it well suited to the needs of adaptive design. The approach is demonstrated on a human reach experiment where the selection of sampling points adapts successfully to the stature and perception of the individual test subject. Results from the initial study indicate that superEGO is able to create experimental designs that yield more accurate models using fewer points than the original testing procedure.

Most states in the Western US have high rates of drug poisoning death (DPD), especially New Mexico, Nevada, Arizona and Utah (UT). This seems paradoxical in UT where illicit drug use, smoking and drinking rates are low. To investigate this, spatial analysis of county level DPD data and other relevant factors in the Western US and UT was undertaken. Poisson kriging was used to smooth the DPD data, populate data gaps and improve the reliability of rates recorded in sparsely populated counties. Links between DPD and economic, environmental, health, lifestyle, and demographic factors were investigated at four scales using multiple linear regression. LDS church membership and altitude, factors not previously considered, were included. Spatial change in the strength and sign of relationships was investigated using geographically weighted regression and significant DPD clusters were identified using the Local Moran's I. Economic factors, like the sharp social gradient between rural and...

This paper compares the prediction performances of three multivariate algorithms that allow the incorporation of secondary information that is known at all locations to be estimated (linear regression, simple krig-ing with varying local means derived from the secondary attribute, kriging with an external drift) and the more general cokriging with one or two un-biasedness constraints. A case study shows that cokriging performs better than the three other algorithms, in particular when a single unbiasedness constraint is considered. For all methods, the prediction error is reduced by replacing the raw measurements of the secondary variable (topsoil Co) by their regional components, which are estimated using factorial kriging, because of their better correlation with the primary variable (topsoil Cu).

[1] Before answering Mr. Julian’s comments, the authors would like to emphasize the fact that the ‘‘Spatial and Temporal Phosphorus Distribution Changes in a Large Wetland Ecosystem’’ paper is a scientific study aiming at presenting an original methodology to analyze data sets and to assess water quality total phosphorus (TP) levels and trends in a large ecosystem. This is not a regulatory or a policy driven paper aiming at assessing compliance with any more or less specific Floridian test. [2] We thank Mr. Julian for what reads as apparently regulatory-based comments on our article. We support Mr. Julian’s comments on the importance of data screening when compiling databases from different sources. However, we disagree with Mr. Julian’s interpretation that water quality thresholds, indicative of an ecological imbalance, should only be applied to specific regulatory-type data. Apart from taking an unusual regulatory twist in his comments, he is also making incorrect assumptions about our analysis suggesting that, as we compare water quality TP levels to two predetermined ecological thresholds (10 and 15 mg L ), we are improperly applying some regulatory tests. Rather, we are presenting a novel analytical method for compiling databases to create maps and assess spatial and temporal distribution of long-term and short-term changes of surface water TP in the Everglades. As explained below, the 10 mg L 1 level considered in our work is the currently broadly recognized ecological threshold––exceeding this value in the water column is, as generally agreed to, the cause of an ecological imbalance responsible for a major and broad ecological system change. Florida Everglades, being a historically phosphorus-limited eco-system, is particularly sensitive to increased and increasing crucial threshold phosphorus levels as reflected by those found in the water column. [3] While compiling the carefully selected data sets (from renown agencies or universities), the authors considered the uncertainties associated with a multiple year nutrient changes assessment in a large ecosystem linked to the fact that different agencies have different field sampling techniques and analytical methodologies, resulting in varied measurement related errors. Descriptions of the methodologies and protocols are reported in the cited original sources. The authors deem that it is outside the scope of this paper to explain these in detail or to question the integrity of the data originating from federal and state government funded projects (National Science Foundation Florida Coastal Everglades Long Term Ecological Research, U.S. Geological Survey, U.S. Environmental Protection Agency, Florida Department of Environmental Protection and U.S. Environmental Protection Agency) that followed strict quality assurance and quality control procedures before being published. [4] Other authors cited in our study have recognized the unique challenges of working with spatial and temporal environmental data and temporal trajectories of ecosystems properties, including different field protocols adopted for sampling design, density and total number of observations, positional accuracy of sampling locations, analytical methods, precision of laboratory measurements, and errors in statistical and/or geostatistical processing methods [Grunwald et al., 2008]. In our study, data were carefully scrutinized and flagged for the presence of outliers or duplicated records, and the appropriate exploratory analysis was conducted, prior to the geostatistical analysis. Data with flags and outliers outside three standard deviations were excluded from the analysis. In addition, in order to compare sampling events from different years and sources and to minimize bias due to different sampling densities and geospatial designs, we constrained the number of samples to only include sites located close to each other, following geostatistical procedures used in similar studies [Bruland et al., 2007; Grunwald et al., 2008]. [5] On another note, we commend Mr. Julian’s attempt to explain the regulatory framework highlighting the rigorous work performed by the Florida Department of Environmental Protection (FDEP) while developing the Water Quality Based Effluent Limit (WQBEL) dated 2012. However, the current paper is not about the Stormwater Treatment Area (STA) discharge effluent limit but rather about the Everglades Protection Area (EvPA) water quality. [6] We understand that the four-part test specific to the EvPA is applied to stations codified into impacted and Everglades Foundation, Science Department, Palmetto Bay, Florida, USA. Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona, USA. College of Environment and Design, University of Georgia, Athens, Georgia, USA. BioMedware, Inc., Ann Arbor, Michigan, USA.

ABSTRACT There are different sources of uncertainty attached to UXO site characterization. One of them is associated with prior information regarding locations of ordnance targets, which are used initially for designing sampling schemes. The accuracy of information is ...

Since Flint returned to its pre-crisis source of drinking water close to 25,000 water samples have been collected and tested for lead and copper in more than 10,000 residences. This paper presents the first analysis and time trend modeling of lead data, providing new insights about the impact of this intervention. The analysis started with geocoding all water lead levels (WLL) measured during an 11-month period following the return to the Detroit water supply. Each data was allocated to the corresponding tax parcel unit and linked to secondary datasets, such as the composition of service lines, year built, or census tract poverty level. Only data collected on residential parcels within the City limits were used in the analysis. One key feature of Flint data is their collection through two different sampling initiatives: (i) voluntary or homeowner-driven sampling whereby concerned citizens decided to acquire a testing kit and conduct sampling on their own (non-sentinel sites), and (ii) State-controlled sampling where data were collected bi-weekly at selected sites after training of residents by technical teams (sentinel sites). Temporal trends modeled from these two datasets were found to be statistically different with fewer sentinel data exceeding WLL thresholds ranging from 10 to 50 g/L. Even after adjusting for housing characteristics the odds ratio (OR) of measuring WLL above 15 g/L at non-sentinel sites is significantly greater than 1 (OR=1.480) and it increases with the threshold (OR= 2.055 for 50 g/L). Joinpoint regression showed that the city-wide percentage of WLL data above 15 g/L displayed four successive trends since the return to Detroit Water System. Despite the recent improvement in water quality, the culprit for differences between sampling programs needs to be identified as it impacts exposure assessment and might influence whether there is compliance or not with the Lead and Copper Rule.

Geostatistical techniques that account for spatially varying population sizes and spatial patterns in the filtering of choropleth maps of cancer mortality were recently developed. Their implementation was facilitated by the initial assumption that all geographical units are the same size and shape, which allowed the use of geographic centroids in semivariogram estimation and kriging. Another implicit assumption was that the population at risk is uniformly distributed within each unit. This paper presents a generalization of Poisson kriging whereby the size and shape of administrative units, as well as the population density, is incorporated into the filtering of noisy mortality rates and the creation of isopleth risk maps. An innovative procedure to infer the point-support semivariogram of the risk from aggregated rates (i.e. areal data) is also proposed. The novel methodology is applied to age-adjusted lung and cervix cancer mortality rates recorded for white females in two contras...

Smoothing methods have been developed to improve the reliability of risk cancer estimates from sparsely populated geographical entities. Filtering local details of the spatial variation of the risk leads however to the detection of larger clusters of low or high cancer risk while most spatial outliers are filtered out. Static maps of risk estimates and the associated prediction variance also fail to depict the uncertainty attached to the spatial distribution of risk values and does not allow its propagation through local cluster analysis. This paper presents a geostatistical methodology to generate multiple realizations of the spatial distribution of risk values. These maps are then fed into spatial operators, such as in local cluster analysis, allowing one to assess how risk spatial uncertainty translates into uncertainty about the location of spatial clusters and outliers. This novel approach is applied to age-adjusted breast and pancreatic cancer mortality rates recorded for whit...

Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Quality of decision-making thus relies on an accurate quantification of risks from observed rates which can be very unreliable when computed from sparsely populated geographical units or recorded for minority populations. This paper presents a geostatistical methodology that accounts for spatially varying population sizes and spatial patterns in the processing of cancer mortality data. Simulation studies are conducted to compare the performances of Poisson kriging to a few simple smoothers (i.e. population-weighted estimators and empirical Bayes smoothers) under different scenarios for the disease frequency, the population size, and the spatial pattern of risk. A public-domain executable with example datasets is provided. The analysis of age-adjusted mortality rates for breast and cervix cancers illustrated some key features of commonly used smoothi...

... Correspondence: Enza Gucciardi, PhD, Assistant Professor, School of Nutrition, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada. E-mail: egucciar@ryerson.ca. Publication History. ... 1 Gary TL, Genkinger JM, Guallar E, Peyrot M, Brancati FL. ...

... Geostatistics, which is based on the theory of regionalized variables (Journel and Huijbregts, 1978 ... and Salas, 1985; Phillips et al., 1992) have shown that geostatistical prediction techniques ... A multivariate extension of kriging, known as cokriging, has been used for merging ...

In the north-east of Belgium an extensive area has been contaminated with cadmium as a result of past industrial activities. The performances of two geostatistical algorithms (ordinary kriging with a global trend model and indicator kriging) for estimating the topsoil Cd content at 276 ...

Identifying the spatial variability and risk areas for southern root-knot nematode [Meloidogyne incognita (Kofoid & White) Chitwood] (RKN) is key for site-specific management (SSM) of cotton (Gossypium hirsutum L.) fields. The objectives of this study were to: (i) determine the soil properties that influence RKN occurrence at different scales; and (ii) delineate risk areas of RKN by indicator kriging. The study site was a cotton field located in the southeastern coastal plain region of the USA. Nested semivariograms indicated that RKN samples, collected from a 50×50 m grid, exhibited a local and regional scale of variation describing small and large clusters of RKN population density. Factorial kriging decomposed RKN and soil properties variability into different spatial components. Scale dependent correlations between RKN data showed that the areas with high RKN population remained stable though the growing season. RKN data were strongly correlated with slope (SL) at local scale and with apparent soil electrical conductivity deep (EC(a-d)) at both local and regional scales, which illustrate the potential of these soil physical properties as surrogate data for RKN population. The correlation between RKN data and soil chemical properties was soil texture mediated. Indicator kriging (IK) maps developed using either RKN, the relation between RKN and soil electrical conductivity or a combination of both, depicted the probability for RKN population to exceed the threshold of 100 second stage juveniles/100 cm(3) of soil. Incorporating EC(a-d) as soft data improved predictions favoring the reduction of the number of RKN observations required to map areas at risk for high RKN population.

... Cases (N = 421), recruited from the Michigan Cancer Registry and controls (N = 573), enrolled using random digit dialing of age-weighted lists, answered questionnaires about water and dietary consumption, residential and occupational histories, and historical sources of ...