Variowin: Software for Spatial Data Analysis

Ziegel, Eric R.; Pannatier, Yvan

doi:10.2307/1271331

Cited by 15 publications

(8 citation statements)

References 1 publication

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“…Dutilleul's modified t tests were conducted using PASSAGE [ Rosenberg , 2001]. Variogram analyses were conducted using VARIOWIN [ Pannatier , 1996]. All GIS analyses were conducted using ArcView GIS Spatial Analyst [ ESRI, Inc. , 1998].…”

Section: Methodsmentioning

confidence: 99%

Landscape‐scale vegetation dynamics inferred from spatial patterns of soil δ¹³C in a subtropical savanna parkland

Bai

Boutton

et al. 2009

J. Geophys. Res.

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[1] Grasslands and savannas around the world have experienced woody plant encroachment during the past 100 years, but we know little regarding the manner in which woody plants spread across the landscape. We used soil d 13 C, aerial photography, and geostatistics to quantify patterns of woody encroachment in a 160 Â 100 m georeferenced grid subdivided into 10 Â 10 m cells in a savanna parkland landscape in southern Texas. d 13 C contour maps revealed that centers of closed contours coincided with centers of woody patches, and that larger woody patches developed from smaller woody plant clusters that spread laterally and coalesced. Areas where woody patches were expanding into grassland were characterized by low densities of soil d 13 C contour lines, and indicated the direction and extent of woody encroachment. Conversely, areas with high contour densities represented grassland-woodland boundaries that were temporally stable. Indeed, aerial photos from 1930, 1941, 1982, and 2003 confirmed that woody patches with low spatial variability in d 13 C corresponded to areas where woody plants had encroached during the past 30-75 years. While aerial photos can only record vegetation cover at the photo acquisition time, kriged maps of soil d 13 C allowed us to accurately reconstruct long-term temporal dynamics of woody plant encroachment into grassland. This approach can reliably reconstruct landscape-scale vegetation changes in areas where historical aerial photography or satellite imagery are unavailable and provides a strong spatial context for studies aimed at understanding the functional consequences of vegetation change.

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Section: Methodsmentioning

confidence: 99%

Landscape‐scale vegetation dynamics inferred from spatial patterns of soil δ¹³C in a subtropical savanna parkland

Bai

Boutton

et al. 2009

J. Geophys. Res.

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“…Data points used for analysis were the disease incidence from each quadrat in the plot. Geostatistical software, VARIOWIN (29), was used to detect the spatial dependence or correlation among sample sites in this study. Semivariograms were calculated for four directions, 0, 45, 90, and 135º, where 0º represents the direction along the rows and 90º represents the direction across rows and those between.…”

Section: Methodsmentioning

confidence: 99%

Comparative Analyses of Lettuce Drop Epidemics Caused by Sclerotinia minor and S. sclerotiorum

Hao

Subbarao

2005

Plant Disease

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Temporal and spatial patterns of lettuce (Lactuca sativa) drop caused by Sclerotinia minor and S. sclerotiorum were determined in lettuce fields in the Salinas, Santa Maria, and San Joaquin Valleys in California during 1995 to 1998. Of the 25 commercial fields assessed, 14 had predominantly S. minor, 9 had predominantly S. sclerotiorum, and 2 had varying levels of both species. Sclerotinia infections were classified based on symptoms: those caused directly by eruptive germination of sclerotia (type I) and those caused by the airborne ascospores (type II). The precise location of diseased and healthy plants was mapped and lettuce drop progress was determined at different crop growth stages. Spatial patterns of disease incidence were analyzed using 1-by-1-, 2-by-2-, 3-by-3-, and 4-by-4-m quadrat sizes. Regardless of the analytical method employed, disease incidence with type I infection showed an aggregated pattern in a majority of the fields evaluated and random patterns in fields where incidence was low. In all fields with type I infection, disease progress followed the monomolecular model, typical of soilborne diseases. For fields with aggregated distribution, spatial dependence was observed up to 10 m and was either isotropic or random in direction, suggesting the potential influence of tillage operations on inoculum distribution and disease incidence. Lettuce drop incidence in fields with type II infection was erratic in time and peaked within a very short time. However, disease incidence showed an aggregated pattern in all fields evaluated. Spatial dependence of quadrats generally was detected in two adjacent directions, suggesting a directional gradient perhaps caused by wind direction during ascospore dissemination. Increasing quadrat sizes usually increased the degree of aggregation of lettuce drop, but not the distribution pattern itself. These results demonstrate that the source of inoculum and the type of infections they cause are most likely to determine spatial patterns of lettuce drop in the field.

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“…The indicator variogram, 2b γ h; k ð Þ, measures how often two SLs are of different compositions (i.e., transition frequency) as a function of their separation distance (Goovaerts, 1997). To account for the targeted inspection and geographical clustering of residences with LSL or GSL, the following rescaled semivariogram estimator (Goovaerts et al, 2005;Pannatier, 1996) was implemented:…”

Section: Modeling Spatial Patternsmentioning

confidence: 99%

Geospatial model of composition of water service lines in Flint,Michigan: Validation using excavation data and a new compositional geostatistical approach

Goovaerts¹

2023

AWWA Water Science

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Following the Flint drinking water crisis, a service line (SL) replacement program was implemented to replace lead SLs and galvanized SLs connecting residences to Flint's water system, leading to the excavation and inspection over a 5‐year period (2016–2020) of a total of 26,750 lines, representing close to 50% of all tax parcels in the City of Flint. These recent data were used to validate an earlier geospatial model created by residual indicator kriging (IK) to predict the probability that a home has a lead, galvanized, or copper private‐side SL based on neighboring house inspections (i.e., 3254 homes visited in 2017) and secondary information (i.e., built year and city records on SL composition). Receiver operating characteristic curves indicated an average frequency of detection (i.e., area under the curve [AUC]) of 0.9 for copper and galvanized material and 0.6 for lead service lines. Predicting the composition of SL at unmonitored residences by IK, however, can result in negative probabilities of occurrence and probabilities that do not sum to 1. These limitations were overcome by adopting simplicial IK, whereby data undergo a logratio transform before the geospatial analysis. This first application of a compositional approach to SL data improved the detection of lead SLs (AUC = 0.8 vs. 0.6) while providing coherent predictions. Incorporating secondary information, in particular using standardized cokriging and a new rescaled cross‐semivariogram estimator introduced to correct for geographical clustering of house inspections, increased the accuracy of the prediction.

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Variowin: Software for Spatial Data Analysis

Cited by 15 publications

References 1 publication

Landscape‐scale vegetation dynamics inferred from spatial patterns of soil δ¹³C in a subtropical savanna parkland

Landscape‐scale vegetation dynamics inferred from spatial patterns of soil δ¹³C in a subtropical savanna parkland

Comparative Analyses of Lettuce Drop Epidemics Caused by Sclerotinia minor and S. sclerotiorum

Geospatial model of composition of water service lines in Flint,Michigan: Validation using excavation data and a new compositional geostatistical approach

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Variowin: Software for Spatial Data Analysis

Cited by 15 publications

References 1 publication

Landscape‐scale vegetation dynamics inferred from spatial patterns of soil δ13C in a subtropical savanna parkland

Landscape‐scale vegetation dynamics inferred from spatial patterns of soil δ13C in a subtropical savanna parkland

Comparative Analyses of Lettuce Drop Epidemics Caused by Sclerotinia minor and S. sclerotiorum

Geospatial model of composition of water service lines in Flint,Michigan: Validation using excavation data and a new compositional geostatistical approach

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Product

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Landscape‐scale vegetation dynamics inferred from spatial patterns of soil δ¹³C in a subtropical savanna parkland

Landscape‐scale vegetation dynamics inferred from spatial patterns of soil δ¹³C in a subtropical savanna parkland