Spatial point‐process statistics: concepts and application to the analysis of lead contamination in urban soil
Identifieur interne : 00A230 ( Main/Curation ); précédent : 00A229; suivant : 00A231Spatial point‐process statistics: concepts and application to the analysis of lead contamination in urban soil
Auteurs : Christian Walter [Australie, France] ; Alex. B. Mcbratney [Australie] ; Raphael A. Viscarra Rossel [Australie] ; Julie A. Markus [Australie]Source :
- Environmetrics [ 1180-4009 ] ; 2005-06.
Descripteurs français
- Pascal (Inist)
- Wicri :
English descriptors
- KwdEn :
- Agriculture food, Arbitrary points, Australian journal, Basic statistics, Cartographic representation, Centile, Contamination, Contour line, Copyright, Covariance function, Decile, Dense sample, Different point patterns, Different point processes, Different processes, Diffusion range, Diffusion ranges, Diggle, Disjunctive kriging, Distance, Distance functions, Distribution functions, Empirical distribution function, Empirical limits, Environmental pollution, Environmental science, Environmetrics, Experimental data, Experimental point pattern, Experimental variogram, Experimental variogram modelled, Extreme values, Geostatistical, Geostatistical analysis, Geostatistical simulation, Geostatistical simulations, Geostatistics, Glebe, Glebe data, Glebe topsoil, Heavy metals, High values, Higher values, Inhibition process, Intensity function, Intensity maps, John wiley sons, Kriging, Large diffusion range, Large range, Larger distances, Larger particles, Less variability, Monte carlo simulations, More localised contamination, Nearest event, Northern part, Oxford university press, Pisces, Point pattern, Point pattern analysis, Point pattern events, Point pattern statistics, Point patterns, Point process, Point processes, Poisson cluster process, Quantile, Quantile pattern, Quantiles, Quartic kernel function, Quartile, Random process, Random sampling, Regular grid, Regular ssip, Same intensity, Sampling design, Sampling points, Sampling process, Sampling scheme, Short radius, Side square, Simulation, Simulation envelope, Simulation limits, Site locations, Soil pollution, Soil research, Soil science, Sparse sample, Spatial, Spatial analysis, Spatial dependence, Spatial distribution, Spatial pattern, Spatial patterns, Spatial point pattern, Spatial point pattern analysis, Spatial point patterns, Spatial point process, Spatial point process analysis, Spatial point processes, Spatial processes, Spatial sampling quantile point pattern, Spatial sampling quantile point patterns statistics, Spatial statistics, Spatial variability, Spatial variation, Sppa, Sppa analysis, Ssip, Ssqpp, Study area, Third quartile, Topsoil, Unit area, Upper quartile, Variable diffusion range, Variogram, Variograms, Whole area, concentration, contamination, diffusion, experimental studies, geostatistics, heavy metals, hot spots, lead, sampling, simulation, soils, statistical analysis, statistics, stochastic processes, urban areas, urban environment, variograms.
- Teeft :
- Agriculture food, Arbitrary points, Australian journal, Basic statistics, Cartographic representation, Centile, Contamination, Contour line, Copyright, Covariance function, Decile, Dense sample, Different point patterns, Different point processes, Different processes, Diffusion range, Diffusion ranges, Diggle, Disjunctive kriging, Distance functions, Distribution functions, Empirical distribution function, Empirical limits, Environmental pollution, Environmental science, Environmetrics, Experimental data, Experimental point pattern, Experimental variogram, Experimental variogram modelled, Extreme values, Geostatistical, Geostatistical analysis, Geostatistical simulation, Geostatistical simulations, Geostatistics, Glebe, Glebe data, Glebe topsoil, Heavy metals, High values, Higher values, Inhibition process, Intensity function, Intensity maps, John wiley sons, Kriging, Large diffusion range, Large range, Larger distances, Larger particles, Less variability, Monte carlo simulations, More localised contamination, Nearest event, Northern part, Oxford university press, Point pattern, Point pattern analysis, Point pattern events, Point pattern statistics, Point patterns, Point process, Point processes, Poisson cluster process, Quantile, Quantile pattern, Quantiles, Quartic kernel function, Quartile, Random process, Random sampling, Regular grid, Regular ssip, Same intensity, Sampling design, Sampling points, Sampling process, Sampling scheme, Short radius, Side square, Simulation, Simulation envelope, Simulation limits, Site locations, Soil pollution, Soil research, Soil science, Sparse sample, Spatial, Spatial analysis, Spatial dependence, Spatial distribution, Spatial pattern, Spatial patterns, Spatial point pattern, Spatial point pattern analysis, Spatial point patterns, Spatial point process, Spatial point process analysis, Spatial point processes, Spatial processes, Spatial sampling quantile point pattern, Spatial sampling quantile point patterns statistics, Spatial statistics, Spatial variability, Spatial variation, Sppa, Sppa analysis, Ssip, Ssqpp, Study area, Third quartile, Topsoil, Unit area, Upper quartile, Variable diffusion range, Variogram, Variograms, Whole area.
Abstract
This article explores the use of spatial point‐process analysis as an aid to describe topsoil lead distribution in urban environments. The data used were collected in Glebe, an inner suburb of Sydney. The approach focuses on the locations of punctual events defining a point pattern, which can be statistically described through local intensity estimates and between‐point distance functions. F‐, G‐ and K‐surfaces of a marked spatial point pattern were described and used to estimate nearest distance functions over a sliding band of quantiles belonging to the marking variable. This provided a continuous view of the point pattern properties as a function of the marking variable. Several random fields were simulated by selecting points from random, clustered or regular point processes and diffusing them. Recognition of the underlying point process using variograms derived from dense sampling was difficult because, structurally, the variograms were very similar. Point‐event distance functions were useful complimentary tools that, in most cases, enabled clear recognition of the clustered processes. Spatial sampling quantile point pattern analysis was defined and applied to the Glebe data set. The analysis showed that the highest lead concentrations were strongly clustered. The comparison of this data set with the simulation confidence limits of a Poisson process, a short‐radius clustered point process and a geostatistical simulation showed a random process for the third quartile of lead concentrations but strong clustering for the data in the upper quartile. Thus the distribution of topsoil lead concentrations over Glebe may have resulted from several contamination processes, mainly from regular or random processes with large diffusion ranges and short‐range clustered processes for the hot spots. Point patterns with the same characteristics as the Glebe experimental pattern could be generated by separate additive geostatistical simulation. Spatial sampling quantile point patterns statistics can, in an easy and accurate way, be used complementarily with geostatistical methods. Copyright © 2005 John Wiley & Sons, Ltd.
Url:
DOI: 10.1002/env.705
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Viscarra Rossel</name><affiliation wicri:level="4"><country xml:lang="fr">Australie</country><wicri:regionArea>Faculty of Agriculture Food & Natural Resources, The University of Sydney, NSW, 2006</wicri:regionArea><orgName type="university">Université de Sydney</orgName><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Markus, Julie A" sort="Markus, Julie A" uniqKey="Markus J" first="Julie A." last="Markus">Julie A. Markus</name><affiliation wicri:level="4"><country xml:lang="fr">Australie</country><wicri:regionArea>Faculty of Agriculture Food & Natural Resources, The University of Sydney, NSW, 2006</wicri:regionArea><orgName type="university">Université de Sydney</orgName><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Environmetrics</title><title level="j" type="alt">ENVIRONMETRICS</title><idno type="ISSN">1180-4009</idno><idno type="eISSN">1099-095X</idno><imprint><biblScope unit="vol">16</biblScope><biblScope unit="issue">4</biblScope><biblScope unit="page" from="339">339</biblScope><biblScope unit="page" to="355">355</biblScope><biblScope unit="page-count">17</biblScope><publisher>John Wiley & Sons, Ltd.</publisher><pubPlace>Chichester, UK</pubPlace><date type="published" when="2005-06">2005-06</date></imprint><idno type="ISSN">1180-4009</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1180-4009</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Agriculture food</term><term>Arbitrary points</term><term>Australian journal</term><term>Basic statistics</term><term>Cartographic representation</term><term>Centile</term><term>Contamination</term><term>Contour line</term><term>Copyright</term><term>Covariance function</term><term>Decile</term><term>Dense sample</term><term>Different point patterns</term><term>Different point processes</term><term>Different processes</term><term>Diffusion range</term><term>Diffusion ranges</term><term>Diggle</term><term>Disjunctive kriging</term><term>Distance</term><term>Distance functions</term><term>Distribution functions</term><term>Empirical distribution function</term><term>Empirical limits</term><term>Environmental pollution</term><term>Environmental science</term><term>Environmetrics</term><term>Experimental data</term><term>Experimental point pattern</term><term>Experimental variogram</term><term>Experimental variogram modelled</term><term>Extreme values</term><term>Geostatistical</term><term>Geostatistical analysis</term><term>Geostatistical simulation</term><term>Geostatistical simulations</term><term>Geostatistics</term><term>Glebe</term><term>Glebe data</term><term>Glebe topsoil</term><term>Heavy metals</term><term>High values</term><term>Higher values</term><term>Inhibition process</term><term>Intensity function</term><term>Intensity maps</term><term>John wiley sons</term><term>Kriging</term><term>Large diffusion range</term><term>Large range</term><term>Larger distances</term><term>Larger particles</term><term>Less variability</term><term>Monte carlo simulations</term><term>More localised contamination</term><term>Nearest event</term><term>Northern part</term><term>Oxford university press</term><term>Pisces</term><term>Point pattern</term><term>Point pattern analysis</term><term>Point pattern events</term><term>Point pattern statistics</term><term>Point patterns</term><term>Point process</term><term>Point processes</term><term>Poisson cluster process</term><term>Quantile</term><term>Quantile pattern</term><term>Quantiles</term><term>Quartic kernel function</term><term>Quartile</term><term>Random process</term><term>Random sampling</term><term>Regular grid</term><term>Regular ssip</term><term>Same intensity</term><term>Sampling design</term><term>Sampling points</term><term>Sampling process</term><term>Sampling scheme</term><term>Short radius</term><term>Side square</term><term>Simulation</term><term>Simulation envelope</term><term>Simulation limits</term><term>Site locations</term><term>Soil pollution</term><term>Soil research</term><term>Soil science</term><term>Sparse sample</term><term>Spatial</term><term>Spatial analysis</term><term>Spatial dependence</term><term>Spatial distribution</term><term>Spatial pattern</term><term>Spatial patterns</term><term>Spatial point pattern</term><term>Spatial point pattern analysis</term><term>Spatial point patterns</term><term>Spatial point process</term><term>Spatial point process analysis</term><term>Spatial point processes</term><term>Spatial processes</term><term>Spatial sampling quantile point pattern</term><term>Spatial sampling quantile point patterns statistics</term><term>Spatial statistics</term><term>Spatial variability</term><term>Spatial variation</term><term>Sppa</term><term>Sppa analysis</term><term>Ssip</term><term>Ssqpp</term><term>Study area</term><term>Third quartile</term><term>Topsoil</term><term>Unit area</term><term>Upper quartile</term><term>Variable diffusion range</term><term>Variogram</term><term>Variograms</term><term>Whole area</term><term>concentration</term><term>contamination</term><term>diffusion</term><term>experimental studies</term><term>geostatistics</term><term>heavy metals</term><term>hot spots</term><term>lead</term><term>sampling</term><term>simulation</term><term>soils</term><term>statistical analysis</term><term>statistics</term><term>stochastic processes</term><term>urban areas</term><term>urban environment</term><term>variograms</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Analyse statistique</term><term>Concentration</term><term>Contamination</term><term>Diffusion</term><term>Distance</term><term>Echantillonnage</term><term>Etude expérimentale</term><term>Géostatistique</term><term>Milieu urbain</term><term>Métal lourd</term><term>Pisces</term><term>Plomb</term><term>Point chaud</term><term>Pollution sol</term><term>Processus stochastique</term><term>Simulation</term><term>Sol</term><term>Statistique</term><term>Variogramme</term><term>Zone urbaine</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Agriculture food</term><term>Arbitrary points</term><term>Australian journal</term><term>Basic statistics</term><term>Cartographic representation</term><term>Centile</term><term>Contamination</term><term>Contour line</term><term>Copyright</term><term>Covariance function</term><term>Decile</term><term>Dense sample</term><term>Different point patterns</term><term>Different point processes</term><term>Different processes</term><term>Diffusion range</term><term>Diffusion ranges</term><term>Diggle</term><term>Disjunctive kriging</term><term>Distance functions</term><term>Distribution functions</term><term>Empirical distribution function</term><term>Empirical limits</term><term>Environmental pollution</term><term>Environmental science</term><term>Environmetrics</term><term>Experimental data</term><term>Experimental point pattern</term><term>Experimental variogram</term><term>Experimental variogram modelled</term><term>Extreme values</term><term>Geostatistical</term><term>Geostatistical analysis</term><term>Geostatistical simulation</term><term>Geostatistical simulations</term><term>Geostatistics</term><term>Glebe</term><term>Glebe data</term><term>Glebe topsoil</term><term>Heavy metals</term><term>High values</term><term>Higher values</term><term>Inhibition process</term><term>Intensity function</term><term>Intensity maps</term><term>John wiley sons</term><term>Kriging</term><term>Large diffusion range</term><term>Large range</term><term>Larger distances</term><term>Larger particles</term><term>Less variability</term><term>Monte carlo simulations</term><term>More localised contamination</term><term>Nearest event</term><term>Northern part</term><term>Oxford university press</term><term>Point pattern</term><term>Point pattern analysis</term><term>Point pattern events</term><term>Point pattern statistics</term><term>Point patterns</term><term>Point process</term><term>Point processes</term><term>Poisson cluster process</term><term>Quantile</term><term>Quantile pattern</term><term>Quantiles</term><term>Quartic kernel function</term><term>Quartile</term><term>Random process</term><term>Random sampling</term><term>Regular grid</term><term>Regular ssip</term><term>Same intensity</term><term>Sampling design</term><term>Sampling points</term><term>Sampling process</term><term>Sampling scheme</term><term>Short radius</term><term>Side square</term><term>Simulation</term><term>Simulation envelope</term><term>Simulation limits</term><term>Site locations</term><term>Soil pollution</term><term>Soil research</term><term>Soil science</term><term>Sparse sample</term><term>Spatial</term><term>Spatial analysis</term><term>Spatial dependence</term><term>Spatial distribution</term><term>Spatial pattern</term><term>Spatial patterns</term><term>Spatial point pattern</term><term>Spatial point pattern analysis</term><term>Spatial point patterns</term><term>Spatial point process</term><term>Spatial point process analysis</term><term>Spatial point processes</term><term>Spatial processes</term><term>Spatial sampling quantile point pattern</term><term>Spatial sampling quantile point patterns statistics</term><term>Spatial statistics</term><term>Spatial variability</term><term>Spatial variation</term><term>Sppa</term><term>Sppa analysis</term><term>Ssip</term><term>Ssqpp</term><term>Study area</term><term>Third quartile</term><term>Topsoil</term><term>Unit area</term><term>Upper quartile</term><term>Variable diffusion range</term><term>Variogram</term><term>Variograms</term><term>Whole area</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Droit d'auteur</term><term>Métal lourd</term><term>Plomb</term><term>Simulation</term><term>Pollution du sol</term><term>Science des sols</term><term>Statistique</term><term>Zone urbaine</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This article explores the use of spatial point‐process analysis as an aid to describe topsoil lead distribution in urban environments. The data used were collected in Glebe, an inner suburb of Sydney. The approach focuses on the locations of punctual events defining a point pattern, which can be statistically described through local intensity estimates and between‐point distance functions. F‐, G‐ and K‐surfaces of a marked spatial point pattern were described and used to estimate nearest distance functions over a sliding band of quantiles belonging to the marking variable. This provided a continuous view of the point pattern properties as a function of the marking variable. Several random fields were simulated by selecting points from random, clustered or regular point processes and diffusing them. Recognition of the underlying point process using variograms derived from dense sampling was difficult because, structurally, the variograms were very similar. Point‐event distance functions were useful complimentary tools that, in most cases, enabled clear recognition of the clustered processes. Spatial sampling quantile point pattern analysis was defined and applied to the Glebe data set. The analysis showed that the highest lead concentrations were strongly clustered. The comparison of this data set with the simulation confidence limits of a Poisson process, a short‐radius clustered point process and a geostatistical simulation showed a random process for the third quartile of lead concentrations but strong clustering for the data in the upper quartile. Thus the distribution of topsoil lead concentrations over Glebe may have resulted from several contamination processes, mainly from regular or random processes with large diffusion ranges and short‐range clustered processes for the hot spots. Point patterns with the same characteristics as the Glebe experimental pattern could be generated by separate additive geostatistical simulation. Spatial sampling quantile point patterns statistics can, in an easy and accurate way, be used complementarily with geostatistical methods. Copyright © 2005 John Wiley & Sons, Ltd.</div></front></TEI><double idat="1180-4009:2005:Walter C:spatial:point:process"><INIST><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Spatial point-process statistics: concepts and application to the analysis of lead contamination in urban soil</title><author><name sortKey="Walter, Christian" sort="Walter, Christian" uniqKey="Walter C" first="Christian" last="Walter">Christian Walter</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Faculty of Agriculture Food & Natural Resources, The University of Sydney</s1><s2>NSW, 2006</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName><orgName type="university">Université de Sydney</orgName></affiliation><affiliation wicri:level="3"><inist:fA14 i1="02"><s1>UMR SAS, INRA/Agrocampus Rennes</s1><s2>35042 Rennes</s2><s3>FRA</s3><sZ>1 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Région Bretagne</region><settlement type="city">Rennes</settlement></placeName></affiliation></author><author><name sortKey="Mcbratney, Alex B" sort="Mcbratney, Alex B" uniqKey="Mcbratney A" first="Alex B." last="Mcbratney">Alex B. Mcbratney</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Faculty of Agriculture Food & Natural Resources, The University of Sydney</s1><s2>NSW, 2006</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName><orgName type="university">Université de Sydney</orgName></affiliation></author><author><name sortKey="Viscarra Rossel, Raphael A" sort="Viscarra Rossel, Raphael A" uniqKey="Viscarra Rossel R" first="Raphael A." last="Viscarra Rossel">Raphael A. Viscarra Rossel</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Faculty of Agriculture Food & Natural Resources, The University of Sydney</s1><s2>NSW, 2006</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName><orgName type="university">Université de Sydney</orgName></affiliation></author><author><name sortKey="Markus, Julie A" sort="Markus, Julie A" uniqKey="Markus J" first="Julie A." last="Markus">Julie A. Markus</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Faculty of Agriculture Food & Natural Resources, The University of Sydney</s1><s2>NSW, 2006</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName><orgName type="university">Université de Sydney</orgName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">05-0265538</idno><date when="2005">2005</date><idno type="stanalyst">PASCAL 05-0265538 INIST</idno><idno type="RBID">Pascal:05-0265538</idno><idno type="wicri:Area/PascalFrancis/Corpus">004938</idno><idno type="wicri:Area/PascalFrancis/Curation">001765</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">004375</idno><idno type="wicri:explorRef" wicri:stream="PascalFrancis" wicri:step="Checkpoint">004375</idno><idno type="wicri:doubleKey">1180-4009:2005:Walter C:spatial:point:process</idno><idno type="wicri:Area/Main/Merge">00B025</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Spatial point-process statistics: concepts and application to the analysis of lead contamination in urban soil</title><author><name sortKey="Walter, Christian" sort="Walter, Christian" uniqKey="Walter C" first="Christian" last="Walter">Christian Walter</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Faculty of Agriculture Food & Natural Resources, The University of Sydney</s1><s2>NSW, 2006</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName><orgName type="university">Université de Sydney</orgName></affiliation><affiliation wicri:level="3"><inist:fA14 i1="02"><s1>UMR SAS, INRA/Agrocampus Rennes</s1><s2>35042 Rennes</s2><s3>FRA</s3><sZ>1 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Région Bretagne</region><settlement type="city">Rennes</settlement></placeName></affiliation></author><author><name sortKey="Mcbratney, Alex B" sort="Mcbratney, Alex B" uniqKey="Mcbratney A" first="Alex B." last="Mcbratney">Alex B. Mcbratney</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Faculty of Agriculture Food & Natural Resources, The University of Sydney</s1><s2>NSW, 2006</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName><orgName type="university">Université de Sydney</orgName></affiliation></author><author><name sortKey="Viscarra Rossel, Raphael A" sort="Viscarra Rossel, Raphael A" uniqKey="Viscarra Rossel R" first="Raphael A." last="Viscarra Rossel">Raphael A. Viscarra Rossel</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Faculty of Agriculture Food & Natural Resources, The University of Sydney</s1><s2>NSW, 2006</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName><orgName type="university">Université de Sydney</orgName></affiliation></author><author><name sortKey="Markus, Julie A" sort="Markus, Julie A" uniqKey="Markus J" first="Julie A." last="Markus">Julie A. Markus</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Faculty of Agriculture Food & Natural Resources, The University of Sydney</s1><s2>NSW, 2006</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName><orgName type="university">Université de Sydney</orgName></affiliation></author></analytic><series><title level="j" type="main">EnvironMetrics : (London, Ont.)</title><title level="j" type="abbreviated">EnvironMetrics : (Lond., Ont.)</title><idno type="ISSN">1180-4009</idno><imprint><date when="2005">2005</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">EnvironMetrics : (London, Ont.)</title><title level="j" type="abbreviated">EnvironMetrics : (Lond., Ont.)</title><idno type="ISSN">1180-4009</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Distance</term><term>Pisces</term><term>Soil pollution</term><term>concentration</term><term>contamination</term><term>diffusion</term><term>experimental studies</term><term>geostatistics</term><term>heavy metals</term><term>hot spots</term><term>lead</term><term>sampling</term><term>simulation</term><term>soils</term><term>statistical analysis</term><term>statistics</term><term>stochastic processes</term><term>urban areas</term><term>urban environment</term><term>variograms</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Statistique</term><term>Analyse statistique</term><term>Plomb</term><term>Contamination</term><term>Zone urbaine</term><term>Sol</term><term>Milieu urbain</term><term>Distance</term><term>Variogramme</term><term>Echantillonnage</term><term>Concentration</term><term>Simulation</term><term>Pisces</term><term>Processus stochastique</term><term>Diffusion</term><term>Point chaud</term><term>Etude expérimentale</term><term>Métal lourd</term><term>Pollution sol</term><term>Géostatistique</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Statistique</term><term>Plomb</term><term>Zone urbaine</term><term>Simulation</term><term>Métal lourd</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This article explores the use of spatial point-process analysis as an aid to describe topsoil lead distribution in urban environments. The data used were collected in Glebe, an inner suburb of Sydney. The approach focuses on the locations of punctual events defining a point pattern, which can be statistically described through local intensity estimates and between-point distance functions. F-, G- and K-surfaces of a marked spatial point pattern were described and used to estimate nearest distance functions over a sliding band of quantiles belonging to the marking variable. This provided a continuous view of the point pattern properties as a function of the marking variable. Several random fields were simulated by selecting points from random, clustered or regular point processes and diffusing them. Recognition of the underlying point process using variograms derived from dense sampling was difficult because, structurally, the variograms were very similar. Point-event distance functions were useful complimentary tools that, in most cases, enabled clear recognition of the clustered processes. Spatial sampling quantile point pattern analysis was defined and applied to the Glebe data set. The analysis showed that the highest lead concentrations were strongly clustered. The comparison of this data set with the simulation confidence limits of a Poisson process, a short-radius clustered point process and a geostatistical simulation showed a random process for the third quartile of lead concentrations but strong clustering for the data in the upper quartile. Thus the distribution of topsoil lead concentrations over Glebe may have resulted from several contamination processes, mainly from regular or random processes with large diffusion ranges and short-range clustered processes for the hot spots. Point patterns with the same characteristics as the Glebe experimental pattern could be generated by separate additive geostatistical simulation. Spatial sampling quantile point patterns statistics can, in an easy and accurate way, be used complementarily with geostatistical methods.</div></front></TEI></INIST><ISTEX><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Spatial point‐process statistics: concepts and application to the analysis of lead contamination in urban soil</title><author><name sortKey="Walter, Christian" sort="Walter, Christian" uniqKey="Walter C" first="Christian" last="Walter">Christian Walter</name></author><author><name sortKey="Mcbratney, Alex B" sort="Mcbratney, Alex B" uniqKey="Mcbratney A" first="Alex. B." last="Mcbratney">Alex. B. Mcbratney</name></author><author><name sortKey="Viscarra Rossel, Raphael A" sort="Viscarra Rossel, Raphael A" uniqKey="Viscarra Rossel R" first="Raphael A." last="Viscarra Rossel">Raphael A. Viscarra Rossel</name></author><author><name sortKey="Markus, Julie A" sort="Markus, Julie A" uniqKey="Markus J" first="Julie A." last="Markus">Julie A. Markus</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:8C20CED3D438FF9CE0E847F91BB702BA52CF9BE7</idno><date when="2005" year="2005">2005</date><idno type="doi">10.1002/env.705</idno><idno type="url">https://api.istex.fr/document/8C20CED3D438FF9CE0E847F91BB702BA52CF9BE7/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001A45</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001A45</idno><idno type="wicri:Area/Istex/Curation">001A45</idno><idno type="wicri:Area/Istex/Checkpoint">001786</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">001786</idno><idno type="wicri:doubleKey">1180-4009:2005:Walter C:spatial:point:process</idno><idno type="wicri:Area/Main/Merge">00AD94</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Spatial point‐process statistics: concepts and application to the analysis of lead contamination in urban soil<ref type="note" target="#fn1"></ref></title><author><name sortKey="Walter, Christian" sort="Walter, Christian" uniqKey="Walter C" first="Christian" last="Walter">Christian Walter</name><affiliation wicri:level="4"><country xml:lang="fr">Australie</country><wicri:regionArea>Faculty of Agriculture Food & Natural Resources, The University of Sydney, NSW, 2006</wicri:regionArea><orgName type="university">Université de Sydney</orgName><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation><affiliation wicri:level="3"><country xml:lang="fr">France</country><wicri:regionArea>UMR SAS, INRA/Agrocampus Rennes, 35042 Rennes</wicri:regionArea><placeName><region type="region" nuts="2">Région Bretagne</region><settlement type="city">Rennes</settlement></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation><affiliation wicri:level="4"><country xml:lang="fr">Australie</country><wicri:regionArea>Correspondence address: Faculty of Agriculture Food & Natural Resources, The University of Sydney, NSW, 2006</wicri:regionArea><orgName type="university">Université de Sydney</orgName><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Mcbratney, Alex B" sort="Mcbratney, Alex B" uniqKey="Mcbratney A" first="Alex. B." last="Mcbratney">Alex. B. Mcbratney</name><affiliation wicri:level="4"><country xml:lang="fr">Australie</country><wicri:regionArea>Faculty of Agriculture Food & Natural Resources, The University of Sydney, NSW, 2006</wicri:regionArea><orgName type="university">Université de Sydney</orgName><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Viscarra Rossel, Raphael A" sort="Viscarra Rossel, Raphael A" uniqKey="Viscarra Rossel R" first="Raphael A." last="Viscarra Rossel">Raphael A. Viscarra Rossel</name><affiliation wicri:level="4"><country xml:lang="fr">Australie</country><wicri:regionArea>Faculty of Agriculture Food & Natural Resources, The University of Sydney, NSW, 2006</wicri:regionArea><orgName type="university">Université de Sydney</orgName><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Markus, Julie A" sort="Markus, Julie A" uniqKey="Markus J" first="Julie A." last="Markus">Julie A. Markus</name><affiliation wicri:level="4"><country xml:lang="fr">Australie</country><wicri:regionArea>Faculty of Agriculture Food & Natural Resources, The University of Sydney, NSW, 2006</wicri:regionArea><orgName type="university">Université de Sydney</orgName><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Environmetrics</title><title level="j" type="alt">ENVIRONMETRICS</title><idno type="ISSN">1180-4009</idno><idno type="eISSN">1099-095X</idno><imprint><biblScope unit="vol">16</biblScope><biblScope unit="issue">4</biblScope><biblScope unit="page" from="339">339</biblScope><biblScope unit="page" to="355">355</biblScope><biblScope unit="page-count">17</biblScope><publisher>John Wiley & Sons, Ltd.</publisher><pubPlace>Chichester, UK</pubPlace><date type="published" when="2005-06">2005-06</date></imprint><idno type="ISSN">1180-4009</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1180-4009</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Agriculture food</term><term>Arbitrary points</term><term>Australian journal</term><term>Basic statistics</term><term>Cartographic representation</term><term>Centile</term><term>Contamination</term><term>Contour line</term><term>Copyright</term><term>Covariance function</term><term>Decile</term><term>Dense sample</term><term>Different point patterns</term><term>Different point processes</term><term>Different processes</term><term>Diffusion range</term><term>Diffusion ranges</term><term>Diggle</term><term>Disjunctive kriging</term><term>Distance functions</term><term>Distribution functions</term><term>Empirical distribution function</term><term>Empirical limits</term><term>Environmental pollution</term><term>Environmental science</term><term>Environmetrics</term><term>Experimental data</term><term>Experimental point pattern</term><term>Experimental variogram</term><term>Experimental variogram modelled</term><term>Extreme values</term><term>Geostatistical</term><term>Geostatistical analysis</term><term>Geostatistical simulation</term><term>Geostatistical simulations</term><term>Geostatistics</term><term>Glebe</term><term>Glebe data</term><term>Glebe topsoil</term><term>Heavy metals</term><term>High values</term><term>Higher values</term><term>Inhibition process</term><term>Intensity function</term><term>Intensity maps</term><term>John wiley sons</term><term>Kriging</term><term>Large diffusion range</term><term>Large range</term><term>Larger distances</term><term>Larger particles</term><term>Less variability</term><term>Monte carlo simulations</term><term>More localised contamination</term><term>Nearest event</term><term>Northern part</term><term>Oxford university press</term><term>Point pattern</term><term>Point pattern analysis</term><term>Point pattern events</term><term>Point pattern statistics</term><term>Point patterns</term><term>Point process</term><term>Point processes</term><term>Poisson cluster process</term><term>Quantile</term><term>Quantile pattern</term><term>Quantiles</term><term>Quartic kernel function</term><term>Quartile</term><term>Random process</term><term>Random sampling</term><term>Regular grid</term><term>Regular ssip</term><term>Same intensity</term><term>Sampling design</term><term>Sampling points</term><term>Sampling process</term><term>Sampling scheme</term><term>Short radius</term><term>Side square</term><term>Simulation</term><term>Simulation envelope</term><term>Simulation limits</term><term>Site locations</term><term>Soil pollution</term><term>Soil research</term><term>Soil science</term><term>Sparse sample</term><term>Spatial</term><term>Spatial analysis</term><term>Spatial dependence</term><term>Spatial distribution</term><term>Spatial pattern</term><term>Spatial patterns</term><term>Spatial point pattern</term><term>Spatial point pattern analysis</term><term>Spatial point patterns</term><term>Spatial point process</term><term>Spatial point process analysis</term><term>Spatial point processes</term><term>Spatial processes</term><term>Spatial sampling quantile point pattern</term><term>Spatial sampling quantile point patterns statistics</term><term>Spatial statistics</term><term>Spatial variability</term><term>Spatial variation</term><term>Sppa</term><term>Sppa analysis</term><term>Ssip</term><term>Ssqpp</term><term>Study area</term><term>Third quartile</term><term>Topsoil</term><term>Unit area</term><term>Upper quartile</term><term>Variable diffusion range</term><term>Variogram</term><term>Variograms</term><term>Whole area</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Agriculture food</term><term>Arbitrary points</term><term>Australian journal</term><term>Basic statistics</term><term>Cartographic representation</term><term>Centile</term><term>Contamination</term><term>Contour line</term><term>Copyright</term><term>Covariance function</term><term>Decile</term><term>Dense sample</term><term>Different point patterns</term><term>Different point processes</term><term>Different processes</term><term>Diffusion range</term><term>Diffusion ranges</term><term>Diggle</term><term>Disjunctive kriging</term><term>Distance functions</term><term>Distribution functions</term><term>Empirical distribution function</term><term>Empirical limits</term><term>Environmental pollution</term><term>Environmental science</term><term>Environmetrics</term><term>Experimental data</term><term>Experimental point pattern</term><term>Experimental variogram</term><term>Experimental variogram modelled</term><term>Extreme values</term><term>Geostatistical</term><term>Geostatistical analysis</term><term>Geostatistical simulation</term><term>Geostatistical simulations</term><term>Geostatistics</term><term>Glebe</term><term>Glebe data</term><term>Glebe topsoil</term><term>Heavy metals</term><term>High values</term><term>Higher values</term><term>Inhibition process</term><term>Intensity function</term><term>Intensity maps</term><term>John wiley sons</term><term>Kriging</term><term>Large diffusion range</term><term>Large range</term><term>Larger distances</term><term>Larger particles</term><term>Less variability</term><term>Monte carlo simulations</term><term>More localised contamination</term><term>Nearest event</term><term>Northern part</term><term>Oxford university press</term><term>Point pattern</term><term>Point pattern analysis</term><term>Point pattern events</term><term>Point pattern statistics</term><term>Point patterns</term><term>Point process</term><term>Point processes</term><term>Poisson cluster process</term><term>Quantile</term><term>Quantile pattern</term><term>Quantiles</term><term>Quartic kernel function</term><term>Quartile</term><term>Random process</term><term>Random sampling</term><term>Regular grid</term><term>Regular ssip</term><term>Same intensity</term><term>Sampling design</term><term>Sampling points</term><term>Sampling process</term><term>Sampling scheme</term><term>Short radius</term><term>Side square</term><term>Simulation</term><term>Simulation envelope</term><term>Simulation limits</term><term>Site locations</term><term>Soil pollution</term><term>Soil research</term><term>Soil science</term><term>Sparse sample</term><term>Spatial</term><term>Spatial analysis</term><term>Spatial dependence</term><term>Spatial distribution</term><term>Spatial pattern</term><term>Spatial patterns</term><term>Spatial point pattern</term><term>Spatial point pattern analysis</term><term>Spatial point patterns</term><term>Spatial point process</term><term>Spatial point process analysis</term><term>Spatial point processes</term><term>Spatial processes</term><term>Spatial sampling quantile point pattern</term><term>Spatial sampling quantile point patterns statistics</term><term>Spatial statistics</term><term>Spatial variability</term><term>Spatial variation</term><term>Sppa</term><term>Sppa analysis</term><term>Ssip</term><term>Ssqpp</term><term>Study area</term><term>Third quartile</term><term>Topsoil</term><term>Unit area</term><term>Upper quartile</term><term>Variable diffusion range</term><term>Variogram</term><term>Variograms</term><term>Whole area</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Droit d'auteur</term><term>Simulation</term><term>Pollution du sol</term><term>Science des sols</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This article explores the use of spatial point‐process analysis as an aid to describe topsoil lead distribution in urban environments. The data used were collected in Glebe, an inner suburb of Sydney. The approach focuses on the locations of punctual events defining a point pattern, which can be statistically described through local intensity estimates and between‐point distance functions. F‐, G‐ and K‐surfaces of a marked spatial point pattern were described and used to estimate nearest distance functions over a sliding band of quantiles belonging to the marking variable. This provided a continuous view of the point pattern properties as a function of the marking variable. Several random fields were simulated by selecting points from random, clustered or regular point processes and diffusing them. Recognition of the underlying point process using variograms derived from dense sampling was difficult because, structurally, the variograms were very similar. Point‐event distance functions were useful complimentary tools that, in most cases, enabled clear recognition of the clustered processes. Spatial sampling quantile point pattern analysis was defined and applied to the Glebe data set. The analysis showed that the highest lead concentrations were strongly clustered. The comparison of this data set with the simulation confidence limits of a Poisson process, a short‐radius clustered point process and a geostatistical simulation showed a random process for the third quartile of lead concentrations but strong clustering for the data in the upper quartile. Thus the distribution of topsoil lead concentrations over Glebe may have resulted from several contamination processes, mainly from regular or random processes with large diffusion ranges and short‐range clustered processes for the hot spots. Point patterns with the same characteristics as the Glebe experimental pattern could be generated by separate additive geostatistical simulation. Spatial sampling quantile point patterns statistics can, in an easy and accurate way, be used complementarily with geostatistical methods. Copyright © 2005 John Wiley & Sons, Ltd.</div></front></TEI></ISTEX></double></record>Pour manipuler ce document sous Unix (Dilib)
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