Publicação
Nonparametric approaches for estimating risk maps
| Resumo: | Assessment of environmental contamination is increasingly a concern in nowadays soci- ety. The maximum levels for pollutants are heavily regulated, being necessary to ensure compliance. Consequently, it becomes important to construct probability maps of the observation region, showing the complementary value of the distribution function of the variable involved at regulatory thresholds. These are usually called risk maps in the environmental setting. In this work, two kernel-type estimators of the spatial distribution function are constructed, which de- part from approximating the distribution at the sampled sites and then obtaining a weighted average of the resulting values, to derive a valid estimator at any random location. Consistency of both ap- proaches is proved under rather general conditions, such as local stationarity and the existence of a number of derivatives of the distribution function. Unlike other alternatives, the new proposals pro- vide non-decreasing functions and do not require a previous estimation of the indicator variogram or the trend function. However, appropriate bandwidths parameters are needed and selection of them in practice needs to be addressed. Numerical studies are carried out, aiming at comparing the current proposal with more usual methods, such as those based on the sill estimation or the indicator kriging, described in Journel (1983) or Goovaerts (1997), respectively, and redesigned in García-Soidán and Menezes (2012). Finally, the new proposal is applied to arsenic data from Portugal, so that pollution risk maps of the referred region are constructed. Moreover, accuracy maps of the probability estimates might be constructed based on bootstrap replicas, as described in García-Soidán, Menezes and Rubiños (2014). |
|---|---|
| Autores principais: | García-Soidán, Pilar |
| Outros Autores: | Menezes, Raquel |
| Assunto: | Spatial data Distribution function Kernel function Stationarity |
| Ano: | 2016 |
| País: | Portugal |
| Tipo de documento: | outro |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866876744070332416 |
|---|---|
| author | García-Soidán, Pilar |
| author2 | Menezes, Raquel |
| author2_role | author |
| author_facet | García-Soidán, Pilar Menezes, Raquel |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"García-Soidán, Pilar\"},{\"Person.name\":\"Menezes, Raquel\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | García-Soidán, Pilar Menezes, Raquel |
| datacite.date.Accepted.fl_str_mv | 2016-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2018-02-19T11:06:14Z |
| datacite.date.embargoed.fl_str_mv | 2018-02-19T11:06:14Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Spatial data Distribution function Kernel function Stationarity |
| datacite.titles.title.fl_str_mv | Nonparametric approaches for estimating risk maps |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | García-Soidán, Pilar Menezes, Raquel |
| dc.date.Accepted.fl_str_mv | 2016-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2018-02-19T11:06:14Z |
| dc.date.embargoed.fl_str_mv | 2018-02-19T11:06:14Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/50632 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Universidad de Valencia. Fundación Universidad-Empresa (ADEIT) |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Spatial data Distribution function Kernel function Stationarity |
| dc.title.fl_str_mv | Nonparametric approaches for estimating risk maps |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_1843 |
| description | Assessment of environmental contamination is increasingly a concern in nowadays soci- ety. The maximum levels for pollutants are heavily regulated, being necessary to ensure compliance. Consequently, it becomes important to construct probability maps of the observation region, showing the complementary value of the distribution function of the variable involved at regulatory thresholds. These are usually called risk maps in the environmental setting. In this work, two kernel-type estimators of the spatial distribution function are constructed, which de- part from approximating the distribution at the sampled sites and then obtaining a weighted average of the resulting values, to derive a valid estimator at any random location. Consistency of both ap- proaches is proved under rather general conditions, such as local stationarity and the existence of a number of derivatives of the distribution function. Unlike other alternatives, the new proposals pro- vide non-decreasing functions and do not require a previous estimation of the indicator variogram or the trend function. However, appropriate bandwidths parameters are needed and selection of them in practice needs to be addressed. Numerical studies are carried out, aiming at comparing the current proposal with more usual methods, such as those based on the sill estimation or the indicator kriging, described in Journel (1983) or Goovaerts (1997), respectively, and redesigned in García-Soidán and Menezes (2012). Finally, the new proposal is applied to arsenic data from Portugal, so that pollution risk maps of the referred region are constructed. Moreover, accuracy maps of the probability estimates might be constructed based on bootstrap replicas, as described in García-Soidán, Menezes and Rubiños (2014). |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | other |
| fulltext.url.fl_str_mv | https://prod-dspace.uminho.pt/bitstreams/69590e62-45ba-47dc-862d-761ef0767ad7/download |
| id | rum_1db77f499bbfeeea25cdbe9915112b3e |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/50632 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/50632 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | García-Soidán, Pilar Menezes, Raquel |
| publishDate | 2016 |
| publisher.none.fl_str_mv | Universidad de Valencia. Fundación Universidad-Empresa (ADEIT) |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engUniversidad de Valencia. Fundación Universidad-Empresa (ADEIT)porAssessment of environmental contamination is increasingly a concern in nowadays soci- ety. The maximum levels for pollutants are heavily regulated, being necessary to ensure compliance. Consequently, it becomes important to construct probability maps of the observation region, showing the complementary value of the distribution function of the variable involved at regulatory thresholds. These are usually called risk maps in the environmental setting. In this work, two kernel-type estimators of the spatial distribution function are constructed, which de- part from approximating the distribution at the sampled sites and then obtaining a weighted average of the resulting values, to derive a valid estimator at any random location. Consistency of both ap- proaches is proved under rather general conditions, such as local stationarity and the existence of a number of derivatives of the distribution function. Unlike other alternatives, the new proposals pro- vide non-decreasing functions and do not require a previous estimation of the indicator variogram or the trend function. However, appropriate bandwidths parameters are needed and selection of them in practice needs to be addressed. Numerical studies are carried out, aiming at comparing the current proposal with more usual methods, such as those based on the sill estimation or the indicator kriging, described in Journel (1983) or Goovaerts (1997), respectively, and redesigned in García-Soidán and Menezes (2012). Finally, the new proposal is applied to arsenic data from Portugal, so that pollution risk maps of the referred region are constructed. Moreover, accuracy maps of the probability estimates might be constructed based on bootstrap replicas, as described in García-Soidán, Menezes and Rubiños (2014).application/pdfporNonparametric approaches for estimating risk mapsGarcía-Soidán, PilarMenezes, RaquelHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISBNIsPartOf978-84-608-8468-22018-02-19T11:06:14Z20162016-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/50632http://purl.org/coar/access_right/c_abf2open accessSpatial dataDistribution functionKernel functionStationarity68337 bytesother research producthttp://purl.org/coar/resource_type/c_1843otherhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/69590e62-45ba-47dc-862d-761ef0767ad7/download |
| spellingShingle | Nonparametric approaches for estimating risk maps García-Soidán, Pilar Spatial data Distribution function Kernel function Stationarity |
| status | SINGLETON |
| subject.fl_str_mv | Spatial data Distribution function Kernel function Stationarity |
| title | Nonparametric approaches for estimating risk maps |
| title_full | Nonparametric approaches for estimating risk maps |
| title_fullStr | Nonparametric approaches for estimating risk maps |
| title_full_unstemmed | Nonparametric approaches for estimating risk maps |
| title_short | Nonparametric approaches for estimating risk maps |
| title_sort | Nonparametric approaches for estimating risk maps |
| topic | Spatial data Distribution function Kernel function Stationarity |
| topic_facet | Spatial data Distribution function Kernel function Stationarity |
| url | https://hdl.handle.net/1822/50632 |
| visible | 1 |