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The use of geographical information systems in studies on environment and health.

World health statistics quarterly. Rapport trimestriel de statistiques sanitaires mondiales

Briggs DJ, Elliott P.
PMID: 8585238
World Health Stat Q. 1995;48(2):85-94.

Geographical information systems (GIS) provide a powerful technology for the spatial analysis of environmental and health data. Major areas of application include the assessment and mapping of environmental exposure, mapping of health outcome, and the analysis of spatial relationships...

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Briggs DJ, Elliott P. The use of geographical information systems in studies on environment and health. World Health Stat Q. 1995;48(2):85-94
Briggs, D. J., & Elliott, P. (1995). The use of geographical information systems in studies on environment and health. World health statistics quarterly. Rapport trimestriel de statistiques sanitaires mondiales, 48(2), 85-94.
Briggs, D J, and Elliott, P. "The use of geographical information systems in studies on environment and health." World health statistics quarterly. Rapport trimestriel de statistiques sanitaires mondiales vol. 48,2 (1995): 85-94.
Briggs DJ, Elliott P. The use of geographical information systems in studies on environment and health. World Health Stat Q. 1995;48(2):85-94. PMID: 8585238.
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Indicator quality for multidisciplinary systems.

Novartis Foundation symposium

Riley J.
PMID: 10231831
Novartis Found Symp. 1999;220:178-86; discussion 186-90. doi: 10.1002/9780470515600.ch10.

In January 1998, a project studying the Unification of Indicator Quality for Assessment of Impact of Multidisciplinary Systems (UNIQUAIMS) began at IACR-Rothamsted. This three year European Union-funded project will examine the choice of indicators for the assessment of the...

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Riley J. Indicator quality for multidisciplinary systems. Novartis Found Symp. 1999;220:178-86; discussion 186-90doi: 10.1002/9780470515600.ch10.
Riley, J. (1999). Indicator quality for multidisciplinary systems. Novartis Foundation symposium, 220178-86; discussion 186-90. https://doi.org/10.1002/9780470515600.ch10
Riley, J. "Indicator quality for multidisciplinary systems." Novartis Foundation symposium vol. 220 (1999): 178-86; discussion 186-90. doi: https://doi.org/10.1002/9780470515600.ch10
Riley J. Indicator quality for multidisciplinary systems. Novartis Found Symp. 1999;220:178-86; discussion 186-90. doi: 10.1002/9780470515600.ch10. PMID: 10231831.
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[Possibilities and limits of human biomonitoring].

Schriftenreihe des Vereins fur Wasser-, Boden- und Lufthygiene

Angerer J.
PMID: 10981303
Schriftenr Ver Wasser Boden Lufthyg. 2000;106:42-8.

No abstract available.

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Angerer J. Möglichkeiten und Grenzen des Human-Biomonitoring. [Possibilities and limits of human biomonitoring]. Schriftenr Ver Wasser Boden Lufthyg. 2000;106:42-8
Angerer, J. (2000). Möglichkeiten und Grenzen des Human-Biomonitoring. [Possibilities and limits of human biomonitoring]. Schriftenreihe des Vereins fur Wasser-, Boden- und Lufthygiene, 10642-8.
Angerer, J. "Möglichkeiten und Grenzen des Human-Biomonitoring." [Possibilities and limits of human biomonitoring]. Schriftenreihe des Vereins fur Wasser-, Boden- und Lufthygiene vol. 106 (2000): 42-8.
Angerer J. Möglichkeiten und Grenzen des Human-Biomonitoring. [Possibilities and limits of human biomonitoring]. Schriftenr Ver Wasser Boden Lufthyg. 2000;106:42-8. German. PMID: 10981303.
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Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?.

Environmental monitoring and assessment

Brus DJ, Nieuwenhuizen W, Koomen A.
PMID: 16738763
Environ Monit Assess. 2006 Nov;122(1):153-69. doi: 10.1007/s10661-005-9171-8.

Seventy-two squares of 100 ha were selected by stratified random sampling with probabilities proportional to size (pps) to survey landscape changes in the period 1996-2003. The area of the plots times the urbanization pressure was used as a size...

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Brus DJ, Nieuwenhuizen W, Koomen A. Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?. Environ Monit Assess. 2006;122(1):153-69doi: 10.1007/s10661-005-9171-8.
Brus, D. J., Nieuwenhuizen, W., & Koomen, A. (2006). Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?. Environmental monitoring and assessment, 122(1), 153-69. https://doi.org/10.1007/s10661-005-9171-8
Brus, D J, et al. "Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?." Environmental monitoring and assessment vol. 122,1 (2006): 153-69. doi: https://doi.org/10.1007/s10661-005-9171-8
Brus DJ, Nieuwenhuizen W, Koomen A. Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?. Environ Monit Assess. 2006 Nov;122(1):153-69. doi: 10.1007/s10661-005-9171-8. PMID: 16738763.
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Comparing spatial regression to random forests for large environmental data sets.

PloS one

Fox EW, Ver Hoef JM, Olsen AR.
PMID: 32203555
PLoS One. 2020 Mar 23;15(3):e0229509. doi: 10.1371/journal.pone.0229509. eCollection 2020.

Environmental data may be "large" due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods,...

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Fox EW, Ver Hoef JM, Olsen AR. Comparing spatial regression to random forests for large environmental data sets. PLoS One. 2020;15(3):e0229509doi: 10.1371/journal.pone.0229509.
Fox, E. W., Ver Hoef, J. M., & Olsen, A. R. (2020). Comparing spatial regression to random forests for large environmental data sets. PloS one, 15(3), e0229509. https://doi.org/10.1371/journal.pone.0229509
Fox, Eric W, et al. "Comparing spatial regression to random forests for large environmental data sets." PloS one vol. 15,3 (2020): e0229509. doi: https://doi.org/10.1371/journal.pone.0229509
Fox EW, Ver Hoef JM, Olsen AR. Comparing spatial regression to random forests for large environmental data sets. PLoS One. 2020 Mar 23;15(3):e0229509. doi: 10.1371/journal.pone.0229509. eCollection 2020. PMID: 32203555; PMCID: PMC7089425.
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Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine.

PloS one

Tiwari V, Kumar V, Matin MA, Thapa A, Ellenburg WL, Gupta N, Thapa S.
PMID: 32813701
PLoS One. 2020 Aug 19;15(8):e0237324. doi: 10.1371/journal.pone.0237324. eCollection 2020.

Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR...

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Tiwari V, Kumar V, Matin MA, et al. Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PLoS One. 2020;15(8):e0237324doi: 10.1371/journal.pone.0237324.
Tiwari, V., Kumar, V., Matin, M. A., Thapa, A., Ellenburg, W. L., Gupta, N., & Thapa, S. (2020). Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PloS one, 15(8), e0237324. https://doi.org/10.1371/journal.pone.0237324
Tiwari, Varun, et al. "Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine." PloS one vol. 15,8 (2020): e0237324. doi: https://doi.org/10.1371/journal.pone.0237324
Tiwari V, Kumar V, Matin MA, Thapa A, Ellenburg WL, Gupta N, Thapa S. Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PLoS One. 2020 Aug 19;15(8):e0237324. doi: 10.1371/journal.pone.0237324. eCollection 2020. PMID: 32813701; PMCID: PMC7437901.
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Mixed models for assessing correlation in the presence of replication.

Journal of the Air & Waste Management Association (1995)

Hamlett A, Ryan L, Serrano-Trespalacios P, Wolfinger R.
PMID: 12708508
J Air Waste Manag Assoc. 2003 Apr;53(4):442-50. doi: 10.1080/10473289.2003.10466174.

The need to assess correlation in settings where multiple measurements are available on each of the variables of interest often arises in environmental science. However, this topic is not covered in introductory statistics texts. Although several ad hoc approaches...

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Hamlett A, Ryan L, Serrano-Trespalacios P, et al. Mixed models for assessing correlation in the presence of replication. J Air Waste Manag Assoc. 2003;53(4):442-50doi: 10.1080/10473289.2003.10466174.
Hamlett, A., Ryan, L., Serrano-Trespalacios, P., & Wolfinger, R. (2003). Mixed models for assessing correlation in the presence of replication. Journal of the Air & Waste Management Association (1995), 53(4), 442-50. https://doi.org/10.1080/10473289.2003.10466174
Hamlett, Anthony, et al. "Mixed models for assessing correlation in the presence of replication." Journal of the Air & Waste Management Association (1995) vol. 53,4 (2003): 442-50. doi: https://doi.org/10.1080/10473289.2003.10466174
Hamlett A, Ryan L, Serrano-Trespalacios P, Wolfinger R. Mixed models for assessing correlation in the presence of replication. J Air Waste Manag Assoc. 2003 Apr;53(4):442-50. doi: 10.1080/10473289.2003.10466174. PMID: 12708508.
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Why most conservation monitoring is, but need not be, a waste of time.

Journal of environmental management

Legg CJ, Nagy L.
PMID: 16112339
J Environ Manage. 2006 Jan;78(2):194-9. doi: 10.1016/j.jenvman.2005.04.016. Epub 2005 Aug 19.

Ecological conservation monitoring programmes abound at various organisational and spatial levels from species to ecosystem. Many of them suffer, however, from the lack of details of goal and hypothesis formulation, survey design, data quality and statistical power at the...

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Legg CJ, Nagy L. Why most conservation monitoring is, but need not be, a waste of time. J Environ Manage. 2005;78(2):194-9doi: 10.1016/j.jenvman.2005.04.016.
Legg, C. J., & Nagy, L. (2006). Why most conservation monitoring is, but need not be, a waste of time. Journal of environmental management, 78(2), 194-9. https://doi.org/10.1016/j.jenvman.2005.04.016
Legg, Colin J, and Nagy, Laszlo. "Why most conservation monitoring is, but need not be, a waste of time." Journal of environmental management vol. 78,2 (2006): 194-9. doi: https://doi.org/10.1016/j.jenvman.2005.04.016
Legg CJ, Nagy L. Why most conservation monitoring is, but need not be, a waste of time. J Environ Manage. 2006 Jan;78(2):194-9. doi: 10.1016/j.jenvman.2005.04.016. Epub 2005 Aug 19. PMID: 16112339.
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The positive effects of population-based preferential sampling in environmental epidemiology.

Biostatistics (Oxford, England)

Antonelli J, Cefalu M, Bornn L.
PMID: 27324413
Biostatistics. 2016 Oct;17(4):764-78. doi: 10.1093/biostatistics/kxw026. Epub 2016 Jun 20.

In environmental epidemiology, exposures are not always available at subject locations and must be predicted using monitoring data. The monitor locations are often outside the control of researchers, and previous studies have shown that "preferential sampling" of monitoring locations...

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Antonelli J, Cefalu M, Bornn L. The positive effects of population-based preferential sampling in environmental epidemiology. Biostatistics. 2016;17(4):764-78doi: 10.1093/biostatistics/kxw026.
Antonelli, J., Cefalu, M., & Bornn, L. (2016). The positive effects of population-based preferential sampling in environmental epidemiology. Biostatistics (Oxford, England), 17(4), 764-78. https://doi.org/10.1093/biostatistics/kxw026
Antonelli, Joseph, et al. "The positive effects of population-based preferential sampling in environmental epidemiology." Biostatistics (Oxford, England) vol. 17,4 (2016): 764-78. doi: https://doi.org/10.1093/biostatistics/kxw026
Antonelli J, Cefalu M, Bornn L. The positive effects of population-based preferential sampling in environmental epidemiology. Biostatistics. 2016 Oct;17(4):764-78. doi: 10.1093/biostatistics/kxw026. Epub 2016 Jun 20. PMID: 27324413; PMCID: PMC6424414.
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Environmental and health data needed to develop national surveillance systems in industrially contaminated sites.

Epidemiologia e prevenzione

Martin-Olmedo P, Hams R, Santoro M, Ranzi A, Hoek G, de Hoogh K, Leonardi GS.
PMID: 30322232
Epidemiol Prev. 2018 Sep-Dec;42(5):11-20. doi: 10.19191/EP18.5-6.S1.P011.084.

BACKGROUND: this paper is based upon work from COST Action ICSHNet. Public health surveillance (PHS) of industrially contaminated sites (ICSs) is likely to play a role in supporting the monitoring of harmful aspects of ICSs and related interventions. Environmental...

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Martin-Olmedo P, Hams R, Santoro M, et al. Environmental and health data needed to develop national surveillance systems in industrially contaminated sites. Epidemiol Prev. 2018;42(5):11-20doi: 10.19191/EP18.5-6.S1.P011.084.
Martin-Olmedo, P., Hams, R., Santoro, M., Ranzi, A., Hoek, G., de Hoogh, K., & Leonardi, G. S. (2018). Environmental and health data needed to develop national surveillance systems in industrially contaminated sites. Epidemiologia e prevenzione, 42(5), 11-20. https://doi.org/10.19191/EP18.5-6.S1.P011.084
Martin-Olmedo, Piedad, et al. "Environmental and health data needed to develop national surveillance systems in industrially contaminated sites." Epidemiologia e prevenzione vol. 42,5 (2018): 11-20. doi: https://doi.org/10.19191/EP18.5-6.S1.P011.084
Martin-Olmedo P, Hams R, Santoro M, Ranzi A, Hoek G, de Hoogh K, Leonardi GS. Environmental and health data needed to develop national surveillance systems in industrially contaminated sites. Epidemiol Prev. 2018 Sep-Dec;42(5):11-20. doi: 10.19191/EP18.5-6.S1.P011.084. PMID: 30322232.
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Power analysis and practical strategies for environmental monitoring.

Environmental research

Green RH.
PMID: 2676509
Environ Res. 1989 Oct;50(1):195-205. doi: 10.1016/s0013-9351(89)80058-1.

What sample size n is needed to have 1 - beta chance of detecting a magnitude delta response to pollution impact? A design where the same sites are sampled both before and after impact is more efficient than a...

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Green RH. Power analysis and practical strategies for environmental monitoring. Environ Res. 1989;50(1):195-205doi: 10.1016/s0013-9351(89)80058-1.
Green, R. H. (1989). Power analysis and practical strategies for environmental monitoring. Environmental research, 50(1), 195-205. https://doi.org/10.1016/s0013-9351(89)80058-1
Green, R H. "Power analysis and practical strategies for environmental monitoring." Environmental research vol. 50,1 (1989): 195-205. doi: https://doi.org/10.1016/s0013-9351(89)80058-1
Green RH. Power analysis and practical strategies for environmental monitoring. Environ Res. 1989 Oct;50(1):195-205. doi: 10.1016/s0013-9351(89)80058-1. PMID: 2676509.
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Environmental statistics: analysing data for environmental policy. Introduction.

Novartis Foundation symposium

Barnett V.
PMID: 10231822
Novartis Found Symp. 1999;220:1-7.

No abstract available.

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Barnett V. Environmental statistics: analysing data for environmental policy. Introduction. Novartis Found Symp. 1999;220:1-7
Barnett, V. (1999). Environmental statistics: analysing data for environmental policy. Introduction. Novartis Foundation symposium, 2201-7.
Barnett, V. "Environmental statistics: analysing data for environmental policy. Introduction." Novartis Foundation symposium vol. 220 (1999): 1-7.
Barnett V. Environmental statistics: analysing data for environmental policy. Introduction. Novartis Found Symp. 1999;220:1-7. PMID: 10231822.
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Showing 1 to 12 of 16 entries