Dataset Information

Regional differences in the association between land cover and West Nile virus disease incidence in humans in the United States.

ABSTRACT: West Nile virus (WNV) is generally considered to be an urban pathogen in the United States, but studies associating land cover and disease incidence, seroprevalence, or infection rate in humans, birds, domesticated and wild mammals, and mosquitoes report varying and sometimes contradictory results at an array of spatial extents. Human infection can provide insight about basic transmission activity; therefore, we analyzed data on the incidence of WNV disease in humans to obtain a comprehensive picture of how human disease and land cover type are associated across the United States. Human WNV disease incidence in Northeastern regions was positively associated with urban land covers, whereas incidence in the Western United States was positively associated with agricultural land covers. We suggest that these regional associations are explained by the geographic distributions of prominent WNV vectors: Culex pipiens complex (including Cx. pipiens and Cx. quinquefasciatus) in the Northeast and Cx. tarsalis in the Western United States.

SUBMITTER: Bowden SE

PROVIDER: S-EPMC3029173 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Regional differences in the association between land cover and West Nile virus disease incidence in humans in the United States.

Bowden Sarah E SE Magori Krisztian K Drake John M JM

The American journal of tropical medicine and hygiene 20110201 2

West Nile virus (WNV) is generally considered to be an urban pathogen in the United States, but studies associating land cover and disease incidence, seroprevalence, or infection rate in humans, birds, domesticated and wild mammals, and mosquitoes report varying and sometimes contradictory results at an array of spatial extents. Human infection can provide insight about basic transmission activity; therefore, we analyzed data on the incidence of WNV disease in humans to obtain a comprehensive pi ...[more]

PMID: 21292890

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Project description:Research on spatial non-stationarity of land-cover classification accuracy has been ongoing for over two decades with most of the work focusing on single date maps. We extend the understanding of thematic map accuracy spatial patterns by: (1) quantifying spatial patterns of map-reference agreement for class-specific land-cover change rather than class-specific land cover for both omission and commission expressions of map error; (2) reporting goodness-of-fit estimates for the empirical models, which have been lacking in previous assessments, and; (3) using the empirical model results to map the locations of the relative likelihoods of map-reference agreement for specific land-cover change classes. We evaluated 10 map-based explanatory variables in single and multivariable logistic regression models to predict the likelihood of agreement between map and reference land-cover change (2001-2011) labels using the National Land Cover Database (NLCD) 2011 land cover and accuracy data. Logistic models for omission error had better goodness-of-fit estimates than models for commission error. For the omission error models, the explanatory variable, density of the mapped class-specific change in the immediate neighbourhood surrounding the sample pixel, produced the best model fit results (Tjur coefficient of discrimination, D, ranged from 0.59 to 0.98) compared to multivariable models and all other single explanatory variable models. Maps of the predicted likelihood of map-reference agreement produced from the best fitting omission error models provide a spatially explicit description of spatial variation of classification uncertainty at both local and regional scales. Application of the models indicated higher likelihoods of agreement (>50%) comprised a greater proportion of the land-cover change class area than the proportion of the land-cover change class with lower likelihoods of agreement. NLCD users can apply reported equations to map land-cover change uncertainty.

Project description:The National Land Cover Database (NLCD) is an operational land cover monitoring program providing updated land cover and related information for the United States at five-year intervals. NLCD2016 extends temporal coverage to 15 years (2001 - 2016). We collected land cover reference data for the 2011 and 2016 nominal dates to report land cover accuracy for the NLCD2016 database 2011 and 2016 land cover components at Level II and Level I and for Level I 2011 - 2016 land cover change using two definitions of agreement. For both the 2011 and 2016 land cover components, single-date Level II overall accuracies (OA) were 72% (standard error of ± 0.9%) when agreement was defined as match between the map label and primary reference label only and 86% (± 0.7%) when agreement also included the alternate reference label. The corresponding level I OA for both dates were 79% (± 0.9%) and 91% (± 1.0%). The 2011 - 2016 user's and producer's accuracies (UA and PA) were ~75% for forest loss and PA for water loss, grassland loss, and grass gain were > 70% when agreement included a match between the map label and either the primary or alternate reference label. Depending on agreement definition and level of the classification hierarchy, OA for the 2011 land cover component of the NLCD2016 database was about 4% to 7% higher than OA for the 2011 land cover component of the NLCD2011 database, suggesting that the changes in mapping methodologies initiated for production of the NLCD2016 database have led to improved product quality. Additionally, we used the reference dataset collected for assessment of the NLCD2011 database to assess the 2001 and 2006 land cover components of the NLCD2016 database. OA for the 2001 and 2006 land cover components was 1% - 5% lower than OA for the 2011 and 2016 land cover components of the NLCD2016 database. Higher OA for 2011 and 2016 land cover components of the NLCD2016 database relative to OA for its 2001 and 2006 components may be attributable to differences in reference data quality.

Dataset Information

Regional differences in the association between land cover and West Nile virus disease incidence in humans in the United States.

Publications

Regional differences in the association between land cover and West Nile virus disease incidence in humans in the United States.

Similar Datasets

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