Spatial patterns of the United States National Land Cover Dataset (NLCD) land-cover change thematic accuracy (2001-2011).

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.

Accuracy assessment of NLCD 2011 impervious cover data for the Chesapeake Bay region, USA.

The National Land Cover Database (NLCD) contains three eras (2001, 2006, 2011) of percentage urban impervious cover (%IC) at the native pixel size (30 m-×-30 m) of the Landsat Thematic Mapper satellite. These data are potentially valuable to environmental managers and stakeholders because of the utility of %IC as an indicator of watershed and aquatic condition, but lack an accuracy assessment because of the absence of suitable reference data. Recently developed 1 m2 land cover data for the Chesapeake Bay region makes it possible to assess NLCD %IC accuracy for a 262,000 km2 region based on a census rather than a sample of reference data. We report agreement between the two %IC datasets for watersheds and the riparian zones within watersheds and four additional square units. The areas of the six assessment units were 40 ha cell, 433 ha (riparian mean), 2756 ha cell, 5626 ha cell, 8569 ha (watershed mean) and 22,500 ha cell. Mean Absolute Deviation (MAD) and Mean Deviation (MD) were about 1.5% and -1.5%, respectively, for each of the assessment units except for the riparian unit, for which MAD and MD were 0.88 and 0.62, respectively. NLCD reliably reproduced %IC from the 1 m2 data with a small, consistent tendency for underestimation. Results were sensitive to assessment unit choice. The results for the four largest assessment units had very similar regression parameters, R2 values, and bias patterns. Results for the riparian assessment were different from those for the watershed unit and the other three larger units. MAD was about 50% less for the riparian zones than it was for the watersheds, the direction of bias was less consistent, and NLCD %IC was uniformly higher than 1 m2 %IC in urbanized riparian zones. For the smallest unit, bias patterns were more similar to the riparian unit and regression results were more similar to the four larger units. MAD and MD were also sensitive to the amount of urbanization, increasing as NLCD %IC increased. The low overall bias and positive relationship between bias and urbanization suggest that the benefits of obtaining 1 m2 IC data outside of urban areas may not outweigh the costs of obtaining such data.

Methods of hospital use control in health maintenance organizations.

The results of the study upon which this article is based show that most HMO plans do not rely solely on incentives as control methods; they impose several other controls including direct constraints on physician decisions to use hospital resources. HMO managers seem to have developed a technology of hospital use control that is applicable regardless of the model, age, or size.

Some pitfalls in creating competition between HMOs and fee-for-service delivery.

This paper examines four propositions inherent in competitive approaches to containing the growth of health care expenditures: (1) that health maintenance organizations can deliver health care less expensively than the fee-for-service system; (2) that under certain competitive conditions, HMOs would prosper; (3) that HMO successes would force FFS insurers and providers to become more efficient; and (4) that creating the competitive conditions would be politically feasible. Reasons for doubting the latter three propositions are plentiful, and the strategy is therefore judged unlikely to succeed.