Estimates of cancer incidence and mortality in Europe in 1995

The Lee–Carter modelling framework is extended through the introduction of a wider class of generalised, parametric, non-linear models. This permits the modelling and extrapolation of age-specific cohort effects as well as the more familiar age-specific period effects. The choice of error distribution is generalised.

A cohort-based extension to the Lee–Carter model for mortality reduction factors

Global positioning systems (GPSs) and geographical information systems (GISs) have been widely used to collect and synthesize spatial data from a variety of sources. New advances in satellite imagery and remote sensing now permit scientists to access spatial data at several different resolutions. The Internet facilitates fast and easy data acquisition. In any one study, several different types of data may be collected at differing scales and resolutions, at different spatial locations, and in different dimensions. Many statistical issues are associated with combining such data for modeling and inference. This article gives an overview of these issues and the approaches for integrating such disparate data, drawing on work from geography, ecology, agriculture, geology, and statistics. Emphasis is on state-of-the-art statistical solutions to this complex and important problem.

Combining Incompatible Spatial Data

Aggregated demographic datasets are associated with analytical and cartographic problems due to the arbitrary nature of areal unit partitioning. This article describes a methodology for generating a surface-based representation of population that mitigates these problems. This methodology uses dasymetric mapping and incorporates areal weighting and empirical sampling techniques to assess the relationship between categorical ancillary data and population distribution. As a demonstration, a 100-meter-resolution population surface is generated from U.S. Census block group data for the southeast Pennsylvania region. Remote-sensing-derived urban land-cover data serve as ancillary data in the dasymetric mapping. ∗The author would like to thank Cory Eicher and Alan MacEacren for many helpful conversations regarding dasymetric mapping and Barbara Buttenfield for her constructive comments on a previous draft of this article.

Generating Surface Models of Population Using Dasymetric Mapping

Dasymetric maps display statistical data in meaningful spatial zones Such maps can be preferable to choropleth maps that show data by enumeration zones, because dasymetric zones more accurately represent underlying data distributions Though dasymetric mapping has existed for well over a century, the methods for producing these maps have not been thoroughly examined In contrast, research on areal interpolation has been more thorough and has examined methods of transferring data from one set of map zones to another, an issue that is applicable to dasymetric mapping Inspired by this work, we tested five dasymetric mapping methods, including methods derived from work on areal interpolation Dasymetric maps of six socio-economic variables were produced fm a study area of 159 counties in the eastern US using county choropleth data and ancillary land-use data Both polygonal (vector) and grid (raster) dasymetric methods were tested We evaluated map accuracy using both statistical analyses and visual prese

Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation

This manual describes how GLIM 4 may be used for statistical analysis in its most general sense, including data manipulation and display, model fitting, and prediction. This is a thorough re-working of the previous GLIM manual to take account of updates to the software - essential reading for all research statisticians everywhere.

GLIM system release 4 manual

This paper is a review and extension of the authors' research project on areal interpolation. It is concerned with problems arising when a region is divided into different sets of zones for different purposes, and data available for one set of zones (source zones) are needed for a different set (target zones). Standard approaches are based on the assumption that source zone data are evenly distributed within each zone, but our approach allows additional information about the target zones to be taken into account so that more accurate target zone estimates can be derived. The method used is based on the EM algorithm. Most of the work reported so far (e.g. Flowerdew and Green 1989) has been concerned with count data whose distribution can be modelled using a Poisson assumption. Such data are frequently encountered in censuses and surveys. Other types of data are more appropriately regarded as having continuous distributions. This paper is primarily concerned with areal interpolation of normally distributed data. A method is developed suitable for such data and is applied to house price data for Preston, Lancashire, starting with mean house prices in 1990 for local government wards and estimating mean house prices for postcode sectors.

Developments in areal interpolation methods and GIS

This paper reports on a research project concerned with the areal interpolation problem — the problem of comparing different data sets when they have been made available for different zonal systems. Our approach is based on using additional information to guide the interpolation process. This paper emphasizes recent work applying the method to Poisson and binomially distributed data. There is also discussion of how the method can best be implemented in a geographic information system.

Using areal interpolation methods in geographic information systems

A field experiment was set up to investigate the retention of PAHs by pasture grass following sludge application. In addition to information on compound-specific loss rates, the influence of meteorological variables and sludge-pasture contact time on the weathering process were investigated. The rates of loss were compound specific with half-lives ranging from <1 to 10 d. The lighter PAHs were rapidly lost from the grass surface primarily by volatilization. For the intermediate and heavier PAHs, loss was slower with rain playing an important role in the weathering process. Growth dilution was also important in reducing the contaminant levels in the grass. The effect of sludge-pasture contact time prior to weathering by rain on the residual levels remains unclear. In terms of risk of exposure, there is the potential for persistent organic contaminants to be introduced into the grazing animal food chain if sewage sludge is applied to pasture land.

Behavior of sewage sludge-derived PAHs on pasture.

Summary. Statistical methods of ecological analysis that attempt to reduce ecological bias are empirically evaluated to determine in which circumstances each method might be practicable. The method that is most successful at reducing ecological bias is stratified ecological regression. It allows individual level covariate information to be incorporated into a stratified ecological analysis, as well as the combination of disease and risk factor information from two separate data sources, e.g. outcomes from a cancer registry and risk factor information from the census sample of anonymized records data set. The aggregated individual level model compares favourably with this model but has convergence problems. In addition, it is shown that the large areas that are covered by local authority districts seem to reduce between-area variability and may therefore not be as informative as conducting a ward level analysis. This has policy implications because access to ward level data is restricted.

Reducing bias in ecological studies: an evaluation of different methodologies

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