CiteULike: neteler's error

A Spatial View of the Ecological Inference Problem

Carol Gotway — 2007-10-01T08:57:06-00:00

(2004), pp. 233-244.

Ecological Inference: New Methodological Strategies (Analytical Methods for Social Research)

Gary King — 2007-10-01T08:46:25-00:00

(13 September 2004)

This collection of essays brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half-decade has witnessed an explosion of research in ecological inference--the process of trying to infer individual behavior from aggregate data. Although uncertainties and information lost in aggregation make ecological inference one of the most problematic types of research to rely on, these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, by business in marketing research, and by governments in policy analysis.

Combining Incompatible Spatial Data

CA Gotway — 2007-10-01T08:17:28-00:00

Journal of the American Statistical Association, Vol. 97, No. 458. (June 2002), pp. 632-648.

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.

Pattern Discovery and Detection: A Unified Statistical Methodology

David Hand — 2007-09-17T20:21:31-00:00

Journal of Applied Statistics, Vol. 31, No. 8. (2004), pp. 885-924.

Modern statistical data analysis is predominantly model-driven, seeking to decompose an observed data distribution in terms of major underlying descriptive features modified by some stochastic variation. A large part of data mining is also concerned with this exercise. However, another fundamental part of data mining is concerned with detecting anomalies amongst the vast mass of the data: the small deviations, unusual observations, unexpected clusters of observations, or surprising blips in the data, which the model does not explain. We call such anomalies patterns. For sound reasons, which are outlined in the paper, the data mining community has tended to focus on the algorithmic aspects of pattern discovery, and has not developed any general underlying theoretical base. However, such a base is important for any technology: it helps to steer the direction in which the technology develops, as well as serving to provide a basis from which algorithms can be compared, and to indicate which problems are the important ones waiting to be solved. This paper attempts to provide such a theoretical base, linking the ideas to statistical work in spatial epidemiology, scan statistics, outlier detection, and other areas. One of the striking characteristics of work on pattern discovery is that the ideas have been developed in several theoretical arenas, and also in several application domains, with little apparent awareness of the fundamentally common nature of the problem. Like model building, pattern discovery is fundamentally an inferential activity, and is an area in which statisticians can make very significant contributions.

Issues of scale and uncertainty in the global remote sensing of disease.

PM Atkinson — 2007-08-17T14:40:05-00:00

Adv Parasitol, Vol. 62 (2006), pp. 79-118.

Scale and uncertainty are important issues for the global prediction of disease. Disease mapping over the entire surface of the Earth usually involves the use of remotely sensed imagery to provide environmental covariates of disease risk or disease vector density. It further implies that the spatial resolution of such imagery is relatively coarse (e.g., 8 or 1km). Use of a coarse spatial resolution limits the information that can be extracted from imagery and has important effects on the results of epidemiological analyses. This paper discusses geostatistical models for (i) characterizing the scale(s) of spatial variation in data and (ii) changing the scale of measurement of both the data and the geostatistical model. Uncertainty is introduced, highlighting the fact that most epidemiologists are interested in accuracy, aspects of which can be estimated with measurable quantities. This paper emphasizes the distinction between data- and model-based methods of accuracy assessment and gives examples of both. The key problem of validating global maps is considered.