Paul Williamson

Bio: Paul Williamson is an academic researcher from University of Liverpool. The author has contributed to research in topics: Population & Microsimulation. The author has an hindex of 14, co-authored 38 publications receiving 1012 citations. Previous affiliations of Paul Williamson include University of Leeds.

The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records

Abstract: "Traditionally, estimates of the number of people in small areas (the smallest geographical units for which data are available) have been disaggregated only by age and sex. More recently, much research effort has been directed towards developing some form of enhanced small-area population estimation, in which the population in a small area is disaggregated not only by age and sex, but also by a wide range of additional economic and social characteristics. Solutions to this problem currently include account-based demographic models, often used by local authorities."

An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata

Abstract: Population microdata comprise a list of households and individuals each with an associated list of characteristics. Unfortunately for Britain no small-area microdata exist that offers a broad range of demographic and socioeconomic variables contained in the national census. There are a number of approaches to the reconstruction of such spatially detailed microdata including data fusion synthetic reconstruction (chain imputation) and reweighting. One variant of the reweighting approach involves the selection of a combination of households from the 1% household Samples of Anonymized Records that best fits known small-area are constraints (the published census tabulations). In this paper the implementation of this "combinatorial optimization" technique is more thoroughly examined. First the combinatorial optimization process is reviewed. Then a number of methodological innovations designed to improve the accuracy and consistency of resulting outputs are reported. Subsequently the problems of evaluating the outputs are discussed and a new strategy is outlined for assessing the quality of synthetic microdata. The strategy proposed is generally applicable to all such data irrespective of their means of generation. The paper goes on to provide an extensive assessment of the quality of synthetic microdata produced using the combinatorial optimization approach. This represents the first time that such an evaluation has ever been undertaken. The results highlight the degree to which such data may be able to meet specific user needs. The paper concludes by offering an illustration of the "added value" that can be obtained by combining information from public-use microdata with published small-area tabulations. (authors)

Evaluating Goodness-of-Fit Measures for Synthetic Microdata

Abstract: Goodness-of-fit tests are widely used by geographers, but choice remains difficult. Our overview starts with a conceptual examination of the nature of fit. Three important approaches to assessing the goodness-of-fit of categorical data are reviewed and appraised: statistics tested against the h ² distribution, the normal Z score and its variants, and measures derived from information theory. The forms of the phi and psi statistics highlighted in earlier geographical work are shown to be closely approximated by a simple measure of absolute error. Empirical examples help to illustrate the relative utility of these tests for a range of purposes.

The census data system

Abstract: List of Contributors Acknowledgements Forewords (John Pullinger, Ian Diamond, Reg Carr) How to Use the Book Census Data Resources in the United Kingdom (Philip Rees, David Martin, and Paul Williamson) Part I Geography and Lookup Tables The debate about census geography (Philip Rees and David Martin) Output areas for 2001 (David Martin) Designing your own geographies (Seraphim Alvanides, Stan Openshaw, and Philip Rees) Lookup tables and new area statistics for the 1971, 1981, and 1991 Censuses (James Harris, Danny Dorling, David Owen, Mike Coombes, and Tom Wilson) Part II Boundary Data and Visualization Handling and accessing census boundary data (William Mackaness and Alistair Towers) Visualizing census data (Jason Dykes, Jackie Carter, and Danny Dorling) Part III Area Statistics Disseminating census area statistics over the Web (James Harris, Justin Hayes, and Keith Cole) Deprivation indicators (Martyn Senior) Census population surfaces (David Martin) ONS classifications and GB profiles: census typologies for researchers (Philip Rees, Chris Denham, John Charlton, Stan Openshaw, Marcus Blake, and Linda See) Dealing with the census undercount (Stephen Simpson) Population statistics after the census (Paul Williamson and Stephen Simpson) Part IV Microdata Microdata from the census: Samples of Anonymised Records (Angela Dale and Andy Teague) Online tabulation for the Samples of Anonymised Records (Ian Turton) The ONS Longitudinal Study: linked census and event data to 2001 (Rosemary Creeser, Brian Dodgeon, Heather Joshi, and Jillian Smith) Synthetic microdata (Paul Williamson) Part V Interaction Data Migration data from the census (Philip Rees, Frank Thomas, and Oliver Duke Williams) Workplace data from the census (Keith Cole, Martin Frost, and Frank Thomas) Part VI Planning for 2001 Census Outputs New questions for the 2001 Census (John Dixie and Danny Dorling) A one number census (Ian Diamond, Marie Cruddas, and Jennet Woolford) An output strategy for the 2001 Census (Chris Denham and Philip Rees) Metadata for the 2001 UK Census: recommendations (Paul Williamson and Neil Lander Brinkley) Testing user requested geographies (Oliver Duke Williams and Phil Rees) Glossary References Index

The diversity of diversity: a critique of geodemographic classification

Abstract: We report on an examination of two geodemographic classification systems based on an analysis of 1991 census variables, for districts, wards and census enumeration districts in England and Wales. We also review the associations among the variables examined, the extent to which certain underlying components might account for the overall variation and the types of areas that are least typical. The results show that small areas are different in many different ways; a few dimensions cannot provide enough information to describe an area fully. Diversity on most scales remains even after geodemographicclassification, emphasizing the advantages of task-specific classification.

Gender differences in depression in 20 European countries: cross-national variation in the true gender gap in depression

Abstract: One of the most consistent findings in the social epidemiology of mental health is the gender gap in depression. Depression is approximately twice as prevalent among women as it is among men. However, the absence of comparable data hampers cross-national comparisons of the prevalence of depression in general populations. Using information about the frequency and severity of depressive symptoms from the third wave of the European Social Survey (ESS-3), we are able to fill the gap the absence of comparable data leaves. In the ESS-3, depression is measured with an eight-item version of the Center for Epidemiological Studies-Depression Scale. In the current study, we examine depression among men and women aged 18-75 in 23 European countries. Our results indicate that women report higher levels of depression than men do in all countries, but there is significant cross-national variation in this gender gap. Gender differences in depression are largest in some of the Eastern and Southern European countries and smallest in Ireland, Slovakia and some Nordic countries. Hierarchical linear models show that socioeconomic as well as family-related factors moderate the relationship between gender and depression. Lower risk of depression is associated in both genders with marriage and cohabiting with a partner as well as with having a generally good socioeconomic position. In a majority of countries, socioeconomic factors have the strongest association with depression in both men and women. This research contributes new findings, expanding the small existing body of literature that presents highly comparable data on the prevalence of depression in women and men in Europe.

Neighbourhood deprivation and health: does it affect us all equally?

Abstract: Background Neighbourhood socioeconomic status (SES) may affect rich and poor residents differentially. Two models are proposed. Model 1: living in a non-deprived neighbourhood is better for health because better collective material and social resources are available. Model 2: being poor (rich) relative to the neighbourhood average is associated with worse (better) health because of the discrepancy between an individual's situation and those around them. Methods Individual data from the Whitehall II study covering health, SES, and perceived status were linked to census data on neighbourhood deprivation. Results Both individual and neighbourhood deprivation increased the risk of poor general and mental health. There was a suggestion that the effect of living in a deprived area was more marked for poorer individuals, although interactions were not statistically significant. Poor people in poor neighbourhoods reported more financial and neighbourhood problems and rated themselves lowest on the ladder of society. Conclusions We found no evidence that personal poverty combined with affluent neighbourhood had negative health consequences. Rather, living in a deprived neighbourhood may have the most negative health effects on poorer individuals, possibly because they are more dependent on collective resources in the neighbourhood.

Urban Water Demand Modeling: Review of Concepts, Methods, and Organizing Principles

Abstract: [1] In this paper, we use a theoretical framework of coupled human and natural systems to review the methodological advances in urban water demand modeling over the past 3 decades. The goal of this review is to quantify the capacity of increasingly complex modeling techniques to account for complex human and natural processes, uncertainty, and resilience across spatial and temporal scales. This review begins with coupled human and natural systems theory and situates urban water demand within this framework. The second section reviews urban water demand literature and summarizes methodological advances in relation to four central themes: (1) interactions within and across multiple spatial and temporal scales, (2) acknowledgment and quantification of uncertainty, (3) identification of thresholds, nonlinear system response, and the consequences for resilience, and (4) the transition from simple statistical modeling to fully integrated dynamic modeling. This review will show that increasingly effective models have resulted from technological advances in spatial science and innovations in statistical methods. These models provide unbiased, accurate estimates of the determinants of urban water demand at increasingly fine spatial and temporal resolution. Dynamic models capable of incorporating alternative future scenarios and local stochastic analysis are leading a trend away from deterministic prediction.