A Researcher's Guide to the National Statistics Socio‐economic Classification

This is a study of the changing size of occupational classes, of their concomitant pay structure and the timing and circumstances of changes in this structure, in the period 1906â€“1960. In marshalling the facts about the numbers of people in the various broad occupational classes, Dr Routh has indicated the rise or fall of numbers in the occupations of which each class is composed, and the relation of these changes to the changing industrial structure of the economy. He also shows changes in participation rate by age and sex over the half-century, and makes some comparisons of the broad occupational structure of Britain with that of some other countries.

Occupation and Pay in Great Britain 1906-60.

Australian urban and built environment research covers a multitude of research disciplines investigating social, economic and physical phenomena at a multitude of spatial and temporal scales and across diverse aggregation levels, from individual-level through to cohorts and populations, and across a range of scenarios, e.g. public health, voting patterns. The development of a common software platform (e-Infrastructure) to meet the needs of such research communities involves tackling many challenges associated with data intensive areas of research. This includes dealing with data sets from a multitude of federal, state, municipal, academic and private institutions, which hold vast arrays of heterogeneous data. For many researchers these data sets are difficult to discover, access, interrogate and use more generally. It is also unrealistic to expect researchers to always have the technical capability and capacity to handle such large amounts of data, or to develop data processing tools making use of such data sets, or be able to run computationally intensive simulations and models based on these data sets. Islands of expertise and islands (silos) of data currently exist that have fragmented urban research and thwarted a holistic approach to the study of the Australian urban and built environment system. The Australian Urban Research Infrastructure Network (AURIN - www.aurin.org.au) is a $20m SuperScience initiative established across Australia that seeks to address this directly by creating an e-Infrastructure aiming at bridging these gaps, by allowing researchers to conduct collaborative research in a security-enabled, browser-based environment providing seamless and transparent access to the distributed data and computational resources across Australia. These include metadata services, federated datasets, data integration and interrogation services, together with advanced visualization, collaboration and data storage capabilities. The goal is to provide access to rich datasets, state-of-the-art data processing tools, as well as a knowledge base where good research practices can be followed and used to assist researchers when navigating through vast data holdings to couple appropriate data and analytical tools for a range of urban research endeavors. In this paper, we address the fundamental question behind the establishment of this infrastructure, i.e. how to design a versatile and flexible software platform (e-Infrastructure) for urban research? The approach described is centered on establishment of a range of demonstrator projects addressing specific urban and built environment themes and the challenges they give rise to through a common e-Infrastructure. We believe that such an approach will allow delivery of early functionality in supporting a range of urban research scenarios, and at the same time support novel links between tools traditionally not applied beyond individual research fields. Through a common (core) e-Infrastructure, we expect to develop urban research capability that will offer a step change in how urban research is currently conducted, to support multi- and inter-disciplinary research scenarios whilst preserving full functionality in individual urban research strands. We describe the initial design stages of the AURIN e-Infrastructure from a technical perspective. The approach chosen is based on past experiences from a variety of eResearch initiatives, such as the UK e-Science National e-Infrastructure for Social Simulation (NeISS - www.neiss.org.au) project (Birkin et al., 2010); the Data Management through e-Social Science (DAMES - www.dames.org.au) project (McCafferty et al., 2009); the Spatial Information Software Stack (SISS) eResearch Facility (Liao et al., 2009), and the workflow-based image annotation using geographic information retrieval of TRIPOD (Purves et al., 2010). We illustrate the utility of the approach taken based on an initial set of demonstration projects exploring demographic and economic investigations of the Australian urban system.

/pdf/towards-an-e-infrastructure-for-urban-research-across-1310278j3r.pdf

Towards an e-Infrastructure for urban research across Australia

Purpose – This paper aims to present reasons why social classifications which use occupations should seek to adopt “specific” approaches which are tailored to the country, time period and gender of the subjects under study.Design/methodology/approach – The relative motivations for adopting a specific approach to social classifications are discussed and theoretical perspectives on specificity and empirical evidence on the contribution of specific approaches are reviewed. Also the practical costs of implementing specific social classifications are evaluated, and the authors' development of the “GEODE” data service (grid‐enabled occupational data environment), which seeks to assist this process, is discussed.Findings – Specific approaches make a non‐trivial difference to the conclusions drawn from analyses of occupation‐based social classifications. It is argued that the GEODE service has reduced the practical challenges of implementing specific measures.Research limitations/implications – There remain conce...

/pdf/the-importance-of-specificity-in-occupation-based-social-3y7c7ml37d.pdf

The importance of specificity in occupation-based social classifications

The National e-Infrastructure for Social Simulation (NeISS) is a multi-disciplinary collaboration between computation and social science within the UK Digital Social Research programme. The project aims to develop new tools and services for social scientists and planners to assist in performing ‘what-if’ scenario predictions in a variety of policy contexts. A key part of the NeISS remit is to explore real-world scenarios and evaluate real policy applications. Research into the processes and drivers behind crime is an important application area that has major implications for both improving crime-related policy and developing effective crime prevention strategies. This paper will discuss how the current e-infrastructure and available microsimulation tools can be used to improve an existing agent-based burglary simulation (BurgdSIM) by including a more realistic representation of the victims of crime. Results show that the model produces different spatial patterns when individual-level victim data are used and a risk profile of the synthetic victims suggests which types of people have the largest burglary risk.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2011.0142

Towards Victim-Oriented Crime Modelling in a Social Science e-Infrastructure

The ESRC funded Grid Enabled Occupational Data Environment (GEODE) project is conceived to facilitate and virtualise occupational data access through a grid environment. Through GEODE it is planned that occupational data users from the social sciences can access curated datasets, share micro datasets, and perform statistical analysis within a secure virtual community. The Michigan Data Documentation Initiative (DDI) is used to annotate the datasets with social science specific metadata to provide for better semantics and indexes. GEODE uses the Globus Toolkit and the Open Grid Service Architecture – Data Access and Integration (OGSA-DAI) software as the Grid middleware to provide data access and integration. Users access and use occupational data sets through a GEODE web portal. This portal interfaces with the Grid infrastructure and provides useroriented data searches and services. The processing of CAMSIS (Cambridge Social Interaction and Stratification) measures is used as an illustrative example of how GEODE provides services for linking occupational information. This paper provides an overview of the GEODE work and lessons learned in applying Grid technologies in this domain.

/pdf/geode-sharing-occupational-data-through-the-grid-2hmfvb8zeo.pdf

GEODE - Sharing Occupational Data Through the Grid

The last two decades have seen substantially increased potential for quantitative social science research. This has been made possible by the significant expansion of publicly available social science datasets, the development of new analytical methodologies, such as microsimulation, and increases in computing power. These rich resources do, however, bring with them substantial challenges associated with organizing and using data. These processes are often referred to as 'data management'. The Data Management through e-Social Science (DAMES) project is working to support activities of data management for social science research. This paper describes the DAMES infrastructure, focusing on the data-fusion process that is central to the project approach. It covers: the background and requirements for provision of resources by DAMES; the use of grid technologies to provide easy-to-use tools and user front-ends for several common social science data-management tasks such as data fusion; the approach taken to solve problems related to data resources and metadata relevant to social science applications; and the implementation of the architecture that has been designed to achieve this infrastructure.

A social science data-fusion tool and the Data Management through e-Social Science (DAMES) infrastructure

This paper describes a group of online services which are designed to support social
survey research and the production of statistical results. The 'Grid Enabled Specialist
Data Environment' (GESDE) services constitute three related systems which offer
facilities to search for, extract and exploit supplementary data and metadata concerned
with the measurement and operationalisation of survey variables. The services also offer
users the opportunity to deposit and distribute their own supplementary data resources for
the benefit of dissemination and replication of the details of their own analysis.
The GESDE services focus upon three application areas: specialist data relating to the
measurement of occupations; educational qualifications; and ethnicity (including
nationality, language, religion, national identity). They identify information resources
related to the operationalisation of variables which seek to measure each of these
concepts - examples include coding frames, crosswalk and translation files, and
standardisation and harmonisation recommendations. These resources constitute
important supplementary data which can be usefully exploited in the analysis of survey
data. The GESDE services work by collecting together as much of this supplementary
data as possible, and making it searchable and retrievable to others. This paper discusses
the current features of the GESDE services (which have been designed as part of a wider
programme of ‘e-Science’ research in the UK), and considers ongoing challenges in
providing effective support for variable-oriented statistical analysis in the social sciences.

Mean Squared Error Estimation for Small Area Means, Quantiles and Poverty Indicators: A nonparametric bootstrap approach

Utilising a grid enabled occupational data environment

This is a pre-print version of a paper from 2nd International Digital Curation Conference, 21-22 Nov 2006 published by Digital Curation Centre. http://www.dcc.ac.uk/events/conferences/2nd-international-digital-curation-conference-digital-data-curation-practice.

Data curation standards and the messy world of social science occupational information resources

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