Creating the National Classification of Census Output Areas: Data, Methods and Results

The purpose of this paper is to describe and explain the processes and decisions that were involved in the creation of the National Area Classification of 2001 Census Output Areas (OAs). The project was carried out on behalf of the Office for National Statistics (ONS) by Daniel Vickers of the School of Geography, University of Leeds as part of his PhD. thesis. The paper describes the creation of the classification: selection of the variables, assembly of the classification database, the methods of standardisation and the clustering procedures, some discussion of alternative methodologies that were considered for use. The processes used for creating the clusters, their naming and description are outlined. The classification is mapped and visualised in a number of different ways. The OA Classification fits into the ONS suite of area classifications complementing published classifications at Local Authority, Health Authority and Ward levels. The classification is freely available, and can be downloaded from the ONS Neighbourhood Statistics website at www.statistics.gov.uk

A New Classification Of UK Local Authorities Using 2001 Census Key Statistics

The 2001 Census has been successfully administered and the Census Organisations are currently engaged in processing the returns. A very large and rich dataset will be produced for the 58,789,194 people of the UK. The Census Area Statistics, for example, delivers 190 tables containing about 6 thousand unique counts relating to the characteristics of the UK population, for output areas and all higher geographies. This paper represents the first results of a project that aims to develop, in collaboration with the Office for National Statistics, a set of general purpose classifications at different geographic scales, including households, neighbourhoods, wards, local authorities and to link the classifications at different levels together. The paper reports on the methods used and results of a classification of the UK’s 434 Local Authorities, using the Key Statistics released in February 2003. This initial classification and description of methods will feed into the ONS/GROS/NISRA project to classify Local Authorities for the whole UK. Further data or digital versions of the classification system are available on request

Can social media data be useful in spatial modelling? A case study of ‘museum Tweets’ and visitor flows

This paper explores the potential of volunteered geographical information from social media to inform geographical models of behavior. Based on a case study of museums in Yorkshire, we created a spatial interaction model of visitors to 15 museums from 179 administrative zones to test this potential. Instead of relying on limited official data on the magnitude of flows from different attractions we used volunteered geographic information’ (VGI) to calibrate the model. The method represents the potential of VGI for applications beyond descriptive statistics and visuals and highlights potential uses of georeferenced social media data for geographic models. The main input dataset comprised geo-tagged messages harvested using the Twitter Streaming Application Programming Interface (API). We successfully calibrated the distance decay parameter of the model and conclude that social media data have great potential for aiding models of spatial behavior. However, we also caution that there are dangers associated with the use of social media data. Researchers should weigh up the wider costs and benefits of harnessing such ‘big data’ before blindly harnessing this low quality, high volume resource. Our case study also serves as the basis for discussion of the ethics surrounding the use of privately harvested VGI by publicly funded academics

A classification for English primary schools using open data

England has statutory regulations in place that ensure state funded schools deliver broadly the same curriculum. However there still exists a wide range of contexts in which this education takes place, including: the management of schools; how the schools chose to spend their budgets; individual policies in regards to staffing, behaviour and attendance, and perhaps most importantly, the composition of the pupil population in the school. Given these contexts, one outcome of interest is the attainment profile of schools, and it is important that this performance is judged in context, for the benefits of pupils, parents and schools. To this end, this study develops a classification using contemporary data for English primary schools. The open data used captures aspects of the gender, ethnic, language, staffing and affluence makeup of each school. The nature of these derived groupings is described and made available as a mapping resource. These groupings allow the identification of “families of schools”, to act as a resource to foster better collaboration between schools and more nuanced benchmarking

Food safety vulnerability: Neighbourhood determinants of non-compliant establishments in England and Wales

This paper utilises logistic regression to identify ecological determinants of non-compliant food outlets in England and Wales. We consider socio-demographic, urbanness and business type features to better define vulnerable populations based on the characteristics of the area within which they live. We find a clear gradient of association between deprivation and non-compliance, with outlets in the most deprived areas 25% less likely (OR = 0.75) to meet hygiene standards than those in the least deprived areas. Similarly, we find outlets located in conurbation areas have a lower probability of compliance (OR = 0.678) than establishments located in rural and affluent areas. Therefore, individuals living in these neighbourhoods can be considered more situationally vulnerable than those living in rural and non-deprived areas. Whilst comparing compliance across business types, we find that takeaways and sandwich shops (OR = 0.504) and convenience retailers (OR = 0.905) are significantly less likely to meet hygiene standards compared to restaurants. This is particularly problematic for populations who may be unable to shop outside their immediate locality. Where traditional food safety interventions have failed to consider the prospect of increased risk based on proximity to unsafe and unhygienic food outlets, we re-assess the meaning of vulnerability by considering the type of neighbourhoods within which non-compliant establishments are located. In-lieu of accurate foodborne illness data, we recommend prioritised inspections for outlets in urban and deprived areas. Particularly takeaways, sandwich shops and small convenience retailers

Estimating Small Area Income Deprivation: An Iterative Proportional Fitting Approach

Small area estimation and in particular the estimation of small area income deprivation has potential value in the development of new or alternative components of multiple deprivation indices. These new approaches enable the development of income distribution threshold based as opposed to benefit count based measures of income deprivation and so enable the alignment of regional and national measures such as the Households Below Average Income with small area measures. This paper briefly reviews a number of approaches to small area estimation before describing in some detail an iterative proportional fitting based spatial microsimulation approach. This approach is then applied to the estimation of small area HBAI rates at the small area level in Wales in 2003-5. The paper discusses the results of this approach, contrasts them with contemporary ‘official’ income deprivation measures for the same areas and describes a range of ways to assess the robustness of the results

Evaluating the performance of Iterative Proportional Fitting for spatial microsimulation: new tests for an established technique

Iterative Proportional Fitting (IPF), also known as biproportional fitting, ‘raking’ or the RAS algorithm, is an established procedure used in a variety of applications across the social sciences. Primary amongst these for urban modelling has been its use in static spatial microsimulation to generate small area microdata — individual level data allocated to administrative zones. The technique is mature, widely used and relatively straight-forward. Although IPF is well described mathematically, accessible examples of the algorithm written in modern programming languages are rare. Therefore, there is a tendency for researchers to ‘start from scratch’, resulting in a variety of ad hoc implementations and little evidence about the relative merits of different approaches. These knowledge gaps mean that answers to certain methodological questions must be guessed: How can ‘empty cells’ be identified and how do they influence model fit? Can IPF be made more computationally efficient? This paper tackles these questions and more using a systematic methodology with publicly available code and data. The results demonstrate the sensitivity of the results to initial conditions, notably the presence of ‘empty cells’, and the dramatic impact of software decisions on computational efficiency. The paper concludes by proposing an agenda for robust and transparent future tests in the field

Selection on Crop-Derived Traits and QTL in Sunflower (Helianthus annuus) Crop-Wild Hybrids under Water Stress

Locally relevant conditions, such as water stress in irrigated agricultural regions, should be considered when assessing the risk of crop allele introgression into wild populations following hybridization. Although research in cultivars has suggested that domestication traits may reduce fecundity under water stress as compared to wild-like phenotypes, this has not been investigated in crop-wild hybrids. In this study, we examine phenotypic selection acting on, as well as the genetic architecture of vegetative, reproductive, and physiological characteristics in an experimental population of sunflower crop-wild hybrids grown under wild-like low water conditions. Crop-derived petiole length and head diameter were favored in low and control water environments. The direction of selection differed between environments for leaf size and leaf pressure potential. Interestingly, the additive effect of the crop-derived allele was in the direction favored by selection for approximately half the QTL detected in the low water environment. Selection favoring crop-derived traits and alleles in the low water environment suggests that a subset of these alleles would be likely to spread into wild populations under water stress. Furthermore, differences in selection between environments support the view that risk assessments should be conducted under multiple locally relevant conditions