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Temporal and Spatial Consistency

Temporal and Spatial Consistency

Oliver Duke-Williams, John Stillwell
Copyright: © 2013 |Pages: 22
ISBN13: 9781466620384|ISBN10: 1466620382|EISBN13: 9781466620391
DOI: 10.4018/978-1-4666-2038-4.ch101
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MLA

Duke-Williams, Oliver, and John Stillwell. "Temporal and Spatial Consistency." Geographic Information Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 1675-1696. https://doi.org/10.4018/978-1-4666-2038-4.ch101

APA

Duke-Williams, O. & Stillwell, J. (2013). Temporal and Spatial Consistency. In I. Management Association (Ed.), Geographic Information Systems: Concepts, Methodologies, Tools, and Applications (pp. 1675-1696). IGI Global. https://doi.org/10.4018/978-1-4666-2038-4.ch101

Chicago

Duke-Williams, Oliver, and John Stillwell. "Temporal and Spatial Consistency." In Geographic Information Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1675-1696. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2038-4.ch101

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Abstract

One of the major problems challenging time series research based on stock and flow data is the inconsistency that occurs over time due to changes in variable definition, data classification and spatial boundary configuration. The census of population is a prime example of a source whose data are fraught with these problems, resulting in even the simplest comparison between the 2001 Census and its predecessor in 1991 being difficult. The first part of this chapter introduces the subject of inconsistencies between related data sets, with general reference to census interaction data. Various types of inconsistency are described. A number of approaches to dealing with inconsistency are then outlined, with examples of how these have been used in practice. The handling of journey to work data of persons who work from home is then used as an illustrative example of the problems posed by inconsistencies in base populations. Home-workers have been treated in different ways in successive UK censuses, a factor which can cause difficulties not only for researchers interested in such working practices, but also for those interested in other aspects of commuting. The latter set of problems are perhaps more pernicious, as users are less likely to be aware of the biases introduced into data sets that are being compared. In the second half of this chapter, we make use of a time series data set of migration interaction data that does have temporal consistency to explore how migration propensities and patterns in England and Wales have changed since 1999 and in particular since the year prior to the 2001 Census. The data used are those that are produced by the Office of National Statistics based on comparisons of NHS patient records from one year to the next and adjusted using data on NHS patients re-registering in different health authorities. The analysis of these data suggests that the massive exodus of individuals from major metropolitan across the country that has been identified in previous studies is continuing apace, particularly from London whose net losses doubled in absolute terms between 1999 and 2004 before reducing marginally in 2005 and 2006. Whilst this pattern of counterurbanisation is evident for all-age flows, it conceals significant variations for certain age groups, not least those aged between 16 and 24, whose migration propensities are high and whose net redistribution is closely connected with the location of universities. The time series analyses are preceded by a comparison of patient register data with corresponding data from the 2001 Census. This suggests strong correlation between the indicators selected and strengthens the argument that patient register data in more recent years provide reliable evidence for researchers and policy makers on how propensities and patterns change over time.

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