Estimating Spatially Consistent Interaction Flows Across Three Censuses

Estimating Spatially Consistent Interaction Flows Across Three Censuses

Zhiqiang Feng (University of St Andrews, United Kingdom) and Paul Boyle (University of St Andrews, United Kingdom)
DOI: 10.4018/978-1-61520-755-8.ch013
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Abstract

A significant problem facing geographical researchers who wish to compare migration and commuting flows over time is that the boundaries of the geographical areas, between which flows are recorded, often change. This chapter describes an innovative method for re-estimating the migration and commuting data collected in the 1981 and 1991 Censuses for the geographical units used in the 2001 Census. The estimated interaction data are provided as origin-destination flow matrices for wards in England and Wales and pseudo-postcode sectors in Scotland. Altogether, there were about 10,000 zones in 1981, 1991 and 2001, providing huge but sparsely populated matrices of 10,000 by 10,000 cells. Because of the changing boundaries during inter-censal periods, virtually no work has attempted to compare local level migration and commuting flows in the two decades, 1981-91 and 1991-2001. The re-estimated spatially consistent interaction flows described here allow such comparisons to be made and we use migration change in England and commuting change in Liverpool to demonstrate the value of these new data.
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Introduction

Census interaction data or origin-destination flow statistics provide the UK academic community with a potentially rich source of information on spatial mobility. Census interaction data comprise the Special Migration Statistics (SMS) and the Special Workplace Statistics (SWS). However, in the 2001 Census, Special Travel Statistics (STS) were released for Scotland which include travel to work as well as travel to study data. Census interaction data have been collected and published from 1981, theoretically providing an opportunity for researchers to study spatial patterns and trends for small geographical areas over the two decades from 1981 to 2001.

Unfortunately, it is not a straightforward task to compare census interaction data through time. Many difficulties arise including: changes in the definition of census questions; changes in the selection of census topics; alterations to the sample coverage; and the adoption of different disclosure control methods (see Chapter 3). Boundary changes between censuses are also a major problem that hinders the comparison of census data though time (Gregory and Ell, 2005; Champion, 1995). At a relatively small area scale, wards are a commonly used set of geographical units for analysing census data, but they change frequently because they are electoral areas and boundary changes are necessary to satisfy electoral equality. Hence, between 1981 and 2001 wards in Britain have experienced constant review and adjustment by the Boundary Commissions of local governments (Rees, 1995). According to our calculations, for example, based on the ONS Ward History Database, about 46% of wards in England and Wales have been subject to one or more geographical boundary change in the 1990s. At a more aggregate spatial scale, major changes took place in the mid 1990s at the district scale with the reform of local government and at the regional scale with the emergence of Government Office Regions.

The conventional response to the problem of boundary change has been to examine socio-demographic changes using larger, more consistent areal units. Thus, Boyle (1994; 1995), Champion (1994) and Champion et al. (1998) used relatively large areas to study population migration. Similarly, Frost et al. (1996; 1997; 1998) compared commuting data for 1981 and 1991, but were forced to aggregate wards into concentric bands from metropolitan centres, or to summarise flows for entire inner cities. These approaches are limited because, on the one hand, most people tend to move or commute over short distances and, on the other hand, studies at more aggregate levels may misinterpret the true factors underlying these flow patterns. Hence the need to develop a suitable methodology to estimate interaction flows from censuses for spatially consistent zones so that they could be compared both spatially and temporally.

In the following sections, we briefly introduce the characteristics of the SMS and SWS for 1981, 1991 and 2001, highlight why estimating flow data for different geographies requires a novel methodology, and describe the main stages of the estimation strategy and explain how we solved the various problems associated with integrating interaction data through time. We also present two separate examples involving the investigation of migration change in England and commuting patterns in Liverpool to demonstrate the use of the estimated migration and commuting flow data.

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