Analysing Interaction Data

Abstract

Large and complex interaction data sets present researchers with analytical challenges and this chapter attempts to identify and illustrate a number of ways to analyse origin-destination flows. Given the impossible task of providing a comprehensive review in such a limited space, certain analytical measures, modelling methods and visualisation techniques have been selected for inclusion, following an introduction to the notation commonly employed to represent interaction variables. Various Census and NHS patient register data sets are used to exemplify interaction measures, beginning with simple net balances and inflow/outflow ratios and moving onto indices of connectivity, inequality and distance moved. The multiplicative component framework is introduced as a particularly useful analytical approach. More sophisticated methods of modelling interaction data using statistical or mathematical calibration techniques are reviewed, examples of log-linear regression and spatial interaction model structure are highlighted in the context of historical calibration and a brief discussion of the use models for future projection is included. Maps that show patterns of geographical movement function as effective illustrative and research tools. Computerized mapping of geographical movement has evolved since the 1970s and 1980s and, in this chapter, we introduce a new method of mapping flows using vectors and illustrate this approach with micro data on pupils travelling to school. The chapter aims to provide a broad introduction to analysis methods for interaction data, many of which are subsequently applied in later chapters of the book.