Models and Methods for Spatial Interaction Data

2011 ◽  
pp. 47-59 ◽  
Author(s):  
Manfred M. Fischer ◽  
Jinfeng Wang
Keyword(s):  
Spatial Interaction ◽  
Interaction Data

Functional Regionalization of Spatial Interaction Data: A Comment

1981 ◽  
Vol 13 (5) ◽  
pp. 645-646 ◽  
Author(s):  
A Findlay ◽  
P B Slater
Keyword(s):  
Spatial Interaction ◽  
Interaction Data ◽  
Functional Regionalization ◽  
Independent Effects

A recent paper by Masser and Scheurwater (1980) favoured the adoption of the intramax procedure for functional regionalization, without satisfactorily investigating the independent effects of the approach of the procedure to standardization and to clustering. From an examination of these phases of the intramax procedure it is shown that the superiority of Masser and Scheurwater's approach is based on doubtful criteria.

Measuring neighbourhood segregation using spatial interaction data

2017 ◽  
pp. 65-90 ◽  
Author(s):  
Christopher D. Lloyd ◽  
Gemma Catney ◽  
Ian G. Shuttleworth
Keyword(s):  
Spatial Interaction ◽  
Interaction Data

Analysing Interaction Data

2010 ◽  
pp. 69-88
Author(s):  
John Stillwell ◽  
Kirk Harland
Keyword(s):  
Spatial Interaction ◽  
Interaction Model ◽  
Data Sets ◽  
Interaction Data ◽  
Interaction Variables ◽  
Movement Function ◽  
Impossible Task ◽  
Log Linear ◽  
Calibration Techniques ◽  
Modelling Methods

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.

An Empirical Study of the Competing Destinations Model Using Japanese Interaction Data

1987 ◽  
Vol 19 (10) ◽  
pp. 1359-1373 ◽  
Author(s):  
Y Ishikawa
Keyword(s):  
Spatial Interaction ◽  
Interaction Data ◽  
Competition Effect ◽  
Spatial Interaction Models ◽  
Agglomeration Effect ◽  
First Ray ◽  
Constrained Model ◽  
Using Data ◽  
Empirical Implications ◽  
Distance Parameter

The misspecification issue, that the estimated distance parameter in spatial-interaction models might be biased by the spatial structure under investigation, has remained unsettled. However, the competing destinations model recently developed by Fotheringham is the first ray of hope for a solution. In this paper, the empirical validity of Fotheringham's model is examined using data of migration and of university enrollment among Japanese prefectures. It becomes clear that the origin-specific estimates of the distance-decay parameter, calibrated from the production-constrained model, are, on the whole, less negatively biased. It is also confirmed that the dominance of the agglomeration effect for one set of data, and of the competition effect for other sets of data, are the sources of the misspecification issue. The empirical implications are discussed.

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