Transitivity, Spatially Separable Utility Functions, and United States Migration Streams, 1935–1970

1978 ◽  
Vol 10 (4) ◽  
pp. 399-414 ◽  
Author(s):  
T R Smith ◽  
C Clayton

The predictions of spatial-interaction models applied to migration systems may be viewed as the outcome of expected-utility maximization in which the average beliefs and preferences at a given origin are spatially separable. This theory indicates that a test of the spatial separability of the utilities may be performed by examining the degree of transitivity in probabilities and gross flows that is predicted by the spatial-interaction models. United States migration data for four periods between 1935 and 1970 were examined for transitivity at three spatial scales of resolution. These flows all exhibited significantly high degrees of transitivity, although for no period or scale of resolution were migration flows completely without some statistically significant intransitivities in either the probabilities or gross flows. The regions involved in intransitivities varied greatly from period to period, and only weak evidence indicated lower degrees of intransitivity for local aggregates of regions. The hypothesis of spatially separable utilities must be rejected for the migration data examined. Theoretical discussion indicates that several causes may lead to intransitivities, which in turn lead to problems in applying spatial-interaction models to migration data.


Energy ◽  
2011 ◽  
Vol 36 (11) ◽  
pp. 6555-6558 ◽  
Author(s):  
Sicong Wang ◽  
Shifeng Wang


2018 ◽  
Vol 2 (2) ◽  
pp. 33-58
Author(s):  
Adam Dennett

Background   Spatial Interaction Models have been used for decades to explain and predict flows (of migrants, capital, traffic, trade etc.) between geographic locations.Aims   This paper will guide users through the process of fitting and calibrating spatial interaction models in order to understand, explain and predict internal migration flows in Australia. Data and methods   Internal migration data from the Australian 2011 Census of Population and Housing, which records people who have moved between Greater Capital City Statistical Areas over 5-year periods, is used to exemplify the modelling process. The R statistical software is used to process and visualise the data as well as run the models. Results   The full suite of Wilson’s family of spatial interaction models is fitted to the internal migration data, revealing that distance and origin/destination populations are some of the most important influencing factors affecting internal migration flows. We see whether constraining the model to known flows about origins and/or destinations will improve the fits and model estimates. Conclusions   Spatial interaction modelling has been a tool in the box of some population geographers for a number of decades. However, recent advances in more forgiving programming languages such as R and Python now mean that this powerful modelling methodology is no longer only available to those who also possess advanced computer programming skills. This guide has exemplified the process of fitting and calibrating spatial interaction models on Australian internal migration data, but the methods could easily be applied to other flow data sets in other contexts.



1981 ◽  
Vol 13 (2) ◽  
pp. 217-224 ◽  
Author(s):  
J Ledent

This paper compares the system of equations underlying Alonso's theory of movement with that of Wilson's standard family of spatial-interaction models. It is shown that the Alonso model is equivalent to one of Wilson's four standard models depending on the assumption at the outset about which of the total outflows and/or inflows are known. This result turns out to supersede earlier findings—inconsistent only in appearance—which were derived independently by Wilson and Ledent. In addition to this, an original contribution of this paper—obtained as a byproduct of the process leading to the aforementioned result—is to provide an exact methodology permitting one to solve the Alonso model for each possible choice of the input data.



2013 ◽  
Vol 15 (3) ◽  
pp. 249-264 ◽  
Author(s):  
Giuseppe Arbia ◽  
Francesca Petrarca




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