A Statistical Evaluation of the Accuracy of a Family of Gravity Models.

Black, J. Andrew; Salter, R. J.

doi:10.1680/iicep.1975.3839

Cited by 20 publications

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“…However, the R 2 statistics has been argued by several researchers due to its insensitivity to variations in model specification and missing concept to evaluate model performance across different data sets (Black and Salter, 1975;Wilson, 1976). The information-based statistics originated from "information gain statistics" in Kullback and Leibler (1951) includes the phi statistics, the psi statistics, and the measure of absolute entropy difference.…”

Section: Validation Stepmentioning

confidence: 99%

Synthetic Population Techniques in Activity-Based Research

Cho

Bellemans

Creemers

et al. 2014

Data Science and Simulation in Transportation Research

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Section: Validation Stepmentioning

confidence: 99%

Synthetic Population Techniques in Activity-Based Research

Cho

Bellemans

Creemers

et al. 2014

Data Science and Simulation in Transportation Research

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“…The attractiveness exponent was set at 0.5, for example, and the OAC beta values are visually rather than programmatically calibrated. Other impedance functions could be evaluated and tested through more detailed statistical evaluation (see Black and Salter 1975).…”

Section: Concluding Commentsmentioning

confidence: 99%

Geodemographics and spatial interaction: an integrated model for higher education

2010

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Spatial interaction modelling and geodemographic analysis have each developed as quite separate research traditions. In this paper, we present an integrated model that harnesses the power of spatial interaction modelling to behavioural insights derived from a geodemographic classification. This approach is applied to the modelling of participation in higher education (HE). A novel feature of the paper is the integration of national schools, colleges and HE data; a national model is then calibrated and tested against actual recorded flows of students into HE. The model is implemented within a Java framework and is presented as a first step towards providing a quantitative tool that can be used by HE stakeholders to explore policies relating to such topics as widening access to under-represented groups.

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“…The coefficient was also evaluated by Black & Salter (1975), who selected it as one of three measures for evaluating the fit of gravity models. They noted that the correlation coefficient was not very sensitive.…”

Section: Introductionmentioning

confidence: 99%

A note on the use of correlation coefficients for assessing goodness-of-fit in spatial interaction models

Black

1991

Transportation

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The use of correlation coefficients to evaluate the accuracy of spatial interaction models is inappropriate unless such models have been fitted using least squares techniques. In other cases the correlation involves an implicit intercept value and a regression coefficient that may significantly modify the interaction model's estimates. Researchers have not acknowledged the role of these two parameters when the correlation is used. A generalized root mean square error is proposed as an alternative indicator of accuracy that may be used with any model.There have been significant advances in the modeling of spatial interaction over the past thirty years (Black 1989). In general, spatial interaction models, which are more often called trip distribution models by planners, have their origins in an academic setting. Researchers evaluate the models developed using goodness-of-fit indices or statistics. If the models appear to be accurate they may be used in transport planning studies. In such applications the model is often calibrated to fit the study area flows. This calibration may involve the same indices or statistics used in the model's development, but more often it involves comparison of mean trip length or trip length frequency distributions for the observed and estimated flows and adjustments as merited.Advances in the development and testing of indices or statistics for measuring the accuracy or goodness-of-fit of models have not been as impressive as those in model development. Accuracy and goodness-of-fit are terms that have come to indicate the extent to which a set of model generated values replicate a set of observed data. It is apparent that these terms have something to say about error. For example, the better the goodness-of-fit or the more accurate the model, the lower the level of error. This note uses these terms interchangeably. 200Although there is no generally accepted indicator for assessing accuracy, the coefficients of correlation (r) and determination (r 2) are frequently utilized for this purpose. It will be demonstrated here that these coefficients are erroneous statistics for assessing the accuracy or goodness-of-fit of a flow model's elements, Eij , at replicating observed flows, Oij.The primary concern here is evaluating the accuracy of transport planning models during development. However, the increasing importance of geographic information systems in transportation and the anticipated availability of trip matrices from census collection efforts suggest that more accurate methods for assessing goodness-of-fit may be of concern for both the development and application fields in the near future.The note that follows briefly summarizes the application of correlation methods in flow modeling and notes criticisms that have been made regarding the use of these coefficients with flow matrices. An explanation and demonstration of why these coefficients do not usually measure the goodness-of-fit in the spatial interaction case follows. The note concludes with a recommended index of accuracy that...

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A Statistical Evaluation of the Accuracy of a Family of Gravity Models.

Cited by 20 publications

References 0 publications

Synthetic Population Techniques in Activity-Based Research

Synthetic Population Techniques in Activity-Based Research

Geodemographics and spatial interaction: an integrated model for higher education

A note on the use of correlation coefficients for assessing goodness-of-fit in spatial interaction models

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