Geographical Analysis

doi: 10.1111/j.1538-4632.1988.tb00158.xpmid: N/A

Anselin, Luc

doi: 10.1111/j.1538-4632.1988.tb00159.xpmid: N/A

Several diagnostics for the assessment of model misspecification due to spatial dependence and spatial heterogeneity are developed as an application of the Lagrange Multiplier principle. The starting point is a general model which incorporates spatially lagged dependent variables, spatial residual autocorrelation and heteroskedasticity. Particular attention is given to tests for spatial residual autocorrelation in the presence of spatially lagged dependent variables and in the presence of heteroskedasticity. The tests are formally derived and illustrated in a number of simple empirical examples.

Nijkamp, Peter; Reggiani, Aura

doi: 10.1111/j.1538-4632.1988.tb00160.xpmid: N/A

This paper deals with the design of general classes of dynamic spatial interaction models. On the basis of a general (well‐behaved) multiperiod objective function and of a dynamic model representing the evolution of a spatial interaction system, an optimal control model is constructed. Particular attention is given to the equilibrium and stability conditions. It turns out that it is possible to identify steady‐state solutions for a dynamic spatial interaction model. Furthermore, it can also be demonstrated that the entropy model is a specific case of this spatial interaction system. A simple illustration for urban dynamics is given as well.

Openshaw, Stan

doi: 10.1111/j.1538-4632.1988.tb00161.xpmid: N/A

A methodology is described for harnessing the power of supercomputer hardware to build an automated modeling system. The problems of applying this new approach to mathematical modeling in geography are discussed by reference to the design of better performing spatial interaction models. It is also possible that the results may provide clues about new theories and knowledge.

Fischer, Manfred M.; Aufhauser, Elisabeth

doi: 10.1111/j.1538-4632.1988.tb00162.xpmid: N/A

The paper integrates several important elements of the institutionalized and regulated nature of the housing market and analyzes the relationship between household type and housing choice in Vienna within a nested multinomial logit framework. In particular, the concept of household specific choice sets is used to account for institutional, informational and income‐based constraints of the choice process. Government subsidies (such as housing and rent allowances, non‐interest‐bearing state loans) are explicitly taken into account in the definition of variables. Housing choice is considered in three stages: the choice of a dwelling unit given dwelling type and residential zone, the choice of a dwelling type given residential zone, and the marginal choice of a residential zone. The coefficients are derived by means of a sequential ML‐procedure. The empirical results clearly indicate that demographic variables have significant impacts on housing choice behavior. Income as a single explanatory variable as well as its interacting with size measures and the quality of the house are found to be important criteria for dwelling unit choice, as housing cost variables for dwelling type choice behavior. The dwelling unit and dwelling type choice submodels fit reasonably well, whereas the residential zone model is less successful.

Griffith, Daniel A.

doi: 10.1111/j.1538-4632.1988.tb00163.xpmid: N/A

Zhang, Wei Bin

doi: 10.1111/j.1538-4632.1988.tb00164.xpmid: N/A

Moran, Warren

doi: 10.1111/j.1538-4632.1988.tb00165.xpmid: N/A

doi: 10.1111/j.1538-4632.1988.tb00166.xpmid: N/A

doi: 10.1111/j.1538-4632.1988.tb00167.xpmid: N/A