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This paper discusses the impact of housing affordability on the spatial distribution of productivity and wages. The key theoretical contribution is to phrase the problem as an issue of the composition (rather than the level) of housing demand and link it to heterogeneous preferences and characteristics of households. Using a simple simulation methodology, the study estimates levels of amenity values and wages that would make current house prices as affordable as they were in 1995 in all English local authority districts. Although average wages would be unlikely to increase if housing was more affordable, productivity across England would probably be higher as the spatial distribution of economic activity would change. The key conclusions are that (1) unaffordable housing has significant economic implications; and (2) policy aimed at improving housing affordability should consider targeting housing demand as well as supply. [ABSTRACT FROM AUTHOR]

The increasing development of three-dimensional virtual city models and leadingedge computer software applications is providing innovative possibilities for analyzing the performance of existing city-centre public squares. In the design and assessment of city squares, the use of accurate virtual city models is often limited to visual geometrical assessment alone. There is little evidence that such models are being adapted to carry out urban performance simulations. There are, however, existing and emerging tools that can simulate a number of performance aspects—pedestrian movement, noise level, wind movement, and temperature—that show scope for integrating virtual city models to aid in the assessment of public squares. This paper describes a study which investigates the interoperability of off-the-shelf three-dimensional virtual city models to integrate with selected ‘urban performance’ software to contribute to a more integrated approach to the assessment of existing public squares and the future sustainability of virtual city models. Methodologies for utilising virtual city models within ‘urban performance’ software are established, with results demonstrating that the integration of virtual city model data can aid in both the visual and performance assessment of existing public squares, with scope for application to new proposals. The argument is also made that the application of virtual city models in this manner also contributes towards the sustainability of virtual city models, one that takes a more multifunctional approach. This paper acknowledges that the majority of the evaluated software is not related directly to urban design—indeed, there is no software currently available that brings together all the performance aspects and relates them to geometrical characteristics. However, this study offers a significant contribution to this subject and identifies the need for future research into the evolution of information-rich virtual city models. [ABSTRACT FROM AUTHOR]

This paper is concerned with the way in which airspaces are organised, managed, and understood by virtual representations—software simulations that are tested and used both preemptively and in real time. We suggest that, while airspaces are often understood as simulations themselves—models and blueprints for real-world futures—they are among the most mediated of all contemporary social environments, produced not only through code, but based on scenarios which predict and plan for future events—real virtualities that might come true. Drawing on historical and contemporary examples of aeronautical software simulation employed by civilian and military aviation, we explore how code has become increasingly sophisticated and ubiquitous in response to the challenges set by the mobilities the simulations model and the affective susceptibility of the corporeal body that uses them. The paper explores how software simulations work to structure and mediate behaviour by producing specific emotional and affective experiences in order to prepare the body for future encounters. [ABSTRACT FROM AUTHOR]

We evaluate by means of Monte Carlo simulations the W-based spatial autoregressive model and the structural equation model with latent variables (SEM) to handle two types of simultaneously occurring weak spatial dependence: (i) spillover from a hotspot, and (iia) spillover from first-order, queen contiguous neighbours, (iib) inversedistance- related spatial units, (iic) the first three nearest neighbours. Although it is possible to account for several different types of weak spatial lag dependence by several different weights matrices, the W-based approach usually proceeds on the basis of a single weights matrix that is implicitly assumed to adequately capture all types of spatial dependence. SEM on the other hand has been developed to explicitly distinguish between different types of weak spatial dependence in a model. In addition, whereas conventional higher order spatial econometric models entail specification and parameter space definition problems, SEM spatial dependence models can be routinely specified and estimated. In this paper we briefly discuss the pitfalls of including several types of weak spatial lag dependence (and thus different W matrices) in a standard W-based lag model and how these problems are mitigated by the SEM approach. Furthermore, the single W-based and the SEM approach are compared by means of simulations in terms of bias and root mean squared error (RMSE) for different values of the spatial lag parameters, specifications of the weights matrices, and sample sizes. We also include in the comparison the W-based approach with spatial dependence accounted for by the two weights matrices used to generate the data: that is, the correctly specified model. The simulation results show that compared with the single W-based models, SEM frequently has smaller bias and RMSE. Furthermore, SEM even outperforms the correctly specified W-based models (based on the same two weights matrices used for data generation) in many cases. These trends increase when the values of the spatial lag parameters increase. The dominance of SEM also increases with sample size. Finally, SEM is more stable in terms of both bias and RMSE over various dimensions. [ABSTRACT FROM AUTHOR]

This paper explores the question of whether natural hazard risks for urban areas are growing or diminishing over time. While trends such as population growth in hazardous areas increase the potential for loss in disaster events, other factors, such as improved building codes, tend to reduce this risk. The net effects of such urban changes are examined through the use of simulation models that estimate disaster losses. In a comparative static approach, losses are modeled for the same hypothetical hazard event striking at different points in an urban area's development history. The analysis focuses on a case study of seismic hazard for the metropolitan area of Vancouver, Canada. Results of a casualty-estimation model applied retrospectively indicate that, for the same M7.3 seismic event, human casualties would have been similar if the earthquake had struck in 2006 as in 1971, even though the population at risk had doubled in the interim. This improvement in per capita casualty rate is largely due to building-code upgrades and changes in new construction. Other dimensions of risk worsened, however: the estimated population in significantly damaged buildings increased by some 60% for the region and, in some fast-growing municipalities, more than doubled. Using a similar analytical approach, a second modeling effort focused on transportation systems and considered the risk implications of future growth. Analysis compared transportation disruption in 2004 and 2021 for a constant earthquake hazard but a forecast 21% increase in traffic. Results indicate that, for the Vancouver metropolitan area overall, disaster risk to transportation is expected to decrease slightly due to the shifting geography of residences and workplaces. The spatial distribution of risk would change, however: in particular, it would worsen for the older urban core. Moreover, the relative importance of key bridges changed over time. The analyses here indicate that in this case study, risk is not worsening significantly overall but is undergoing notable spatial redistribution; however, the extent to which this finding can be generalized to other urban areas and types of hazards remains to be investigated. [ABSTRACT FROM AUTHOR]

Model simulations of residential segregation have shown that even modest levels of social homophily (or wishing to live near residents with similar social characteristics) gives rise to distinct spatial patterns of residential segregation. However, this proposition has been contested where social homophily is modest. This paper contrasts two explanations for urban sociospatial patterns (socioeconomic and demographic spatial patterns) in a region where social homophily is modest-South East Queensland (SEQ). The research question is whether sociospatial patterns are better explained by social homophily or by structural homophily. In other words, are they better explained by residents wishing to live in neighborhoods with similar people (social homophily), or by residents with similar social characteristics finding similar neighborhood physical attributes important, and thus moving to neighborhoods with similar people (structural homophily). SEQ residents were asked how important various reasons were in choosing their neighborhood. The survey data were linked to neighborhood social characteristics from census data with the aid of geographic information systems. Six neighborhood social characteristics in SEQ were investigated. Social homophily explained a small, though statistically significant, level of spatial variation in socioeconomic and ethnic (non-Western) environments. However, it did not explain any variation in the other four neighborhood social characteristics which related to household structure: that is, younger nonnuclear household environments; nuclear family environments; and older nonnuclear household environments, or disadvantaged environments. Moreover, structural homophily explained much more variation than did social homophily in all six neighborhood social characteristics. In regions such as SEQ, spatial patterns can largely be explained by structural homophily. Thus, modest levels of social homophily are not necessarily important in explaining sociospatial patterning. [ABSTRACT FROM AUTHOR]

Simulation models are increasingly used in applied research to create synthetic micro- populations and predict possible individual-level outcomes of policy intervention. Previous research highlights the relevance of simulation techniques in estimating the potential outcomes of changes in areas such as taxation and child benefit policy, crime, education, or health inequalities. To date, however, there is very little published research on the creation, calibration, and testing of such micropopulations and models, and little on the issue of how well synthetic data can fit locally as opposed to globally in such models. This paper discusses the process of improving the process of synthetic micropopulation generation with the aim of improving and extending existing spatial micro- simulation models. Experiments using different variable configurations to constrain the models are undertaken with the emphasis on producing a suite of models to match the different sociodemographic conditions found within a typical city. The results show that creating processes to generate area-specific synthetic populations, which reflect the diverse populations within the study area, provides more accurate population estimates for future policy work than the traditional global model configurations. [ABSTRACT FROM AUTHOR]

This paper addresses interagent interactions, an issue that has received limited attention in travel behavior research. Drawing upon the theory of externalities and the sociological notion of social networks, we develop a discrete choice model that incorporates elements of social influence in addition to more conventional factors such as the attributes of alternatives and the characteristics of decisionmakers. Using simulation, we apply the model to the case of telecommuting—that is, the decision to telecommute or not—over two waves. The experiment suggests that some marginal adopters of telecommuting are influenced heavily in the second wave by the decisions of others in the first wave. Furthermore, the example illustrates the importance of social influence on new adopters of telecommuting in the second wave. [ABSTRACT FROM AUTHOR]

In this paper we revisit the excess-commuting technique and its links with urban form. The uncertainty in measurement is highlighted, as are the problems relating to changes in excess com- muting over time. The measure of the theoretical maximum commute is proposed and added to the traditional excess-commuting measure so that the use of both the minimum and maximum levels can capture the concept of commuting potential. This measure is what we call the ‘extended excess-commuting measure’. These concepts are tested through the use of a simulation exercise. As well as arguing for the inclusion of socioeconomic variables in analysis, we demonstrate that decentralisation in urban spatial structure can lead to either an increase or a decrease in average commuting distance. Some of the inconsistencies in the use of excess commuting can be reduced through the use of actual commutes together with the commuting range, as these factors in combination lead to a clearer understanding of commuting efficiency. [ABSTRACT FROM AUTHOR]

A mixed geographically weighted regression (MGWR) model is a kind of regression model in which some coefficients of the explanatory variables are constant, but others vary spatially. It is a useful statistical modelling tool in a number of areas of spatial data analysis. After an MGWR model is identified and calibrated, which has been well studied recently, one of the important inference problems is to evaluate the influence of the explanatory variables in the constant-coefficient part on the response of the model. This is useful in the selection of the variables and for the purpose of explanation. In this paper, a statistical inference framework for this issue is suggested and, besides the F-approximation, which has been frequently used in the literature of the geographically weighted regression technique, a bootstrap procedure for deriving the p-value of the test is also suggested. The performance of the test is investigated by extensive simulations. It is demonstrated that both the F-approximation and the bootstrap procedure work satisfactorily. [ABSTRACT FROM AUTHOR]

Geographic information systems (GIS) offer attractive tools for modelling recreational behaviour. In this paper GIS is combined with a discrete choice modelling approach to investigate the importance of landscape attributes for determining forest recreational choices. A new large Danish national dataset (28 947 recreational trips) is used in conjunction with an area information system and information on other site attributes to estimate a recreational choice model. A wide set of landscape characteristics influencing recreational choice is identified. These are the size of the forest patch and adjacency or distance to other land-cover types. The area of the forest patch was found to have a positive marginally declining effect. Adjacency to other seminatural areas was found to have a positive effect. The results show that the parameter estimates depend critically on the choice-set specification and that the size of the choice set needs to be considerably larger than previous research suggests. [ABSTRACT FROM AUTHOR]