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Centrographic measures of spatial dispersion, such as the standard distance, provide a numerical value to summarize the radial scattering of a set of points around their centre of gravity or centroid. This paper develops a procedure to test for the statistical significance of differences in dispersion between two sets of phenomena intertwined in space. The significance test is implemented using a resampling randomization procedure based on the pooled locations from both sets to estimate the sampling distribution of their differences. Repeated thousands of times, that yields empirical frequency thresholds of the sampling distributions to assess the statistical significance of the observed differences. Case studies based on residential locations of lone‐parent families and retired couples in the Quebec City Metropolitan Area illustrate the procedure. This paper shows how randomization procedures can be used to adapt classical tests to assess the statistical significance of differences between indices of spatial dispersion. [ABSTRACT FROM AUTHOR]

Laws and bylaws are regularly challenged for their effectiveness in containing urbanisation sprawl and, conversely, for the constraints they put on development projects. From a French case study, we used survival analysis to disentangle a complex mix of influences on the distribution of residential construction over a 42-year observation period at plot scale when almost everything changes simultaneously. We found that integrated laws and bylaws can slow down coastal urbanisation but do not stop it. Although land planning is becoming more effective, it still provides ample opportunity for residential development because other factors, like distance to existing infrastructure, exert a far stronger influence than the protection of coastal areas. Therefore, this article contributes to filling a knowledge gap about the founding role of public policies on land use dynamics. [ABSTRACT FROM AUTHOR]

It is now almost impossible to deal with spatial data without considering some explicit specification that captures possible spatial effects. One valuable feature of spatial econometrics models is their decomposition of marginal effects into spatial spillover effect and spatial externalities. Progress in interpreting spatial econometrics models has now been extended to the spatial-panel case. However, little consideration has been given to the possible interpretation of models using spatial data pooled over time. This paper proposes a spatio-temporal difference-in-differences (STDID) estimator to measure the effect of urban externalities, such as transport infrastructures, as revealed through real-estate prices. Based on an empirical application for a new development of commuter trains in the Montreal suburbs, this paper shows how such propositions can help us to better understand and evaluate changes in mass transit systems. [ABSTRACT FROM AUTHOR]