Using bootstrap methods to investigate coefficient non-stationarity in regression models: an empirical case study

In this study, parametric bootstrap methods are used to test for spatial non-stationarity in the coefficients of regression models (i.e. test for relationship non-stationarity). Such a test can be rather simply conducted by comparing a model such as geographically weighted regression (GWR) as an alternative to a standard regression, the null hypothesis. However here, three spatially autocorrelated regressions are also used as null hypotheses: (i) a simultaneous autoregressive error model; (ii) a moving average error model; and (iii) a simultaneous autoregressive lag model. This expansion of null hypotheses, allows an investigation as to whether the spatial variation in the coefficients obtained using GWR could be attributed to some other spatial process, rather than one depicting non-stationary relationships. In this short presentation, the bootstrap approach is applied empirically to an educational attainment data set for Georgia, USA. Results suggest value in the bootstrap approach, providing a more informative test than any related test that is commonly applied

Is `Statistix Inferens' Still the Geographical Name for a Wild Goose?

After the recent death of Peter Gould, I decided to look once again at his insightful paper `Is Statistix Inferens the Geographical Name for a Wild Goose?' (Gould 1970) - hence the title for this guest editorial. For those readers who have not seen this article, Gould outlines a number of shortcomings of the common statistical practices of geographers of the day

‘This is not a cinema’ : the projectionist’s tale

‘This is not a cinema’, proclaims the screen in my local Vue cinema in 2017, just before the main feature is shown. This declaration is followed by footage of sports, opera and music festivals, and the claim of a new, plural identity, ‘This is big screen entertainment’, which is spelled out in words and images as ‘Big screen theatre’, ‘Big screen sports’, ‘Big screen opera’ and ‘Big screen festival’. This campaign is a branding ident with a difference for Vue cinemas, the smallest of the three major British chains.1 For what is being negotiated is the identity of the medium, and the drive of the promotion is to diminish the association of cinema buildings and feature films, and replace it with an image of more diverse big screen ‘content delivery’. This is not a cinema – which, as a dedicated building, might seem a bit twentieth century – but something rather more modern, like a portal. A portal – or a computer screen. However, as most of the audience will have very powerful, very small personal computers in their pockets, this association is disavowed through the repetition of ‘BIG’. This is not a cinema; it is somewhere that other events can be experienced both large and loud at premium prices. This policy of the redesignation of cinema, however, is shot through with ambivalence. The short promotional film advocating the spatial and temporal transcendence now available in this not-cinema building is immediately followed by an advertisement for the benefits of Sony 4K, with an instructional edge, informing viewers that now is the time to settle down, turn everything off and enjoy the dark. To behave as if it is a cinema

Quantitative methods I: Reproducible research and quantitative geography.

Reproducible quantitative research is research that has been documented sufficiently rigorously that a third party can replicate any quantitative results that arise. It is argued here that such a goal is desirable for quantitative human geography, particularly as trends in this area suggest a turn towards the creation of algorithms and codes for simulation and the analysis of Big Data. A number of examples of good practice in this area are considered, spanning a time period from the late 1970s to the present day. Following this, practical aspects such as tools that enable research to be made reproducible are discussed, and some beneficial side effects of adopting the practice are identified. The paper concludes by considering some of the challenges faced by quantitative geographers aspiring to publish reproducible research

Geographical and temporal weighted regression (GTWR)

Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19-year set of house price data in London from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling