Single and Multiscale Models of Process Spatial Heterogeneity

Recent work in local spatial modeling has affirmed and broadened interest in multivariate local spatial analysis. Two broad approaches have emerged: Geographically Weighted Regression (GWR) which follows a frequentist perspective and Bayesian Spatially Varying Coefficients models. Although several comparisons between the two approaches exist, recent developments, particularly in GWR, mean that these are incomplete and missing some important axes of comparison. Consequently, there is a need for a more thorough comparison of the two families of local estimators, including recent developments in multiscale variants and their relative performance under controlled conditions. We find that while both types of local models generally perform similarly on a series of criteria, some interesting and important differences exist.     

Death by a Thousand Cuts: an archaeological assessment of souveniring and salvage on the Australian cruiser HMAS Perth (I)

In 2014, the Australian National Maritime Museum (ANMM) received reports from recreational divers that the shipwreck site of HMAS Perth (I) was being systematically salvaged by commercial divers. After extensive discussions with Indonesian Government departments and agencies the ANMM led the first Australian/Indonesian remote sensing survey of Perth in December 2016. This was followed by an in‐water survey in May 2017. These investigations revealed Perth has been devastated by systematic, large‐scale unauthorized salvage. Following the survey, ANMM and its Indonesian research partner Pusat Penelitian Arkeologi Nasional (ARKENAS), working in conjunction with the Royal Australian Navy and Australian Department of Foreign Affairs and Trade, successfully lobbied the Indonesian Government to have the site declared Indonesia's first Marine Protected Area.     

Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

Spatial nonstationarity is a condition in which a simple “global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geographically weighted regression, which attempts to capture this variation by calibrating a multiple regression model which allows different relationships to exist at different points in space. This technique is loosely based on kernel regression. The method itself is introduced and related issues such as the choice of a spatial weighting function are discussed. Following this, a series of related statistical tests are considered which can be described generally as tests for spatial nonstationarity. Using Monte Carlo methods, techniques are proposed for investigating the null hypothesis that the data may be described by a global model rather than a non-stationary one and also for testing whether individual regression coefficients are stable over geographic space. These techniques are demonstrated on a data set from the 1991 U.K. census relating car ownership rates to social class and male unemployment. The paper concludes by discussing ways in which the technique can be extended.