As part of the 2018 National Seismic Hazard Assessment (NSHA), we compiled the geographic information system (GIS) dataset to enable end-users to view and interrogate the NSHA18 outputs on a spatially enabled platform. It is intended to ensure the NSHA18 outputs are openly available, discoverable and accessible to both internal and external users. This geospatial product is derived from the dataset generated through the development of the NSHA18 and contains uniform probability hazard maps for a 10% and 2% chance of exceedance in 50 years. These maps are calculated for peak ground acceleration (PGA) and a range of response spectral periods, Sa(T), for T = 0.1, 0.2, 0.3, 0.5, 1.0, 2.0 and 4.0 s. Additionally, hazard curves for each ground-motion intensity measure as well as uniform hazard spectra at the nominated exceedance probabilities are calculated for key localities.

Soil is a ubiquitous material at the Earth's surface with potential to be a useful evidence class in forensic and intelligence applications. Compositional data from a soil survey over North Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier Transform InfraRed spectroscopy (FTIR) and geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for both total and aqua regia-soluble trace elements) are obtained from the survey's 268 topsoil samples (0–5 cm depth; 1 sample per km2). The simultaneous provenancing approach is underpinned by (i) the calculation of Spearman's correlation coefficients (rS) between an evidentiary sample and all the samples in the database for all variables generated by each analytical method; and (ii) the preparation of an interpolated raster grid of rS for each evidentiary sample and method resulting in a series of provenance rasters (“heat maps”). The simultaneous provenancing method is tested on the North Canberra soil survey with three “blind” samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR (mineralogy) and XRF (geochemistry) analytical methods offer the most precise and accurate provenance predictions. Maximizing the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications. <b>Citation:</b> de Caritat, P, Woods, B, Simpson, T, Nichols, C, Hoogenboom, L, Ilheo, A, et al. Forensic soil provenancing in an urban/suburban setting: A simultaneous multivariate approach. <i>J Forensic Sci</i>. 2022; 67: 927–935. https://doi.org/10.1111/1556-4029.14967

Compositional data from a soil survey over north Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier Transform InfraRed spectroscopy (FTIR) and Magnetic Susceptibility (MS), and geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for both total and aqua regia-soluble trace elements), are performed on the survey’s 268 topsoil samples (0-5 cm depth; 1 sample per km2). Principal components (PCs) are calculated after imputation of censored data and centred logratio transformation. The sequential provenancing approach is underpinned by (i) the preparation of interpolated raster grids of the soil properties (including PCs); (ii) the explicit quantification and propagation of uncertainty; (iii) the intersection of the soil property rasters with the values of the evidentiary sample (± uncertainty); and (iv) the computation of cumulative provenance rasters (‘heat maps’) for the various analytical techniques. The sequential provenancing method is tested in the north Canberra soil survey with three ‘blind’ samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR and MS (mineralogy), as well as XRF and total ICP-MS (geochemistry) analytical methods offer the most precise and accurate provenance predictions. Inclusions of PCs in provenancing adds marginally to the performance. Maximising the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations and gaps, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications. <b>Citation:</b> de Caritat, P., Woods, B., Simpson, T., Nichols, C., Hoogenboom, L., Ilheo, A., Aberle, M.G. and Hoogewerff, J. (2021), Forensic soil provenancing in an urban/suburban setting: A sequential multivariate approach. <i>J Forensic Sci</i>, 66: 1679-1696. https://doi.org/10.1111/1556-4029.14727

Soil is a common evidence type used in forensic and intelligence operations. Where soil composition databases are lacking or inadequate, we propose to use publicly available soil attribute rasters to reduce forensic search areas. Soil attribute rasters, which have recently become widely available at high spatial resolutions, typically three arc-seconds (~90 m), are predictive models of the distribution of soil properties (with confidence limits) derived from data mining the inter-relationships between these properties and several environmental covariates. Each soil attribute raster is searched for pixels that satisfy the compositional conditions of the evidentiary soil sample (target value ± confidence limits). We show through an example that the search area for an evidentiary soil sample can be reduced to <10% of the original investigation area. This Predictive Soil Provenancing (PSP) approach is a transparent, reproducible and objective method of efficiently and effectively reducing the likely provenance area of forensic soil samples. <b>Citation:</b> de Caritat, P., Simpson, T. and Woods, B. (2019), Predictive Soil Provenancing (PSP): An Innovative Forensic Soil Provenance Analysis Tool. <i>J Forensic Sci</i>, 64: 1359-1369. https://doi.org/10.1111/1556-4029.14060