Development of robbery risk analysis tools: using the Australian and New Zealand Standard

2006 ◽  
Vol 6 (4/5/6) ◽  
pp. 456
Author(s):  
Rick Draper ◽  
Emma-Kate Rose
2020 ◽  
Author(s):  
Max Travers ◽  
Emma Colvin

In this paper we seek to review the rapid rise in remand in custody rates in Australia. In particular, and in response, we ask and discuss three specific questions: 1. To what extent do defendants applying for bail have vulnerabilities? 2. To what extent can risk analysis tools that seek to predict breach of bail terms be relied upon? 3. To what extent can the emerging pre-trial services programs in Australia reduce remand in custody populations?


Acta Tropica ◽  
2016 ◽  
Vol 158 ◽  
pp. 248-257 ◽  
Author(s):  
David F. Attaway ◽  
Kathryn H. Jacobsen ◽  
Allan Falconer ◽  
Germana Manca ◽  
Nigel M. Waters

EcoHealth ◽  
2016 ◽  
Vol 14 (S1) ◽  
pp. 30-41 ◽  
Author(s):  
Antonia Eleanor Dalziel ◽  
Anthony W. Sainsbury ◽  
Kate McInnes ◽  
Richard Jakob-Hoff ◽  
John G. Ewen

1992 ◽  
Vol 49 (5) ◽  
pp. 922-930 ◽  
Author(s):  
R. I C. C. Francis

Risk analysis can enhance the value of scientific advice to fishery managers by expressing the uncertainty inherent in stock assessments in terms of biological risk. I present a case study involving an overexploited population of orange roughy (Hoplostethus atlanticus) on the Chatham Rise, New Zealand. This analysis quantifies the risk to the fishery and shows how this decreases as the rate of reduction in total allowble catch increases. The technique helps fishery managers balance biological risk against economic risk. Ways of generalizing the technique are discussed.


2007 ◽  
Vol 64 (2) ◽  
pp. 256-270 ◽  
Author(s):  
Marnie L. Campbell ◽  
Charmaine Gallagher

Abstract Campbell, M. L. and Gallagher, C. 2007. Assessing the relative effects of fishing on the New Zealand marine environment through risk analysis – ICES Journal of Marine Science, 64: 256–270. Risk analysis is a tool often used by management to aid decision-making. We present a risk-analysis framework that was developed to facilitate managing New Zealand fisheries. Using catch-effort and observer data, the likelihood that a certain fishery will impact upon five effects of fishing (EoF) issues (non-target species, biodiversity, habitat, trophic interactions, and legislated protected species) is determined. The consequences (impact and/or change) of such events are then determined to determine a relative risk ranking across fisheries. Consequence matrices were developed to assess each of the five EoF categories. To illustrate the model, a 13-y data set of New Zealand fisheries catch-effort and observer data was analysed, using orange roughy (Hoplostethus atlanticus) as an example fishery. The New Zealand fisheries management framework follows a traditional model in which socio-political imperatives are determined (through risk assessment) after ecological impacts are assessed. By maintaining separation between ecological and socio-political imperatives, a transparent and objective framework is established.


2012 ◽  
Vol 1 (1) ◽  
pp. 17
Author(s):  
Mohammad Farhan Qudratullah

Since the signed memorandum of understanding between BAPEPAM with Dewan Syariah Nasional-Majelis Ulama Indonesia (DSN-MUI) on the principle of Islamic capital market in 2003, the Islamic capital market in Indonesia has developed significantly. In each investment, including Islamic capital market investment, there are 2 (two) fundamental things that always accompany it, the return and risks. This paper discusses the analysis of return and risk of sharia stocks that always go in Jakarta Islamic Index (JII) after the global crisis in 2008, risk analysis tools using Value at risk (VaR) approach to model the Generalized Autoregressive Conditional  Heteroscedastic (GARCH), then proceed with the analysis of the typology to determine the characteristics of these stocks. The results that shares sharia can be grouped into 4 (four) :  6 (six) shares entering the low return and low risk (TLKM, UNVR, SMGR, AALI, ELSA, and SGRO), 3 (three ) shares into group of low-return but high risk (INCO, ANTM, and TINS), 3 (three) shares enter the group of low risk but high return (PTBA, LSIP, and KLBF), and 4 (four) shares enter the group high return but high risk (ITMG, ASII, INTP, and BMTR).


2018 ◽  
Vol 11 (4) ◽  
pp. 91-116
Author(s):  
Paulo Mannini ◽  
Edmir Parada Vasques Prado

Risk management is one of the fundamental points for the success of projects to implement an Enterprise Resource Planning (ERP). One aspect that significantly influences the projects and that should be considered in the risk analysis is the seasonality, although it has been low discussed in the literature. In this sense, this work aims to identify and analyze the most appropriate risk analysis tools for ERP implementation projects influenced by seasonal uncertainties. To achieve the goal, this research was composed by a Systematic Review of Literature and the application of the Delphi technique with Project Management Professionals. The result obtained with this research was the identification of eight more suitable tools to analyze risks in ERP implementation projects influenced by seasonal uncertainties. It was also analyzed separately were the amounts assigned to the risk analysis tools by the Delphi Panel participants.


2021 ◽  
Author(s):  
◽  
Gabriele Hufschmidt

<p>The aim of this research is to identify temporal changes of risk from landsliding for several locations in New Zealand (the Western Hutt Hills, close to Wellington; Te Arai, close to Gisborne; Mt.Cook/Aoraki Village, South Island). While risk analysis usually targets a particular point in time, this research includes several five-year intervals (based on census years) starting in 1981 until 2006. The scale of this analysis is the community level. Risk is not expressed as an absolute level of loss, for example a dollar value or the number of fatalities. Risk is rather considered as the probability and extent of adverse effects on a community inferred from landsliding. As such, risk is relative: the aim is to quantify risk for a community relative to another point in time, and relative to other communities. In addition, the degree to which risk levels vary between communities is quantified. The objectives of the risk analysis are to: 1. establish landslide hazard, i.e. the frequency and magnitude of landsliding for each location, 2. develop an index of social vulnerability per census year and community, 3. develop an index of social resilience per census year and community, 4. combine 1.-3. and, together with exposure ('elements at risk'), determine risk from landsliding for each community through time.</p>


2010 ◽  
Author(s):  
Stuart Anderson ◽  
Keith Molenaar ◽  
Cliff Schexnayder ◽  
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