Mining work, family and community : a spatially-oriented approach to the impact of the Ravensthorpe nickel mine closure in remote Australia

While changes in work and employment practices in the mining sector have been profound, the literature addressing mining work is somewhat partial as it focuses primarily on the workplace as the key (or only) site of analysis, leaving the relationship between mining work and families and communities under-theorized. This article adopts a spatially oriented, case-study approach to the sudden closure of the Ravensthorpe nickel mine in the south-west of Western Australia to explore the interplay between the new scales and mobilities of labour and capital and work–family–community connections in mining. In the context of the dramatically reconfigured industrial arena of mining work, the study contributes to a theoretical engagement between employment relations and the spatial dimensions of family and community in resource-affected communities.

Granule mining and its application for network traffic characterization

Decision table and decision rules play an important role in rough set based data analysis, which compress databases into granules and describe the associations between granules. Granule mining was also proposed to interpret decision rules in terms of association rules and multi-tier structure. In this paper, we further extend granule mining to describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other ganules, it provides a kind of novel knowledge in databases. Some experiments are conducted to test the proposed new concepts for describing the characteristics of a real network traffic data collection. The results show that the proposed concepts are promising.

A data mining driven risk profiling method for road asset management

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach, which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this chapter we propose two approaches which measure multi-level association rules to help evaluate their interestingness by considering the database’s underlying taxonomy. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Structured feature extraction using association rules

As of today, opinion mining has been widely used to iden- tify the strength and weakness of products (e.g., cameras) or services (e.g., services in medical clinics or hospitals) based upon people's feed- back such as user reviews. Feature extraction is a crucial step for opinion mining which has been used to collect useful information from user reviews. Most existing approaches only find individual features of a product without the structural relationships between the features which usually exists. In this paper, we propose an approach to extract features and feature relationship, represented as tree structure called a feature hi- erarchy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature hierarchy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that the proposed feature extraction approach can identify more correct features than the baseline model. Even though the datasets used in the experiment are about cameras, our work can be ap- plied to generate features about a service such as the services in hospitals or clinics.

Oscar Pistorius and the death of Reeva Steenkamp : mapping public opinions on Twitter using frame analysis and data mining

This paper uses innovative content analysis techniques to map how the death of Oscar Pistorius' girlfriend, Reeva Steenkamp, was framed on Twitter conversations. Around 1.5 million posts from a two-week timeframe are analyzed with a combination of syntactic and semantic methods. This analysis is grounded in the frame analysis perspective and is different than sentiment analysis. Instead of looking for explicit evaluations, such as “he is guilty” or “he is innocent”, we showcase through the results how opinions can be identified by complex articulations of more implicit symbolic devices such as examples and metaphors repeatedly mentioned. Different frames are adopted by users as more information about the case is revealed: from a more episodic one, highly used in the very beginning, to more systemic approaches, highlighting the association of the event with urban violence, gun control issues, and violence against women. A detailed timeline of the discussions is provided.

The Australian mining industry and the ideal mining woman : mobilizing a public business case for gender equality

Despite ongoing ‘boom’ conditions in the Australian mining industry, women remain substantially and unevenly under-represented in the sector, as is the case in other resource-dependent countries. Building on the literature critiquing business-case rationales and strategies as a means to achieve women’s equality in the workplace, we examine the business case for employing more women as advanced by the Australian mining industry. Specifically, we apply a discourse analysis to seven substantial, publically-available documents produced by the industry’s national and state peak organizations between 2005 and 2013. Our study makes two contributions. First, we map the features of the business case at the sectoral rather than firm or workplace level and examine its public mobilization. Second, we identify the construction and deployment of a normative identity – ‘the ideal mining woman’ – as a key outcome of this business-case discourse. Crucially, women are therein positioned as individually responsible for gender equality in the workplace.

Interpretation of association rules in multi-tier structures

Dealing with the large amount of data resulting from association rule mining is a big challenge. The essential issue is how to provide efficient methods for summarizing and representing meaningful discovered knowledge from databases. This paper presents a new approach called multi-tier granule mining to improve the performance of association rule mining. Rather than using patterns, it uses granules to represent knowledge that is implicitly contained in relational databases. This approach also uses multi-tier structures and association mappings to interpret association rules in terms of granules. Consequently, association rules can be quickly assessed and meaningless association rules can be justified according to these association mappings. The experimental results indicate that the proposed approach is promising

Using the SWAP to connect water, human rights and mining

It is important that industries’ water interactions respect the human right to water. Historically, within the mining industry there has been a disconnect between the management of sites’ internal water interactions and the consequences of their external impacts, including human rights impacts. This poses a challenge for the mining industry as it attempts to put the Ruggie Guiding Principles for Business and Human Rights into practice, particularly as United Nations has recently recognised the human right to water. A technical framework such as the Minerals Council of Australia’s Water Accounting Framework (WAF) can help to bridge this disconnect and to integrate human rights considerations into business practice by connecting a site’s external and internal water interactions and by encouraging regular monitoring of performance. However, at present the connection is limited since the WAF lacks the capability to formalise a site’s social water context. This work presents the Social Water Assessment Protocol (SWAP), a scoping tool consisting of a set of questions organised into taxonomic themes that capture a site’s social water context and that can be combined with the WAF to better connect human rights with mine water interactions.

Mining, sex work and STIs: why force a connection?

Can the mining boom be blamed for the rising rates of sexually transmitted infections (STIs) in some states? The Australian Medical Association thinks so, with its Queensland president Dr Richard Kidd attributing rising rates of gonorrhoea, syphilis and chlamydia in Queensland and Western Australia to bored and cashed-up miners.

Obstacle detection for a mining vehicle using a 2D laser

This paper discusses a number of key issues for the development of robust obstacle detection systems for autonomous mining vehicles. Strategies for obstacle detection are described and an overview of the state-of-the-art in obstacle detection for outdoor autonomous vehicles using lasers is presented, with their applicability to the mining environment noted. The development of an obstacle detection system for a mining vehicle is then detailed. This system uses a 2D laser scanner as the prime sensor and combines dead-reckoning data with laser data to create local terrain maps. The slope of the terrain maps is then used to detect potential obstacles.

Issues in obstacle detection for autonomous mining and construction vehicles

This paper discusses a Dumber of key issues for the development of robust, obstacle detection systems for autonomous mining and construction vehicles. A taxonomy of obstacle detection systems is described; An overview of the state-of- the-art in obstacle detection for outdoor autonomous vehicles is presented with their applicability to the mining and construction environments noted. The issue of so-called fail-safe obstacle detection is then discussed. Finally, we describe the development of an obstacle detection system for a mining vehicle.

Swing load stabilization for mining and construction applications

This paper discusses the issue of sensing and control for stabilizing a swinging load. Our work has focused in particular on the dragline as used for overburden stripping in open-pit coal mining, but many of the principles would also be applicable to construction cranes. Results obtained from experimental work on a full-scale production dragline are presented.

Visual control of an electrohydraulic manipulator for underground mining applications

A large range of underground mining equipment makes use of compliant hydraulic arms for tasks such as rock-bolting, rock breaking, explosive charging and shotcreting. This paper describes a laboratory model electo-hydraulic manipulator which is used to prototype novel control and sensing techniques. The research is aimed at improving the safety and productivity of these mining tasks through automation, in particular the application of closed-loop visual positioning of the machine's end-effector.