Resolving the conflicts of sustainable world heritage landscapes in cities : fully open or limited access for visitors?

World Heritage Landscapes (WHLs) are receiving increased attention from researchers, urban planners, managers, and policy makers and many heritage values and resources are becoming irreversibly lost. This phenomenon is especially prominent for WHLs located in cities, where greater development opportunities are involved. Decision making for sustainable urban landscape planning, conservation and management of WHLs often takes place from an economic perspective, especially in developing countries. This, together with the uncertain source of funding to cover WHL operating and maintenance costs, has resulted in many urban managers seeking private sector funding either in the form of visitor access fees or leasing part of the site for high-rental facilities such as five star hotels, clubs and expensive restaurants. For the former, this can result in low-income urban citizens being unable to afford the access fees and hence contradicting the principle of equal access for all; while, for the latter, the principle of open access for all is equally violated. To resolve this conflict, a game model is developed to determine how urban managers should allocate WHL spaces to maximize the combination of economic, social and ecological benefits and cultural values. A case study is provided of the Hangzhou's West Lake Scenic Area, a WHL located at the centre of Hangzhou city, in which several high-rental facilities have recently been closed down by the local authorities due to charges of elitism and misuse of public funds by government officials. The result shows that the best solution is to lease a small space with high rents and leave the remainder of the site to the public. This solution is likely to be applicable only in cities with a strong economy.

Practical analysis of big acoustic sensor data for environmental monitoring

Monitoring the environment with acoustic sensors is an effective method for understanding changes in ecosystems. Through extensive monitoring, large-scale, ecologically relevant, datasets can be produced that can inform environmental policy. The collection of acoustic sensor data is a solved problem; the current challenge is the management and analysis of raw audio data to produce useful datasets for ecologists. This paper presents the applied research we use to analyze big acoustic datasets. Its core contribution is the presentation of practical large-scale acoustic data analysis methodologies. We describe details of the data workflows we use to provide both citizen scientists and researchers practical access to large volumes of ecoacoustic data. Finally, we propose a work in progress large-scale architecture for analysis driven by a hybrid cloud-and-local production-grade website.

Do Big-Four affilitates earn audit fee premiums in emerging markets?

The paper investigates whether Big-Four affiliated (B4A) firms earn audit premiums in an emerging economy context, using Bangladesh as a case. The joint determination of audit and non-audit service fees is also examined using a sample of 122 companies listed in the Dhaka Stock Exchange. Our findings reveal that although the B4A firms do not generally earn a fee premiumin Bangladesh, they charge higher audit fees for clients not purchasing non-audit services. This suggests that the B4A firms may actually lower audit fees to attract non-audit services, and cross subsidizes audit fees through non-audit-services fees. The lack of a B4A premium implies that there is lack of quality audit in emerging markets. We also document that audit and non-audit service fees are jointly determined in Bangladesh. Thus, we provide evidence of joint determination of audit and non-audit service fees in an emerging economy context.

Hydrostratigraphic analysis of the MADE site with full-resolution GPR and direct-push hydraulic profiling

Full-resolution 3D Ground-Penetrating Radar (GPR) data were combined with high-resolution hydraulic conductivity (K) data from vertical Direct-Push (DP) profiles to characterize a portion of the highly heterogeneous MAcro Dispersion Experiment (MADE) site. This is an important first step to better understand the influence of aquifer heterogeneities on observed anomalous transport. Statistical evaluation of DP data indicates non-normal distributions that have much higher similarity within each GPR facies than between facies. The analysis of GPR and DP data provides high-resolution estimates of the 3D geometry of hydrostratigraphic zones, which can then be populated with stochastic K fields. The lack of such estimates has been a significant limitation for testing and parameterizing a range of novel transport theories at sites where the traditional advection-dispersion model has proven inadequate.

Mineralogy and Magnetic Properties of Basaltic Substrate Soils: Kaho'olawe and Big Island, Hawaii

Magnetic behavior of soils can seriously hamper the performance of geophysical sensors. Currently, we have little understanding of the types of minerals responsible for the magnetic behavior, as well as their distribution in space and evolution through time. This study investigated the magnetic characteristics and mineralogy of Fe-rich soils developed on basaltic substrate in Hawaii. We measured the spatial distribution of magnetic susceptibility (χlf) and frequency dependence (χfd%) across three test areas in a well-developed eroded soil on Kaho'olawe and in two young soils on the Big Island of Hawaii. X-ray diffraction spectroscopy, x-ray fluorescence spectroscopy (XFCF), chemical dissolution, thermal analysis, and temperature-dependent magnetic studies were used to characterize soil development and mineralogy for samples from soil pits on Kaho'olawe, surface samples from all three test areas, and unweathered basalt from the Big Island of Hawaii. The measurements show a general increase in magnetic properties with increasing soil development. The XRF Fe data ranged from 13% for fresh basalt and young soils on the Big Island to 58% for material from the B horizon of Kaho'olawe soils. Dithionite-extractable and oxalate-extractable Fe percentages increase with soil development and correlate with χlf-and χfd%, respectively. Results from the temperature-dependent susceptibility measurements show that the high soil magnetic properties observed in geophysical surveys in Kaho'olawe are entirely due to neoformed minerals. The results of our studies have implications for the existing soil survey of Kaho'olawe and help identify methods to characterize magnetic minerals in tropical soils.

Nutrient discharge to groundwater at the Kaharoa rainfall recharge site, Rotorua [GNS Science Consultancy Report 2008/320]

Environment Bay of Plenty Commissioned GNS Science to measure nitrogen and phosphorus concentrations in rainfalla nd rainfall recharge to groundwater at the Kaharoa rainfall recharge site. The aim of this work is to determine nutrient concentrations in rainfall recharge to groundwater and rainfall under pasoral land use.

Gone Fishing

Gone Fishing is the touching story of a boy and old man coming to terms with bereavement through their shared love of fishing, and the legend of Goliath, the biggest pike ever caught! Financed by 150 film makers, Gone Fishing is a short film directed by Guerilla Film Makers Handbook Author Chris Jones. Thirteen minutes of magic that will touch and move you. The story is in part autobiographical - while growing up, director Chris Jones and his friends would often attempt to catch the legendary 'Oscar the pike', a fish that according to local myth terrorized the lake at the end of his road, the aptly named Blue Lagoon. This was the inspiration behind the legend of 'Goliath', the big fish in this even bigger tale. Think 'Jaws' but for kids...

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Data as an asset: what the oil and gas sector can learn from other industries about “Big Data”

The upstream oil and gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data” is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil and gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This viewpoint examines existing data management practices in the upstream oil and gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the way in Big Data. The comparison shows that, in companies that are widely considered to be leaders in Big Data analytics, data is regarded as a valuable asset—but this is usually not true within the oil and gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how the industry could potentially extract more value from data, and concludes with a series of policy-related questions to this end.

Deciphering historic landscapes: A case study of slender West Lake in Yangzhou, China

Historic landscapes today are changing gradually or abruptly, and the abrupt changes have caused the loss of much historic information. How to identify and protect the significant evidence of dynamic landscapes is a question that must be answered by each cultural community. This paper establishes a decipherment process – an operational guide for landscape assessment in China. This is a methodology using European methods integrated with traditional Chinese ways of landscape appreciation, providing an effective approach to translate the cultural landscape framework into the conservation inventory. Using Slender West Lake as a case study, the decipherment process has expanded the existing landscape investigation theory using the factor of artistic conception to integrate intangible values into the assessment process. It has also established a unit-based method to classify and represent historic landscapes.