Identifying fisheries regions in New Zealand: Some conceptual difficulties

The importance of fisheries to nations is reflected in the production and employment statistics of the country. It is also reflected in socio-cultural symbols (for instance songs, tales), and in socio-political hegemonies. Just as these may vary from one nation to another, they may also vary from region to region within a nation. Several nations speak openly in terms of 'fisheries regions' and there have been a number of attempts to identify such regions in the social science literature. An understanding of these regions is seen as step towards defining appropriate policies for the sustainable management of their resources. In 1986, New Zealand established an innovative fishery management system based on individually transferable quota (ITQ), and subsequently removed the (never-implemented) region-based, fishery management planning structure from the statutes. These changes might be indicative of a loss of geography, a flattening of the nation's "fishing topography", and might be expected to result in significant changes to the nature and location of fisheries regions. This paper outlines the changes in the management structure of New Zealand's fisheries. We then attempt a preliminary analysis of fisheries regions in New Zealand as the basis for a "new regional" geography of New Zealand's fisheries. In the process we discuss various criteria for defining fishery regions and present our initial categorisation of New Zealand into those regions. The relationship between these regions and related institutional structures is then discussed. This raises a number of additional questions regarding the concept of a fisheries region, especially in the context of a resurgent indigenous (Maaori) culture, the emergence of new fishing peoples in New Zealand, and the respective size of recreational and commercial fishing sectors

Detecting demand outliers in transport systems

Optimisation routines used for demand management in transport systems strongly depend on accurate forecasts. Outliers caused by systematic shifts in demand cause erroneous forecasts for both current services and future services whose forecasts are based on historic demand. Transport service providers often rely on analysts to identify outlier demand and make adjustments accordingly. However, previous research on judgemental forecasting shows that such adjustments can be biased and even superfluous. Literature on automated detection and evaluation of outlier demand in this context is scarce. To date, most literature on forecasting and optimisation in transport planning does not account for demand outliers despite the negative impacts it can have. This thesis presents a novel methodology, which combines network clustering with functional data analysis and time series forecasting, to detect outliers in demand for transport systems. This thesis also contributes a simulation framework for evaluating the performance of the proposed outlier detection procedure and for quantifying the effects of outlier demand on different optimisation routines. The use of such a method as a decision support tool for analyst adjusted forecasts, and how the outlier alerts may be best communicated, is also considered. Computational studies highlight the benefits of different adjustments that analysts may take after the identification of outlier demand. Multiple empirical studies will demonstrate how the method can be applied in practice to different types of transport systems, with analyses of Deutsche Bahn railway booking data and Capital Bikeshare usage data

Identifying and Responding to Outlier Demand in Revenue Management

Revenue management strongly relies on accurate forecasts. Thus, when extraordinary events cause outlier demand, revenue management systems need to recognise this and adapt both forecast and controls. Many passenger transport service providers, such as railways and airlines, control the sale of tickets through revenue management. State-of-the-art systems in these industries rely on analyst expertise to identify outlier demand both online (within the booking horizon) and offline (in hindsight). So far, little research focuses on automating and evaluating the detection of outlier demand in this context. To remedy this, we propose a novel approach, which detects outliers using functional data analysis in combination with time series extrapolation. We evaluate the approach in a simulation framework, which generates outliers by varying the demand model. The results show that functional outlier detection yields better detection rates than alternative approaches for both online and offline analyses. Depending on the category of outliers, extrapolation further increases online detection performance. We also apply the procedure to a set of empirical data to demonstrate its practical implications. By evaluating the full feedback-driven system of forecast and optimisation, we generate insight on the asymmetric effects of positive and negative demand outliers. We show that identifying instances of outlier demand and adjusting the forecast in a timely fashion substantially increases revenue compared to what is earned when ignoring outliers

Detecting outlying demand in multi-leg bookings for transportation networks

Network effects complicate demand forecasting in general, and outlier detection in particular. For example, in transportation networks, sudden increases in demand for a specific destination will not only affect the legs arriving at that destination, but also connected legs nearby in the network. Network effects are particularly relevant when transport service providers, such as railway or coach companies, offer many multi-leg itineraries. In this paper, we present a novel method for generating automated outlier alerts, to support analysts in adjusting demand forecasts accordingly for reliable planning. To create such alerts, we propose a two-step method for detecting outlying demand from transportation network bookings. The first step clusters network legs to appropriately partition and pool booking patterns. The second step identifies outliers within each cluster to create a ranked alert list of affected legs. We show that this method outperforms analyses that independently consider each leg in a network, especially in highly-connected networks where most passengers book multi-leg itineraries. We illustrate the applicability on empirical data obtained from Deutsche Bahn and with a detailed simulation study. The latter demonstrates the robustness of the approach and quantifies the potential revenue benefits of adjusting for outlying demand in networks

Pravastatin for early-onset pre-eclampsia:a randomised, blinded, placebo-controlled trial

Objective: Women with pre-eclampsia have elevated circulating levels of soluble fms-like tyrosine kinase-1 (sFlt-1). Statins can reduce sFlt-1 from cultured cells and improve pregnancy outcome in animals with a pre-eclampsia-like syndrome. We investigated the effect of pravastatin on plasma sFlt-1 levels during pre-eclampsia. Design: Blinded (clinician and participant), proof of principle, placebo-controlled trial. Setting: Fifteen UK maternity units. Population: We used a minimisation algorithm to assign 62 women with early-onset pre-eclampsia (24 +0–31 +6 weeks of gestation) to receive pravastatin 40 mg daily (n = 30) or matched placebo (n = 32), from randomisation to childbirth. Primary outcome: Difference in mean plasma sFlt-1 levels over the first 3 days following randomisation. Results: The difference in the mean maternal plasma sFlt-1 levels over the first 3 days after randomisation between the pravastatin (n = 27) and placebo (n = 29) groups was 292 pg/ml (95% CI −1175 to 592; P = 0.5), and over days 1–14 was 48 pg/ml (95% CI −1009 to 913; P = 0.9). Women who received pravastatin had a similar length of pregnancy following randomisation compared with those who received placebo (hazard ratio 0.84; 95% CI 0.50–1.40; P = 0.6). The median time from randomisation to childbirth was 9 days [interquartile range (IQR) 5–14 days] for the pravastatin group and 7 days (IQR 4–11 days) for the placebo group. There were three perinatal deaths in the placebo-treated group and no deaths or serious adverse events attributable to pravastatin. Conclusions: We found no evidence that pravastatin lowered maternal plasma sFlt-1 levels once early-onset pre-eclampsia had developed. Pravastatin appears to have no adverse perinatal effects. Tweetable abstract: Pravastatin does not improve maternal plasma sFlt-1 or placental growth factor levels following a diagnosis of early preterm pre-eclampsia #clinicaltrial finds

Analysing and visualising bike-sharing demand with outliers

Bike-sharing is a popular component of sustainable urban mobility. It requires anticipatory planning, e.g. of station locations and inventory, to balance expected demand and capacity. However, external factors such as extreme weather or glitches in public transport, can cause demand to deviate from baseline levels. Identifying such outliers keeps historic data reliable and improves forecasts. In this paper we show how outliers can be identified by clustering stations and applying a functional depth analysis. We apply our analysis techniques to the Washington D.C. Capital Bikeshare data set as the running example throughout the paper, but our methodology is general by design. Furthermore, we offer an array of meaningful visualisations to communicate findings and highlight patterns in demand. Last but not least, we formulate managerial recommendations on how to use both the demand forecast and the identified outliers in the bike-sharing planning process