Using Self-Organising Maps (SOMs) to assess synchronies: an application to historical eucalypt flowering records

Data clustering: application and trends

Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.

Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals.

Predicting the timing of ecological phenomena using dates of species occurrence records: a methodological approach and test case with mushrooms

Spatiotemporal predictions of ecological phenomena are highly useful and significant in scientific and socio-economic applications. However, the inadequate availability of ecological time-series data often impedes the development of statistical predictions. On the other hand, considerable amounts of temporally discrete biological records (commonly known as 'species occurrence records') are being stored in public databases, and often include the location and date of the observation. In this paper, we describe an approach to develop spatiotemporal predictions based on the dates and locations found in species occurrence records. The approach is based on 'time-series classification', a field of machine learning, and consists of applying a machine-learning algorithm to classify between time series representing the environmental variation that precedes the occurrence records and time series representing the full range of environmental variation that is available in the location of the records. We exemplify the application of the approach for predicting the timing of emergence of fruiting bodies of two mushroom species (Boletus edulis and Macrolepiota procera) in Europe, from 2009 to 2015. Predictions made from this approach were superior to those provided by a 'null' model representing the average seasonality of the species. Given the increased availability and information contained in species occurrence records, particularly those supplemented with photographs, the range of environmental events that could be possible to predict using this approach is vast.

The rise of phenology with climate change: an evaluation of IJB publications

In recent decades, phenology has become an important tool by which to measure both the impact of climate change on ecosystems and the feedback of ecosystems to the climate system. However, there has been little attempt to date to systematically quantify the increase in the number of scientific publications with a focus on phenology and climate change. In order to partially address this issue, we examined the number of articles (original papers, reviews and short communications) containing the terms 'phenology' and 'climate change' in the title, abstract or keywords, published in the International Journal of Biometeorology in the 60 years since its inception in 1957. We manually inspected all issues prior to 1987 for the search terms and subsequently used the search facility on the Web of Science online database. The overall number of articles published per decade remained relatively constant (255-378) but rose rapidly to 1053 in the most recent decade (2007-2016), accompanied by an increase (41-172) in the number of articles containing the search terms. A number of factors may have contributed to this rise, including the recognition of the value of phenology as an indicator of climate change and the initiation in 2010 of a series of conferences focusing on phenology which subsequently led to two special issues of the journal. The word 'phenology' was in use from the first issue, whereas 'climate change' only emerged in 1987 and peaked in 2014. New technologies such as satellite remote sensing and the internet led to an expansion of and greater access to a growing reservoir of phenological information. The application of phenological data included determining the impact of warming of phenophases, predicting wine quality and the pollen season, demonstrating the potential for mismatch to occur and both reconstructing and forecasting climate. Even though this analysis was limited to one journal, it is likely to be indicative of a similar trend across other scientific publications.

Sixty years of the International Journal of Biometeorology

The International Journal of Biometeorology (IJB) has continuously evolved since its first publications in 1957. In this paper, we examine these changes using a database that includes all manuscript titles and author information. A brief history considers the development of the journal and shifts over time. With an interdisciplinary focus, publications draw on a wide array of subdisciplines. Using content analysis, we evaluate the themes found within IJB. Some research themes have maintained prominence throughout the journal's history, while other themes have waxed or waned over time. Similarly, the most influential manuscripts throughout the past 60 years reveal that human biometeorological papers, particularly regarding thermal comfort, have been influential throughout the journal's history, with other themes, including phenology and animal biometeorology, more concentrated in specific periods. Dominated by North America and Europe in the early years, publication authorship has shifted over the last decade to be more globally representative. Recent inclusion of special issues devoted to regional biometeorological issues, as well as to Students and New Professionals, offer insight into the future direction of the IJB.