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Data, Volume 4, Issue 4 (December 2019)

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Open AccessArticle
Capacity Allocation of Game Tickets Using Dynamic Pricing
Data 2019, 4(4), 141; https://doi.org/10.3390/data4040141 (registering DOI) - 18 Oct 2019
Abstract
This study examines a pricing approach that is applicable in the field of online ticket sales for game tickets. The mathematical principle of dynamic programing is combined with empirical data analysis to determine demand functions for university football game tickets. Based on the [...] Read more.
This study examines a pricing approach that is applicable in the field of online ticket sales for game tickets. The mathematical principle of dynamic programing is combined with empirical data analysis to determine demand functions for university football game tickets. Based on the calculated demand functions, the application of DP strategies is found to generate more revenues than a fixed price strategy. The other important result is the capacity distribution of tickets according to the football game intensity. Prior studies have shown that it is sometimes more profitable or football clubs to allocate a share of tickets to a retailer and earn a commission based on the sales, rather than selling the entire capacity of tickets by itself. This paper finds that in a high intensity game, where the demand is generally high, it is optimal for the club to sell all tickets by itself. Whereas, for less popular games, where there is considerable fluctuation in demand, the capacity allocation problem for maximized revenues from ticket sales, becomes a harder optimization challenge for the club. According to DP optimization, when the demand for tickets is relatively low, it is optimal for the club to retain 20–40% of the tickets and the rest of the capacity should be sold to online retailers. In the real world, this pricing technique has been used by football clubs and thus the secondary market online retailers like Ticketmaster and Vivid Seats have become popular in the last decade. Full article
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Open AccessData Descriptor
Lifestyles and Cycling Behavior—Data from a Cross-Sectional Study
Data 2019, 4(4), 140; https://doi.org/10.3390/data4040140 - 17 Oct 2019
Viewed by 110
Abstract
Cycling experiences a remarkable renaissance as an everyday mode of transport and in an increasing number of cities, cycling substantially contributes to the overall traffic. However, cyclists are not a homogeneous group of road users, but very diverse in terms of behavior, motivators, [...] Read more.
Cycling experiences a remarkable renaissance as an everyday mode of transport and in an increasing number of cities, cycling substantially contributes to the overall traffic. However, cyclists are not a homogeneous group of road users, but very diverse in terms of behavior, motivators, and deterrents. In order to gain better insights into driving forces and behavior patterns of cyclists, we conducted an opt-in online survey, in which socio-demographic, lifestyle, and mobility behavior data were collected. In total, 1234 responses with a completion rate of 87% (1073 complete survey) were collected between 3 May and 3 June 2019. With reference to complete responses, the gender ratio is balanced (53% female) and the mean age is 42 (σ = 12.75). A relative majority of participants cycles frequently. The fully anonymized dataset contains 107 data points per response, including survey metadata. Full article
Open AccessData Descriptor
Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications
Data 2019, 4(4), 139; https://doi.org/10.3390/data4040139 - 12 Oct 2019
Viewed by 128
Abstract
Recently, deep learning-based methods for solving multi-modal tasks such as image captioning, multi-modal classification, and cross-modal retrieval have attracted much attention. To apply deep learning for such tasks, large amounts of data are needed for training. However, although there are several Korean single-modal [...] Read more.
Recently, deep learning-based methods for solving multi-modal tasks such as image captioning, multi-modal classification, and cross-modal retrieval have attracted much attention. To apply deep learning for such tasks, large amounts of data are needed for training. However, although there are several Korean single-modal datasets, there are not enough Korean multi-modal datasets. In this paper, we introduce a KTS (Korean tourist spot) dataset for Korean multi-modal deep-learning research. The KTS dataset has four modalities (image, text, hashtags, and likes) and consists of 10 classes related to Korean tourist spots. All data were extracted from Instagram and preprocessed. We performed two experiments, image classification and image captioning with the dataset, and they showed appropriate results. We hope that many researchers will use this dataset for multi-modal deep-learning research. Full article
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Open AccessFeature PaperArticle
Snow Cover Evolution in the Gran Paradiso National Park, Italian Alps, Using the Earth Observation Data Cube
Data 2019, 4(4), 138; https://doi.org/10.3390/data4040138 - 09 Oct 2019
Viewed by 318
Abstract
Mountainous regions are particularly vulnerable to climate change, and the impacts are already extensive and observable, the implications of which go far beyond mountain boundaries and the environmental sectors. Monitoring and understanding climate and environmental changes in mountain regions is, therefore, needed. One [...] Read more.
Mountainous regions are particularly vulnerable to climate change, and the impacts are already extensive and observable, the implications of which go far beyond mountain boundaries and the environmental sectors. Monitoring and understanding climate and environmental changes in mountain regions is, therefore, needed. One of the key variables to study is snow cover, since it represents an essential driver of many ecological, hydrological and socioeconomic processes in mountains. As remotely sensed data can contribute to filling the gap of sparse in-situ stations in high-altitude environments, a methodology for snow cover detection through time series analyses using Landsat satellite observations stored in an Open Data Cube is described in this paper, and applied to a case study on the Gran Paradiso National Park, in the western Italian Alps. In particular, this study presents a proof of concept of the preliminary version of the snow observation from space algorithm applied to Landsat data stored in the Swiss Data Cube. Implemented in an Earth Observation Data Cube environment, the algorithm can process a large amount of remote sensing data ready for analysis and can compile all Landsat series since 1984 into one single multi-sensor dataset. Temporal filtering methodology and multi-sensors analysis allows one to considerably reduce the uncertainty in the estimation of snow cover area using high-resolution sensors. The study highlights that, despite this methodology, the lack of available cloud-free images still represents a big issue for snow cover mapping from satellite data. Though accurate mapping of snow extent below cloud cover with optical sensors still represents a challenge, spatial and temporal filtering techniques and radar imagery for future time series analyses will likely allow one to reduce the current cloud cover issue. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes)
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Open AccessData Descriptor
Matrix Metalloproteinases as Markers of Acute Inflammation Process in the Pulmonary Tuberculosis
Data 2019, 4(4), 137; https://doi.org/10.3390/data4040137 - 05 Oct 2019
Viewed by 202
Abstract
The main factors of pathogenesis in the pulmonary tuberculosis are not only the bacterial virulence and sensitivity of the host immune system to the pathogen, but also the degree of destruction of the lung tissue. Such destruction processes lead to the development of [...] Read more.
The main factors of pathogenesis in the pulmonary tuberculosis are not only the bacterial virulence and sensitivity of the host immune system to the pathogen, but also the degree of destruction of the lung tissue. Such destruction processes lead to the development of caverns, in most cases requiring surgical interventions besides the drug therapy. Identification of special biochemical markers allowing to assess the necessity of surgery or therapy prolongation remains a challenge. We consider promising markers—metalloproteinases—analyzing the data obtained from patients with pulmonary tuberculosis infected by different strains of Mycobacterium tuberculosis. We argue that the presence of drug-resistant strains in lungs leading to complicated clinical prognosis could be justified not only by the difference in medians of biomarkers concentration (as determined by the Mann–Whitney test for small samples), but also by the qualitative difference in their probability distributions (as detected by the Kolmogorov–Smirnov test). Our results and the provided raw data could be used for further development of precise biochemical data-based diagnostic and prognostic tools for pulmonary tuberculosis. Full article
(This article belongs to the Special Issue Benchmarking Datasets in Bioinformatics)
Open AccessArticle
Geometrical Platform of Big Database Computing for Modeling of Complex Physical Phenomena in Electric Current Treatment of Liquid Metals
Data 2019, 4(4), 136; https://doi.org/10.3390/data4040136 - 05 Oct 2019
Viewed by 170
Abstract
According to the principles of multiphysical, multiscale simulations of phenomena and processes which take place during the electric current treatment of liquid metals, the need to create an adjustable and concise geometrical platform for the big database computing of mathematical models and simulations [...] Read more.
According to the principles of multiphysical, multiscale simulations of phenomena and processes which take place during the electric current treatment of liquid metals, the need to create an adjustable and concise geometrical platform for the big database computing of mathematical models and simulations is justified. In this article, a geometrical platform was developed based on approximations of boundary contours using arcs for application of the integral equations method and matrix transformations. This method achieves regular procedures using multidimensional scale matrixces for big data transfer and computing. The efficiency of this method was verified by computer simulation and used for different model contours, which are parts of real contours. The obtained results showed that the numerical algorithm was highly accurate based on the presented geometrical platform of big database computing and that it possesses a potential ability for use in the organization of computational processes regarding the modeling and simulation of electromagnetic, thermal, hydrodynamic, wave, and mechanical fields (as a practical case in metal melts treated by electric current). The efficiency of this developed approach for big data matrices computing and equation system formation was displayed, as the number of numerical procedures, as well as the time taken to perform them, were much smaller when compared to the finite element method used for the same model contours. Full article
(This article belongs to the Special Issue Machine Learning and Materials Informatics)
Open AccessConcept Paper
A Transformative Concept: From Data Being Passive Objects to Data Being Active Subjects
Data 2019, 4(4), 135; https://doi.org/10.3390/data4040135 - 02 Oct 2019
Viewed by 165
Abstract
The exploitation of potential societal benefits of Earth observations is hampered by users having to engage in often tedious processes to discover data and extract information and knowledge. A concept is introduced for a transition from the current perception of data as passive [...] Read more.
The exploitation of potential societal benefits of Earth observations is hampered by users having to engage in often tedious processes to discover data and extract information and knowledge. A concept is introduced for a transition from the current perception of data as passive objects (DPO) to a new perception of data as active subjects (DAS). This transition would greatly increase data usage and exploitation, and support the extraction of knowledge from data products. Enabling the data subjects to actively reach out to potential users would revolutionize data dissemination and sharing and facilitate collaboration in user communities. The three core elements of the transformative DAS concept are: (1) “intelligent semantic data agents” (ISDAs) that have the capabilities to communicate with their human and digital environment. Each ISDA provides a voice to the data product it represents. It has comprehensive knowledge of the represented product including quality, uncertainties, access conditions, previous uses, user feedbacks, etc., and it can engage in transactions with users. (2) A knowledge base that constructs extensive graphs presenting a comprehensive picture of communities of people, applications, models, tools, and resources and provides tools for the analysis of these graphs. (3) An interaction platform that links the ISDAs to the human environment and facilitates transaction including discovery of products, access to products and derived knowledge, modifications and use of products, and the exchange of feedback on the usage. This platform documents the transactions in a secure way maintaining full provenance. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes)
Open AccessData Descriptor
Assessing Urban Livability through Residential Preference—An International Survey
Data 2019, 4(4), 134; https://doi.org/10.3390/data4040134 - 01 Oct 2019
Viewed by 169
Abstract
Livability is a popular term for describing the satisfaction of residents with living in a city. The assessment of livability can be of high relevance for urban planning; however, existing assessment methods have various limitations, especially in terms of transferability. In our main [...] Read more.
Livability is a popular term for describing the satisfaction of residents with living in a city. The assessment of livability can be of high relevance for urban planning; however, existing assessment methods have various limitations, especially in terms of transferability. In our main research article, we developed a conceptual framework and an assessment workflow to provide a transferable way of assessing livability, also considering intra-urban differences of the identified livability assessment factors to use for further geospatial analysis. As a key part of this assessment, we developed a survey to investigate residential preference and satisfaction concerning different urban factors. The current Data Descriptor introduces the questionnaire we used, the distribution of the responses, and the most important findings for the socioeconomic and demographic parameters influencing urban livability. We found that the development of an area, the number of persons in the household, and the income level are significant circumstances in assessing how satisfied a person would be with living in a given city. Full article
Open AccessReview
A Lack of “Environmental Earth Data” at the Microhabitat Scale Impacts Efforts to Control Invasive Arthropods That Vector Pathogens
Data 2019, 4(4), 133; https://doi.org/10.3390/data4040133 - 29 Sep 2019
Viewed by 216
Abstract
We currently live in an era of major global change that has led to the introduction and range expansion of numerous invasive species worldwide. In addition to the ecological and economic consequences associated with most invasive species, invasive arthropods that vector pathogens (IAVPs) [...] Read more.
We currently live in an era of major global change that has led to the introduction and range expansion of numerous invasive species worldwide. In addition to the ecological and economic consequences associated with most invasive species, invasive arthropods that vector pathogens (IAVPs) to humans and animals pose substantial health risks. Species distribution models that are informed using environmental Earth data are frequently employed to predict the distribution of invasive species, and to advise targeted mitigation strategies. However, there are currently substantial mismatches in the temporal and spatial resolution of these data and the environmental contexts which affect IAVPs. Consequently, targeted actions to control invasive species or to prepare the population for possible disease outbreaks may lack efficacy. Here, we identify and discuss how the currently available environmental Earth data are lacking with respect to their applications in species distribution modeling, particularly when predicting the potential distribution of IAVPs at meaningful space-time scales. For example, we examine the issues related to interpolation of weather station data and the lack of microclimatic data relevant to the environment experienced by IAVPs. In addition, we suggest how these data gaps can be filled, including through the possible development of a dedicated open access database, where data from both remotely- and proximally-sensed sources can be stored, shared, and accessed. Full article
(This article belongs to the Special Issue Overcoming Data Scarcity in Earth Science)
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Open AccessArticle
Handling Data Gaps in Reported Field Measurements of Short Rotation Forestry
Data 2019, 4(4), 132; https://doi.org/10.3390/data4040132 - 25 Sep 2019
Viewed by 264
Abstract
Filling missing data in forest research is paramount for the analysis of primary data, forest statistics, land use strategies, as well as for the calibration/validation of forest growth models. Consequently, our main objective was to investigate several methods of filling missing data under [...] Read more.
Filling missing data in forest research is paramount for the analysis of primary data, forest statistics, land use strategies, as well as for the calibration/validation of forest growth models. Consequently, our main objective was to investigate several methods of filling missing data under a reduced sample size. From a complete dataset containing yearly first-rotation tree growth measurements over a period of eight years, we gradually retrieved two and then four years of measurements, hence operating on 72% and 43% of the original data. Secondly, 15 statistical models, five forest growth functions, and one biophysical, process-oriented, tree growth model were employed for filling these data gap representations accounting for 72% and 43% of the available data. Several models belonging to (i) regression analysis, (ii) statistical imputation, (iii) forest growth functions, and (iv) tree growth models were applied in order to retrieve information about the trees from existing yearly measurements. Subsequently, the findings of this study could lead to finding a handy tool for both researchers and practitioners dealing with incomplete datasets. Moreover, we underline the paramount demand for far-sighted, long-term research projects for the expansion and maintenance of a short rotation forestry (SRF) repository. Full article
(This article belongs to the Special Issue Forest Monitoring Systems and Assessments at Multiple Scales)
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Open AccessData Descriptor
Horsing Around—A Dataset Comprising Horse Movement
Data 2019, 4(4), 131; https://doi.org/10.3390/data4040131 - 22 Sep 2019
Viewed by 262
Abstract
Movement data were collected at a riding stable over seven days. The dataset comprises data from 18 individual horses and ponies with 1.2 million 2-s data samples, of which 93,303 samples have been tagged with labels (labeled data). Data from 11 subjects were [...] Read more.
Movement data were collected at a riding stable over seven days. The dataset comprises data from 18 individual horses and ponies with 1.2 million 2-s data samples, of which 93,303 samples have been tagged with labels (labeled data). Data from 11 subjects were labeled. The data from six subjects and six activities were labeled more extensively. Data were collected during horse riding sessions and when the horses freely roamed the pasture over seven days. Sensor devices were attached to a collar that was positioned around the neck of horses. The orientation of the sensor devices was not strictly fixed. The sensors devices contained a three-axis accelerometer, gyroscope, and magnetometer and were sampled at 100 Hz. Full article
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