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Copyright of Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As part of the progressive process of extending spatial plans to cover an increasing number of marine areas, with the aim of objectively balancing the interests of various users of the marine area, it has become necessary to establish the value of marine areas as a yardstick or determinant of the user group for which a given marine area is of greater value. This study seeks to fill a research gap by attempting to develop a method to calculate the value of marine areas for the commercial shipping industry. This is done to make it possible in the future to prepare the ground for policy regulating the spatial rent of the sea, whose most important users are shipowners and their ships. We use the homogeneous basin of the Polish Marine Areas (PMA) in the Baltic Sea. Based on a literature review, we conclude that such a method does not exist, posing a significant challenge in the process of marine/maritime spatial planning (MSP) and maritime policy formulation. Conducting an in-depth analysis of 2020 data on ship traffic in the basin noted above, combined with a financial analysis of shipowners' operating costs and profitability indicators, we can determine the value of marine areas both in aggregate for all shipping in the studied basin and for each of the five segments of shipping -- the bulk cargo, ro-ro cargo, container, tanker, and passenger segments. In addition, through a dynamic analysis of ship traffic, it is possible to determine the value of sea area in Polish seawaters per unit of area (1 km²) at the average level and for the five specified market segments. The obtained values show that the total profits of shipowners in the Polish Marine Areas, which are at the level of more than EUR 103 million per year, and the average value of profits per 1 km² of marine area used by a ship provide future decision-makers with an objective point of reference to shape future policies for the fiscalization of public space, including the sea. [ABSTRACT FROM AUTHOR]

Purpose: Surveys are one of the most popular data acquisition tools used in economics and management sciences. The results of surveys provide a lot of information and allow for fast response to changes in the socio-economic environment. Unfortunately, in many cases there are missing data in surveys, which can be caused by various reasons. Design/methodology/approach: One of the most common reasons are the respondent’s reluctance to provide an answer or distraction while completing the questionnaire. This study presents a novel approach for filling gaps in the survey data. Findings: The main idea of the proposed method is to use the associations between the answers to given sets of questions for different respondents. Originality/value: The obtained association rules were used as input variables and a number of well-known machine learning tools were applied for filling data gaps. The results of numerical experiments confirmed a very high performance of the proposed novel method for filling data gaps in surveys. [ABSTRACT FROM AUTHOR]

Maritime and coastal areas are the lifeblood of many countries. They are vital to the prosperity of countries because they provide trade routes, regulate the climate, are a source of organic and inorganic resources and energy, and provide a place for the public to live and relax. Increasingly, however, there are divergences and barriers to the use of marine areas. On the one hand, available and developed technology and knowledge enable increasingly improved use of the sea. On the other hand, the combined effect of these activities leads to conflicts of interest and the deterioration of the marine environment. The purpose of this study is to analyze the feasibility of using automatic identification system (AIS) maritime traffic data, in terms of its suitability, to correctly assess the utilization of the potential of a specific fleet within the offshore shipping industry. In addition, the authors undertake the task of determining to what extent activities relating to the GPS position of the ship, ship type (i.e., cargo or passenger), ship status (i.e., aground, anchored, moored, not under command, restricted maneuverability, underway sailing, or underway using its engine), ship draught, service speed, total engine power, and deadweight constitute areas and methods for optimizing the use of the offshore fleet under all the conditions previously described that limit this optimization. Given the stated goal, this paper uses both a literature review procedure and statistical methods to conduct a comparative analysis. [ABSTRACT FROM AUTHOR]

Anomaly (outlier) detection is one of the most important problems of modern data analysis. The sources of anomalies are varying. They can be the results of database users’ mistakes, operational errors, or just missing values. The problem is very important because of the fast growth of large datasets. Therefore, in this article, we present detailed results of work on the concept of granular computing-based approach to anomaly detection, classification, and gradation. The aim of the study is to introduce an innovative solution that allows the use of information granules to identify and classify anomalies. The novelty of the proposed solution consists in the use of fuzzy semantics implied by the statistical properties of the data considered. Moreover, instead of the classic approach to detecting anomalies in the data, it is proposed to determine the degree of anomaly for the data transformed to the new resulting state space. Thanks to the use of an innovative approach using the universal descriptor space, it is possible to determine the degree of anomaly, and by using various aggregation methods one can also specify its type. [ABSTRACT FROM AUTHOR]

Estimating travel time is one of the most important processes in logistics as well as in everyday life. In particular, when it comes to transportation services, efficient time management can be a competitive advantage, not to mention customer satisfaction, which can be easily translated into business success. Therefore, in this study we analyze various travel time estimation methods in combination with a well-known Fuzzy C-Means clustering algorithm. The proposed FCM-based solution has significant advantages, allowing for the determination of the optimal travel time. In an extensive numerical experiment, we present the application of the proposed method to estimate the time of a taxi trip around New York. Due to division of the city area into detailed areas and taking into account information about the travel time in the analysis, a model was obtained, that perfectly forecasts speed of taxi travel. In this study we consider various, competitive approaches to build such a model. [ABSTRACT FROM AUTHOR]

In this study, we develop a process of estimation of importance of features considered in face recognition by making use of the analytic hierarchy process (AHP). The AHP method of pairwise comparisons realized at three levels of hierarchy becomes crucial to realize a comprehensive weighting of cues so that sound estimates of weights associated with the individual features of faces can be formed. We demonstrate how to carry out an efficient process of face description by using a collection of linguistic descriptors of the features and their groups. Numerical dependencies between the features are quantified with the help of experienced criminology and psychology experts. Finally, we present an entropy-based method of evaluation of the relevance of the estimation process completed by the individuals. The intuitively appealing results of experiments are presented and analyzed in detail. [ABSTRACT FROM AUTHOR]

• Data gaps are a common problem in ecological research. • The monitoring data of the breeding success of the white stork was used. • We showed the consequences of ignoring and imputing of missing data. • Methods for dealing with data gaps should find wider interest in ecology. Ecological datasets often contain gaps, outliers or even incorrect data. Ignoring the problem of missing data can lead to reduction in the statistical power of the models used, estimation of biased parameters and incorrect conclusions about the phenomenon studied. In this study, using simulated and real ecological data (seven-year monitoring of offspring production in 239 white stork (Ciconia ciconia) nests), we show how results differ when ignoring missing data, filling the gaps using single methods (including fuzzy methods) and using multiple-imputation and aggregation techniques. Based on simulation data, we showed that data gaps can be filled with high accuracy if the appropriate method (model) is used (96.5% of perfectly matched cases). Based on empirical data, we showed how results can differ when accepting or filling the missing data. These differences concerned both general indicators of breeding success (e.g. total number of offspring, mean annual productivity per nest), differences in trends (e.g. increase or decrease in productivity between years) and more detailed analyses, such as ranks of the most productive nests. The observed differences in the results could lead to formulation of incorrect conclusions about the state of the stork population, the condition of its habitat or conservation guidelines. We highlight that well-developed set of data-imputation methods dedicated to monitoring the white stork could increase the accuracy of modern estimates and re-analyse rich historical data. Similar data pre-processing solutions to fill data gaps should also find wider application in other ecological research. [ABSTRACT FROM AUTHOR]

• Detecting anomalies in the space use patterns of the European bison. • A new approach to detecting anomalies in spatial behavior using granular computing. • Using fuzzy statistical semantics to describe the space use patterns of the European bison Nowadays, in the era of automation of ecological measurements, more and more often we are dealing with large data sets in which various unexpected anomalies may occur. Their detection is often crucial for a proper assessment of ecological trends and processes. Therefore, methods allowing for identification of anomalous data are especially important for a deep understanding of ecological phenomena and their relationships in practical domains. In this study, we present an innovative application of information granules to the in-depth study of spatial behavior of the European bison (Bison bonasus), based on GPS data. As evidenced by a series of numerical experiments, this granular computing-based approach allows to detect both anomalies and regularities in the atypical behavior of the European bison, a species important for local ecosystems. The transformation of the original data space into a new semantic multidimensional space, defining the degree of membership in the anomaly class represented by new descriptors allows for more unambiguous analysis of non-standard animal behavior. [ABSTRACT FROM AUTHOR]

• Urbanization is a relatively new evolutionary factor strongly affecting wildlife. • We studied the activity patterns of populations affected or unaffected by urbanization. • We used particle swarm optimization, neural networks, decision trees and cluster analysis. • Urban small mammals are active around the clock, while rural ones are nocturnal. • Daily activity analysis has a great potential to apply new statistical approaches. As a result of the widespread use of camera-traps, the analysis of the daily activity of animals based on field data has become a common practice, which is addressed in ecological studies. The more frequent consideration of this issue in ecological research, however, has not led to any advancement in the techniques of analysis of these activity patterns. In this work, we have two main aims: ecological and methodological. Firstly, using camera-traps in the winter period, we examine the differences in the daily activity of wild small mammal populations, which are affected or unaffected by urbanization; we treated changes in daily activity as indicators of species adaptation to urban conditions. Secondly, we test four different approaches to data analysis regarding the determination and comparison of activity patterns, which are not based on the traditional methods that have been used to date, such as particle swarm optimization (PSO), neural networks, decision trees and cluster analysis. We found that the urbanized environment modifies the daily activity patterns of the mammals studied. Animals from the urban population have a longer active period than their rural counterparts and can forage under more favourable thermal conditions, so that the energetic cost of foraging is lower. PSO and neural networks allow for a more detailed analysis of patterns of daily activity than traditional methods, and their results correspond well with each other. Daily activity analysis shows great potential in the application of new statistical approaches that could supplement and enrich the traditionally used methods (e.g. the kernel density estimation). Our approach may help researchers to gain a broader perspective during their analysis of daily activity patterns and lead to a better description of the ecology of the species or even to more balanced wildlife management decisions. [ABSTRACT FROM AUTHOR]