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Habitat loss due to land use and land cover change (LUCC) has been identified as the main cause of global environmental change, responsible for biodiversity decline and the deterioration of ecological processes. Habitat loss and fragmentation have been driven by
processes of LUCC such as deforestation, agricultural expansion and intensification, urbanization, and globalization. The objective of this research was to determine the effects of LUCC on the process of habitat loss and the patterns of fragmentation in the surrounding landscape of the Pacuare Reserve (PR) in the Caribbean lowlands of Costa Rica. The PR is a protected area of 800 ha surrounded by an agricultural landscape with a history of over 150 years of bananas monocultures. Landsat satellite images from 1978 to 2020 were used to conduct a temporal analysis of LUCC around the PR. Patterns of change were explored using landscape metrics from the land classification images. To explore potential connectivity routes, the least cost path analysis was used to connect the PR to other protected areas. Overall, forest cover decreased in the study area at a rate of -4.8% per year during the period of 1992-1997. In the year 2001 it reached its lowest cover and then increased at a mean annual rate of 1.6%. A mean overall accuracy of 92% was obtained for the land classification process. A clear fragmentation process was observed, as shown by a decreased in forest mean patch area and largest patch index and by the increase in patch density. Although forest cover increased in the last decade, fragmentation metrics suggest this recover happened in a spatially scattered manner, due to agricultural land abandonment. Connectivity maps showed the importance of forest fragments and of the already established biological corridors for the movement of species to and from the PR, however it also evidenced the lack of connectivity between the coastal forest fragments and further inside the country located protected areas, as well as the need to promote reforestation projects, particularly between fragments of the corridors identified.
Eine gängige Form der Qualitätskontrolle von Quellcode sind Code Reviews. Der Fokus von Code Reviews liegt allerdings oft auf syntaktischer Analyse, wodurch weniger Zeit für eine semantische Überprüfung bleibt und zusätzliche Kosten verursacht werden. Code Reviews lassen sich zwar teilweise durch "Linter" automatisieren, dennoch können sie nur syntaktische Fehlermuster identifizieren, welche vorher definiert wurden. Zudem kann ein Linter nur darauf hinweisen, dass möglicherweise ein Fehler vorliegt, da die Fehler nicht durch logische Inferenz ermittelt werden. Die vorliegende Arbeit prüft, ob ein Deep Learning Modell den regelbasierten Ansatz von Lintern ablösen und die semantische Ebene erschließen kann. Dazu wurde eine Stichprobe von Java Methoden zusammengestellt und im Anschluss mit einem Supervised Learning Ansatz binär klassifiziert. Da die Analyse von Quellcode der Textanalyse stark ähnelt wird ein gängiger Ansatz für Textklassifikation verwendet. Dadurch kann gezeigt werden, dass eine Präzision von 85% bei der Erkennung von Quellcodeproblemen durch Deep Learning möglich ist.
The topic for the thesis originated from the CAP4ACCESS project run by the European Commission and its partners, which deals towards the sensiti-zation of people and development of tools for awareness about people with movement disabilities. The explorative analysis is never ending and to explore and find interest-ing patterns and the results is a tedious task. Therefore, a scientific approach was very important. To start with, familiarizing the domain and the data sources were done. Thereafter, selection of methodology for data analysis was done which resulted in the use of CRISP-DM methodology. The data sources are the source of blood to the analysis methodology, and as there were two sources of data that is MICROM and OSM Wheelchair History(OWH), it was important to integrate them together to extract relevant datasets. Therefore a functional and technically impure data warehouse was created, from which the datasets are extracted and analysed.The next task was to select appropriate tools for analysis. This task was very important as the data set although was not big data but con-tained a large number of rows. After careful analysis, Apache spark and its machine learning library were utilized for building and testing supervised models. DataFrame API for Python, Pandas, the machine learning library Sci-kit learn provided unsupervised algorithms for analysis, the association rule analysis was performed using WEKA. Tableau[21] and Matplotlib[24] provide attractive visualizations for representation and analysis.
Im Zusammenhang mit dem Begriff Big Data können nicht nur immer größere Datenmengen verarbeitet werden, sondern auch neue Arten von Datenquellen genutzt werden. Insbesondere Web 2.0-Inhalte bieten dabei vielfältige Potenziale.
So können beispielsweise mit Hilfe einer Sentiment-Analyse Meinungen und Stimmungen zu Produkten und Unternehmen in sozialen Netzwerken beobachtet werden. Diese Infor-mationen sind für sich gesehen bereits wertvoll für viele Unternehmen. Jedoch ist eine effiziente Analyse und Auswertung der Informationen nur in Kombination mit weiteren Unternehmensdaten möglich, die typischerweise in einem Data Warehouse liegen. Diese Arbeit diskutiert die Unter-schiede, Möglichkeiten und Herausforde-rungen diese Kombination zu realisieren. Veranschaulicht wird dies durch einen Show-Case, der eine Ende-zu-Ende-Umsetzung
am Beispiel der Fernsehsendung Tatort zeigt. Dabei werden Zuschauerkommentare
aus Twitter extrahiert, mit einer Sentiment-Analyse bewertet und schließlich in einem Data Warehouse ausgewertet. Dabei können klassische BI-Kennzahlen, wie beispiels- weise Einschaltquoten, Folgen pro Ermittler etc. den Ergebnissen der Sentiment-Analyse gegenübergestellt werden.
This paper is grounded in the emerging field of web science and shall contribute to its further classification and demarcation by illustrating the current state of »web-native research methods«. It builds upon an initial arraying work of Richard Rogers, who coined the term »Digital Methods« for research with methods that were »born« in the web, and illustrated and organized them in his eponymous book in 2013. This paper attempts to develop a more appropriate illustration of the Digital Methods by following the web’s very own, hypertextual, network-like nature, in particular by construing an ontological representation on the base of the Web Ontology Language (OWL). By virtue of decomposing the book into granular information units and their subsequent reassembly into OWL entities, immediate access to the entire knowledge domain can be provided, and coherencies, interrelations and distinctions between concepts become apparent. The ontology’s structure was induced narrowly along the provided examples of research projects and subsequently clustered in topic groups, of which the three most important ones were (a) the Digital Methods as an arraying space of web-native methodology, (b) a collection of concrete applications of these Digital Methods in research projects, and (c) a hierarchical scheme of traditional sciences with a distinct interest in answering research questions with help of Digital Methods. Subsequently, the ontology was evaluated in three general dimensions: Deriving user stories and scenarios provided means to validate the utilization quality; the accuracy and reliability of the resulting structure was validated with help of a control group of web-native research projects; and process control instruments served as a validator for the ontology’s correctness. Despite the ontology itself, this paper also resulted in a first interpretation of the produced information: Statements about research practise in social science, politics and philosophy were as possible as findings about commonly applied varieties of methods. Concluding, the present paper proposes a process of ontology engineering, an evaluation of the ontology’s value, and an interpretation of the ontology’s content.