LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Vegetation Index and Dynamics

The book contemplates different ways of approaching the study of vegetation as well as the type of indices to be used. However, all the works pursue the same objective: to know and interpret nature from different points of view, either through knowledge of nature in situ or the use of technology and mapping using satellite images. Chapters analyze the ecological parameters that affect vegetation, the species that make up plant communities, and the influence of humans on vegetation

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available

Explainable machine learning in soil mapping: Peeking into the black box

Während des Anthropozäns und insbesondere in den letzten Jahrzehnten hat sich die Umwelt der Erde stark verändert. Die planetarischen Grenzen stehen zunehmend unter Druck. Da der Boden als wichtiger Teil der Kohlenstoff- und Stickstoffkreisläufe das Klima beeinflusst, ist er eine wichtige Ressource bei der Bewältigung dieser Umweltprobleme. Folglich spielt das Wissen über den Boden, Bodenprozesse und Bodenfunktionen eine wesentliche Rolle bei der Erforschung und Lösung dieser schwerwiegenden ökologischen und sozioökonomischen Herausforderungen. Die Kartierung und Modellierung des Bodens liefert räumliche Kenntnis über den Zustand des Bodens und seine Veränderungen im Laufe der Zeit. Dies ermöglicht es, Methoden der Bodenbewirtschaftung und Lösungsansätze für Umweltprobleme zu beurteilen und zu bewerten. Methoden des maschinellen Lernens haben sich für die räumliche Kartierung und Modellierung des Bodens als geeignet erwiesen. Oft handelt es sich dabei aber um Black Boxes und die Modellentscheidungen und -ergebnisse werden nicht erklärt. Allerdings würden erklärbare Bodenmodelle auf der Grundlage des maschinellen Lernens die Erkennung von Umweltveränderungen erleichtern, zur Entscheidungsfindung für den Umweltschutz beitragen und die Akzeptanz von Wissenschaft, Politik in Gesellschaft fördern. Daher sind die jüngsten Bemühungen im Bereich des maschinellen Lernens darauf ausgerichtet, den konventionellen Rahmen des maschinellen Lernens auf er¬klärbares maschinelles Lernen zu erweitern, um 1) Entscheidungen zu begründen, 2) die Modelle besser zu steuern und 3) zu verbessern und 4) neues Wissen zu generieren. Die Kernelemente für erklärbares maschinelles Lernen sind Transparenz, Interpretierbarkeit und Erklärbarkeit. Darüber hinaus sind domain knowledge und wissenschaftliche Konsistenz entscheidend. Bei der Bodenmodellierung spielten die Konzepte des erklärbaren maschinellen Lernens jedoch bisher eine geringe Rolle. Ziel dieser Arbeit war es, zu untersuchen und zu beschreiben, wie Transparenz, Interpretierbarkeit und Erklärbarkeit im Rahmen der Bodenmodellierung erreicht werden können. Die Fallbeispiele zeigten, wie Konsistenz mit Modellvergleichen bewertet werden kann und domain knowledge in die Modelle einfließt. Ebenso zeigten die Studien, wie Transparenz mit reproduzierbarer Proben- und Variablenauswahl erreicht werden kann und wie die Interpretation der Modelle mit domain knowledge verknüpft werden kann, um die Modellergebnisse besser zu erklären und in Bezug zu bodenkundlichem Wissen zu setzen sind.During the Anthropocene and especially in the past decades earth’s environment has undergone major changes. The planetary boundaries are increasingly under pressure. Since soil affects climate as compartment of the carbon and nitrogen cycles, it is an important resource in approaching these environmental problems. Consequently, knowledge about soil, soil processes and soil functions plays an essential role in research on and solutions for these severe environmental and socio-economic challenges. The mapping and modelling of soil provides spatial knowledge of soil status and changes over time, which allows to assess and evaluate soil management practices and attempts to solve to environmental problems. Machine learning methods have proven to be suitable for spatial mapping and modelling of soil, but often are black boxes and the model decisions and prediction results remain unexplained. However, explainable soil models based on machine learning would facilitate detection of environmental changes, contribute to decision making for environmental protection and foster acceptance in science, politics, and society. Therefore, latest efforts in machine learning were to expand the conventional machine learning framework to explainable machine learning to 1) justify decisions, 2) control, and 3) improve models and 4) to discover new knowledge. The core elements for explainable machine learning are transparency, interpretability and explainability. Additionally, domain knowledge and scientific consistency are crucial. However, to date the concepts of explainable machine learning played a marginal role in soil modelling and mapping. Objective of this thesis was to explore and describe how transparency, interpretability and explainability can be achieved in the soil mapping framework. The example studies showed how scientific consistency can be evaluated with model comparison and domain knowledge was and incorporated in DSM models. The studies showed how transparency can be accomplished with reproducible sample and covariate selection, and how interpretation of the models can be linked with domain knowledge about soil formation and processes to explain the model results

Investigating the 3D chromatin architecture with fluorescence microscopy

Chromatin is an assembly of DNA and nuclear proteins, which on the one hand has the function to properly store the 2 meters of DNA of a diploid human nucleus in a small volume and on the other hand regulates the accessibility of specific DNA segments for proteins. Many cellular processes like gene expression and DNA repair are affected by the three-dimensional architecture of chromatin. Cohesin is an important and well-studied protein that affects three-dimensional chromatin organization. One of the functions of this motor protein is the active generation of specific domain structures (topologically associating domains (TADs)) by the process of loop extrusion. Studies of cohesin depleted cells showed that TAD structures were lost on a population average. Due to this finding, the question arose, to what extent the functional nuclear architecture, that can be detected by confocal and structured illumination microscopy, is impaired when cells were cohesin depleted. The work presented in this thesis could show that the structuring of the nucleus in areas with different chromatin densities including the localization of important nuclear proteins as well as replication patterns was retained. Interestingly, cohesin depleted cells proceeded through an endomitosis leading to the formation of multilobulated nuclei. Obviously, important structural features of chromatin can form even in the absence of cohesin. In the here presented work, fluorescence microscopic methods were used throughout, and an innovative technique was developed, that allows flexible labeling of proteins with different fluorophores in fixed cells. With this technique DNA as well as peptide nucleic acid (PNA) oligonucleotides can be site-specifically coupled to antibodies via the Tub-tag technology and visualized by complementary fluorescently labeled oligonucleotides. The advantages and disadvantages of PNAs as docking strands are discussed in this thesis as well as the use of PNAs in fluorescence in situ hybridization (FISH). In the next study, which is part of this work, a combination of FISH and super-resolution microscopy was used. There it could be shown that DNA segments of 5 kb can form both compact and elongated configurations in regulatory active as well as inactive chromatin. Coarse-grained modeling of these microscopic data, in agreement with published data from other groups, has suggested that elongated configurations occur more frequently in DNA segments in which the occupancy of nucleosomes is reduced. The microscopically measured distance distributions could only be simulated with models that assume different densities of nucleosomes in the population. Another result of this study was that inactive chromatin - as expected - shows a high level of compaction, which can hardly be explained with common coarse-grained models. It is possible that environmental effects that are difficult to simulate play a role here. Chromatin is a highly dynamic structure, and its architecture is constantly changing, be it through active processes such as the effect of cohesin investigated here or through thermodynamic interactions of nucleosomes as they are simulated in coarse-grained models. It will take a long time until we adequately understand these dynamic processes and their interplay