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Veröffentlichung im Rahmen des European Parking Association Congress 2015, Berlin.
Connected ambient assistance systems support elderly people - or people in need of care - for a self-determined, healthy and safe living in their familiar home until very old age. The systems typically communicate with nursing relatives or remote professional caregiving providers. The basic functions of stationary assistance systems include the detection of a) falls and b) deviant behavior indicating a health hazard. These kernel tasks of assistance have been amended in the course of time by a plenitude of more general assistant functions. Wearable devices, like programmable smartwatches, extend the reach of stationary assistance beyond the spatial boundaries of the familiar home. They extend the scope of wellbeing monitoring by analyzing the wearer’s heart rate in relation to the current physical activity and can also verify sufficient liquid ingestion, drinking, thus compensating the diminishing natural sensation of thirst at increasing age. Home robots also extend the scope of wellbeing monitoring within the home and relieve from the necessity of a comprehensive sensoric instrumentation of the home. So far, ambient assistant systems can primarily provide assistance only in the physiological dimension. Cognitive ambient assistance, which allows the participation in the social communication on an equal footing, today is beyond their abilities. The advances in artificial intelligence are about to change the picture. From an economic point of view, the dissemination and success of assistance systems has been slowed down within the last decade by lacking wide-spread interaction standards as well as the shortage of necessary multivalent utility of such systems. In contrast, the smart home concept offering increased comfort and sustainability has gained a lot of attractivity in the same time. Therefore, commercially successful assistance systems must provide their utility in all three dimensions: assistance, but also simultaneously in comfort/safety and sustainability.
The advancing use of modern technology in nursing care, especially the development of technical assistance systems through robotics, digitization and Artificial Intelligence (Al), can open up new opportunities for those affected. At the same time, however, the use of these technologies also carries risks for this group of people, especially for their fundamental right to informational self-determination, due to the extensive processing of personal data. This article describes the risks for data subjects and explains the current legal framework regarding the protection of personal data in the European Union. The authors conclude, that applying data protection and data security to technical assistance systems, robots and Al from the beginning not only leads to legally compliant practices but also strengthens the trust of users and society as a whole in the use of these systems. The use of Al-based systems raises further (legal} questions that go beyond data protection and data security. The authors first address the various definitions of the term „Artificial Intelligence” in the academic literature. They then describe the European Union’s various regulatory approaches to the use of Al starting with the European Commission's “Strategy for Artificial Intelligence” published in 2018, followed by the “Al White Paper” published in 2020 and ending with the European Commission’s proposal for a Regulation laying down harmonized Rules on Artificial Intelligence- Artificial Intelligence Act (AIA) published in April 2021. Here, in particular, aspects of „scope“, „transparency” and „impact on the healthcare sector” will be examined in more detail and the still necessary need for legal policy discussion will be highlighted.
Working Paper Nr. 18 des Fachbereichs 3: Wirtschaft und Recht
Master's Thesis at Frankfurt University of Applied Sciences, Faculty 1: Architecture, Civil Engineering, Geomatics, Master Program "Urban Agglomerations".
Abstract
At the intersection of issues of water scarcity and rapid urbanisation is the need to investigate sustainable urban water management practices in today’s cities. This is especially important with the undeniable impact of climate change adding to its complexity. Johannesburg is a prime example of a city facing a myriad of challenges regarding water and is therefore the focus of the thesis. This research focuses on how the city of Johannesburg can pave its way into becoming a Water Sensitive City (WSC)- an aspirational vision of a city that integrates sustainable urban water management practices. To discover this, case study research on Johannesburg is undertaken by exploring the challenges and opportunities in relation to water demand and supply in the city. A few challenges include pollution of water and issues relating to the dependency on surface water. Additionally, an Urban Water Mass Balance (UWMB) analysis was undertaken to explore the potential of alternative water service options in a city that relies predominantly on surface water. It was found that rainwater harvesting had the highest potential to replace the existing centralized system. Based on the results of the case study research and the UWMB analysis a program was proposed to assist Johannesburg’s transition into a WSC. The proposal includes proactive recommendations relating to public participation and the protection of the environment.
Abstract English
Urban area tessellation is a crucial aspect in many spatial analyses. While regular tessellation methods, like square-grid or hexagon-grid, are suitable for addressing pure geometry problems, they cannot take the unique characteristics of different subareas into account. Irregular tessellation methods allow the border between the subareas to be defined more realistically based on the urban features like road network or POI data. This paper studies and compares five different tessellation methods: Squares, hexagons, adaptive squares, Voronoi diagrams, and city blocks. We explain how (open-source) POI data can be integrated into the tessellation process to build what we call “Local Geo-graphic Units” (POI-based tiles). These units are flexible and adaptable to the structure of the studied area and underlying data and could improve the performance of further analyses. The results of the various tessellation methods are demonstrated for the city of Frankfurt am Main in Germany. A simple clustering of Local Geographic Units for the studied city indicates that city blocks perform better than the other methods in the city segmentation in terms of reflecting the structure of this city.
Abstract Deutsch
Die Tessellierungen urbaner Gebiete ist ein entscheidender Aspekt bei räumlichen Analysen. Regelmäßige Tessellierungen, wie die Unterteilung in Quadrate oder Hexagons, eignen sich zwar für Probleme rein geometrischer Natur, berücksichtigen aber die Charakteristika der enthaltenen kleineren geographischen Einheiten nicht. Unregelmäßige Tessellierungen ermöglichen eine realitätsnahe Unterteilung basierend auf städtischen Merkmalen, wie dem Straßennetz oder POI-Daten. In diesem Beitrag werden fünf verschiedene Tessellierungsmethoden vorgestellt und verglichen: Quadrate, Hexagons, adaptive Quadrate, Voronoi-Diagramme und City-Blocks. Die Integration von (Open-Source) POI-Daten in den Tessellierungsprozess führt zu sogenannten „Lokalen Geographischen Einheiten“. Diese POI-basierten Einheiten sind flexibel und passen sich sowohl der Struktur des zu untersuchenden Gebiets, als auch der zugrundeliegenden Daten an und erlaube dadurch darauf aufbauende, detailliertere Analysen. Alle vorgestellten Tessellierungsmethoden werden an dem Beispiel Frankfurt am Main durchgeführt und präsentiert. Ein einfaches „Clustering“ der Lokalen Geographischen Einheiten zeigt, dass City-Blocks die Struktur der Stadt besser abbilden können, als die anderen vorgestellten Methoden.