Uncertainty And Context In Giscience And Geography
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Uncertainty and Context in GIScience and Geography
Uncertainty and context pose fundamental challenges in GIScience and geographic research. Geospatial data are imbued with errors (e.g., measurement and sampling) and various types of uncertainty that often obfuscate any understanding of the effects of contextual or environmental influences on human behaviors and experiences. These errors or uncertainties include those attributable to geospatial data measurement, model specifications, delineations of geographic context in space and time, and the use of different spatiotemporal scales and zonal schemes when analyzing the effects of environmental influences on human behaviors or experiences. In addition, emerging sources of geospatial big data – including smartphone data, data collected by GPS, and various types of wearable sensors (e.g., accelerometers and air pollutant monitors), volunteered geographic information, and/ or location- based social media data (i.e., crowd- sourced geographic information) – inevitably contain errors, and their quality cannot be fully controlled during their collection or production. Uncertainty and Context in GIScience and Geography: Challenges in the Era of Geospatial Big Data illustrates how cutting- edge research explores recent advances in this area, and will serve as a useful point of departure for GIScientists to conceive new approaches and solutions for addressing these challenges in future research. The seven core chapters in this book highlight many challenges and opportunities in confronting various issues of uncertainty and context in GIScience and geography, tackling different topics and approaches. The chapters in this book were originally published as a special issue of the International Journal of Geographical Information Science.
The Routledge Handbook of Methodologies in Human Geography
The Routledge Handbook of Methodologies in Human Geography is the defining reference for academics and postgraduate students seeking an advanced understanding of the debates, methodological developments and methods transforming research in human geography. Divided into three sections, Part I reviews how the methods of contemporary human geography reflect the changing intellectual history of human geography and events both within human geography and society in general. In Part II, authors critically appraise key methodological and theoretical challenges and opportunities that are shaping contemporary research in various parts of human geography. Contemporary directions within the discipline are elaborated on by established and emerging researchers who are leading ontological debates and the adoption of innovative methods in geographic research. In Part III, authors explore cross-cutting methodological challenges and prompt questions about the values and goals underpinning geographical research work, such as: Who are we engaging in our research? Who is our research ‘for’? What are our relationships with communities? Contributors emphasize examples from their research and the research of others to reflect the fluid, emotional and pragmatic realities of research. This handbook captures key methodological developments and disciplinary influences emerging from the various sub-disciplines of human geography.
Deep Learning in Internet of Things for Next Generation Healthcare
This book presents the latest developments in deep learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with deep learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of deep learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with deep learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, Alzheimer's disease, and genomes. Presents practical approaches of using the IoT with deep learning vision and how it deals with human dynamics Offers novel solution for medical imaging including skin lesion detection, cancer detection, enhancement techniques for MRI images, automated disease prediction, fraud detection, genomes, and many more Includes the latest technological advances in the IoT and deep learning with their implementations in healthcare Combines deep learning and analysis in the unified framework to understand both IoT and deep learning applications Covers the challenging issues related to data collection by sensors, detection and tracking of moving objects and solutions to handle relevant challenges Postgraduate students and researchers in the departments of computer science, working in the areas of the IoT, deep learning, machine learning, image processing, big data, cloud computing, and remote sensing will find this book useful.