As metropolises continue to see a growth in population, planners are continually searching for trending methods for utilizing space and seeking the best geographical arrangements for these cities. Professionals have continually used geographic information systems (GIS) to solve these issues; however, limitations in this technology remain prevalent. Integrating multiple-criteria decision analysis and evolutionary computing tools with GIS has created an array of robust solutions for spatial optimization problems in densely populated areas. Interdisciplinary Approaches to Spatial Optimization Issues is a pivotal reference source that provides vital research on advancements within the field of GIS and evolutionary solutions for spatial optimization issues. While highlighting topics such as computing machinery, vehicular routing, and operational research, this publication is ideally designed for practitioners, technicians, developers, academicians, students, government officials, planners, and researchers seeking current research on applications and improvements within spatial optimization and GIS.

Urbanization is giving rise to environmental concerns including urban flooding, which generally occurs due to the construction of houses in the low-lying areas; loss of green cover leading to a disturbance in the ecological cycle; water scarcity due to growing needs; and deforestation leading to habitat fragmentation, wildlife corridors disturbance, forest fires, and climate change. In order to correct these issues, a consolidated balance between human, nature, and spatial aspects must be resolved and spatial solutions integrated on a common platform. Addressing Environmental Challenges Through Spatial Planning is devoted to addressing environmental concerns and technology innovations in domains such as pollution, water insecurity, and resources management. This text works to bridge the gap between engineering considerations and spatial aspects of planning. Covering topics such as sustainable housing, environmental restoration, and air emissions, this text is essential for environmental engineers, planning researchers, faculty, environmental and civil administrators, architects, consultants, environmental activists, town and country planning organizations, and professionals in all industries who aspire to have an environmentally friendly atmosphere and to provide a sustainable way of dealing with the environment in their respective domains for process efficiency and cost optimization.

While megacities are a reality, so too are the environmental disturbances that they cause, including air and water pollution. These disturbances can be modeled with technology and data obtained by modern methods, such as by drone, to monitor cities in near real-time as well as help to simulate risk situations and propose future solutions. These solutions can be inspired by the theoretical principles of sustainable urbanism. Methods and Applications of Geospatial Technology in Sustainable Urbanism is a collection of innovative research that combines theory and practice on analyzing urban environments and applying sustainability principles to them. Highlighting a wide range of topics including geographic information systems, internet mapping technologies, and green urbanism, this book is ideally designed for urban planners, public administration officials, landscape analysts, geographers, engineers, entrepreneurs, academicians, researchers, and students.

With the emergence of smart technology and automated systems in today’s world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.

In recent decades, the industrial revolution has increased economic growth despite its immersion in global environmental issues such as climate change. Researchers emphasize the adoption of circular economy practices in global supply chains and businesses for better socio-environmental sustainability without compromising economic growth. Integrating blockchain technology into business practices could promote the circular economy as well as global environmental sustainability. Integrating Blockchain Technology Into the Circular Economy discusses the technological advancements in circular economy practices, which provide better results for both economic growth and environmental sustainability. It provides relevant theoretical frameworks and the latest empirical research findings in the applications of blockchain technology. Covering topics such as big data analytics, financial market infrastructure, and sustainable performance, this book is an essential resource for managers, operations managers, executives, manufacturers, environmentalists, researchers, industry practitioners, students and educators of higher education, and academicians.

In geospatial analysis, navigating the complexities of data interpretation and analysis presents a formidable challenge. Traditional methods often need to efficiently handle vast volumes of geospatial data while providing insightful and actionable results. Scholars and practitioners grapple with manual or rule-based approaches, hindering progress in understanding and addressing pressing issues such as climate change, urbanization, and resource management. Ethics, Machine Learning, and Python in Geospatial Analysis offers a solution to the challenges faced by leveraging the extensive library support and user-friendly interface of Python and machine learning. The book’s meticulously crafted chapters guide readers through the intricacies of Python programming and its application in geospatial analysis, from fundamental concepts to advanced techniques.

Today, the relentless depletion of natural resources has reached a critical juncture, demanding innovative solutions. Advanced Geospatial Practices in Natural Environment Resource Management dives into the intricate tapestry of issues jeopardizing ecosystems. This book systematically dissects the fundamental drivers, traces the historical evolution, and elucidates the underlying causes that have led to this precarious point. From deforestation to pollution, from climate change to habitat destruction, these challenges are multifaceted. By harnessing the power of data-driven decision-making and predictive modeling, this book advocates for a profound shift in the approach to environmental issues. This book also explores the complexities of water, soil, and air-related challenges, offering technically sound solutions that strike a balance between legislative, economic, social, and political considerations. The holistic approach championed in these pages holds the potential to benefit governments, businesses, and communities alike. This book is ideal for a diverse audience, including students, scholars, and researchers, as well as environmental science enthusiasts, geospatial technology professionals, and AI/ML practitioners. Policymakers, environmental engineers, and professionals engaged in governmental and corporate sustainability initiatives will also discover practical solutions to address the pressing challenges.

Academics and researchers currently grapple with a pressing issue; the demand for precise and insightful geographical information has surged across various fields, encompassing urban planning, environmental monitoring, agriculture, and disaster management. This surge has revealed a substantial knowledge gap, underscoring the need for effective applications that can bridge the gap between cutting-edge technologies and practical usage. Geospatial Application Development Using Python Programming emerges as the definitive solution to this challenge. This comprehensive book equips academics, researchers, and professionals with the essential tools and insights required to leverage the capabilities of Python programming in the realm of spatial analysis. It goes beyond merely connecting these two realms; it actively fosters their collaboration. By advancing knowledge in spatial sciences and highlighting Python's pivotal role in data analysis and application development, this book plays a crucial part in addressing the challenge of effectively harnessing geographical data.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

Advances in Mechanics: Theoretical, Computational and Interdisciplinary Issues covers the domain of theoretical, experimental and computational mechanics as well as interdisciplinary issues, such as industrial applications. Special attention is paid to the theoretical background and practical applications of computational mechanics.This volume