This book studies the increasing use of data analytics and technology in urban planning and development in developing nations. It examines the application of urban science and engineering in different sectors of urban planning and looks at the challenges involved in planning 21st-century cities, especially in India. The volume analyzes various key themes such as auditory/visual sensing, network analysis and spatial planning, and decision-making and management in the planning process. It also studies the application of big data, geographic information systems, and information and communications technology in urban planning. Finally, it provides data-driven approaches toward holistic and optimal urban solutions for challenges in transportation planning, housing, and conservation of vulnerable urban zones like coastal areas and open spaces. Well supplemented with rigorous case studies, the book will be of interest to scholars and researchers of architecture, architectural and urban planning, and urban analytics. It will also be useful for professionals involved in smart city planning, planning authorities, urban scientists, and municipal and local bodies.

Originally published in 1980, this book draws together a wide range of studies dealing with various aspects of land use in a text specifically designed to guide students through the complexities of the subject. It examines the history of the subject, its techniques, applications, the models that it applies and the frameworks within which it has been carried out. Land use remains a central political and practical issue in contemporary society.

Location-Based Services (LBS) are the delivery of data and information services where the content of those services is tailored to the current location and context of a mobile user. This is a new and fast-growing technology sector incorporating GIS, wireless technologies, positioning systems and mobile human-computer interaction. Geo-Information (GI) Engineering is the design of dependably engineered solutions to society's use of geographical information and underpins applications such as LBS. These are brought together in this comprehensive text that takes the reader through from source data to product delivery.

This book will appeal to professionals and researchers in the areas of GIS, mobile telecommunications services and LBS. It provides a comprehensive view and in-depth knowledge for academia and industry alike. It serves as essential reading and an excellent resource for final year undergraduate and postgraduate students in GIScience, Geography, Mobile Computing or Information Systems who wish to develop their understanding of LBS.

Master GIS Applications on Modelling and Mapping the Risks of Diseases Infections transmitted by mosquitoes, ticks, triatomine bugs, sandflies, and black flies cause significant rates of death and disease, especially in developing countries. Why are certain places more susceptible to vector-borne diseases? Modelling Interactions Between Vector-Borne Diseases and Environment Using GIS reveals how using geographic information systems (GISs) can provide a greater understanding of how vector-borne diseases are spread and explores the use of geographical techniques in vector-borne disease monitoring, management, and control. This text provides readers with a better understanding of the vector-borne disease problem and its impact on public health. Introduces New Spatial Approaches Based on Location and Environment The book exposes readers to information on how to identify vector hotspots, determine when and where they can occur, and eliminate vector breeding sites. Utilizing simple illustrations based on real data, as well as the authors' more than 20 years of experience in the field, this text combines key spatial analysis techniques available in modern GIS with real-world applications. It offers step-by-step instruction on developing vector-borne disease risk models at different spatial and temporal scales and helps practitioners formulate disease causation hypotheses and identify areas at risk. In addition, it addresses medical geography, GIS, spatial analysis, and modelling, and covers other factors related to the spread of vector-borne diseases. This book: Gives an overview of common vector-borne diseases, GIS-based mapping and modelling, impacts of climate change on vector distributions, and availability and importance of accurate epidemiologically relevant spatial data Describes modelling and simulating the prevalence of vector-borne diseases around the world Summarizes some key spatial techniques and how they can be used to aid in the analysis of geographical and attributed data Defines the concept of establishing and characterizing spatial data systems, including their quality, errors, references, and issues of scale, and building such a system from often quite separate, disparate sources Shows how to develop weather-based predictive modelling, which can be used to predict the weekly trend of vector abundance Provides a GIS case study for modelling the future potential distribution of vector-borne disease based on different climatic change scenarios Modelling Interactions Between Vector-Borne Diseases and Environment Using GIS combines spatial analysis techniques available in modern GIS, together with real-world applications to provide you with a better understanding of ways to map, model, prevent, and control vector-borne diseases.

Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field. A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc. Although theory will be covered, the applied component will be equally as important with lots of practical examples offered. Features: Discusses the very latest developments on multivariate and multidimensional mapping. Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches. Balances epidemiological and statistical points-of-view. Requires no previous knowledge of disease mapping. Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets. Supplies R code for the examples in the book so that they can be reproduced by the reader. About the Authors: Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center. He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master. Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration. Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.

Social Sensing and Big Data Computing for Disaster Management captures recent advancements in leveraging social sensing and big data computing for supporting disaster management. Specifically, analysed within this book are some of the promises and pitfalls of social sensing data for disaster relevant information extraction, impact area assessment, population mapping, occurrence patterns, geographical disparities in social media use, and inclusion in larger decision support systems. Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events. Social sensing enables all citizens to become part of a large sensor network which is low cost, more comprehensive, and always broadcasting situational awareness information. However, data collected with social sensing is often massive, heterogeneous, noisy, and unreliable in some aspects. It comes in continuous streams, and often lacks geospatial reference information. Together, these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress. Meanwhile, big data computing methods and technologies such as high-performance computing, deep learning, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion. This book was originally published as a special issue of the International Journal of Digital Earth.

With urbanization as a global phenomenon, there is a need for data and information about these terrains. Urban remote sensing techniques provide critical physical input and intelligence for preparing base maps, formulating planning proposals, and monitoring implementations. Likewise these methodologies help with understanding the biophysical properties, patterns, and process of urban landscapes, as well as mapping and monitoring urban land cover and spatial extent. Advanced sensor technologies and image processing methodologies such as deep learning, data mining, etc., facilitate the wide applications of remote sensing technology in urban areas. This book presents advanced image processing methods and algorithms focused on three very important roots of urban remote sensing: 3D urban modelling using different remotely sensed data, urban orthophotomap generation, and urban feature extraction, which are also today's real challenges in high resolution remote sensing. Data generated by remote sensing, with its repetitive and synoptic viewing and multispectral capabilities, constitutes a powerful tool for mapping and monitoring emerging changes in the city's urban core, as well as in peripheral areas. Features: Provides advances in emerging methods and algorithms in image processing and technology Uses algorithms and methodologies for handling high-resolution imagery from a ground sampling distance (GSD) less than 1.0 meter Focuses on 3D urban modelling, orthorectification methodologies, and urban feature extraction algorithms from high-resolution remotely sensed imagery Demonstrates how to apply up-to-date techniques to the problems identified and how to analyze research results Presents methods and algorithms for monitoring, analyzing, and modeling urban growth, urban planning, and socio-economic developments In this book, readers are provided with valuable research studies and applications-oriented chapters in areas such as urban trees, soil moisture mapping, city transportation, urban remote sensing big data, etc.

Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer codes. It develops statistical methods for the analysis of optical/infrared and synthetic aperture radar (SAR) imagery, including wavelet transformations, kernel methods for nonlinear classification, as well as an introduction to deep learning in the context of feed forward neural networks. New in the Fourth Edition: An in-depth treatment of a recent sequential change detection algorithm for polarimetric SAR image time series. The accompanying software consists of Python (open source) versions of all of the main image analysis algorithms. Presents easy, platform-independent software installation methods (Docker containerization). Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API). Examines deep learning examples including TensorFlow and a sound introduction to neural networks, Based on the success and the reputation of the previous editions and compared to other textbooks in the market, Professor Canty's fourth edition differs in the depth and sophistication of the material treated as well as in its consistent use of computer codes to illustrate the methods and algorithms discussed. It is self-contained and illustrated with many programming examples, all of which can be conveniently run in a web browser. Each chapter concludes with exercises complementing or extending the material in the text.

As the use of geographical information systems develops apace, a significant strand of research activity is being directed to the fundamental nature of geographic information. This volume contains a collection of essays and discussions on this theme. What is geographic information? What fundamental principles are associated with it? How can it be represented? How does it represent the world? How can geographic information be quantified? How can it be communicated and related to the other information sciences? How does HCI tie in with it? A number of other more specific but relevant issues are considered, such as Spatio-temporal relationships, boundaries, granularity and taxonomy. This book is a revised and updated version of a collection of presentations given by a group of distinguished researchers in the field of Geographic Information Science who gathered in Manchester in July 2001. It should be useful for graduate students as well as researchers and high-level professionals.

Geographical Information System and Crime Mapping features a diverse array of Geographic Information System (GIS) applications in crime analysis, from general issues such as GIS as a communication process, interjurisdictional mapping and data sharing to specific applications in tracking serial killers and predicting violence-prone zones. It supports readers in developing and implementing crime mapping techniques. The distribution of crime is explained with reference to theories of human ecology, transport network, built environment, housing markets, and forms of urban management, including policing. Concepts are supported with relevant case studies and real-time crime data to illustrate concepts and applications of crime mapping. Aimed at senior undergraduate, graduate students, professionals in GIS, Crime Analysis, Spatial Analysis, Ergonomics and human factors, this book: Provides an update of GIS applications for crime mapping studies Highlights growing potential of GIS for crime mapping, monitoring, and reduction through developing and implementing crime mapping techniques Covers Operational Research, Spatial Regression model, Point Analysis and so forth Builds models helpful in police patrolling, surveillance and crime mapping from a technology perspective Includes a dedicated section on case studies including exercises and data samples

The demand on local government to do more with less by improving operations, increasing productivity, and making better and more informed decisions increases constantly. On a departmental level Geographic Information Systems are helping meet this demand but the majority of local government organizations do not take the time to understand the GIS needs and opportunities of each and every department. This book: Discusses how towns, cities and counties and their specific departments should actually use GIS Explains the best ways to use GIS tools through many specific case studies and step by step instructions Emphasises local government needs first before offering solutions Gives readers a practical and understandable way of thinking about managing and making GIS successful This book is the guide that details best GIS applications and practices for the 34 departments in local government that can, and should, use GIS technology. It explains in details how, why, and what each department should implement, a clear and understandable explanation of departmental GIS.

The Open Access version of this book, available at http://www.tandfebooks.com/doi/view/10.1201/9781315146638, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 3.0 license. GIS is used today to better understand and solve urban problems. GIS in Sustainable Urban Planning and Management: A Global Perspective, explores and illustrates the capacity that geo-information and GIS have to inform practitioners and other participants in the processes of the planning and management of urban regions. The first part of the book addresses the concept of sustainable urban development, its different frameworks, the many ways of measuring sustainability, and its value in the urban policy arena. The second part discusses how urban planning can shape our cities, examines various spatial configurations of cities, the spread of activities, and the demands placed on different functions to achieve strategic objective. It further focuses on the recognition that urban dwellers are increasingly under threat from natural hazards and climate change. Written by authors with expertise on the applications of geo-information in urban management, this book showcases the importance of GIS in better understanding current urban challenges and provides new insights on how to apply GIS in urban planning. It illustrates through real world cases the use of GIS in analyzing and evaluating the position of disadvantaged groups and areas in cities and provides clear examples of applied GIS in urban sustainability and urban resilience. The idea of sustainable development is still very much central in the new development agenda of the United Nations, and in that sense, it is of particular importance for students from both the Global South and Global North. Professionals, researchers, and students alike will find this book to be an invaluable resource for understanding and solving problems relating to sustainable urban planning and management.

Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of- the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume II, Hyperspectral Indices and Image Classifications for Agriculture and Vegetation evaluates the performance of hyperspectral narrowband or imaging spectroscopy data with specific emphasis on the uses and applications of hyperspectral narrowband vegetation indices in characterizing, modeling, mapping, and monitoring agricultural crops and vegetation. This volume presents and discusses topics such as the non-invasive quantification of foliar pigments, leaf nitrogen concentration of cereal crop, the estimation of nitrogen content in crops and pastures, and forest leaf chlorophyll content, among others. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume II through the editors' perspective. Key Features of Volume II: Provides the fundamentals of hyperspectral narrowband vegetation indices and hyperspectral derivative vegetation indices and their applications in agriculture and vegetation studies. Discusses the latest advances in hyperspectral image classification methods and their applications. Explains the massively big hyperspectral sensing data processing on cloud computing architectures. Highlights the state-of-the-art methods in the field of hyperspectral narrowband vegetation indices for monitoring agriculture, vegetation, and their properties such as plant water content, nitrogen, chlorophyll, and others at leaf, canopy, field, and landscape scales. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.

1. Provides the fundamentals of feature extraction methods and applications along with fundamentals of machine learning. 2. Discusses in detail the advantages of using machine learning in geospatial feature extraction. 3. Explains the methods for estimating object height from optical satellite remote sensing images using Python, R, QGIS, and GRASS GIS implementations. 4. Includes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetric methods for height assessment. 5. Highlights the potential of machine learning and geospatial technology for future project developments.

Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors' perspective. Key Features of Volume I: Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies. Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands. Develops online hyperspectral libraries, proximal sensing and phenotyping for understanding, modeling, mapping, and monitoring crop and vegetation traits. Implements reflectance spectroscopy of soils and vegetation. Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms. Explores methods and approaches for data mining and overcoming data redundancy; Highlights the advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine. Integrates hyperspectral with other data, such as the LiDAR data, in the study of vegetation. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.

This book explores the applicability of multiple remote sensors to acquire information relevant to restoration and conservation efforts in wetlands using data collected from airborne and space multispectral/hyperspectral sensors, light detection and ranging (LiDAR), Unmanned Aircraft Systems (UAS), and a hand-held spectroradiometer. This book also examines digital data processing techniques such as object-based image analysis, machine learning, texture analysis, and data fusion. After an introduction to the Everglades and to remote sensing, the book is divided into four parts based on the sensor systems used. There are chapters on vegetation mapping, biomass and water quality modeling, applications of hyperspectral data for plant stress analysis and coral reef mapping, studies of airborne LiDAR data for coastal vulnerability analysis and DEM improvement, as well as chapters that explore a fusion of multiple sensors for different datasets. Features Introduces concepts, theories, and advanced processing techniques A complete introduction of machine learning, object-based image analysis, data fusion, and ensemble analysis techniques in processing data from multiple remote sensors Explains how multiple remote sensing systems are applied in the wetland ecosystems of Florida The author had been teaching and using both systems and her research is widely recognized Multi-sensor System Applications in the Everglades Ecosystems provides a comprehensive application of remote sensing techniques in the Florida Everglades and its coastal ecosystems. It will prove an invaluable resource for the restoration and conservation of the Florida Everglades and beyond, for global wetlands in general. Any professional, scientist, engineer, or student working with remote sensing and wetland ecosystems will reap enormous benefits from this book.

Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements, but modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Principles and Applications, Second Edition maintains a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This second edition expands to new topics that satisfy a growing need in the GIS, professional surveyor, machine control, and Big Data communities while continuing to embrace the earth center fixed coordinate system as the fundamental point of origin of one, two, and three-dimensional data sets. Ideal for both beginner and advanced levels, this book also provides guidance and insight on how to link to the data collected and stored in legacy systems.

This book shows how to build a "INFelecPHY GPS Unit" (IEP-GPS) tracking system for fleet management that is based on 3G and GPRS modules. This model should provide reliability since it deals with several protocols: 1) HTTP and HTTPS to navigate, download and upload in real time the information to a web server, 2) FTTP and FTTPS to handle in a non-real time the files to the web application, and 3) SMTP and POP3 to send and receive email directly from the unit in case of any alert. Similar to a mobile device, but without screen for display, it is multifunctional because it links to a GPRS module, a camera, a speaker, headphone, a keypad and screen.

Wireless Sensor Networks overcome the difficulties of other monitoring systems. However, they require further efficiencies for Outdoor Environment Monitoring (OEM) applications due to their harsh operational conditions, huge targeted areas, limited energy budget, and required 3D setups. A fundamental issue in defeating these practical challenges is deployment planning. The deployment plan is a key factor of many intrinsic properties of OEM networks, summarized in connectivity, lifetime, fault-tolerance, and cost-effectiveness. This book investigates the problem of WSNs deployments that address these properties in order to overcome the unique challenges and circumstances in OEM applications.

The explosion of public interest in the natural environment can, to a large extent, be attributed to greater public awareness of the impacts of global warming and climate change. This has led to increased research interest and funding directed at studies of issues affecting sensitive, natural environments. Not surprisingly, much of this work has required the innovative application of GIS and has led to a crucial research question: How should the environment be represented, modeled, analyzed, and visualized within a GIS? With contributions from recognized international experts, Representing, Modeling, and Visualizing the Natural Environment explores the interplay between data representation, modeling, and visualization in environmental studies. It reviews state-of-the-art GIS applications for the natural environment and presents them in the context of a range of recent studies. This focus identifies analytical challenges and illustrates broader opportunities for applying GIS within other areas of the sciences and social sciences. The integrated approach reflects the need for a single volume covering all aspects While many texts cover aspects of GIS application within an environmental context, few of these books focus specifically on the natural environment nor do they integrate the questions that encompass the full process of enquiry associated with GIS application in studies of the environment. The thirteenth volume in the widely recognized Innovations of GIS series, this book investigates each of these questions in turn, explicitly addressing all aspects of GIS application in the natural environment.

Urban development and migration from rural to urban areas are impacting prime agricultural land and natural landscapes, particularly in the less developed countries. These phenomena will persist and require serious study by those monitoring global environmental change. To address this need, various models have been devised to analyze urbanization and the physical, socioeconomic, and institutional factors impacting urban development. The most promising and rapidly developing of these paradigms take advantage of new Geographical Information System (GIS) technology. Modelling Urban Development with Geographical Information Systems and Cellular Automata presents one such cutting-edge model that is more than just predictive. It describes how the model simulates the urbanization process, and it provides theoretical context to promote understanding. Starting with a practical overview of the modelling techniques used in urban development research, the author focuses on the cellular automata model and its greatest strength - the incorporation of fuzzy set and fuzzy logic approaches through which urban development can be viewed as a spatially and temporally continuous process. Real-Life Application to Develop Future Planning Methods The text describes a landmark study underway, in which the fuzzy constrained cellular automata model has been implemented in a GIS environment to simulate urban development in Sydney, Australia. Featuring a survey of associated research and a geographical database for the Sydney simulation, this book answers many general "what if" questions for urban planners and details a new approach that they can adapt to their own testing and evaluation needs. This modeling method will provide researchers and planners with the means to not just predict population trends, but to better prepare for their consequences.

GIS for Sustainable Development examines how GIS applications can improve collaboration in decision making among those involved in promoting sustainable development. This volume reviews leading GIScience, providing an overview of research topics and applications that enable GIS newcomers and professionals to apply GIScience methods to sustainable spatial planning. Divided into three parts, the book begins with an introduction to the issues of sustainability, focusing on concepts concerning GIS adoption and use within public organizations that are planning development. Part II focuses on GIScience methods, which can be used to support sustainable development and solve environmental problems. Part III presents research projects and best practices relating to different areas of application within the field. This text provides the latest research findings, delivers complete references to related publications, and supplies you with a complete reference framework for each topic.

This book explains the fundamentals and intricacies of telegeoinformatics. The book is divided into three parts: Theories and Technologies; Integrated Data and Technologies; and Applications. These sections are divided into smaller contributed chapters, each of which addresses a topic crucial to the understanding of telegeoinformatics. This volume covers the concepts and technologies related to GIS and geoprocessing, remote sensing, the GPS, and wireless systems. It also explores the main issues of integrated data and technologies in LBC, LBS, mediated reality, and mobile augmented reality systems. The final part discusses applications of telegeoinformatics in emergency response, mobile inspection data collection, and ITS.

Have you ever considered how much effect information technology has on society throughout the world? Progress often places lower income and marginalized communities at a distinct disadvantage. Community Participation and Geographic Information Systems, however, offers a detailed look at numerous incidences around the world where communities have actually taken control of the technology and really used it to their advantage. This is presented in the form of case studies and models such as Philadelphia's neighborhoods, the Atlanta Project, and neighborhood revitalization in Minneapolis, reflect on public participation in GIS concepts, best practices, constraints and opportunities.

One of the ongoing problems researchers in geography and GIS have is studying data that is inherently spatial over a long period of time. One of the main hurdles they have to overcome is the study of groups of people classified by their socio-economic status (one of the main means for governments, companies and research organisations to group together segments of the population). The amount of data collected by governments, business and research organisations has increased markedly in recent years. Geographic Information Systems have been more widely used than ever before for the storage and analysis of this information. Most GIS can handle this information spatially rather than temporally, and have difficulty with the management of socio-economic time series, which relate to spatial units. Accordingly, this book covers the issues ranging from the formal model to differentiate aspects of spatio-temporal data, through philosophical and fundamental reconsideration of time and space to the development of practical solutions to the problem. This book draws together an interdisciplinary group of scientists in the field of geography, computing, surveying and philosophy. It presents the definitive sourcebook on temporal GIS as applied to socio-economic units.