Spatial Analysis Using Big Data

Spatial Analysis Using Big Data
Author: Yoshiki Yamagata,Hajime Seya
Publsiher: Academic Press
Total Pages: 300
Release: 2019-10
ISBN 10: 9780128131275
ISBN 13: 0128131276
Language: EN, FR, DE, ES & NL

Spatial Analysis Using Big Data Book Review:

Spatial Analysis using Big Data: Econometrical Methods and Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and high-resolution information to potentially reveal new socioeconomic dynamics within urban populations. Each method, written by leading exponents of the discipline, uses real-time urban big data to solve research problems in spatial science. Urban applications of these methods are provided in unsurpassed depth, with chapters on surface temperature mapping, view value analysis, community clustering and spatial-social networks, among many others. Reviews some of the most powerful and challenging modern methods to study big data problems in spatial science Provides computer codes written in R, MATLAB and Python to help implement methods Applies these methods to common problems observed in urban and regional economics

Spatial Analysis Using Big Data

Spatial Analysis Using Big Data
Author: Yoshiki Yamagata,Hajime Seya
Publsiher: Academic Press
Total Pages: 302
Release: 2019-11-03
ISBN 10: 0128131322
ISBN 13: 9780128131329
Language: EN, FR, DE, ES & NL

Spatial Analysis Using Big Data Book Review:

Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and high-resolution information to potentially reveal new socioeconomic dynamics within urban populations. Each method, written by leading exponents of the discipline, uses real-time urban big data to solve research problems in spatial science. Urban applications of these methods are provided in unsurpassed depth, with chapters on surface temperature mapping, view value analysis, community clustering and spatial-social networks, among many others. Reviews some of the most powerful and challenging modern methods to study big data problems in spatial science Provides computer codes written in R, MATLAB and Python to help implement methods Applies these methods to common problems observed in urban and regional economics

Geographical Data Science and Spatial Data Analysis

Geographical Data Science and Spatial Data Analysis
Author: Lex Comber,Chris Brunsdon
Publsiher: SAGE
Total Pages: 360
Release: 2020-12-02
ISBN 10: 1526485435
ISBN 13: 9781526485434
Language: EN, FR, DE, ES & NL

Geographical Data Science and Spatial Data Analysis Book Review:

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges. Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (ie the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a ‘learning by doing’ text book, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.

Big Data Applications in Geography and Planning

Big Data Applications in Geography and Planning
Author: Mark Birkin,Graham Clarke,Jonathan Corcoran,Robert Stimson
Publsiher: Edward Elgar Publishing
Total Pages: 288
Release: 2021-05-28
ISBN 10: 1789909791
ISBN 13: 9781789909791
Language: EN, FR, DE, ES & NL

Big Data Applications in Geography and Planning Book Review:

This unique book demonstrates the utility of big data approaches in human geography and planning. Offering a carefully curated selection of case studies, it reveals how researchers are accessing big data, what this data looks like and how such data can offer new and important insights and knowledge.

Geographical Data Science and Spatial Data Analytics in R

Geographical Data Science and Spatial Data Analytics in R
Author: Lex Comber,Chris Brunsdon
Publsiher: Spatial Analytics and GIS
Total Pages: 336
Release: 2021-01-09
ISBN 10: 9781526449368
ISBN 13: 1526449366
Language: EN, FR, DE, ES & NL

Geographical Data Science and Spatial Data Analytics in R Book Review:

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial - it is collected some-where - and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges. Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (ie the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a 'learning by doing' text book, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.

Spatial Big Data Science

Spatial Big Data Science
Author: Zhe Jiang,Shashi Shekhar
Publsiher: Springer
Total Pages: 131
Release: 2017-07-13
ISBN 10: 3319601954
ISBN 13: 9783319601953
Language: EN, FR, DE, ES & NL

Spatial Big Data Science Book Review:

Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book. This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed. This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference.

Big Data for Remote Sensing Visualization Analysis and Interpretation

Big Data for Remote Sensing  Visualization  Analysis and Interpretation
Author: Nilanjan Dey,Chintan Bhatt,Amira S. Ashour
Publsiher: Springer
Total Pages: 154
Release: 2018-05-23
ISBN 10: 3319899236
ISBN 13: 9783319899237
Language: EN, FR, DE, ES & NL

Big Data for Remote Sensing Visualization Analysis and Interpretation Book Review:

This book thoroughly covers the remote sensing visualization and analysis techniques based on computational imaging and vision in Earth science. Remote sensing is considered a significant information source for monitoring and mapping natural and man-made land through the development of sensor resolutions that committed different Earth observation platforms. The book includes related topics for the different systems, models, and approaches used in the visualization of remote sensing images. It offers flexible and sophisticated solutions for removing uncertainty from the satellite data. It introduces real time big data analytics to derive intelligence systems in enterprise earth science applications. Furthermore, the book integrates statistical concepts with computer-based geographic information systems (GIS). It focuses on image processing techniques for observing data together with uncertainty information raised by spectral, spatial, and positional accuracy of GPS data. The book addresses several advanced improvement models to guide the engineers in developing different remote sensing visualization and analysis schemes. Highlights on the advanced improvement models of the supervised/unsupervised classification algorithms, support vector machines, artificial neural networks, fuzzy logic, decision-making algorithms, and Time Series Model and Forecasting are addressed. This book guides engineers, designers, and researchers to exploit the intrinsic design remote sensing systems. The book gathers remarkable material from an international experts' panel to guide the readers during the development of earth big data analytics and their challenges.

Spatial Analysis Modelling and Planning

Spatial Analysis  Modelling and Planning
Author: Jorge Rocha,José António Tenedório
Publsiher: BoD – Books on Demand
Total Pages: 268
Release: 2018-11-28
ISBN 10: 1789842395
ISBN 13: 9781789842395
Language: EN, FR, DE, ES & NL

Spatial Analysis Modelling and Planning Book Review:

New powerful technologies, such as geographic information systems (GIS), have been evolving and are quickly becoming part of a worldwide emergent digital infrastructure. Spatial analysis is becoming more important than ever because enormous volumes of spatial data are available from different sources, such as social media and mobile phones. When locational information is provided, spatial analysis researchers can use it to calculate statistical and mathematical relationships through time and space. This book aims to demonstrate how computer methods of spatial analysis and modeling, integrated in a GIS environment, can be used to better understand reality and give rise to more informed and, thus, improved planning. It provides a comprehensive discussion of spatial analysis, methods, and approaches related to planning.

Applied Spatial Statistics and Econometrics

Applied Spatial Statistics and Econometrics
Author: Katarzyna Kopczewska
Publsiher: Routledge
Total Pages: 594
Release: 2020-11-26
ISBN 10: 1000079783
ISBN 13: 9781000079784
Language: EN, FR, DE, ES & NL

Applied Spatial Statistics and Econometrics Book Review:

This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises. This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data.

Spatial Planning in the Big Data Revolution

Spatial Planning in the Big Data Revolution
Author: Voghera, Angioletta,La Riccia, Luigi
Publsiher: IGI Global
Total Pages: 359
Release: 2019-03-15
ISBN 10: 1522579281
ISBN 13: 9781522579281
Language: EN, FR, DE, ES & NL

Spatial Planning in the Big Data Revolution Book Review:

Through interaction with other databases such as social media, geographic information systems have the ability to build and obtain not only statistics defined on the flows of people, things, and information but also on perceptions, impressions, and opinions about specific places, territories, and landscapes. It is thus necessary to systematize, integrate, and coordinate the various sources of data (especially open data) to allow more appropriate and complete analysis, descriptions, and elaborations. Spatial Planning in the Big Data Revolution is a critical scholarly resource that aims to bring together different methodologies that combine the potential of large data analysis with GIS applications in dedicated tools specifically for territorial, social, economic, environmental, transport, energy, real estate, and landscape evaluation. Additionally, the book addresses a number of fundamental objectives including the application of big data analysis in supporting territorial analysis, validating crowdsourcing and crowdmapping techniques, and disseminating information and community involvement. Urban planners, architects, researchers, academicians, professionals, and practitioners in such fields as computer science, data science, and business intelligence will benefit most from the research contained within this publication.

Modern Technologies for Big Data Classification and Clustering

Modern Technologies for Big Data Classification and Clustering
Author: Seetha, Hari,Murty, M. Narasimha,Tripathy, B. K.
Publsiher: IGI Global
Total Pages: 360
Release: 2017-07-12
ISBN 10: 1522528067
ISBN 13: 9781522528067
Language: EN, FR, DE, ES & NL

Modern Technologies for Big Data Classification and Clustering Book Review:

Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.

Spatial Analysis

Spatial Analysis
Author: Tonny J. Oyana,Florence Margai
Publsiher: CRC Press
Total Pages: 323
Release: 2015-07-28
ISBN 10: 1498707645
ISBN 13: 9781498707640
Language: EN, FR, DE, ES & NL

Spatial Analysis Book Review:

An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis—containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS—as well as detailed illustrations and numerous case studies. The book enables readers to: Identify types and characterize non-spatial and spatial data Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results Construct testable hypotheses that require inferential statistical analysis Process spatial data, extract explanatory variables, conduct statistical tests, and explain results Understand and interpret spatial data summaries and statistical tests Spatial Analysis: Statistics, Visualization, and Computational Methods incorporates traditional statistical methods, spatial statistics, visualization, and computational methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Topics covered include: spatial descriptive methods, hypothesis testing, spatial regression, hot spot analysis, geostatistics, spatial modeling, and data science.

Spatial Data Handling in Big Data Era

Spatial Data Handling in Big Data Era
Author: Chenghu Zhou,Fenzhen Su,Francis Harvey,Jun Xu
Publsiher: Springer
Total Pages: 237
Release: 2017-05-04
ISBN 10: 9811044244
ISBN 13: 9789811044243
Language: EN, FR, DE, ES & NL

Spatial Data Handling in Big Data Era Book Review:

This proceedings volume introduces recent work on the storage, retrieval and visualization of spatial Big Data, data-intensive geospatial computing and related data quality issues. Further, it addresses traditional topics such as multi-scale spatial data representations, knowledge discovery, space-time modeling, and geological applications. Spatial analysis and data mining are increasingly facing the challenges of Big Data as more and more types of crowd sourcing spatial data are used in GIScience, such as movement trajectories, cellular phone calls, and social networks. In order to effectively manage these massive data collections, new methods and algorithms are called for. The book highlights state-of-the-art advances in the handling and application of spatial data, especially spatial Big Data, offering a cutting-edge reference guide for graduate students, researchers and practitioners in the field of GIScience.

Spatial Analysis with R

Spatial Analysis with R
Author: Tonny J. Oyana
Publsiher: CRC Press
Total Pages: 334
Release: 2020-09-01
ISBN 10: 100017347X
ISBN 13: 9781000173475
Language: EN, FR, DE, ES & NL

Spatial Analysis with R Book Review:

In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes. New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data mining This second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.

Distributed and Parallel Architectures for Spatial Data

Distributed and Parallel Architectures for Spatial Data
Author: Alberto Belussi,Sara Migliorini
Publsiher: MDPI
Total Pages: 170
Release: 2021-01-20
ISBN 10: 3039367501
ISBN 13: 9783039367504
Language: EN, FR, DE, ES & NL

Distributed and Parallel Architectures for Spatial Data Book Review:

This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data.

Big Data Analytics for Sustainable Computing

Big Data Analytics for Sustainable Computing
Author: Haldorai, Anandakumar,Ramu, Arulmurugan
Publsiher: IGI Global
Total Pages: 263
Release: 2019-09-20
ISBN 10: 1522597522
ISBN 13: 9781522597520
Language: EN, FR, DE, ES & NL

Big Data Analytics for Sustainable Computing Book Review:

Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.

Hierarchical Modeling and Analysis for Spatial Data

Hierarchical Modeling and Analysis for Spatial Data
Author: Sudipto Banerjee
Publsiher: CRC Press
Total Pages: 472
Release: 2003-12-17
ISBN 10: 020348780X
ISBN 13: 9780203487808
Language: EN, FR, DE, ES & NL

Hierarchical Modeling and Analysis for Spatial Data Book Review:

Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,

Big Data Computing for Geospatial Applications

Big Data Computing for Geospatial Applications
Author: Zhenlong Li,Wenwu Tang,Qunying Huang,Eric Shook,Qingfeng Guan
Publsiher: MDPI
Total Pages: 222
Release: 2020-11-23
ISBN 10: 3039432443
ISBN 13: 9783039432448
Language: EN, FR, DE, ES & NL

Big Data Computing for Geospatial Applications Book Review:

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.

Big Data

Big Data
Author: Hassan A. Karimi
Publsiher: CRC Press
Total Pages: 312
Release: 2014-02-18
ISBN 10: 1466586516
ISBN 13: 9781466586512
Language: EN, FR, DE, ES & NL

Big Data Book Review:

Big data has always been a major challenge in geoinformatics as geospatial data come in various types and formats, new geospatial data are acquired very fast, and geospatial databases are inherently very large. And while there have been advances in hardware and software for handling big data, they often fall short of handling geospatial big data efficiently and effectively. Big Data: Techniques and Technologies in Geoinformatics tackles these challenges head on, integrating coverage of techniques and technologies for storing, managing, and computing geospatial big data. Providing a perspective based on analysis of time, applications, and resources, this book familiarizes readers with geospatial applications that fall under the category of big data. It explores new trends in geospatial data collection, such as geo-crowdsourcing and advanced data collection technologies such as LiDAR point clouds. The book features a range of topics on big data techniques and technologies in geoinformatics including distributed computing, geospatial data analytics, social media, and volunteered geographic information. With chapters contributed by experts in geoinformatics and in domains such as computing and engineering, the book provides an understanding of the challenges and issues of big data in geoinformatics applications. The book is a single collection of current and emerging techniques, technologies, and tools that are needed to collect, analyze, manage, process, and visualize geospatial big data.

Big Data and Internet of Things A Roadmap for Smart Environments

Big Data and Internet of Things  A Roadmap for Smart Environments
Author: Nik Bessis,Ciprian Dobre
Publsiher: Springer
Total Pages: 470
Release: 2014-03-11
ISBN 10: 331905029X
ISBN 13: 9783319050294
Language: EN, FR, DE, ES & NL

Big Data and Internet of Things A Roadmap for Smart Environments Book Review:

This book presents current progress on challenges related to Big Data management by focusing on the particular challenges associated with context-aware data-intensive applications and services. The book is a state-of-the-art reference discussing progress made, as well as prompting future directions on the theories, practices, standards and strategies that are related to the emerging computational technologies and their association with supporting the Internet of Things advanced functioning for organizational settings including both business and e-science. Apart from inter-operable and inter-cooperative aspects, the book deals with a notable opportunity namely, the current trend in which a collectively shared and generated content is emerged from Internet end-users. Specifically, the book presents advances on managing and exploiting the vast size of data generated from within the smart environment (i.e. smart cities) towards an integrated, collective intelligence approach. The book also presents methods and practices to improve large storage infrastructures in response to increasing demands of the data intensive applications. The book contains 19 self-contained chapters that were very carefully selected based on peer review by at least two expert and independent reviewers and is organized into the three sections reflecting the general themes of interest to the IoT and Big Data communities: Section I: Foundations and Principles Section II: Advanced Models and Architectures Section III: Advanced Applications and Future Trends The book is intended for researchers interested in joining interdisciplinary and transdisciplinary works in the areas of Smart Environments, Internet of Things and various computational technologies for the purpose of an integrated collective computational intelligence approach into the Big Data era.