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Spatial Analysis Using Big Data

Spatial Analysis Using Big Data
Methods and Urban Applications

by Yoshiki Yamagata,Hajime Seya

  • Publisher : Academic Press
  • Release : 2019-11-03
  • Pages : 302
  • ISBN : 0128131322
  • Language : En, Es, Fr & De
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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

Big Data Applications in Geography and Planning

Big Data Applications in Geography and Planning
An Essential Companion

by Mark Birkin,Graham Clarke,Jonathan Corcoran,Robert Stimson

  • Publisher : Edward Elgar Publishing
  • Release : 2021-05-28
  • Pages : 288
  • ISBN : 1789909791
  • Language : En, Es, Fr & De
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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.

Big Data Analytics for Sustainable Computing

Big Data Analytics for Sustainable Computing
A Book

by Haldorai, Anandakumar,Ramu, Arulmurugan

  • Publisher : IGI Global
  • Release : 2019-09-20
  • Pages : 263
  • ISBN : 1522597522
  • Language : En, Es, Fr & De
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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.

Distributed and Parallel Architectures for Spatial Data

Distributed and Parallel Architectures for Spatial Data
A Book

by Alberto Belussi,Sara Migliorini

  • Publisher : MDPI
  • Release : 2021-01-20
  • Pages : 170
  • ISBN : 3039367501
  • Language : En, Es, Fr & De
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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 Computing for Geospatial Applications

Big Data Computing for Geospatial Applications
A Book

by Zhenlong Li,Wenwu Tang,Qunying Huang,Eric Shook,Qingfeng Guan

  • Publisher : MDPI
  • Release : 2020-11-23
  • Pages : 222
  • ISBN : 3039432443
  • Language : En, Es, Fr & De
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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.

Spatial Analysis, Modelling and Planning

Spatial Analysis, Modelling and Planning
A Book

by Jorge Rocha,José António Tenedório

  • Publisher : BoD – Books on Demand
  • Release : 2018-11-28
  • Pages : 268
  • ISBN : 1789842395
  • Language : En, Es, Fr & De
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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.

Fundamentals of Spatial Analysis and Modelling

Fundamentals of Spatial Analysis and Modelling
A Book

by Jay Gao

  • Publisher : CRC Press
  • Release : 2021-12-15
  • Pages : 376
  • ISBN : 1000519880
  • Language : En, Es, Fr & De
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This textbook provides comprehensive and in-depth explanations of all topics related to spatial analysis and spatiotemporal simulation, including how spatial data are acquired, represented digitally, and spatially aggregated. Also features the nature of space and how it is measured. Descriptive, explanatory, and inferential analyses are covered for point, line, and area data. It captures the latest developments in spatiotemporal simulation with cellular automata and agent-based modelling, and through practical examples discusses how spatial analysis and modelling can be implemented in different computing platforms. A much-needed textbook for a course at upper undergraduate and postgraduate levels.

Big Data and Computational Intelligence in Networking

Big Data and Computational Intelligence in Networking
A Book

by Yulei Wu,Fei Hu,Geyong Min,Albert Y. Zomaya

  • Publisher : CRC Press
  • Release : 2017-12-14
  • Pages : 530
  • ISBN : 1498784879
  • Language : En, Es, Fr & De
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This book presents state-of-the-art solutions to the theoretical and practical challenges stemming from the leverage of big data and its computational intelligence in supporting smart network operation, management, and optimization. In particular, the technical focus covers the comprehensive understanding of network big data, efficient collection and management of network big data, distributed and scalable online analytics for network big data, and emerging applications of network big data for computational intelligence.

Spatial Analysis

Spatial Analysis
Statistics, Visualization, and Computational Methods

by Tonny J. Oyana,Florence Margai

  • Publisher : CRC Press
  • Release : 2015-07-28
  • Pages : 323
  • ISBN : 1498707645
  • Language : En, Es, Fr & De
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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.

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

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

by Nik Bessis,Ciprian Dobre

  • Publisher : Springer
  • Release : 2014-03-11
  • Pages : 470
  • ISBN : 331905029X
  • Language : En, Es, Fr & De
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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.

Big Data for Regional Science

Big Data for Regional Science
A Book

by Laurie A Schintler,Zhenhua Chen

  • Publisher : Routledge
  • Release : 2017-08-07
  • Pages : 350
  • ISBN : 1351983261
  • Language : En, Es, Fr & De
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Recent technological advancements and other related factors and trends are contributing to the production of an astoundingly large and rapidly accelerating collection of data, or ‘Big Data’. This data now allows us to examine urban and regional phenomena in ways that were previously not possible. Despite the tremendous potential of big data for regional science, its use and application in this context is fraught with issues and challenges. This book brings together leading contributors to present an interdisciplinary, agenda-setting and action-oriented platform for research and practice in the urban and regional community. This book provides a comprehensive, multidisciplinary and cutting-edge perspective on big data for regional science. Chapters contain a collection of research notes contributed by experts from all over the world with a wide array of disciplinary backgrounds. The content is organized along four themes: sources of big data; integration, processing and management of big data; analytics for big data; and, higher level policy and programmatic considerations. As well as concisely and comprehensively synthesising work done to date, the book also considers future challenges and prospects for the use of big data in regional science. Big Data for Regional Science provides a seminal contribution to the field of regional science and will appeal to a broad audience, including those at all levels of academia, industry, and government.

Spatial Big Data, BIM and advanced GIS for Smart Transformation

Spatial Big Data, BIM and advanced GIS for Smart Transformation
City, Infrastructure and Construction

by Sara Shirowzhan,Willie Tan,Samad M. E. Sepasgozar

  • Publisher : MDPI
  • Release : 2020-12-02
  • Pages : 166
  • ISBN : 3039360302
  • Language : En, Es, Fr & De
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This book covers a range of topics including selective technologies and algorithms that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This book also presents an agenda for future investigations to address the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities. Some of selected specific tools presented in this book are as a simulator for improving the smart parking practices by modelling drivers with activity plans, a bike optimization algorithm to increase the efficiency of bike stations, an agent-based model simulation of human mobility with the use of mobile phone datasets. In addition, this book describes the use of numerical methods to match the network demand and supply of bicycles, investigate the distribution of railways using different indicators, presents a novel algorithm of direction-aware continuous moving K-nearest neighbor queries in road networks, and presents an efficient staged evacuation planning algorithm for multi-exit buildings.

Spatial Big Data Science

Spatial Big Data Science
Classification Techniques for Earth Observation Imagery

by Zhe Jiang,Shashi Shekhar

  • Publisher : Springer
  • Release : 2017-07-13
  • Pages : 131
  • ISBN : 3319601954
  • Language : En, Es, Fr & De
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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.

Spatial Analysis with R

Spatial Analysis with R
Statistics, Visualization, and Computational Methods

by Tonny J. Oyana

  • Publisher : CRC Press
  • Release : 2020-08-31
  • Pages : 334
  • ISBN : 1000173453
  • Language : En, Es, Fr & De
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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.

Data-centric Regenerative Built Environment

Data-centric Regenerative Built Environment
Big Data for Sustainable Regeneration

by Saeed Banihashemi,Sepideh Zarepour Sohi

  • Publisher : Routledge
  • Release : 2022-03-18
  • Pages : 168
  • ISBN : 1000593193
  • Language : En, Es, Fr & De
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This book examines the use of big data in regenerative urban environment and how data helps in functional planning and design solutions. This book is one of the first endeavors to present the data-driven methods for regenerative built environments and integrate it with the novel design solutions. It looks at four specific areas in which data is used – urban land use, transportation and traffic, environmental concerns and social issues – and draws on the theoretical literature concerning regenerative built environments to explain how the power of big data can achieve the systematic integration of urban design solutions. It then applies an in-depth case study method on Asian metropolises including Beijing and Tehran to bring the developed innovation into a research-led practical context. This book is a useful reference for anyone interested in driving sustainable regeneration of our urban environments through big data-centric design solutions.

COVID-19 Pandemic, Geospatial Information, and Community Resilience

COVID-19 Pandemic, Geospatial Information, and Community Resilience
Global Applications and Lessons

by Abbas Rajabifard,Daniel Paez,Greg Foliente

  • Publisher : CRC Press
  • Release : 2021-06-07
  • Pages : 558
  • ISBN : 1000402924
  • Language : En, Es, Fr & De
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"The Open Access version of this book, available at https://www.taylorfrancis.com/books/oa-edit/10.1201/9781003181590, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license." Geospatial information plays an important role in managing location dependent pandemic situations across different communities and domains. Geospatial information and technologies are particularly critical to strengthening urban and rural resilience, where economic, agricultural, and various social sectors all intersect. Examining the United Nations' SDGs from a geospatial lens will ensure that the challenges are addressed for all populations in different locations. This book, with worldwide contributions focused on COVID-19 pandemic, provides interdisciplinary analysis and multi-sectoral expertise on the use of geospatial information and location intelligence to support community resilience and authorities to manage pandemics.

Advanced Research in Technologies, Information, Innovation and Sustainability

Advanced Research in Technologies, Information, Innovation and Sustainability
First International Conference, ARTIIS 2021, La Libertad, Ecuador, November 25–27, 2021, Proceedings

by Teresa Guarda,Filipe Portela,Manuel Filipe Santos

  • Publisher : Springer Nature
  • Release : 2021-11-17
  • Pages : 741
  • ISBN : 3030902412
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the First International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2021, held in La Libertad, Ecuador, in November 2021. The 53 full papers and 2 short contributions were carefully reviewed and selected from 155 submissions. The volume covers a variety of topics, such as computer systems organization, software engineering, information storage and retrieval, computing methodologies, artificial intelligence, and others. The papers are logically organized in the following thematic blocks: ​Computing Solutions; Data Intelligence; Ethics, Security, and Privacy; Sustainability.

Applied Spatial Statistics and Econometrics

Applied Spatial Statistics and Econometrics
Data Analysis in R

by Katarzyna Kopczewska

  • Publisher : Routledge
  • Release : 2020-11-26
  • Pages : 594
  • ISBN : 1000079783
  • Language : En, Es, Fr & De
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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.

Handbook of Research on Big Data Storage and Visualization Techniques

Handbook of Research on Big Data Storage and Visualization Techniques
A Book

by Segall, Richard S.,Cook, Jeffrey S.

  • Publisher : IGI Global
  • Release : 2018-01-05
  • Pages : 917
  • ISBN : 1522531432
  • Language : En, Es, Fr & De
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The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.

Spatial Analysis Methods and Practice

Spatial Analysis Methods and Practice
Describe – Explore – Explain through GIS

by George Grekousis

  • Publisher : Cambridge University Press
  • Release : 2020-03-31
  • Pages : 129
  • ISBN : 1108585507
  • Language : En, Es, Fr & De
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This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences.