Table Of Content

Spatiotemporal Analytics This book introduces readers to spatiotemporal analytics that are extended from spatial statistics. Spatiotemporal analytics help analysts to quantita- tively recognize and evaluate the spatial patterns and their temporal trends of a set of geographic events or objects. Spatiotemporal analyses are very important in geography, environmental sciences, economy, and many other domains. Spatiotemporal Analytics explains with very simple terms the concepts of spatiotemporal data and statistics, theories, and methods used. Each chapter introduces a case study as an example application for an in- depth learning process. The software used and the provided codes enable readers not only to learn the analytics but also to use them effectively in their projects. • Provides a comprehensive understanding of spatiotemporal analytics to readers with minimum knowledge in statistics. • Written in simple, understandable language with step-by-step instructions. • Includes numerous examples for all theories and methods explained in the book covering a wide range of applications from different disciplines. • Each application includes a software code needed to follow the instructions. • Each chapter also has a set of prepared PowerPoint slides to help instructors of a course on spatiotemporal analytics with the content explained. Undergraduate and graduate students who use Geographic Information Systems or study Geographical Information Science will find this book useful. The subject matter also pertains to an array of disciplines such as agriculture, anthropology, archaeology, architecture, biology, business administration and management, civic engineering, criminal justice, epi- demiology, geography, geology, marketing, political science, and public health. Spatiotemporal Analytics Jay Lee Designed cover image: Image from Shutterstock (Image ID: 1115087306) First edition publisshed 2023 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2023 selection and editorial matter, Jay Lee; individual chapters, the contributors Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www. copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. ISBN: 978-1-032-303055 (hbk) ISBN: 978-1-032-303062 (pbk) ISBN: 978-1-003-304395 (ebk) DOI: 10.1201/9781003304395 Typeset in Times by codeMantra Access the Support Material: www.routledge.com/9781032303055 Contents Editor .......................................................................................................vii Contributors .............................................................................................ix Chapter 1 Introduction to Spatiotemporal Analytics ............................1 Jay Lee Chapter 2 Spatiotemporal Centrography and Dispersion .....................13 Langxue Dang, Jay Lee, and Huiyu Lin Chapter 3 Spatiotemporal Quadrat Analytics .....................................35 Zhuo Chen Chapter 4 Spatiotemporal Nearest Neighbor Analytics .......................53 Qingsong Liu and Jay Lee Chapter 5 Spatiotemporal Ripley’s K and L Functions .......................77 Jay Lee Chapter 6 Spatiotemporal Autocorrelation Analytics ..........................91 Shengwen Li, Xuyang Cheng, Bo Wan, Junfang Gong, and Jay Lee Chapter 7 Spatiotemporal G Statistical Analytics .............................113 Huiyu Lin and Zhuo Chen Chapter 8 Spatiotemporal Kernel Density Estimation .......................127 Junfang Gong, Zhuang Zeng, Bo Wan, Shengwen Li, and Jay Lee Chapter 9 Spatiotemporally Weighted Regression .............................145 Bo Huang and Sensen Wu v vi Contents Chapter 10 Spatiotemporal Bayesian Regression .................................175 Ortis Yankey, Tao Hu, Han Yue, Peixiao Wang, and Xiao Xu Chapter 11 Spatiotemporal Process Analytics and Simulations .........207 Moira O’Neill and Jay Lee Chapter 12 Spatiotemporal Analytical Unit Problems .........................233 Langxue Dang, Huiyu Lin, and Jay Lee Index ......................................................................................................255 Editor Dr. Jay Lee received his PhD in geography from the University of Western Ontario in 1989. Since then, he has taught GIS and quantitative methods in geography at Kent State University. Dr. Lee’s research interests stem from integrating operations research and spatial analysis. He coauthored two edi- tions of Statistical Analysis with GIS. Since 2017, Dr. Lee has worked on extending spatial analytics to spatiotemporal analytics. This book represents the research outcomes from the Applied Geography Laboratory at Kent State University from 2017 to 2022. vii Contributors Zhuo Chen Jay Lee Department of Geography Department of Geography Kent State University Kent State University Kent, Ohio Kent, Ohio Xuyang Cheng Shengwen Li National Engineering Research School of Computer Science Center for Geographic China University of Geosciences Information Systems Wuhan, China China University of Geosciences Wuhan, China Huiyu Lin Department of Geography Langxue Dang Kent State University School of Computer and Kent, Ohio Information Engineering Henan University Qingsong Liu Henan, China Shenzhen e-Traffic Technology Shenzhen, China Junfang Gong School of Geography and Moira O’Neill Information Engineering Department of Geography China University of Geosciences Kent State University Wuhan, China Kent, Ohio Tao Hu Bo Wan Department of Geography School of Computer Science Oklahoma State University China University of Geosciences Stillwater, Oklahoma Wuhan, China Bo Huang Peixiao Wang Department of Geography and Surveying, Mapping, and Remote Resource Management Sensing The Chinese University of Hong Wuhan University Kong Wuhan, China Hong Kong, China ix