Space–Time Analysis

Concepts, Quantitative Methods, and Future Directions

Datos Bibliográficos

ID8375656
AutoresLi An (0000-0002-7933-5174), An Li (0000-0002-8476-8783, San Diego State University, autor de correspondencia), Ming-Hsiang Tsou (0000-0003-3421-486X, San Diego State University, autor de correspondencia), Stephen E S Crook (San Diego State University, autor de correspondencia), Yongwan Chun (0000-0002-4957-1379, The University of Texas at Dallas, autor de correspondencia), Brian H Spitzberg (0000-0003-3838-6052, San Diego State University, autor de correspondencia), Brian Spitzberg, Jean Mark Gawron (0000-0001-6288-4014, San Diego State University, autor de correspondencia), Dipak Kumar Gupta (0000-0002-8415-2970, San Diego State University, autor de correspondencia)
Año2015
Volumen105
Número5
Páginas891-914
Fecha de publicación2015-09-03
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaAnnals of the Association of American Geographers (JOURNAL)
Identificadores de la revistaISSN: 0004-5608 • E-ISSN: 1467-8306
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00045608.2015.1064510
OpenAlexW2100558433
IdiomaEN
Citas recibidas16
Referencias citadas136

Throughout most of human history, events and phenomena of interest have been characterized using space and time as their major characteristic dimensions, in either absolute or relative conceptualizations. Space–time analysis seeks to understand when and where (and sometimes why) things occur. In the context of several of the most recent and substantial advances in individual movement data analysis (time geography in particular) and spatial panel data analysis, we focus on quantitative space–time analytics. Based on more than 700 articles (from 1949 to 2013) we obtained through a key word search on the Web of Knowledge and through the authors' personal archives, this article provides a synthetic overview about the quantitative methodology for space–time analysis. Particularly, we highlight space–time pattern revelation (e.g., various clustering metrics, path comparison indexes, space–time tests), space–time statistical models (e.g., survival analysis, latent trajectory models), and simulation methods (e.g., cellular automaton, agent-based models) as well as their empirical applications in multiple disciplines. This article systematically presents the strengths and weaknesses of a set of prevalent methods used for space–time analysis and points to the major challenges, new opportunities, and future directions of space–time analysis

Cluster analysis · Context (archaeology) · Data mining · Data science · Focus (optics) · Geography · Social science · Sociology · Space (punctuation) · Space time · Spacetime · Time geography · Artificial Intelligence · Computer Science · Human Mobility and Location-Based Analysis · Land Use and Ecosystem Services · Urban Transport and Accessibility