Space–Time Analysis: Concepts, Quantitative Methods, and Future Directions

An, Li; Tsou, Ming‐Hsiang; Crook, Stephen E. S.; Chun, Yongwan; Spitzberg, Brian H.; Gawron, Jean Mark; Gupta, Dipak Kumar

doi:10.1080/00045608.2015.1064510

Cited by 70 publications

(43 citation statements)

References 136 publications

(169 reference statements)

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“…Although not the main focus of this article, the integration of space and time deserves further comment. The transition from purely spatial analysis, as presented first in this section, into truly space‐time considerations demands the reconciliation of the “dual nature” (An et al ) of the two fundamentally different dimensions, expressed in different scales. The two ends of the spectrum considered above—discrete and continuous—to conceptualize space very much apply in this context.…”

Section: Methodological Frameworkmentioning

confidence: 99%

“…All the above studies are situated within the broader developments that the space‐time analysis research field has achieved in its endeavor to analyze and model space‐time data. These developments, which are systematically illustrated by An et al () include various methods which are used to (i) identify patterns, (ii) develop space‐time statistical models, and (iii) simulate processes for either individual movement data or spatial panel data (see table 2 in An et al ). Our article contributes in the pattern revelation for spatial panel data analysis to follow An et al () typology.…”

Section: The Spatial Structure Of Cities: “Old” and “New” Datamentioning

confidence: 99%

“…The lack of temporally fine‐grained data about cities and the focus on slow dynamics has made researchers pay much less attention to the temporal dimension of techniques to measure the spatial structure of cities. As An et al () put it, urban research tends to approach space and time disjointedly, regardless of the consensus on the unified nature of space and time in many areas of geographical research. This methodological gap should not come as a surprise since the “first culture of cities” type of urban methods were developed to tackle data of low spatial and temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterizing the Spatial Structure(s) of Cities “on the fly”: The Space‐Time Calendar

Arribas‐Bel

Tranos

2017

Geographical Analysis

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Our understanding of the spatial structure of cities has been traditionally shaped by the availability of static data. In the last few years, thanks to improvements in geospatial technology as well as computing storage and power, there has been an explosion of geo‐referenced data, which monitor cities and urban activities in real time. Although this shift in the data landscape promises to change and augment the way we measure, understand, and act on cities, it poses significant methodological challenges and uncovers substantial gaps in the analytics required. This article contributes in this direction by delivering insights on two fundamental fronts: first, we compare several methods that conceptualize both space and time in rather different ways, highlighting their main advantages and limitations; second and more important, we propose a novel approach the Space‐Time Calendar that uncovers, characterizes and visualizes in an explicitly spatial way both fast and slow urban dynamics. We illustrate its advantages using a data set derived from over 2 years of mobile phone activity in Amsterdam (the Netherlands). Our findings highlight the advantages of the Space‐Time Calendar approach, but also the benefits of appropriately matching the methodological approach to the nature of the data at hand.

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Section: Methodological Frameworkmentioning

confidence: 99%

Section: The Spatial Structure Of Cities: “Old” and “New” Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterizing the Spatial Structure(s) of Cities “on the fly”: The Space‐Time Calendar

Arribas‐Bel

Tranos

2017

Geographical Analysis

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“…Parallel to this trend, GIScientists face unprecedented challenges and opportunities in "conceptualization, representation, computation, and visualization of spacetime data" (Kwan and Neutens 2014, 851). Considerable recent efforts 1 have been devoted to addressing these challenges, including developing and synthesizing space-time conceptual frameworks (An and Crook accepted;Yuan, Nara, and Bothwell 2014;An et al 2015), eliciting time geography patterns from individual movement and trajectory data (Baer and Butler 2000;Kwan 1998Kwan , 2004Downs, Horner, et al 2014;Liao, Rasouli, and Timmermans 2014), and developing innovative statistical indices, visualization methods, and/or analytical methods (Rey and Janikas 2006;Chen et al 2011). For an overview of space-time analysis, including its origin, working definition, historic development, quantitative methods, and strengths and weaknesses, see Yuan, Nara, and Bothwell (2014) and An et al (2015).…”

Section: Introductionmentioning

confidence: 99%

“… The number of publications in relation to space‐time analysis, according to an online search from web of knowledge, has experienced an exponential increase since around 1990–1993 (An et al ).…”

mentioning

confidence: 99%

Latent trajectory models for space‐time analysis: An application in deciphering spatial panel data

Tsou

Spitzberg

et al. 2016

Geographical Analysis

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This article introduces latent trajectory models (LTMs), an approach often employed in social sciences to handle longitudinal data, to the arena of GIScience, particularly space‐time analysis. Using the space‐time data collected at county level for the whole United States through webpage search on the keyword “climate change,” we show that LTMs, when combined with eigenvector filtering of spatial dependence in data, are very useful in unveiling temporal trends hidden in such data: the webpage‐data derived popularity measure for climate change has been increasing from December 2011 to March 2013, but the increase rate has been slowing down. In addition, LTMs help reveal potential mechanisms behind observed space‐time trajectories through linking the webpage‐data derived popularity measure about climate change to a set of socio‐demographic covariates. Our analysis shows that controlling for population density, greater drought exposure, higher percent of people who are 16 years old or above, and higher household income are positively predictive of the trajectory slopes. Higher percentages of Republicans and number of hot days in summer are negatively related to the trajectory slopes. Implications of these results are examined, concluding with consideration of the potential utility of LTMs in space‐time analysis and more generally in GIScience.

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Spatiotemporal Analysis

Crook

2017

International Encyclopedia of Geography

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This entry discusses the concepts and methods related to spatiotemporal analysis. From early civilizations to modern times, space and time have been the two fundamental domains under which humans have characterized events and phenomena of interest. The conceptualization of absolute versus relative space–time has exerted a major impact on so‐called spatiotemporal analysis, which seeks to understand “when” and “where” (and to some extent “why”) things occur. This entry covers significant concepts and methods in relation to modern spatiotemporal analysis, such as time geography, analysis of time‐series spatial data, and exploratory space–time analysis. A set of statistical (e.g., Bayesian space–time models, survival analysis, multilevel models, latent trajectory models) and simulation approaches (e.g., cellular automaton, agent‐based models) have been presented in regard to their strengths and weaknesses for spatiotemporal analysis. The future directions of spatiotemporal analysis are pointed out to conclude the entry.

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Space–Time Analysis: Concepts, Quantitative Methods, and Future Directions

Cited by 70 publications

References 136 publications

Characterizing the Spatial Structure(s) of Cities “on the fly”: The Space‐Time Calendar

Characterizing the Spatial Structure(s) of Cities “on the fly”: The Space‐Time Calendar

Latent trajectory models for space‐time analysis: An application in deciphering spatial panel data

Spatiotemporal Analysis

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