Special Issue on Spatio-Temporal Theories and Models for Environmental, Urban and Social Sciences: Where Do We Stand?

Claramunt, Christophe; Stewart, Kathleen

doi:10.1080/13875868.2015.1004178

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, discovering patterns hidden in large-scale spatiotemporal datasets is challenging and thus attracts a lot of attention from the GIScience community (Hardisty and Klippel 2010;Scholz and Lu 2014;Claramunt and Stewart 2015).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal enabled Content-based Image Retrieval

Belgiu¹,

Sudmanns²,

Tiede³

et al. 2016

GIScience

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) are widely used for monitoring and observation of dynamic phenomena. A sensor in WSNs covers only a limited region, depending on its sensing and communicating ranges, as well as the environment configuration. For efficient deployment of sensors in a WSN the coverage estimation is a critical issue. Probabilistic methods are among the most accurate models proposed for sensor coverage estimation. However, most of these methods are based on raster representation of the environment for coverage estimation which limits their quality. In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on 3D vector representation of the environment.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatiotemporal enabled Content-based Image Retrieval

Belgiu¹,

Sudmanns²,

Tiede³

et al. 2016

GIScience

View full text Add to dashboard Cite

show abstract

“…However, discovering patterns hidden in large-scale spatiotemporal datasets is challenging and thus attracts a lot of attention from the GIScience community (Hardisty and Klippel 2010;Demšar and Virrantaus 2010;Scholz and Lu 2014;Claramunt and Stewart 2015).…”

Section: Introductionmentioning

confidence: 99%

Identifying Local Spatiotemporal Autocorrelation Patterns of Taxi Pick-ups and Dropoffs

Gao¹,

Zhu²,

Mai³

2016

GIScience

View full text Add to dashboard Cite

Analyzing spatiotemporal autocorrelation would be helpful to understand the underlying dynamic patterns in space and time simultaneously. In this work, we aim to extend the conventional spatial autocorrelation statistics to a more general framework considering both spatial and temporal dimensions. Specifically, we focus on the spatiotemporal version of Getis-Ord's G * . The proposed indicator STG * can quantify the local association of adjacent features in space and time. As a proof of concept, the proposed method is then applied in a large-scale GPS-enabled taxi dataset to identify local spatiotemporal autocorrelation patterns of taxi pick-ups and drop-offs in New York City.

show abstract