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Learning citywide patterns of life from trajectory monitori..:

, In: Proceedings of the 30th International Conference on Advances in Geographic Information Systems,

Tenzer, Mark ; Rasheed, Zeeshan ; Shafique, Khurram - p. 1-12 , 2022

Link: https://dl.acm.org/doi/10.1145/3557915.3560978

RT 
T1 <cTitle>Proceedings of the 30th International Conference on Advances in Geographic Information Systems</cTitle>: T1 <Title>Learning citywide patterns of life from trajectory monitoring</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3560978&Exemplar=1&LAN=DE
				
A1 Tenzer, Mark
A1 Rasheed, Zeeshan
A1 Shafique, Khurram
PB ACM
YR 2022
K1 anomaly detection
K1 biological neural networks
K1 geospatial analysis
K1 patterns of life
K1 self-organizing feature maps
K1 Information systems
K1 Information systems applications
K1 Spatial-temporal systems
K1 Geographic information systems
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Bio-inspired approaches
K1 Applied computing
K1 Operations research
K1 Transportation
K1 Learning settings
K1 Online learning settings
K1 Learning paradigms
K1 Unsupervised learning
K1 Anomaly detection
SP 1
OP 12
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3557915.3560978
DO https://dl.acm.org/doi/10.1145/3557915.3560978
SF ELIB - SuUB Bremen

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