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Discovering co-location patterns with aggregated spatial transactions and dependency rules
2017
International Journal of Data Science and Analytics
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By transforming spatial data to transaction data, the co-location pattern mining problem can be reduced to an association rule mining problem and such methods can be used to find co-location patterns robustly ...
Furthermore, we evaluated the resulting patterns to find spatial common and contrast sets, which are two special types of co-location patterns, to compare spatial regions and gain more insights. ...
Acknowledgements The authors would like to acknowledge the funding of CIHR/NSERC Collaborative Health Research Program and the Canadian Neonatal Network for providing adverse birth outcome data. ...
doi:10.1007/s41060-017-0079-5
dblp:journals/ijdsa/JabbarBZO18
fatcat:dsqwtkvllzdt7po4jrs3h4kgra
A multi-relational approach to spatial classification
2009
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
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A formal set of additions to the multi-relational data mining framework is proposed, to be able to represent spatial aggregations as well as spatial features and literals. ...
One such problem is that spatial relationships are embedded in space, unknown a priori, and it is part of the algorithm's task to determine which relationships are important and what properties to consider ...
INTRODUCTION This paper is concerned with Data Mining in spatial data. Specifically, we are interested in the discovery of predictive patterns that help solve a spatial classification task [ 4] . ...
doi:10.1145/1557019.1557058
dblp:conf/kdd/FrankEK09
fatcat:tho4wqlubrdhhhvv4n6wmmnt6e
Geographic Data Mining and Knowledge Discovery An Overview
[chapter]
2009
Geographic Data Mining and Knowledge Discovery, Second Edition
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KNOWLEDGE DISCOVERY AND DATA MINING In this section of the chapter, we provide a general overview of knowledge discovery and data mining. ...
Therefore, traditional spatial analytical techniques cannot easily discover new and unexpected patterns, trends and relationships that can be hidden deep within very large and diverse geographic datasets ...
Acknowledgments: Thanks to Mark Gahegan and Phoebe McNeally for some helpful comments on this chapter. ...
doi:10.1201/9781420073980.ch1
fatcat:bfh4kasvlbhylgwu2afp5wrqbq
Spatiotemporal Data Mining: A Computational Perspective
2015
ISPRS International Journal of Geo-Information
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In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling ...
The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. ...
HM1582-08-1-0017 and HM0210-13-1-0005, and the University of Minnesota under OVPR U-Spatial. We would like to thank Kim Koffolt for the helpful comments in improving the readability of the paper. ...
doi:10.3390/ijgi4042306
fatcat:hwnwbw7wm5hx5c4ncrvv533qju
Big spatio-temporal data mining for emergency management information systems
2019
IET Intelligent Transport Systems
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In this study, the authors developed and evaluated several algorithms and a framework for big spatio-temporal data mining in the Russian EMIS GLONASS + 112112. ...
They detected spatio-temporal outliers and spatial autocorrelation. Finally, they evaluated the scalability and time performance of algorithms. ...
The term transaction means the set of events that occur together in spatial proximity and in time. ...
doi:10.1049/iet-its.2019.0171
fatcat:giblf7gzpvdofnrqda5odjgcfq
Spatio-Temporal Data Mining: A Survey of Problems and Methods
[article]
2017
arXiv
pre-print
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mining, anomaly detection, and relationship mining. ...
Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and temporal attributes are available ...
, and relationship mining. ...
arXiv:1711.04710v2
fatcat:di3fxigwobeb3db5kcdvlhbe7i
Privacy Issues in Geospatial Visual Analytics
[chapter]
2012
Lecture Notes in Geoinformation and Cartography
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The privacy issues particularly related to the use of visual and interactive methods are currently studied neither in the areas of visualization and visual analytics nor in the area of data mining and ...
Furthermore, humans can note such kinds of patterns and relationships that are hard to formalize and detect by computational techniques. ...
The taxonomy of context should also include the possible types of relationships that may occur between moving objects and elements of the context (e.g. spatial proximity, temporal proximity). ...
doi:10.1007/978-3-642-24198-7_16
fatcat:i6fmnonqojebrodsouullml4zq
Knowledge Discovery In GIS Data
[article]
2016
arXiv
pre-print
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The main difference between traditional KDD techniques and GKD techniques is hidden in the nature of spatial data sets. ...
The spatial outlier detection is one of the most popular spatial data mining techniques which is used to detect spatial objects whose non-spatial attributes values are extremely different from those of ...
Data mining is considered the main step in KDD process to find patterns and relationships between these patterns by building models. ...
arXiv:1601.07241v1
fatcat:ljjln7jzcneodflc5v7x5ddd7m
A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring
2015
Ecological Informatics
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Firstly, data is pre-processed in order to obtain different spatial proximities. Later, we apply a standard algorithm to extract sequential patterns. ...
Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and river monitoring pressure data. ...
Spatial decomposition and aggregation are pre-processing steps in which spatial data is mapped to sequences according to different spatial relationships (e.g., station proximity, watercourse). ...
doi:10.1016/j.ecoinf.2014.05.011
fatcat:a62bfebob5hqva5nk5znk5wvue
Characterization of Particulate Matter Species in an Area Impacted by Aggregate and Limestone Mining North of San Antonio, TX, USA
2022
Sustainability
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Aggregate and limestone mining in San Antonio's Bexar and Comal counties in Texas, USA, has caused considerable health concerns as of late. ...
Aggregate mining actions can result in localized air quality issues in any neighborhood. Furthermore, heavy truck traffic, hauling, and transportation of the mined material contribute to pollution. ...
Introduction Aggregate and limestone mining, albeit beneficial to society, comes at a high environmental cost. ...
doi:10.3390/su14074288
fatcat:4u34z3modbaspddz4jni4psexe
Clustering Algorithm for Spatial Data Mining: An Overview
2013
International Journal of Computer Applications
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Spatial data mining practice for the extraction of useful information and knowledge from massive and complex spatial database. ...
It shows that spatial data mining is a promising field, with fruitful research results and many challenging issues. ...
Goals There are different goals of spatial data mining are ordered below, Understanding spatial data Discovering spatial relationships and relationships between spatial and non-spatial data Constructing ...
doi:10.5120/11617-7014
fatcat:ahsmcq6oojgtdgfctw2ff55ana
Spatiotemporal Neighborhood Discovery for Sensor Data
[chapter]
2010
Lecture Notes in Computer Science
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Real-time analysis of dynamic and distributed data, including streaming and event-based data 2. Mining from continuous streams of time-changing data and mining from ubiquitous data 3. ...
ABSTRACT The focus of this paper is the discovery of spatiotemporal neighborhoods in sensor datasets where a time series of data is collected at many spatial locations. ...
Acknowledgements This article has been funded in part by the National Oceanic and Atmospheric Administration (Grants NA06OAR4310243 and NA07OAR4170518). ...
doi:10.1007/978-3-642-12519-5_12
fatcat:bu7aenettjbpfku5vzuk2nzmum
Spatiotemporal Data Mining: A Survey
[article]
2022
arXiv
pre-print
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Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. ...
In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. ...
ACKNOWLEDGMENTS We would like to thank Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their comments. ...
arXiv:2206.12753v1
fatcat:jwm4mcxi5na7jbmbciwlrtt554
Adaptive Tessellation Mapping (ATM) for Spatial Data Mining
2015
International Journal of Machine Learning and Computing
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In the research of spatial data mining, gridding/tessellation mapping is a common technique to aggregate the locational data points in smaller regions (namely grids or tiles) so that properties of those ...
It is a natural way to study spatial related information because such information is dependent to the locational proximity in most of the cases, and it significantly reduces the effort needed to learn ...
Spatial Data Mining Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. ...
doi:10.7763/ijmlc.2014.v6.458
fatcat:kpiyatwmozdyxagi7fub7dusqe
Detecting communities in time-evolving proximity networks
2011
2011 IEEE Network Science Workshop
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In this paper, we introduce the notion of spatio-temporal communities that attempts to capture the structure in spatial connections as well as temporal changes in a network. ...
For example, such communities are lost if the temporal interactions are aggregated in a single weighted network since the concurrency information is lost. ...
The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory ...
doi:10.1109/nsw.2011.6004643
fatcat:q4zzaiii5je2jchg5bexkifh2i
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