Event Centric Modeling Approach in Colocation Pattern Analysis From Spatial Data

Venkatesan, M.; Thangavelu, Arunkumar; Prabhavathy, P.

doi:10.5121/ijdms.2011.3311

Cited by 4 publications

(4 citation statements)

References 11 publications

(16 reference statements)

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“…Further studies on spatial colocation patterns mainly focus on three aspects, namely, method improvement, spatial colocation rule innovation, and data type expansion. Among these aspects, the most typical methods include feature‐centric (Yoo, Shekhar, and Celik 2005), window‐centric (Venkatesan and Thangavelu 2013), and event‐centric (Wang et al 2008; Venkatesan, Thangavelu, and Prabhavathy 2011) models. A series of distance‐focused methods, including optimization algorithm with focus on pruning strategy (Al‐Naymat 2008; Wang et al 2009) and K‐nearest neighbor (Qian et al 2013), traffic network (Kudic 2015; Yu 2017), and dynamic neighborhood (Qian, He, and He 2009) constraints, have been proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Colocation Flow Patterns in the Geographical Interaction Data

Zhang¹,

Zhou

Tang³

et al. 2021

Geographical Analysis

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The detection of colocation pattern is an important and widely used method to analyze the spatial associations of geographical objects and events. Existing studies primarily focus on discovering colocation patterns and association rules based on point data. A broad range of flow data types, such as population flow, logistics, and information flow, have emerged in recent years. However, colocation patterns and association rules based on flow data are difficult to detect because of their complex structure. This work proposes a colocation pattern detection and spatial association rule discovery approach that treats origin‐destination (OD) flow as Boolean spatial features, while considering the spatial proximity of the origins and destinations of OD flows and its direction similarity. The effectiveness of this approach is verified by an artificial data set. Finally, this work analyzes the data of tourists who are traveling from different countries or regions to diverse cities in China. It also proves the application value of the proposed approach, which has general applicability to the mining of colocation patterns and association rules from any type of OD flow data.

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Section: Literature Reviewmentioning

confidence: 99%

Detecting Colocation Flow Patterns in the Geographical Interaction Data

Zhang¹,

Zhou

Tang³

et al. 2021

Geographical Analysis

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“…From the results, it is ensure that this proposed technique outperformed of about more than 50% of previous algorithm in time and memory usage. Venkatesan et al (2011) have mainly focused on spatial co-location patterns. That is the subsets of Boolean spatial features whose instances are often located in close geographic proximity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Spatial co-location pattern is the subsets of Boolean spatial features whose instances are often located in close geographic proximity (Venkatesan et al, 2011). It is a set of spatial features that are frequently located together in spatial proximity (Venkatesan et al, 2011). Extracting interesting and useful patterns from spatial datasets is more difficult than extracting untrivial patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships and spatial autocorrelation (Wan et al, 2008;Altidor et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Mining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighbours in space . Spatial co-location pattern is the subsets of Boolean spatial features whose instances are often located in close geographic proximity (Venkatesan et al, 2011). It is a set of spatial features that are frequently located together in spatial proximity (Venkatesan et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Thresholding Technique In Spatially Co-Located Objects Mining From Vehicle Moving Data

Anitha¹,

Duraiswamy²

2017

IJBIDM

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Co-location pattern discovery is intended towards the processing data with spatial contexts to discover classes of spatial objects that are frequently located together. The existing moving vehicle location prediction technique not analyses the moving vehicles co-location instance. So, we improve the previous technique process by mining spatially co-located moving objects using spatial data mining techniques. Initially, the neighbour relationship is computed by the prim's algorithm. After that, the candidate co-locations are pruned according to the presence of candidate co-location in the input data and the final stage of co-location instances selection is performed by compute neighbourhood and node membership functions. The values obtained using neighbourhood membership function is compared with the dynamic threshold values. The co-location instances are selected which satisfy the dynamic threshold value. Moreover, the proposed co-location pattern mining with dynamic thresholding technique is compared with the existing co-location pattern mining technique.

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