Software and applications of spatial data mining

Li, Deren; Wang, Shuliang; Yuan, Huang; Li, Deyi

doi:10.1002/widm.1180

Cited by 21 publications

(13 citation statements)

References 40 publications

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“…Enlightened by the previous studies on data field and spatiotemporal clustering analysis [16][17][18][22][23][24][25], we improve the conventional data field potential function by incorporating additional temporal weight [31].…”

Section: Network-based Spatiotemporal Field (Nsf) Clustering Methodsmentioning

confidence: 99%

“…In the data field, each data object is regarded as a mass particle, which radiates its potential energy and is affected by others simultaneously [22,23]. When such an interaction is used, data fields can be applied to characterize the interaction among objects and mine valuable information [24]. Generally, a data field takes the following properties:…”

Section: Spatial Data Fieldmentioning

confidence: 99%

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Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Xia

Chen

et al. 2019

IJGI

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Pick-up and drop-off events of taxi trajectory data contain rich information about residents’ travel activities and road traffic. Such data have been widely applied in urban hotspot detection in recent years. However, few studies have attempted to delimitate the urban hotspot scope using taxi trajectory data. On this basis, the current study firstly introduces a network-based spatiotemporal field (NSF) clustering approach to discover and identify hotspots. Our proposed method expands the notion from spatial to space–time dimension and from Euclidean to network space by comparing with traditional spatial clustering analyses. In addition, a concentration index of hotspot areas is presented to refine the surface of centredness to delimitate the hotspot scope further. This index supports the quantitative depiction of hotspot areas by generating two standard deviation isolines. In the case study, we analyze the spatiotemporal dynamic patterns of hotspots at different days and times of day using the NSF method. Meanwhile, we also validate the effectiveness of the proposed method in identifying hotspots to evaluate the delimitating results. Experimental results reveal that the proposed approach can not only help detect detailed microscale characteristics of urban hotspots but also identify high-concentration patterns of pick-up incidents in specific places.

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Section: Network-based Spatiotemporal Field (Nsf) Clustering Methodsmentioning

confidence: 99%

Section: Spatial Data Fieldmentioning

confidence: 99%

Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Xia

Chen

et al. 2019

IJGI

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“…In particular, it is difficult to mine trivariate relationships. Such data are increasingly common in fields such as genomics, physics, and economics, making this problem an important and growing challenge [ 2 ]. To extract the associations from high-dimensional data, it is basal to identify whether the correlation among variables is strong or not.…”

Section: Introductionmentioning

confidence: 99%

Detecting Trivariate Associations in High-Dimensional Datasets

Liu

Wang

Yuan

et al. 2022

Sensors

Self Cite

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Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.

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“…Visualization of spatial data can be done with cloud-terminal integration GIS to provide convenience in the process of spatial analysis on a large number of spatial datasets [4], aggregation-based spatial datasets information retrieval system [5]. Spatial datasets as the key to the value of big data in spatial data mining (SDM) that refers to the description of attribute data requirements, how the data is obtained, and what AI 366 method is used to perform spatial analysis of the data [6], [4]. Spatial datasets become the basic structure in GIS for the process of spatial analysis algorithms, analyzing algorithm principles, or adapting existing algorithms [7].…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis model for traffic accident-prone roads classification: a proposed framework

Vitianingsih

Suryana

Othman

2021

IJ-AI

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<span id="docs-internal-guid-9754d3d8-7fff-a7d2-6605-1c8f8c5a707a"><span>The classification method in the spatial analysis modeling based on the multi-criteria parameter is currently widely used to manage geographic information systems (GIS) software engineering. The accuracy of the proposed model will play an essential role in the successful software development of GIS. This is related to the nature of GIS used for mapping through spatial analysis. This paper aims to propose a framework of spatial analysis using a hybrid estimation model-based on a combination of multi-criteria decision-making (MCDM) and artificial neural networks (ANNs) (MCDM-ANNs) classification. The proposed framework is based on the comparison of existing frameworks through the concept of a literature review. The model in the proposed framework will be used for future work on the traffic accident-prone road classification through testing with a private or public spatial dataset. Model validation testing on the proposed framework uses metaheuristic optimization techniques. Policymakers can use the results of the model on the proposed framework for initial planning developing GIS software engineering through spatial analysis models.</span></span>

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Software and applications of spatial data mining

Cited by 21 publications

References 40 publications

Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Detecting Trivariate Associations in High-Dimensional Datasets

Spatial analysis model for traffic accident-prone roads classification: a proposed framework

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