Mining Statistically Significant Co-location and Segregation Patterns

Barua, Sajib; Sander, Jöerg

doi:10.1109/tkde.2013.88

Cited by 77 publications

(29 citation statements)

References 23 publications

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“…Colocation patterns often reflect symbiotic relationships, such as that between the Nile Crocodile and Egyptian Plover [33], which often reside in close proximity. Other biological dependencies (e.g., different types of blackberry canes) also have characteristics of colocation [34]. In climate change studies, colocation patterns between global plant growth and climate variables (e.g., precipitation) have also been found [35].…”

Section: Foundations Of Colocation Detectionmentioning

confidence: 99%

“…Statistical significance test is used in colocation pattern detection to remove chance patterns [34]. Colocation pattern detection is concerned with the dependency between different spatial features, while the existence of autocorrelation between instances of the same feature may affect the value of the interest measure and result in colocation patterns detected by chance.…”

Section: Statistical Foundation Of Colocation Detectionmentioning

confidence: 99%

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Transdisciplinary Foundations of Geospatial Data Science

Xie

Eftelioglu

Ali

et al. 2017

IJGI

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Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.

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Section: Foundations Of Colocation Detectionmentioning

confidence: 99%

Section: Statistical Foundation Of Colocation Detectionmentioning

confidence: 99%

Transdisciplinary Foundations of Geospatial Data Science

Xie

Eftelioglu

Ali

et al. 2017

IJGI

View full text Add to dashboard Cite

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“…Therefore, if thresholds are not selected properly, meaningless co-location patterns could be reported, or meaningful co-location patterns could be missed when the prevalence threshold is too high. Barua and Sanders proposed a statistical approach to mine true patterns without using a prevalence measure threshold but this method, too, uses a given neighbourhood threshold (Barua and Sander, 2014).…”

Section: Related Workmentioning

confidence: 99%

“…How- ever, it has been shown that a prevalence threshold based mining approach may fail to find true patterns and may even report meaningless patterns (Barua and Sander, 2014). The prevalence measure value of a set of items can be low, if one participating item has a high participation ratio, but other participating items have low participating ratios due to their large number of occurrences.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the method proposed by Barua and Sander (Barua and Sander, 2014), as well the prevalence based methods (Shekhar and Huang, 2001, Huang et al, 2004, Zhang et al, 2004, Yoo and Shekhar, 2006, Xiao et al, 2008 search for meaningful patterns for a given proximity neighbourhood. Neighbourhood information is given in the form a neighbourhood threshold which is the maximum distance between instances of any two participating items of a pattern.…”

Section: Introductionmentioning

confidence: 99%

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Mining Co-Location Patterns From Spatial Data

Zhou¹,

Xiao²,

Tang³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

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Delaunay triangulation‐based spatial colocation pattern mining without distance thresholds

Tran

Wang

2020

Statistical Analysis

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A spatial colocation pattern is a group of spatial features whose instances frequently appear together in close proximity to each other. The proximity of instances is generally measured by the distance between them. If the distance is smaller than a distance threshold that is specified by users, they have a neighbor relationship. However, it is difficult for users to give a suitable distance threshold and mining results also vary widely with different distance thresholds. In addition, using distance thresholds are hard to accurately obtain neighborhoods of instances in heterogeneous distribution density data sets. In this study, we propose a new method for determining the neighbor relationship of instances in space without the distance threshold based on Delaunay triangulation (DT). We design three filtering strategies, such as a feature invalid edge, a global positive edge, and a local positive edge, to constrain the original DT to accurately extract the neighborhoods of instances in space. Then, a miner called DT-based colocation (DTC) pattern mining is developed. Different from the traditional algorithms which adopt the time-consuming generate-test candidate model, DTC directly collects the table instances of colocation patterns from the constrained DT by building neighboring polygons and filters prevalent patterns.We compare the results mined by DTC with by the traditional algorithms at macrolevel and microlevel on both real and synthetic data sets to prove that the DTC algorithm improves the effectiveness and fineness of mining results. K E Y W O R D Sspatial colocation pattern, distance threshold, Delaunay triangulation, heterogeneous distribution density, neighboring polygon

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Mining Statistically Significant Co-location and Segregation Patterns

Cited by 77 publications

References 23 publications

Transdisciplinary Foundations of Geospatial Data Science

Transdisciplinary Foundations of Geospatial Data Science

Mining Co-Location Patterns From Spatial Data

Delaunay triangulation‐based spatial colocation pattern mining without distance thresholds

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