scholarly journals Discovering Co-location Patterns in Datasets with Extended Spatial Objects

2013 ◽  
pp. 84-96 ◽  
Author(s):  
Aibek Adilmagambetov ◽  
Osmar R. Zaiane ◽  
Alvaro Osornio-Vargas
Keyword(s):  
Spatial Objects ◽  
Location Patterns

scholarly journals ON THE RELATIONSHIPS BETWEEN CLUSTERING AND SPATIAL CO-LOCATION PATTERN MINING

2008 ◽  
Vol 17 (01) ◽  
pp. 55-70 ◽  
Author(s):  
YAN HUANG ◽  
PUSHENG ZHANG ◽  
CHENGYANG ZHANG
Keyword(s):  
Pattern Mining ◽  
Spatial Clustering ◽  
Spatial Proximity ◽  
Location Pattern ◽  
Continuous Space ◽  
Clustering Techniques ◽  
Spatial Objects ◽  
Spatial Features ◽  
Statistical Measures ◽  
Location Patterns

The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include services requested frequently and located together from mobile devices (e.g., PDAs and cellular phones) and symbiotic species in ecology (e.g., Nile crocodile and Egyptian plover). Spatial clustering groups similar spatial objects together. Reusing research results in clustering, e.g. algorithms and visualization techniques, by mapping co-location mining problem into a clustering problem would be very useful. However, directly clustering spatial objects from various spatial features may not yield well-defined co-location patterns. Clustering spatial objects in each layer followed by overlaying the layers of clusters may not applicable to many application domains where the spatial objects in some layers are not clustered. In this paper, we propose a new approach to the problem of mining co-location patterns using clustering techniques. First, we propose a novel framework for co-location mining using clustering techniques. We show that the proximity of two spatial features can be captured by summarizing their spatial objects embedded in a continuous space via various techniques. We define the desired properties of proximity functions compared to similarity functions in clustering. Furthermore, we summarize the properties of a list of popular spatial statistical measures as the proximity functions. Finally, we show that clustering techniques can be applied to reveal the rich structure formed by co-located spatial features. A case study on real datasets shows that our method is effective for mining co-locations from large spatial datasets.

scholarly journals Methods for mining co–location patterns with extended spatial objects

2017 ◽  
Vol 27 (4) ◽  
pp. 681-695 ◽  
Author(s):  
Robert Bembenik ◽  
Wiktor Jóźwicki ◽  
Grzegorz Protaziuk
Keyword(s):  
Spatial Objects ◽  
Location Patterns

AbstractThe paper discusses various approaches to mining co-location patterns with extended spatial objects. We focus on the properties of transaction-free approaches EXCOM and DEOSP, and discuss the differences between the method using a buffer and that employing clustering and triangulation. These theoretical differences between the two methods are verified experimentally. In the performed tests three different implementations of EXCOMare compared with DEOSP, highlighting the advantages and downsides of both approaches.

scholarly journals A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial Objects

2004 ◽  
Author(s):  
Hui Xiong ◽  
Shashi Shekhar ◽  
Yan Huang ◽  
Vipin Kumar ◽  
Xiaobin Ma ◽  
...  
Keyword(s):  
Data Sets ◽  
Spatial Objects ◽  
Location Patterns

Inter-Metropolitan Location Patterns of Craft Breweries in the United States

2016 ◽  
Vol 46 (1) ◽  
pp. 115-129 ◽  
Author(s):  
Ralph B. McLAUGHLIN ◽  
Neil REID ◽  
Michael S. MOORE
Keyword(s):  
United States ◽  
The United States ◽  
Location Patterns

The location patterns of audio-visual communication firms in Barcelona

2021 ◽  
pp. 1-22
Author(s):  
Josep-Maria Arauzo-Carod ◽  
Eva Coll-Martínez
Keyword(s):  
Visual Communication ◽  
Location Patterns

scholarly journals A spatio-temporal specification language and its completeness & decidability

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Tengfei Li ◽  
Jing Liu ◽  
Haiying Sun ◽  
Xiang Chen ◽  
Lipeng Zhang ◽  
...  
Keyword(s):  
Intelligent Transportation System ◽  
Cyber Physical Systems ◽  
Specification Language ◽  
Finite Model ◽  
Critical Systems ◽  
Physical Systems ◽  
Spatial Objects ◽  
Temporal Properties ◽  
Spatio Temporal ◽  
Temporal Specification

AbstractIn the past few years, significant progress has been made on spatio-temporal cyber-physical systems in achieving spatio-temporal properties on several long-standing tasks. With the broader specification of spatio-temporal properties on various applications, the concerns over their spatio-temporal logics have been raised in public, especially after the widely reported safety-critical systems involving self-driving cars, intelligent transportation system, image processing. In this paper, we present a spatio-temporal specification language, STSL PC, by combining Signal Temporal Logic (STL) with a spatial logic S4 u, to characterize spatio-temporal dynamic behaviors of cyber-physical systems. This language is highly expressive: it allows the description of quantitative signals, by expressing spatio-temporal traces over real valued signals in dense time, and Boolean signals, by constraining values of spatial objects across threshold predicates. STSL PC combines the power of temporal modalities and spatial operators, and enjoys important properties such as finite model property. We provide a Hilbert-style axiomatization for the proposed STSL PC and prove the soundness and completeness by the spatio-temporal extension of maximal consistent set and canonical model. Further, we demonstrate the decidability of STSL PC and analyze the complexity of STSL PC. Besides, we generalize STSL to the evolution of spatial objects over time, called STSL OC, and provide the proof of its axiomatization system and decidability.

scholarly journals A Framework for Modelling Economic Regional Location Processes Under Uncertainty

2021 ◽  
Author(s):  
Roy Cerqueti ◽  
Eleonora Cutrini
Keyword(s):  
Theoretical Model ◽  
Theoretical Analysis ◽  
Site Selection ◽  
Decision Problem ◽  
Stochastic Theory ◽  
Spatial Location ◽  
Probabilistic Framework ◽  
Location Patterns ◽  
Regional Location ◽  
Quantitative Tool

AbstractThis paper deals with the theoretical analysis of the spatial concentration and localization of firms and employees over a set of regions. In particular, it provides a simple site-selection theoretical model to describe the probabilistic framework of the location patterns. The adopted quantitative tool is the stochastic theory of urns. The model moves from the empirical evidence of the deviation of the spatial location of companies from the uniform distribution and of employees from the distribution of firms. Factors leading to such deviations are taken into consideration. Specifically, we formalize a decision problem grounded on the economic attributes of the regions and also on the distribution of the existing firms and employees in the territory. To our purpose, the site-selection model is presented as a stepwise process.

Discovering spatial contrast and common sets with statistically significant co-location patterns

2017 ◽  
Author(s):  
Mohomed Shazan ◽  
Mohomed Jabbar ◽  
Osmar R. Zaïane ◽  
Alvaro Osornio-Vargas
Keyword(s):  
Location Patterns

Mining Non-Redundant Co-Location Patterns

2021 ◽  
pp. 1-14
Author(s):  
Xuguang Bao ◽  
Jinjie Lu ◽  
Tianlong Gu ◽  
Liang Chang ◽  
Zhoubo Xu ◽  
...  
Keyword(s):  
Location Patterns
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