The Spatial Clustering Effect of Destination Distribution on Cognitive Distance Estimates and Its Impact on Tourists' Destination Choices

2008 ◽  
Vol 25 (3-4) ◽  
pp. 382-397 ◽  
Author(s):  
Chung‐Hsien Lin ◽  
Duarte B. Morais
Keyword(s):  
Spatial Clustering ◽  
Cognitive Distance ◽  
Destination Choices ◽  
Clustering Effect

scholarly journals Development of a Nonlinear Integer Optimization Model for Tenant Mix Layout in a Shopping Centre

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hongyue Lv ◽  
Ting-Kwei Wang
Keyword(s):  
Optimization Model ◽  
Spatial Clustering ◽  
Spillover Effect ◽  
Main Function ◽  
Integer Optimization ◽  
Shopping Centre ◽  
Physical Information ◽  
Clustering Effect ◽  
Configuration Scheme ◽  
Tenant Mix

The tenant mix layout of shopping malls affects shopper consumption behaviour and the performance of malls. The main function of the tenant mix layout is to increase store sales by increasing footfall. However, although existing studies have shown the importance of the spatial clustering effect and the physical information about tenants, the authors of those studies did not properly consider both the spatial clustering effect and the physical information about tenants at the meantime. Through this study, we aimed to maximize the spillover effect of the stores in the shopping centre while considering both the spatial clustering effect and physical information about tenants. Therefore, we present a problem called the tenant mix problem, which is to determine the optimal tenant configuration scheme for existing shopping centre space segmentation to maximize the rental income of a shopping centre. To solve this problem, a nonlinear integer optimization model with defined characteristics was proposed and solved using a genetic algorithm. A shopping centre case study is also presented to verify the performance of the model.

Is Gambling Contagious? An Analysis of Electronic Gambling Machine Clustering in Germany

2016 ◽  
Vol 10 (3) ◽  
pp. 54-70
Author(s):  
Johannes Jasny
Keyword(s):  
Hot Spots ◽  
Spatial Clustering ◽  
Gambling Activity ◽  
Spatial Econometric ◽  
Socioeconomic Characteristics ◽  
German Regions ◽  
Electronic Gambling ◽  
Agglomeration Effects ◽  
Clustering Effect ◽  
Electronic Gambling Machine

There are sizeable differences in the Electronic Gambling Machine (EGM) supply among German regions. Furthermore, the EGM supply concentrates in certain regions which results in gambling hot spots. Interestingly the spatial clustering of EGM supply is still observed when we control for agglomeration effects caused by population. This leads to the question why the EGM supply concentrates in some regions and remains low in others. We argue that the concentration of supply can be mostly explained by the socioeconomic characteristics of these regions. This paper makes three central contributions to the location based gambling research. First, it visualizes the absolute and relative supply of EGMs in German communities and highlights the spatial clustering of high and low EGM density regions. Second, it implements socioeconomic and geographical control variables for a more distinct description of regional differences. Third, it employs spatial econometric modelling to quantify and explain the occurrence of EGM hot spots. For our analysis we use census and EGM market data. The main finding implies, that there is a clear clustering of the EGM supply across regions at first, but when considering the socioeconomic characteristics / deprivation of the regions, most of the clustering effect is erased. The model explains most of the clustering effect which appears to exist only when there is no slender consideration of the socioeconomic differences across regions. This result supports the hypothesis that high gambling activity in one region does not affect the gambling activity in neighboring regions.

scholarly journals Research on Ship Trajectory Extraction Based on Multi-Attribute DBSCAN Optimisation Algorithm

2021 ◽  
Vol 28 (1) ◽  
pp. 136-148
Author(s):  
Xiaofeng Xu ◽  
Deqaing Cui ◽  
Yun Li ◽  
Yingjie Xiao
Keyword(s):  
Spatial Clustering ◽  
Water Area ◽  
Abnormal Behaviour ◽  
Maritime Traffic ◽  
Dbscan Algorithm ◽  
Maritime Navigation ◽  
Clustering Effect ◽  
And Cluster Analysis ◽  
Day By Day ◽  
Navigation Safety

Abstract With the vigorous development of maritime traffic, the importance of maritime navigation safety is increasing day by day. Ship trajectory extraction and analysis play an important role in ensuring navigation safety. At present, the DBSCAN (density-based spatial clustering of applications with noise) algorithm is the most common method in the research of ship trajectory extraction, but it has shortcomings such as missing ship trajectories in the process of trajectory division. The improved multi-attribute DBSCAN algorithm avoids trajectory division and greatly reduces the probability of missing sub-trajectories. By introducing the position, speed and heading of the ship track point, dividing the complex water area and vectorising the ship track, the function of guaranteeing the track integrity can be achieved and the ship clustering effect can be better realised. The result shows that the cluster fitting effect reaches up to 99.83%, which proves that the multi-attribute DBSCAN algorithm and cluster analysis algorithm have higher reliability and provide better theoretical guidance for the analysis of ship abnormal behaviour.

Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

1996 ◽  
Vol 54 ◽  
pp. 282-283
Author(s):  
Badrinath Roysam ◽  
Hakan Ancin ◽  
Douglas E. Becker ◽  
Robert W. Mackin ◽  
Matthew M. Chestnut ◽  
...  
Keyword(s):  
Image Analysis ◽  
Structural Changes ◽  
Sampling Methods ◽  
Spatial Clustering ◽  
Three Dimensional ◽  
Field Of View ◽  
Cell Counting ◽  
Depth Of Field ◽  
Tissue Cell ◽  
Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

2018 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Lin Zhang ◽  
Yanling He ◽  
Huaizhi Wang ◽  
Hui Liu ◽  
Yufei Huang ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Methylation Level ◽  
Optimal Number ◽  
Generative Model ◽  
Methylation Data ◽  
Sequencing Data ◽  
Number Of Clusters ◽  
Rna Methylation ◽  
Clustering Effect ◽  
Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

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