scholarly journals SPATIAL ANALYSIS OF CRIME OCCURRENCE IN VARIOUS REGIONS OF IRAN WITH AN EMPHASIS ON SAFETY

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sayed Ali HOSSEINI ◽  
Zohreh HADYANI ◽  
Hossein YAGHFOORI
Keyword(s):  
Cluster Analysis ◽  
Social System ◽  
Research Method ◽  
Analysis Method ◽  
Basic Issue ◽  
Cluster Analysis Method ◽  
The Mean ◽  
Wide Gap ◽  
And Cluster Analysis ◽  
Spatial Analysis Of Crime

Safety is a basic issue in every social system and communities consider safety as one of their main priorities. One of the most important factors that put the safety of various communities at risk is the threats caused by crime occurrence. This paper is aimed to spatially analyze crime occurrence in various regions of Iran with an emphasis on safety. The research method is descriptive-analytical and a documentary and library data collection method is used. In this paper, the Similarity, COPRAS, mean rank method, and cluster analysis method are applied. The final results of the cluster analysis based on the mean rank method indicate a wide gap between the provinces of the country in terms of survey indicators, so that the final coefficient obtained for the provinces in the sixth cluster (the most unsafe group) is about 45 times of the final coefficient of the provinces in the first cluster (the safest group).

scholarly journals Evaluation and Analysis of Socio-Economic Development Level and Management in Guangxi province of China

2021 ◽  
Vol 39 (5) ◽  
Author(s):  
Li Zongkeng ◽  
Li Zhuoran ◽  
Andrii Mykhailov ◽  
Wei Shi ◽  
Yang Zhuquan ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Economic Development ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Development Level ◽  
Socio Economic Development ◽  
Cluster Analysis Method ◽  
And Cluster Analysis

This article takes 14 regions in Guangxi as the research object, selects ten indicators that can measure the level of socio-economic development, establishes an index system for evaluating the regional socio-economic development level of Guangxi regions, and uses principal component analysis method and cluster analysis method carry out comprehensive evaluation and difference analysis among the economic development level of Guangxi regions. First, the primary component analysis method uses to comprehensively evaluate the economic development level of 14 regions in Guangxi. The results show that there are vast differences in the economic development levels of regions in Guangxi. Secondly, a systematic cluster analysis method uses to classify and analyze the differences between regions according to the similarity of economic development status. Finally, combined with the results of principal component analysis and cluster analysis, comprehensive evaluation analysis and discussion on the economic development status of various regions in Guangxi, and based on the evaluation results, proposed countermeasures for the socio-economic development and management in Guangxi province of China.

scholarly journals An Enhanced Binary Classifier Incorporating Weighted Scores

2018 ◽  
Vol 8 (2) ◽  
pp. 2853-2858 ◽  
Author(s):  
D. Virmani ◽  
N. Jain ◽  
A. Srivastav ◽  
M. Mittal ◽  
S. Mittal
Keyword(s):  
Cluster Analysis ◽  
Threshold Value ◽  
Large Datasets ◽  
Analysis Method ◽  
Data Points ◽  
Score Method ◽  
Weighted Score ◽  
Cluster Analysis Method ◽  
And Cluster Analysis ◽  
Made In

In this study, an approach is being proposed which will predict the output of an observation based on several parameters which employ the weighted score classification method. We will use the weighted scores concept for classification by representing data points on graph with respect to a threshold value found through the proposed algorithm. Secondly, cluster analysis method is employed to group the observational parameters to verify our approach. The algorithm is simple in terms of calculations required to arrive at a conclusion and provides greater accuracy for large datasets. The use of the weighted score method along with the curve fitting and cluster analysis will improve its performance. The algorithm is made in such a way that the intermediate values can be processed for clustering at the same time. The proposed algorithm excels due to its simplistic approach and provides an accuracy of 97.72%.

Moving Target Fast Tracking Using SURF and Cluster Analysis Method

2011 ◽  
Vol 26 (4) ◽  
pp. 544-550 ◽  
Author(s):  
李英 LI Ying ◽  
李静宇 LI Jing-yu ◽  
徐正平 XU Zheng-ping
Keyword(s):  
Cluster Analysis ◽  
Moving Target ◽  
Analysis Method ◽  
Fast Tracking ◽  
Cluster Analysis Method ◽  
And Cluster Analysis

scholarly journals Detection of drought events in Greece using daily precipitation

2010 ◽  
Vol 41 (2) ◽  
pp. 126-133 ◽  
Author(s):  
N. Kalamaras ◽  
H. Michalopoulou ◽  
H. R. Byun
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Daily Precipitation ◽  
Hierarchical Cluster ◽  
Seasonal Distribution ◽  
Drought Severity ◽  
Severe Drought ◽  
Analysis Method ◽  
Cluster Analysis Method ◽  
Insight Into

In this study a method proposed by Byun & Wilhite, which estimates drought severity and duration using daily precipitation values, is applied to data from stations at different locations in Greece. Subsequently, a series of indices is calculated to facilitate the detection of drought events at these sites. The results provide insight into the trend of drought severity in the region. In addition, the seasonal distribution of days with moderate and severe drought is examined. Finally, the Hierarchical Cluster Analysis method is used to identify sites with similar drought features.

Cluster Analysis Using N-gram Statistics for Daihinmin Programs and Performance Evaluations

2016 ◽  
Vol 4 (2) ◽  
pp. 33-57 ◽  
Author(s):  
Seiya Okubo ◽  
Takaaki Ayabe ◽  
Tetsuro Nishino
Keyword(s):  
Cluster Analysis ◽  
Imperfect Information ◽  
Computer Game ◽  
Computer Experiments ◽  
Performance Evaluations ◽  
Analysis Method ◽  
Cluster Analysis Method ◽  
N Gram ◽  
And Performance ◽  
Feature Values

In this paper, the authors elucidate the characteristics of the computer game Daihinmin, a popular Japanese card game that uses imperfect information. They first propose a method to extract feature values using n-gram statistics and a cluster analysis method that employs feature values. By representing the program card hands as several symbols, and the order of hands as simplified symbol strings, they obtain data that is suitable for feature extraction. The authors then evaluate the effectiveness of the proposed method through computer experiments. In these experiments, they apply their method to ten programs that were used in the UEC Computer Daihinmin Convention. In addition, the authors evaluate the robustness of the proposed method and apply it to recent programs. Finally, they show that their proposed method can successfully cluster Daihinmin programs with high probability.

scholarly journals Market characteristics and cluster analysis of non-wood forest products

2017 ◽  
Vol 141 (3-4) ◽  
pp. 151-162
Author(s):  
Ljiljana Keča ◽  
Špela Pezdevšek-Malovrh ◽  
Sreten Jelić ◽  
Stjepan Posavec ◽  
Milica Marčeta
Keyword(s):  
Cluster Analysis ◽  
Spatial Distribution ◽  
Research Method ◽  
Forest Products ◽  
Aromatic Plants ◽  
Medicinal And Aromatic Plants ◽  
Main Research ◽  
And Cluster Analysis ◽  
Wild Berries

The share of small and medium-sized enterprises (SMEs) is largely present in forestry, especially in the segment related to non-wood forest products (NWFPs) in Europe. They are also a dominant category in entrepreneurship in Serbia. Therefore, the subjects of this research were the companies operating in the sector of NWFPs, within specific statistical regions of Serbia. The database of SMEs was obtained from 119 SMEs and the share of surveyed SMEs was 81.5%. The main research method was two-step cluster analysis. Questionnaire was used for the purpose of the research. The aim of the research was to identify clusters in order to establish similarities within the defined clusters and the differences among them. Spatial distribution of specific categories of NWFPs in nature (mushrooms, medicinal and aromatic plants, honey and wild berries), contributed to the portfolio of the companies. This largely influenced clusters that are created by categories of products that are typical for certain statistical regions in Serbia.

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