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Clustering analysis of traffic accident risk in Turkey.

Tortum A, Atalay A - Iran. J. Public Health (2015)

View Article: PubMed Central - PubMed

Affiliation: Dept. of Civil Engineering, Engineering Faculty, Ataturk University, Erzurum-Turkey.

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Traffic accidents and the deaths, injuries and material damage caused by these accidents still occupy their place as one of the most important problems in Turkey... Clustering analysis of the provinces was conducted according to these risk rates that are determined as health risk and traffic risk rates... The purpose of this study was to determine cities similar to each other in terms of health and traffic risk rates in traffic accidents happening in the provinces... The findings of the study indicate that the provinces with highest rates of health and traffic risk were those with low population density, poorly developed and in general, they were the provinces in rural areas... The fact that death and injury rates were low in urban areas was attributed to the fact that roads in these places were better, that vehicles were new as income level of people was higher and that these places had rehabilitation and emergency relief centers... In the study, differences are seen in the provinces in clusters obtained according to the two methods (Fig. 1 and Fig. 2)... This difference is due to the fact that fuzzy c-means technique was affected less by the initial values as compared to k-means technique... It was observed that fuzzy c-means technique usually produced more stable results... In addition, fuzzy c-means technique was observed to have been affected very much by exceptional data whereas k-means technique was influenced very little.

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Mapping of the provinces according to fuzzy c-means clustering method
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Figure 2: Mapping of the provinces according to fuzzy c-means clustering method

Mentions: In the study, differences are seen in the provinces in clusters obtained according to the two methods (Fig. 1 and Fig. 2). This difference is due to the fact that fuzzy c-means technique was affected less by the initial values as compared to k-means technique. It was observed that fuzzy c-means technique usually produced more stable results. In addition, fuzzy c-means technique was observed to have been affected very much by exceptional data whereas k-means technique was influenced very little (2).


Clustering analysis of traffic accident risk in Turkey.

Tortum A, Atalay A - Iran. J. Public Health (2015)

Mapping of the provinces according to fuzzy c-means clustering method
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4402427&req=5

Figure 2: Mapping of the provinces according to fuzzy c-means clustering method
Mentions: In the study, differences are seen in the provinces in clusters obtained according to the two methods (Fig. 1 and Fig. 2). This difference is due to the fact that fuzzy c-means technique was affected less by the initial values as compared to k-means technique. It was observed that fuzzy c-means technique usually produced more stable results. In addition, fuzzy c-means technique was observed to have been affected very much by exceptional data whereas k-means technique was influenced very little (2).

View Article: PubMed Central - PubMed

Affiliation: Dept. of Civil Engineering, Engineering Faculty, Ataturk University, Erzurum-Turkey.

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

Traffic accidents and the deaths, injuries and material damage caused by these accidents still occupy their place as one of the most important problems in Turkey... Clustering analysis of the provinces was conducted according to these risk rates that are determined as health risk and traffic risk rates... The purpose of this study was to determine cities similar to each other in terms of health and traffic risk rates in traffic accidents happening in the provinces... The findings of the study indicate that the provinces with highest rates of health and traffic risk were those with low population density, poorly developed and in general, they were the provinces in rural areas... The fact that death and injury rates were low in urban areas was attributed to the fact that roads in these places were better, that vehicles were new as income level of people was higher and that these places had rehabilitation and emergency relief centers... In the study, differences are seen in the provinces in clusters obtained according to the two methods (Fig. 1 and Fig. 2)... This difference is due to the fact that fuzzy c-means technique was affected less by the initial values as compared to k-means technique... It was observed that fuzzy c-means technique usually produced more stable results... In addition, fuzzy c-means technique was observed to have been affected very much by exceptional data whereas k-means technique was influenced very little.

No MeSH data available.