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:: Volume 16, Issue 1 (9-2019) ::
JSRI 2019, 16(1): 121-141 Back to browse issues page
Spatial Statistics Analysis to Identify Hot Spots Using Accidental Event Calls Services
Samira Ghashghaie , Saeed Behzadi 1
1- , behzadi.saeed@gmail.com
Abstract:   (1002 Views)
Today, data is produced in large volumes, and from multiple sources, so this has caused problems in service. These problems can also affect the speed and accuracy of emergency services. Therefore, access to various resources and databases, information extraction to evaluate and analyze data and provide appropriate solutions for citizens is inevitable. In this paper evaluation of clusters is used for Getis-ord Gi* statistics and Anselin Local Moran's I statistics to identify the behavioral pattern of data. The data used in this article is a large free dataset of Spatio-temporal emergency call events from the United States. Accidental call events in five years are evaluated from this dataset. Moran statistics are used to identify and detect the events which have the pattern of spatial distribution. A high/low distribution pattern of accidental events was obtained through Hotspot maps, an annual comparison and evaluation are made by survey the distribution map of events. Clustering Hotspots Map with Gi* statistics represents the spatial correlation between positive and negative events. In these distribution patterns, clusters with high value are called Hot-spots, and low-value clusters are called Cold-spots. Similarly, clustering maps of accidental events get evaluated every five years; then the Gi indicator evaluates each cluster for every two years. A positive z-score and G-index indicate that the data have a positive spatial correlation; its results show that the distribution pattern is similar in each year with an average of 93 percent. Then, hot/cold spot clustering maps of 5-year accidental events are also created with the general Moran indicator. Moreover, a confidence level is created after calculating the p-value and z-score. In all 5-year data calculations, the Moran coefficient of accidental events is greater than the expected coefficient. Evaluation of biennial clustering maps with Moran index showed that there is more than 96percent behavioral similarity of dispersion pattern in both years. Raster clustering maps are also created to evaluate the clustering of Moran and Gi indicates. The similarity of raster clusters is more than 95percent per year. The results show that the pattern of accident distribution is the same in 5 years. Although the number of accidents has decreased during this period, the hotspots of accidents have not changed significantly in the city. Furthermore, hotspots indicate a high density of accidental events with 95percent confidence in the study area, and most accidents occur on the South Claiborne and New Orleans highways and at intersections with major streets.
Keywords: Clustering, spatial autocorrelation, accidental event, Getis-ord Gi* statistics, Anselin Morans I statistics.
Full-Text [PDF 8246 kb]   (389 Downloads)    
Type of Study: Research | Subject: General
Received: 2020/03/27 | Accepted: 2021/01/24 | Published: 2019/09/19
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Ghashghaie S, Behzadi S. Spatial Statistics Analysis to Identify Hot Spots Using Accidental Event Calls Services. JSRI 2019; 16 (1) :121-141
URL: http://jsri.srtc.ac.ir/article-1-353-en.html

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Volume 16, Issue 1 (9-2019) Back to browse issues page
مجله‌ی پژوهش‌های آماری ایران Journal of Statistical Research of Iran JSRI
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