[Home ] [Archive]    
Main Menu
Journal Information::
About the Journal
Aims & Scopes
Editorial Board
Indexing & Abstracting
Reviewers (in last year)
Peer Review Process
Home::
Archive::
All Issues
Current Issue
For Authors::
Authors' Guide
Reference Guide
Submit an Article
For Reviewers::
Principles of Transparency::
Publication Ethics Statement
Research Ethics Statement
Open Access Statement
Copyright
Contact us::
::
Search in website

Advanced Search
..
Committed to

AWT IMAGE

Attribution-NonCommercial
CC BY-NC

AWT IMAGE

Open Access Publishing

AWT IMAGE

Prevent Plagiarism
..
Registered in


..
Statistics
Journal volumes: 17
Journal issues: 34
Articles views: 683905
Articles downloads: 342215
Total authors: 581
Unique authors: 422
Repeated authors: 159
Repeated authors percent: 27
Submitted articles: 368
Accepted articles: 266
Rejected articles: 25
Published articles: 219
Acceptance rate: 72.28
Rejection rate: 6.79
Average Time to Accept: 282 days
Average Time to First Review: 27.2 days
Average Time to Publish: 26.1 days
Last 3 years statistics:
Submitted articles: 54
Accepted articles: 37
Rejected articles: 6
Published articles: 17
Acceptance rate: 68.52
Rejection rate: 11.11
Average Time to Accept: 205 days
Average Time to First Review: 6.7 days
Average Time to Publish: 118 days
____
..
:: 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:   (1093 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]   (474 Downloads)    
Type of Study: Research | Subject: General
Received: 2020/03/27 | Accepted: 2021/01/24 | Published: 2019/09/19
References
1. Aghajani, M. A., Dezfoulian, R. S., Arjroody, A. R., and Rezaei, M. (2017). Applying GIS to Identify the Spatial and Temporal Patterns of Road Accidents Using Spatial Statistics (Case Study: Ilam Province, Iran). Transportation Research Procedia, 25, 2126-2138. [DOI:10.1016/j.trpro.2017.05.409]
2. Behzadi, S. (2020). An Intelligent Location and State Reorganization of Traffic Signal. Geodesy and Cartography, 46, 145-150. [DOI:10.3846/gac.2020.10806]
3. Bradley, P. S., Fayyad, U., and Reina, C. (1998). Scaling EM (Expectation-maximization) Clustering to Large Databases.
4. Chen, Y. (2013). New approaches for Calculating Moran's Index of Spatial Autocorrelation. PloS one, 8, e68336. [DOI:10.1371/journal.pone.0068336]
5. Chohlas-Wood, A., Merali, A., Reed, W., and Damoulas, T. (2015). Mining 911 Calls in New York City: Temporal Patterns, Detection, and Forecasting.
6. Ding, L., Chen, K.-L., Liu, T., Cheng, S.-G., and Wang, X. (2015). Spatial-temporal Hotspot Pattern Analysis of Provincial Environmental Pollution Incidents and Related Regional Sustainable Management in China in the Period 1995-2012. Sustainability, 7, 14385-14407. [DOI:10.3390/su71014385]
7. Forero, P. A., Kekatos, V., and Giannakis, G. B. (2012). Robust Clustering Using Outlier-sparsity Regularization. IEEE Transactions on Signal Processing, 60, 4163-4177. [DOI:10.1109/TSP.2012.2196696]
8. Hamoudzadeh, A., and Behzadi, S. (2020). Predicting User's Next Location Using Machine Learning Algorithms. Spatial Information Research, 1-9. [DOI:10.1007/s41324-020-00358-2]
9. Jafarian, H., and Behzadi, S. (2020). Evaluation of PM2. 5 Emissions in Tehran by Means of Remote Sensing and Regression Models. Pollution, 6, 521-529.
10. Kalantari, M., Yaghmaei, B., and Ghezelbash, S. (2016). Spatio-temporal Analysis of Crime by Developing a Method to Detect Critical Distances for the Knox Test. International Journal of Geographical Information Science, 30, 2302-2320. [DOI:10.1080/13658816.2016.1174867]
11. Lee, J., and Wong, D. W. (2001). Statistical Analysis with ArcView GIS. John Wiley & Sons.
12. Melecky, L. (2015). Spatial Autocorrelation Method for Local Analysis of the EU. Procedia Economics and Finance, 23, 1102-1109. [DOI:10.1016/S2212-5671(15)00484-0]
13. Nadian, M., Mirzaei, R., and SoltaniMohammadi, S. (2018). Application of MoransI Autocorrelation in Spatial-Temporal Analysis of PM2. 5 Pollutant (A Case Study: Tehran City). Journal of Environmental Health Enginering, 5, 197-213. [DOI:10.29252/jehe.5.3.197]
14. Norouzi, E., and Behzadi, S. (2019). Evaluating Machine Learning Methods and Satellite Images to Estimate Combined Climatic Indices. International Journal of Numerical Methods in Civil Engineering, 4, 30-38.
15. Peeters, A., Zude, M., Kathner, J., Unlu, M., Kanber, R., Hetzroni, A., Gebbers, R., and Ben-Gal, A. (2015). Getis-Ord's Hot-and Cold-spot Statistics as a Basis for Multivariate Spatial Clustering of Orchard Tree Data. Computers and Electronics in Agriculture, 111, 140-150. [DOI:10.1016/j.compag.2014.12.011]
16. Pfitzner, D., Leibbrandt, R., and Powers, D. (2009). Characterization and Evaluation of Similarity Measures for Pairs of Clusterings. Knowledge and Information Systems, 19, 361. [DOI:10.1007/s10115-008-0150-6]
17. Ryder, B., Dahlinger, A., Gahr, B., Zundritsch, P., Wortmann, F., and Fleisch, E. (2018). Spatial Prediction of Traffic Accidents with Critical Driving Events-Insights from a Nationwide Field Study. Transportation Research Part A: Policy and Practice. [DOI:10.1016/j.tra.2018.05.007]
18. Sheikholeslami, G., Chatterjee, S., and Zhang, A. (1998). Wavecluster: A Multi-resolution Clustering Approach for Very Large Spatial Databases, VLDB.
19. Songchitruksa, P., and Zeng, X. (2010). Getis-Ord Spatial Statistics to Identify Hot Spots by Using Incident Management Data. Transportation Research Record: Journal of the Transportation Research Board (2165), 42-51. [DOI:10.3141/2165-05]
20. Wang, Z.-b., and Fang, C.-l. (2016). Spatial-temporal Characteristics and Determinants of PM2. 5 in the Bohai Rim Urban Agglomeration. Chemosphere, 148, 148-162. [DOI:10.1016/j.chemosphere.2015.12.118]
21. Zhang, T., and Lin, G. (2016). On Moran's I Coefficient under Heterogeneity. Computational Statistics & Data Analysis, 95, 83-94. [DOI:10.1016/j.csda.2015.09.010]
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA

XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
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
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 16, Issue 1 (9-2019) Back to browse issues page
مجله‌ی پژوهش‌های آماری ایران Journal of Statistical Research of Iran JSRI
Persian site map - English site map - Created in 0.05 seconds with 42 queries by YEKTAWEB 4645