1. A Stratified Traffic Accident Analysis
Case Study: City of Richardson, Texas
By: Tope Bello
Masters in Geographic Information Sciences
University of Texas at Dallas
December 2005
Advisor: Michael Tiefelsdorf
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2. Introduction
The occurrences of traffic accidents are rare and random in space and time.
These incidents can be represented spatially as points.
Point pattern analysis will be used to explore the occurrence of traffic accidents in the City of Richardson.
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3. Introduction
This research performs a stratified analysis to check if school age children are involved in traffic accidents more around schools.
For this purpose, data on accident locations, school locations, accident victims, and roads are vital to either support the above statement or reject it.
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4. Introduction
Geographic Information Science and spatial statistics techniques were used to analyze the data for this research.
Conclusions were drawn using visualization and exploratory statistical techniques.
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5. Project Objectives
The specific objectives of this project are to examine the following questions:
Do traffic accidents occur to school age kids more around schools than elsewhere?
Do the slower speeds in school speed zones prevent accidents to school age children?
Does the temporal and spatial pattern of accidents to school age children differ patterns for all accidents?
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6. Research Methodology
Spatial queries to check if slower speeds in school speed zones prevent accidents to school age children.
Kernel Density is used to explore if the temporal and spatial pattern of accidents to school age children differ patterns for all accidents.
Kernel density is used to estimate how the density of events varies across a study area based on a point pattern (White et al 2000).
Bivariate K-Function analysis tests if traffic accidents occur to school age kids more around schools than elsewhere.
The bivariate K- function is defined as the expected number of points of pattern 1 within a distance D of an arbitrary point of pattern 2, divided by the overall density of the points in pattern 1.
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7. Literature Review
Research in epidemiology were used more for addressing the research questions.
The main area of focus was cluster analysis.
V. Gomez-Rubio et al discussed many techniques of detecting clusters of disease using R, a statistical package.
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8. Literature Review
Scan statistics for identifying the concentration of events in space have been modified over time by Openshaw et al. 1987, Besag and Newell 1991, Kulldorf and Nagarwalla 1995.
Bryn Austin et al had a research on clustering of fast food restaurants around schools. It used 400 m radius and 800 meter radius buffer around schools to assess proximity to schools. The bivariate K-function was used to quantify the degree of clustering of fast food restaurants around schools.
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9. Literature Review
Bailey and Gatrell 1996 explained the applicability of K- function analysis to identify a point source for larynx cancer and lung cancer. A case was also stated on elevated risk of respiratory disease along busy main roads.
Andrew Jones et al also used the K-function analysis for the geographic distribution of road accidents in Norfolk, England.
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10. Literature Review
Peter Spooner et al 2004 developed a new technique for analyzing distribution of points on a network, called network K-function. This explores the interaction between points on a network.
We have moved from traffic accident research to disease mapping, and finally to proximity to fast food. What these all have in common is that these events are spatial. Therefore they can be modeled for spatial analysis.
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11. DATA
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12. Data: Overview
Data used for this research were provided by different departments at the City of Richardson.
Base map for city boundary, roads, and schools were provided by the GIS department. These are all spatial files.
Traffic department provided data on traffic accidents.
The Police department also provided traffic accident data.
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13. Data
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14. Data: Traffic Accident Data
The accident data provided by the traffic department was a database used for generating annual reports on traffic accidents occurrences.
The data provided by the police department was a spatial file used to analyze traffic accidents occurrences by the crime analyst at the city of Richardson.
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15. Characteristics of the Data
Police department accident data
Geocoded
Up to date
Not detailed
Restrictions on usage
Traffic department accident database
Not spatial
Detailed
Structured database
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16. Police Department Accident Data
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17. Police Department Accident Data
Address
Speed Limit
Intersection
Distance from Intersection
Direction
Accident Date
Accident Time
Day of the Week
Level of Damage
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18. Traffic Department Accident Data
Year
Accident Number
STREET CODE
Intersecting Street Code
Miles from intersection
Feet From Intersection
Direction from intersection
Date
DOW (Day of the week)
Time
Passenger age for 2 units (potential of 4 passengers)
Pedestrian Age (for 2 potential pedestrians)
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19. PROCESSES
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20. Processes
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21. Data Preparation: Geocoding
There were a total of 10,883 accidents in Richardson between 2000 and 2004.
Address field was generated by concatenating the two field as an intersection.
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22. Data Preparation: Geocoding
Create address locator using the road data.
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23. Data Preparation: Geocoding
Geocode results show the number of records matched were low. This was due to wrong spellings and abbreviations in the data.
Records were matched interactively.
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24. Data Preparation: Geocoding
The statistics shows the final geocode results.
The unmatched records were a result of incomplete records.
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25. Data Preparation: Queries
SQL queries were used to extract traffic accidents involving school age kids (TASK)
TASK is defined as accidents involving kids between 5 and 15 years old, accidents that happened during the weekdays and during daytime.
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26. Data Preparation: Accuracy
Not all accidents happened at intersections
Data contained information on distance away from the intersection.
602 TASK and 8402 other accidents from 2000 – 2004 were used for analysis.
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27. Data
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28. Data
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29. Data Preparation: Speed zones
School speed zone data was also created for this project.
GPS and Pictometry were considered for identifying speed zones around schools.
A six inch spatial resolution image used for pictometry by the city of Richardson GIS department was used to identify the speed zones.
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30. Data Preparation: Speed zones
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31. Data
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32. ANALYSIS
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33. Analysis: Spatial Queries
Do the slower speeds in school speed zones prevent accidents to school age children?
Spatial query to identify TASK that fall in the speed zones.
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34. Analysis
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35. Analysis: Spatial Queries
Perform basic GIS analysis to check for clustering.
TASK that fall within various buffer distances from schools.
Distance
150 ft
250 ft
500 ft
1000 ft
1500 ft
No of TASK
0 (0%)
8 (1.3%)
22 (3.6%)
61 (10.1%)
150 (24.9%)
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36. Analysis: Kernel Densities
Does the temporal and spatial pattern of accidents to school age children differ patterns for all accidents?
Kernel density explores the spatial distribution of TASK and other accidents.
Selection of bandwidth was a challenge, and various bandwidths were explored to generate an heterogeneous pattern.
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37. Analysis
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38. Analysis
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39. Analysis
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40. Analysis: Kernel Densities
Spatial analyst - raster calculator was used to estimate ratio of kernels, using the other accidents as a control population.
Raster calculator – (task - others) / (task + others) = Kernel ratio
This normalizes the distribution of TASK.
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41. Analysis
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42. Analysis: Bivariate K-Function
Do traffic accidents occur to school age kids more around schools than elsewhere?
Bivariate K-function identifies clustering of point pattern A around point pattern B.
This was done using a statistics software called R.
It has various packages that are recommended for different purposes.
SPLANCS (Spatial Point Pattern Analysis Code) package was used for analyzing clustering of TASK around schools.
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43. Analysis: Bivariate K-Function
Foreign package was also used. This enables the use of d-base files in the R environment.
Data used in R contained information on X and Y coordinates of the TASK and schools.
The average shortest distance was relevant to identify the catchments area for each school.
The average shortest distance ( ft) was calculated using Point distance in Arc GIS.
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44. Analysis: Bivariate K-Function
The Bivariate K-function checks the degree of clustering of TASK around schools.
It uses a toroidal shift edge effect correction.
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45. RESULTS AND DISCUSSIONS
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46. Results and Discussions
Do the slower speeds in school speed zones prevent accidents to school age children?
No, Spatial queries show that TASK occur within the speed zones.
Does the temporal and spatial pattern of accidents to school age children differ patterns for all accidents?
Yes, Kernel densities shows a variation in pattern of TASK and other accidents.
Do traffic accidents occur to school age kids more around schools than elsewhere?
No, Bivariate K-function shows no significant deviation from a random pattern.
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47. Results and Discussions
Further studies will consider a heterogeneous background.
Edge effect correction for future research.
The event distance was calculated as the crow flies, network distances should be considered for future studies.
Combination of the datasets from both the police and traffic department could have been more efficient.
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48. Conclusion
Rowlingson and Diggle noted that GIS lacks the basic statistical functionality. Meaning there is limit to the statistical analysis that can be performed by a GIS.
However, my research has shown that a combination of various GIS techniques can test complex statistical hypothesis.
Hence, accidents to school age kids do not happen more around schools in the city of Richardson.
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49. Reference
Austin B, Melly S.J, Sanchez B.N, Patel A, Buka S, Gortmaker S.L (2005) “Clustering of Fast-food restaurant around schools: A novel application of spatial statistics to the study of food environments” American Journal of public health Vol. 95, No 9: 1575 – 1581
Bailey T, Gatrell T (1995) “Interactive Spatial data analysis” Longman Scientific and technical, Essex, UK
Besag J, Newell J (1991) “The detection of cluster in rare diseases”. Journal of the Royal Statistical Society A 154: 143 – 155
Gomez-Rubio V, Ferrandiz-Ferragud J, Lopez- Quilez A (2005) “Detecting clusters of disease with R” Journal of Geographic Systems 7:
Jones A.P, Langford I.H and Bentham G (1996) “The application of K-Function analysis to the geographic distribution of road traffic accident outcomes in Norfolk, England” Social Science and Medicine Vol. 42, No. 6:
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50. Reference
Kulldorf M, Nagarwalla N (1995) “Spatial disease clusters: detection and inference” Statistics in Medicine 14:
Openshaw S, Charlton M, Wymer C, Craft A.W (1987) “A mark I geographical analysis machine for the automated analysis of point data sets” International Journal of Geographical Information Systems 1:
Rowlingson B.S and Diggle P.J (1993) “Splancs supplement – spatial and spatial- temporal analysis” Unpublished report, Lancaster University. U.K
Spooner P.G, Lunt I.D, Okabe A, Shiode S (2004) “Spatial analysis of roadside Acacia populations on a road network using the network K-function” Landscape ecology 19:
White S.J, Ashby D, and Brown P.J (2000) “An introduction to statistical methods for health technology assessment” Health Technology Assessment 2000, 4(8).
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51. Thank you! Questions?
“I not only use all the brains that I have, but all that I can borrow.” Woodrow Wilson
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