1 |
Prasannakumar V, Vijith H, Charutha R, Geetha N. 2011. Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia Soc Behav Sci. 21: 317-325.
DOI
|
2 |
Kang YO, Son SR, Cho NH. 2017. Analysis of Traffic Accidents Injury Severity in Seoul using Decision Trees and Spatiotemporal Data Visualization. Journal of Cadastre & Land InformatiX 47(2): 233-254.
DOI
|
3 |
Sung BJ, Bae GH, Yoo HH. 2015. Analysis of Temporal and Spatial Distribution of Traffic Accidents in Jinju. The Journal of Korean Society for Geospatial Information Science. 23(2): 3-9.
|
4 |
Son SR, Kang YO. 2017. Spatio-temporal Pattern of Traffic Accident of Female Drivers in Seoul, Journal of the Korean Cartographic Association, 17(2): 89-98.
|
5 |
Lee SJ, Cho HS, Song WH, Sohn HG. 2015. A Study on Spatial Characteristic and Influence Factor of Traffic Accident in Seoul. Korean Society for Geospatial Information Science.. 132-133.
|
6 |
Hong SK. 1998. Developing a Visualization System for Spatio - Temporal Linear Point Data. The Journal of Korean Urban Geographical Society. 1(1): 85-100.
|
7 |
Anderson TK. 2009. Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention. 41(3): 359-364.
DOI
|
8 |
Ceder A, Livneh M. 1978. Further evaluation of the relationships between road accidents and average daily traffic. Accident Analysis & Prevention, 10(2): 95-109.
DOI
|
9 |
Chen C, Zhang G, Liu XC, Ci Y, Huang H, Ma J, Chen Y, Guan H. 2016. Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation. Accid Anal Prev. 97: 69-78.
DOI
|
10 |
Elvik R. 2013. Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies. Accid Anal Prev. 60: 254-267.
DOI
|
11 |
Okabe A, Okunuki KI, Shiode S. 2006. SANET: a toolbox for spatial analysis on a network. Geographical analysis, 38(1): 57-66.
DOI
|
12 |
Erdogan S, Yilmaz I, Baybura T, Gullu M. 2008. Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar. Accident Analysis & Prevention, 40(1): 174-181.
DOI
|
13 |
Kang YO, Cho NH, Son SR. 2018. Spatiotemporal characteristics of elderly population's traffic accidents in Seoul using space-time cube and space-time kernel density estimation. PLoS one, 13(5): e0196845.
DOI
|
14 |
Levine N, Kim KE, Nitz LH. 1995. Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. Accident Analysis & Prevention, 27(5): 663-674.
DOI
|
15 |
McSwiggan G, Baddeley A, Nair G. 2017. Kernel density estimation on a linear network. Scandinavian Journal of Statistics, 44(2): 324-345.
DOI
|
16 |
Ng JC, Hauer E. 1989. Accidents on rural two-lane roads: differences between seven states (with discussion and closure) (No. 1238).
|
17 |
Okabe A, Sugihara K. 2012. Spatial analysis along networks: statistical and computational methods. John Wiley & Sons.
|
18 |
Openshaw S. 1984. Ecological fallacies and the analysis of areal census data. Environment and planning A, 16(1): 17-31.
DOI
|
19 |
Xie Z, Yan J. 2008. Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5): 396-406.
DOI
|
20 |
Romano B, Jiang Z. 2017. Visualizing Traffic Accident Hotspots Based on Spatial-Temporal Network Kernel Density Estimation. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (p. 98). 2017, November. ACM.
|
21 |
Yamada I, Thill JC. 2004. Comparison of planar and network K-functions in traffic accident analysis. Journal of Transport Geography, 12(2): 149-158.
DOI
|
22 |
도로교통공단, http://www.index.go.kr
|