환경영향평가 (Journal of Environmental Impact Assessment)

한국환경영향평가학회 (Korean Society of Environmental Impact Assessment)
권호 표지
DOI QR코드

DOI QR Code

공간 군집지역 탐색방법에 따른 로드킬 다발구간 분석

Analysis of Roadkill Hotspot According to the Spatial Clustering Methods

  • 투고 : 2019.10.07
  • 심사 : 2019.12.05
  • 발행 : 2019.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 로드킬 다발구간을 선정하기 위한 공간 군집지역 탐색방법을 비교하기 위하여 영주시, 문경시, 안동시, 청송군 로드킬 핫스팟 분석을 수행하였다. 국지적 공간 자기상관 지수인 Getis-Ord Gi* 통계량은 분석 단위를 달리하여 산출하였으며, 전체 도로면적 기준으로 300m에서 9%, 1km에서 14%의 핫스팟 지역을 도출하였다. 1km 단위 핫스팟 지역의 Z-score를 5개로 등급화한 결과, 예천군과 영주시 경계에 있는 28번 국도 구간의 Z-score가 가장 높게 나타났다. 커널 밀도 방법은 일반 커널 밀도 추정과 네트워크 커널 밀도 추정 분석을 수행하였다. 두 방법 모두 구역 단위의 분석보다 로드킬 다발구간의 시각적 확인이 용이했으나, 통계적으로 유의한 우선순위를 산정하기 어려운 한계가 있었다. 결과적으로 군집 분석방법에 따라 국지적인 핫스팟 지역이 다르게 나타나고 있음이 확인되었으며, 공통적으로 저감 대책이 시급한 지역은 영주시와 예천군을 통과하는 28번 국도의 다발구간으로 나타났다. 본 연구의 결과는 향후 로드킬 다발구간 도출 및 저감 대책 수립시 기초자료로 활용될 수 있을 것으로 판단된다.

This study analyzed roadkill hotspots in Yeongju, Mungyeong-si Andong-si and Cheongsong-gun to compare the method of searching the area of the spatial cluster for selecting the roadkill hotspots. The local spatial autocorrelation index Getis-Ord Gi* statistics were calculated by different units of analysis, drawing hotspot areas of 9% from 300 m and 14% from 1 km on the basis of the total road area. The rating of Z-score in the 1km hotspot area showed the highest Z-score in the 28th National Road section on the border between Yecheon-gun and Yeongj-si. The kernel density method performed general kernel density estimation and network kernel density estimation analysis, both of which made it easier to visualize roadkill hotspots than district unit analysis, but there were limitations that it was difficult to determine statistically significant priority. As a result, local hotspot areas were found to be different according to the cluster analysis method, and areas that are in common need of reduction measures were found to be the hotspot of 28th National Road through Yeongju-si and Yecheon-gun. It is deemed that the results of this study can be used as basic data when identifying roadkill hotspots and establishing measures to reduce roadkill.

키워드

참고문헌

  1. Cho DH. 2013. Trends and methodological issues in spatial cluster analysis for count data. Journal of the Korean Geographical Society 48(5): 768-785. [Korean Literature]
  2. Choi TY, Park JH. 2006. The effect of land use on frequency of mammal roadkill in Korea. Journal of the Korean Institute of Landscape Architecture 34(5): 52-58. [Korean Literature]
  3. Clevenger AP, Chruszcz B, Gunson KE. 2003. Spatial patterns and influencing small vertebrate fauna road-kill aggregations. Biological Conservation 109: 15-26. https://doi.org/10.1016/S0006-3207(02)00127-1
  4. Cromley RG, Hanink DM. 2012. Focal location quotients: specification and application. Geographical Analysis 44(4): 398-410. https://doi.org/10.1111/j.1538-4632.2012.00852.x
  5. EEA (European Environmental Agency), 2011, Landscape fragmentation in Europe, EEA Report, 87pp.
  6. Fahrig L. Rytwinski T. 2009. Effects of roads on animal abundance - an empirical review and synthesis. Ecology and Society
  7. Forman RTT, Alexander LE. 1998. Roads and their major ecological effects. Annual Review of Ecology and Systematics 29: 207-231. https://doi.org/10.1146/annurev.ecolsys.29.1.207
  8. Gilsta D, DeVault TL, DeWoody JA. 2009. A review of mitigation measure for reducing wildlife mortality on roadways. Landscape and Urban Planning 91: 1-7. https://doi.org/10.1016/j.landurbplan.2008.11.001
  9. Girardet X, Contruyt-Rogeon G, Foltete JC. 2015. Does regional landscape connectivity influence the location of roe deer roadkill hotspot?. European Journal of Wildlife Research 61(5): 731-742. https://doi.org/10.1007/s10344-015-0950-4
  10. Jeong KS, Moon TH, Jeong JH. 2010. Hotspot analysis of urban crime using space-time scan statistics. Journal of Korean Association of Geographic Information Studies 13(3): 14-28. https://doi.org/10.11108/kagis.2010.13.3.014
  11. Jo NH, Jun CM, Kang YO. 2018. A visualization of traffic accidents hotspot along the road network. Journal of Cadastre & Land InformatiX 48(1): 201-213. [Korean Literature] https://doi.org/10.22640/LXSIRI.2018.48.1.201
  12. Kim KM, Serret H, Clauzel C, Andersen D, Jang IK. 2019. Spatio-temporal characteristics and predictions of the endangered leopard cat Prionailirus bengalensis euptilura roadkills in the Republic of Korea. Global Ecology and Conservation 19: e00673. https://doi.org/10.1016/j.gecco.2019.e00673
  13. Knox EG. 1989. Detection of clusters, in Elliott P ed. Methodology of Enquiries into Disease Clustering, London: Small Area Health Statistics Unit, 17-20.
  14. Lee KJ, Tak JH, Park SI. 2014. Spatial and temporal patterns on wildlife road-kill on highway in Korea. Journal of Veterinary Clinics 31(4): 282-287. [Korean Literature] https://doi.org/10.17555/ksvc.2014.08.31.4.282
  15. Malo JE, Suarez F, Diez A. 2004. Can we mitigate animal-vehicle accidents using predictive models?. Journal of Applied Ecology 41: 701-710. https://doi.org/10.1111/j.0021-8901.2004.00929.x
  16. Mohaymany AS, Shahri M, Mirbagheri B. 2013. GIS-based method for detecting high-crash-risk road segments using network kernel density estimation. Geo-spatial Information Science 16(2): 113-119. https://doi.org/10.1080/10095020.2013.766396
  17. NIE (National Institute of Ecology). 2018. Fundamental research on the conservation of national ecological network. [Korean Literature]
  18. Oh YJ, Lee KJ. 2018. A study on the spatial correlation between roadkill and traffic accidents on roads in Chungcheongnam-do. Environmental Policy 26(2): 147-166. [Korean Literature]
  19. Okabe A, Okunuki K, Shiode S. 2006. SANET: A Toolbox for Spatial Analysis on a Network. Geographical Analysis 38(1): 57-66. https://doi.org/10.1111/j.0016-7363.2005.00674.x
  20. Park SI, Lee KJ. 2015. A study on building methodology for identifying road-kill hotspots on highways and its empirical application. Journal of Korea Planners Association 50(3): 319-333. [Korean Literature] https://doi.org/10.17208/jkpa.2015.04.50.3.319
  21. Ramp D, Caldwell J, Edwards KA, Warton D, Croft DB. 2005. Modelling of wildlife fatality hotspots along the snowy mountain highway in new south wales, Australia. Biological Conservation 126: 472-490.
  22. Seo CW, Thorne JH, Choi TY, Kwon HS, Park CH. 2015. Disentangling roadkill: the influence of landscape and season on cumulative vertebrate mortality in South Korea. Landscape Ecology Engineering 11: 87-99. https://doi.org/10.1007/s11355-013-0239-2
  23. Seo HJ. 2017. Spatial change analysis and future prediction of urban green spaces in Dadgu - a case study of forests and grasslands -. Dissertation of Ph,D, Kyungpook National University. [Korean Literature]
  24. Seok SM, Lee JY. 2015. A study on the correlation between road-kill hotspot and habitat patches. Journal of Environment Impact Assessment 24(3): 233-243. [Korean Literature] https://doi.org/10.14249/eia.2015.24.3.233
  25. Shilling FM, Waetjen DP. 2015. Wildlife-vehicle collision hotspots at US highway extents: scale and data source effects. Nature Conservation 11: 41-60. https://doi.org/10.3897/natureconservation.11.4438
  26. Silverman BW. 1986. Probability density estimation for statistics and data analysis, Chapman and Hall, NY.
  27. Son HG, Park KH. 2008. A spatial statistical method for exploring hotspots of house price volatility. Journal of the Korean Geographical Society 43(3): 392-411. [Korean Literature]
  28. Teixeira FZ, Coelho IP, Esperandio IB, Oliveira NR, Peter FP, Dornelles SS, Delazeri NR, Tavares M, Martins MB, Kindel A. 2013. Are road-kill hotspots coincident among different vertebrate groups?. Oecologia Australis 17(1): 36-47. https://doi.org/10.4257/oeco.2013.1701.04
  29. Xie Z, Yan J. 2008. Kernel density estimation of traffic accidents in a network space. Computers, Environment, and Urban System 35(5): 396-406.
  30. Xie Z, Yan J. 2013. Detecting traffic accident clusters with network kernel density estimation and local spatial statistics : an integrated approach. Journal of Transport Geography 31: 64-71. https://doi.org/10.1016/j.jtrangeo.2013.05.009
  31. Yang JH, Kim JO, Yu KY. 2016. A selection of high pedestrian accident zones using traffic accident data and GIS : a case study of Seoul. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Catography 34(3): 221-230. [Korean Literature] https://doi.org/10.7848/ksgpc.2016.34.3.221