Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
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Urban Sprawl prediction in 2030 using decision tree

의사결정나무를 활용한 2030년 도시 확장 예측

  • Kim, Geun-Han (Korea Environment Institute Division for Environmental Planning) ;
  • Choi, Hee-Sun (Korea Environment Institute Division for Environmental Planning) ;
  • Kim, Dong-Beom (Kongju National University Department of Geography) ;
  • Jung, Yee-Rim (Seoul National University Graduate School of Environmental Studies) ;
  • Jin, Dae-Yong (Korea Environment Institute, Center for Environmental Data Strategy)
  • 김근한 (한국환경정책.평가연구원 환경계획연구실) ;
  • 최희선 (한국환경정책.평가연구원 환경계획연구실) ;
  • 김동범 (공주대학교 지리학과) ;
  • 정예림 (서울대학교 환경대학원) ;
  • 진대용 (한국환경정책.평가연구원 환경데이터전략센터)
  • Received : 2020.11.02
  • Accepted : 2020.11.17
  • Published : 2020.12.31
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Abstract

The uncontrolled urban expansion causes various social, economic problems and natural/environmental problems. Therefore, it is necessary to forecast urban expansion by identifying various factors related to urban expansion. This study aims to forecast it using a decision tree that is widely used in various areas. The study used geographic data such as the area of use, geographical data like elevation and slope, the environmental conservation value assessment map, and population density data for 2006 and 2018. It extracted the new urban expansion areas by comparing the residential, industrial, and commercial zones of the zoning in 2006 and 2018 and derived a decision tree using the 2006 data as independent variables. It is intended to forecast urban expansion in 2030 by applying the data for 2018 to the derived decision tree. The analysis result confirmed that the distance from the green area, the elevation, the grade of the environmental conservation value assessment map, and the distance from the industrial area were important factors in forecasting the urban area expansion. The AUC of 0.95051 showed excellent explanatory power in the ROC analysis performed to verify the accuracy. However, the forecast of the urban area expansion for 2018 using the decision tree was 15,459.98㎢, which was significantly different from the actual urban area of 4,144.93㎢ for 2018. Since many regions use decision tree to forecast urban expansion, they can be useful for identifying which factors affect urban expansion, although they are not suitable for forecasting the expansion of urban region in detail. Identifying such important factors for urban expansion is expected to provide information that can be used in future land, urban, and environmental planning.

Keywords

References

  1. Arsanjani.Helbich and De Noronha Vaz. 2013. Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran. Cities, 32 : 33-42. https://doi.org/10.1016/j.cities.2013.01.005
  2. Bhatta. 2010. Causes and consequences of urban growth and sprawl. In Analysis of urban growth and sprawl from remote sensing data (pp. 17-36). Springer, Berlin, Heidelberg.
  3. Bui..Nguyen and Choi. 2020. Prediction of slope failure in open-pit mines using a novel hybrid artifcial intelligence model based on decision tree and evolution algorithm. Scientific Reports. 10(1) : 1-17. https://doi.org/10.1038/s41598-019-56847-4
  4. Chen.Li.Hou.Wang.Wang.Panahi.Li. Peng.Guo and Niu. 2018. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Science of The Total Environment. 634 : 853-867. https://doi.org/10.1016/j.scitotenv.2018.04.055
  5. Chen.Li.Xue.Shahabi.Li.Hong.Wan g.Bian.Zhang and Pradhan. 2020. Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. Science of The Total Environment. 701, 134979. https://doi.org/10.1016/j.scitotenv.2019.134979
  6. Cheng.Wang and Zhang. 2010. Implementation of a COM-based decision-tree model with VBA in ArcGIS. Expert Systems with Applications, 37(1) : 12-17. https://doi.org/10.1016/j.eswa.2009.01.006
  7. Ghavami.Taleai and Arentze. 2017. An intelligent spatial land use planning support system using socially rational agents. International Journal of Geographical Information Science. 31(5) : 1022-1041. https://doi.org/10.1080/13658816.2016.1263306
  8. Jeon.Hong.Lee.Lee and Sung. 2007. Introduction of the New Evaluation Criteria in the Forest Sector of Environmental Conservation Value Map Using LiDAR. Korean Journal of environmental restoration technology. 10(5) : 20-30 (in Korean with English summary)
  9. Jeon.Lee.Song.Sung and Park. 2008. Review of Compositional Evaluation Items for Environmental Conservation Value Assessment Map(ECVAM) of National Land in Korea. Korean Journal of environmental restoration technology. 11(1) : 1-13 (in Korean with English summary).
  10. Jeon.Song.Lee and Kang. 2010. Development Strategy for Utilization of ECVAM using the User Survey. Korean Journal of environmental restoration technology. 15(4) : 111-118 (in Korean with English summary).
  11. Kang and Park. 2000. A study on the urban growth forecasting for the Seoul metropolitan area. The Korean Geographical Society. 35(4) : 621-639 (in Korean with English summary).
  12. Kim.Jeon.Song.Kwak and Lee. 2012. Application of ECVAM as a Indicator for Monitoring National Environment in Korea. Korean Journal of environmental restoration technology. 11(2) : 3-16 (in Korean with English summary).
  13. Kim and Koehler. 1994. An investigation on the conditions of pruning an induced decision tree. European Journal of Operational Research, 77(1) : 82-95 (in Korean with English summary). https://doi.org/10.1016/0377-2217(94)90030-2
  14. Kim.Lee.Jung and Jung. 2016. Mapping the Assessment of Natural environment Outstanding Areas of North Korea Using Logistic Regression Analysis. Journal of the Korean Cartographic Association. 16(3) : 75-88 https://doi.org/10.16879/jkca.2016.16.3.075
  15. Mcdonald.Green.Balk.Fekete.Revenga. Todd and Montgomery. 2011. Urban growth, climate change, and freshwater availability. Proceedings of the National Academy of Sciences. 108(15) : 6312-6317. https://doi.org/10.1073/pnas.1011615108
  16. Osei-Bryson. 2007. Post-pruning in decision tree induction using multiple performance measures. Computers & operations research, 34(11) : 3331-3345. https://doi.org/10.1016/j.cor.2005.12.009
  17. Ruiz Hernandez and Shi. 2018. A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis. International journal of remote sensing, 39(4) : 1175-1198. https://doi.org/10.1080/01431161.2017.1395968
  18. Son.Jeon and Choi. 2009. GIS and statistical techniques used in Korea urban expansion trend analysis. Korean Society for Geospatial Information Science. 17(4) : 13-22(in English with Korean summary).
  19. Song.Kim.Jeon.Park and Lee. 2012. Improvement of the Criteria on Naturalness of the Environmental Conservation Value Assessment Map (ECVAM). Korean Journal of environmental restoration technology. 15(2) : 31-40 (in Korean with English summary). https://doi.org/10.13087/kosert.2012.15.2.031
  20. Triantakonstantis and Mountrakis. 2012. Urban growth prediction: a review of computational models and human perceptions. Journal of Geographic Information System. 4 : 555-587. https://doi.org/10.4236/jgis.2012.46060
  21. Wang, Shu, Wang, Guo, Liu and Li. 2019. A random forest classifier based on pixel comparison features for urban LiDAR data. ISPRS journal of photogrammetry and remote sensing, 148 : 75-86. https://doi.org/10.1016/j.isprsjprs.2018.12.009
  22. Won and Hwang. 2018. Simulating Land Use Change Using Decision Tree and SVM Model : A Case Study of North Korea's City after the Unification. The Korea Spatial Planning Review. 97 : 41-56 (in Korean with English summary). https://doi.org/10.15793/KSPR.2018.97..003
  23. Wu, Ke, Chen, Liang, Zhao and Hong. 2020. Application of alternating decision tree with adaboost and bagging ensembles for landslide susceptibility mapping. CATENA, 187, 104396. https://doi.org/10.1016/j.catena.2019.104396