한국조경학회지 (Journal of the Korean Institute of Landscape Architecture)

한국조경학회 (The Korean Institute of Landscape Architecture)
권호 표지

A Comparison of Urban Growth Probability Maps using Frequency Ratio and Logistic Regression Methods

  • Park, So-Young (Dept. of Geoinformatic Engineering, Pukyung National University) ;
  • Jin, Cheung-Kil (Dept. of Geoinformatic Engineering, Pukyung National University) ;
  • Kim, Shin-Yup (Environmental Data and Information Office, Ministry of Environment Republic of Korea) ;
  • Jo, Gyung-Cheol (Environmental Data and Information Office, Ministry of Environment Republic of Korea) ;
  • Choi, Chul-Uong (Dept. of Geoinformatic Engineering, Pukyung National University)
  • 투고 : 2010.03.08
  • 심사 : 2010.12.08
  • 발행 : 2010.12.31
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초록

To predict urban growth according to changes in landcover, probability factors werecal culated and mapped. Topographic, geographic and social and political factors were used as prediction variables for constructing probability maps of urban growth. Urban growth-related factors included elevation, slope, aspect, distance from road,road ratio, distance from the main city, land cover, environmental rating and legislative rating. Accounting for these factors, probability maps of urban growth were constr uctedusing frequency ratio (FR) and logistic regression (LR) methods and the effectiveness of the results was verified by the relative operating characteristic (ROC). ROC values of the urban growth probability index (UGPI) maps by the FR and LR models were 0.937 and 0.940, respectively. The LR map had a slightly higher ROC value than the FR map, but the numerical difference was slight, with both models showing similar results. The FR model is the simplest tool for probability analysis of urban growth, providing a faster and easier calculation process than other available tools. Additionally, the results can be easily interpreted. In contrast, for the LR model, only a limited amount of input data can be processed by the statistical program and a separate conversion process for input and output data is necessary. In conclusion, although the FR model is the simplest way to analyze the probability of urban growth, the LR model is more appropriate because it allows for quantitative analysis.

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