## 5 Conclusions  
Analyzing and predicting the expansion of urban areas has long been an area of interest in remote sensing and urban study sectors. By adopting XAI modeling, this study developed the urban expansion model and predicted the possibility of urbanization in the near future. The study's results suggest that the urban expansion tends to be promoted when a certain area is close to economically developed area with gentle topography. In addition, the existence of mountainous area and legislative regulations on land use were found to significantly reduce the possibility of urban expansion. Our findings can contribute to develop cities' effective strategies in managing sustainable urban expansion in the future.  
Despite the study's contribution in predicting urban expansion, there is still some room for improvement in future research. First, the XGBoost model has resulted in relatively high accuracy in recent urban studies, particularly for transportation sectors [37, 57]; however, the prediction accuracy of urban expansion derived in this study's model was not significantly higher than previous studies that used other methods, including logistic regression and machine learning techniques [7, 24]. It seems to reflect the complex nature of urban expansion processes, which is determined by not only socioeconomic and physical environment, but also political and cultural factors [58, 59]. To increase the accuracy of predicting urban expansion, researchers are required to consider various aspects of the target areas.  
In addition, the study's urban expansion model did not take into account the effects of urban decline, which is currently occurring in many developed countries around the world. In South Korea, for example, the population number has been decreasing in the late 2010s, and small cities are declining in terms of demographic and economic aspects [60]. However, the study predicted that the urban areas tend to continuously expand in 2031, as we developed the urban expansion model using urbanization data from between 2007 and 2019. Future studies need to consider not only factors influencing urban expansion, but also those for urban declines through widening of temporal extent of analysis.  
Conceptualization, M.K. and G.K.; methodology, M.K. and G.K.; software, M.K.; validation, M.K.; formal analysis, M.K.; investigation, M.K.; resources, G.K.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, G.K.; visualization, M.K.; supervision, G.K. project administration, G.K.; funding acquisition, G.K. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1C1C1013582). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. This paper is based on the findings of the research project \"A Study on Data-based Environment Inequality and Influence Analysis Techniques Using Machine Learning and Spatio-temporal Analysis\" (2022-037(R)), which was conducted by the Korea Environment Institute (KEI). The authors declare no conflict of interest.