• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.5B
• /
• pp.603-614
• /
• 2008

End-uses of household water have been changed by a life style, housing type, weather, water rate and water supply facilities etc. and those variables can be considered as an internal and exogenous factors to estimate long-term demand forecasts. Analysis of influential factors on water consumption in households would give an explanation to cause on the change of trend and would help predicting the water demand of end-use in household. The purpose of this study is to analyze the demand trends and patterns of household water uses by metering and questionnaire such as occupation, revenue, numbers of family member, housing types, age, floor area and installation of water saving device, etc. The peak water uses were shown at Saturday among weekdays and July in a year based on the analysis results of water use pattern. A steep increase of total water volume can be found in the analysis of water demand trend according to temperature from $-14^{\circ}C$ to $0^{\circ}C$, while there are no significant variations in the phase of more than $0^{\circ}C$, with an almost stable demand. Washbowl water shows the highest and toilet water shows the lowest relation with temperature in correlation analysis results. In the results of ANOVA to find the significant difference in each unit water use by exogenous factors such as housing type, occupation, number of generation, residential area and income et al., difference was shown in bathtub water by housing type and shown in kitchen, toilet and miscellaneous water by numbers of resident. Especially, definite differences in components except washbowl and bathtub water, could be found by numbers of resident. Based on the result, average residents in a house should be carefully considered and the results can be applied as reference information, in decision making process for predicting water demand and establishing water conservation policy. It is expected that these can be used as design factors in planning stage for water and wastewater facilities.

• Korean Journal of Environment and Ecology
• /
• v.31 no.2
• /
• pp.266-274
• /
• 2017

The purpose of this study was to analyze the number of visitors to the top of Seoseokdae in Mudeungsan National Park, in which visitors are concentrated, and their visiting patterns, thereby suggesting measures to manage the visitors. The number of annual visitors and the numbers of regional and seasonal visitors to Mudeungsan National Park, which affect the concentration of visits to Seoseokdae were analyzed using the data produced by an automatic digitizing device. A field study was conducted to examine the number of seasonal and periodic visitors to Seoseokdae and their visiting patterns. In 2015, the number of visitors was 2,563,651 and 83.9% of the visitors visited via the Jeungsimsa and Wonhyosa area that is near Gwangju City. This area is close to the Seoseokdae area and it is easy to hike between the areas. Therefore, there was an influx of most visitors to Seoseokdae into the Jeungsimsa and Wonhyosa area. In terms of seasonal visitors, the largest number of visitors came in the fall, followed by the summer, spring, and winter in order. However, the seasonal differences were not notable. There was no statistically significant correlation between the number of visitors and meteorological factors. This result may have been because Gwangju citizens frequently visit Mudeungsan regardless of period and weather. Visitors can get to Seoseokdae via the trails into Wonhyosa and Jangbuljae. A slightly larger number of visitors used the trail into Jangbuljae in the fall and winter, whereas a larger number of visitors used the trail into Wonhyosa in the summer. In general, there is a large influx of visitors into Jangbuljae, a strategic visiting point. However, a slightly larger number of visitors may have chosen the trail into Wonhyosa in the summer because they could hike under the shade of trees. In the summer, visitors stayed in Seoseokdae for a short time with a low level of crowdedness. On the other hand, in the fall and winter, visitors stayed in the area longer because they had lunch and rested. During the time, the number of momentary maximum visitors peaked, causing extreme crowdedness. Therefore, some visitors showed the visiting pattern of entering the grassland outside the designate zone. Because this behavior can damage the grassland on the top of Seoseokdae, which can lead to soil erosion, intensive visitor management may be necessary.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.64-80
• /
• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.