Water demand forecasting in the mid- and long-term begins with predicting the design population. For this reason, the design population is the most important factor in planning public service systems, such as waterworks. However, the design population is characterized by uncertainty and variability, due to the nature of long-term planning; thus, it does not necessarily serve as a precise guide and may simply be utilized to determine a vision for the region. Given that proposing a design population unilaterally can create a psychological obstacle that immobilizes future development potential, there is one trend of opinion that its importance is low [1].

However, when the size of the design population is incorrectly estimated, all water demand plans are either over- or underestimated [2]. Insufficient water demand planning creates an unstable water supply; as water is a basic human right, this cause an imbalance in water welfare. Meanwhile, excessive water demand planning reduces the operational efficiency of limited water resources and, further, causes waste at the national level. To ensure the effectiveness of the National Waterworks Master Plan and Waterworks Basic Plan in Korea, an accurate population forecast is required and this means setting a realistic and feasible target. At present, predictions of the design population have too much elasticity. A more accurate and reasonable estimation method is needed.

Korean Ministry of Land, Infrastructure and Transport (MOLIT) and Korean Ministry of Environment (MOE) [3, 4] have presented a methodology for estimating population for various water supply plans, including the Korean National Planning and Local Government Waterworks Basic Plans. Both studies claim that the cohort-component method using the survival model should be employed to predict the design population, while a social increment should be considered as an auxiliary means. The social increment takes into account external migration (EM) for development projects.

Fig. 1 shows the change in population with the implementation of a development project is City A. The external migration rate (EMR) is defined as the ratio between the population entering the development project area and the cities outside the development project area. Regarding the migration mobility diagram shown in Fig. 1, the EMR is defined as Eq. (1) [3, 4]. The terms SM_oc and SM_co, in Fig. 1 are definitions of social migration that occurs irrespective of development projects. SM_ic in the figure also, represents social migration that occurs irrespective of development projects, however, such migrations do not change the population of City A.

MOLIT and MOE [3] have presented the EMR for cities and provinces in Korea, while MOLIT and MOE [4] have demonstrated the value of subdividing development project types, based on the results of previous studies. However, the values in previous studies have indirectly estimated the EMR, due to difficulties in data acquisition. The EMR derived using the indirect method proposed by MOLIT and MOE [3, 4], which is commonly used for designing populations can be calculated using Eq. (2). This method is problematic for the following reasons.

First, the EMR has been derived based on the EMR of 66 residential development projects examined in 2006. It does not reflect the trends of the latest development projects. Second, it has been derived using the indirect method. Indirect method has some shortcomings. It does not take into consideration of the actual EM. The indirect method is based on the assumption that the amount by which the growth rate of city and county population (CPGR) exceeds the average growth rate of the national population (NPGR) represents the increase in the social population. In addition, the increase in social population is assumed to be the result of EM. However, the social population can increase regardless of development projects. This causes a difference in population estimates, given that social population growth occurs in areas without any development projects. In addition, when estimating the EMR, the value derived by dividing the social populationgrowth by the design population of the development project (DP) is used. For this reason, some errors may occur when calculating the EMR. Third, since the EMR is reported for provinces, the different conditions affecting external inflows to different cities in the same province are not taken into account.

To address the above problems and apply an accurate EMR, researchers have been asked to calculate an EMR that takes into consideration the actual transfer population in development project areas, based on the latest development projects trends, and applies the EMR best suited to each city. Accordingly, this study has developed a model that takes into consideration the urban characteristics that affect the overall EMR. The main objective of this study is to provide basic data, based on reasonable population forecast results for predicting water demand.

Balogun et al. [5] examined the impact of population changes on water supply and proposed a practical solution to reduce the gap between water supply and demand. As changes in population may cause great changes to public services such as the water supply, many studies have attempted to estimate the accurate population.

The present study examines prior research related to external migration mobility. As the EMR comprises social population movement, it is necessary to estimate it considering the factors that influence population movements; that is, urban characteristics. The previous studies that have identified urban characteristics that affect population movement are summarized below.

Molloy et al. [6] have argued that job changing has an effect on domestic migration, with gender, age, education, race/ethnicity, nativity, the presence of children in the household, the number of working adults, income, employment status, homeownership, and the percentage of married households made an internal migration in the United States. McCatty [7] has developed three models (migration and the dual sector model of economic development, the family/household migration model, and the Todaro model of migration), concluding that job finding, family changing, health, and education affected migration. Clark and Withers [8] have suggested that patterns of migration change due to changes in family composition. Miheretu [9] has claimed that the distance between two areas, density of population, education, and employment affect population movement.

Previous studies have indicated that social population movements are affected by the characteristics of people behaviors. However, previous studies involving Korea, which is a developing country, have indicated that social population movements are more affected by city characteristics than by people’s behavior. The previous studies that have identified urban characteristics that affecting population movement are summarized below.

In researching factors that influence population movement, Yoo [10] has found that when wage levels and housing supply rates increase, EM also increases. Kim and Jang [11] have observed that the expected income ratio and educational opportunity ratio can create population growth, triggering significant EM. Lee [12] has analyzed the correlation between population movement and influence factors, discovering that the number of pharmacies, the gas coverage rate, and the housing area all affect population movement. Moreover, Lee [13] has discovered that income opportunities, job opportunities, the tax burden, comfort, housing construction, capital area readjustment zone separation, population size, and migration distance as population movement factors in Gyeonggi Province. Lee and Park [14] have noted that population movement into a residential development project area occurs because people want to purchase a house or improve workplace proximity, education, or their residential environment. In particular, those aged 20–29 usually move for the purpose of purchasing a house, those aged 30–39 predominantly move in order to be closer to their workplace, those aged 40–49 relocate for the purpose of education, and those aged 50–59 migrate in order to improve their residential environment. Migration has thus been shown to be associated with the life cycle. Furthermore, the shorter the migration distance, the higher the rate of relocating for individuals aged 20–29 and 40–49, whereas those aged 30–39, there is a higher rate of migration for longer distances.

Kim and Jang [15] have found that in many cases of population movement, migration occurs to areas that have social and economic characteristics that are similar to their existing residential environments. Kim and Choi [16] calculated the EMR of housing development projects in capital and metropolitan cities by analyzing the external inflow rate for 11 residential land development project districts in cities across the country during the 1990s. Kwon [17] has found that age, gender, educational level, manufacturing ratio, labor rate, and rural/urban status are factors that affect population migration to metropolitan areas, while Choi and Kim [18] have found that land development areas, design population, local downtown distances, population density, and industrial structures affect population movement. Lee and Lee [19] have analyzed the characteristics of population movement into new cities, discovering that, in the case of migration within the capital city, educational level, housing scales, occupancy types, and household type had the biggest impact.

Jeon [20] has analyzed the EMR of 594 new apartment buildings in Gyeonggi-do and subsequently comparing the results with the EMR applied when establishing the city basic plan. Areas involving large-scale housing development projects were found to have high EMR, in comparison to other areas. It is necessary to consider the in-migration rate when making such calculations, given the strong correlation between the type of development project, the size of development project, and the EMR. Hur [21] has analyzed the EMR in relation to local government, development project size, development project type, and distance from Seoul by targeting 594 apartment buildings constructed in Gyeonggi Province since 2005. Based on the analysis results, the factors were divided into project district factors and regional factors, and while project type and project enforcer influenced the EMR. As a complement to Hur’s [21] study, Lee [22] has found the EMR to be higher as the distance from Seoul decreases and employment conditions become more favorable.

From this comprehensive analysis of prior research, the common factors influencing the EMR are found to be development project size and type, distance from capital city, and factors related to economic activity. However, these results reflect the fact that most previous studies have used data drawn solely from Gyeonggi Province. In this study, additional city characteristics are identified and applied to the model to predict the EMR.

The prediction method for the EMR used quantification I analysis, which comprises quantitative numerical analysis of qualitative variables. This method was used because, as shown in Table 2, the qualitative variables of the factors influencing the EMR, such as the development project type, were included.

Quantification I analysis can typically be understood as a dummy regression analysis, and multiple regression analysis is an analytical method used to develop a model that can describe or predict the value of a dependent variable using more than two variables. The following Eq. (4) indicates the general formula that was derived through the quantification theory type I analysis.

Here,

Each independent variable was selected as an influencing factor in the EMR, and the quantification I analysis was carried out using SPSS 23.0. The step selection method was used for the process of developing the regression model. In this method, the statistical significance of each independent variable is verified as the variables are added, step by step. If the F value for the input value is greater than the entry value, the variable is included in the regression model. In case of the inclusion of a new variable, when the F value for the value that is input by recalculating the F value is greater than the rejection value, the variable contained in the regression model is removed. When there is no independent variable for which the F value is greater than the entry value and no variable that needs to be removed, the phase of the regression model ends. In this case, as the sample size was small, 0.15 and 0.25 were applied as the significance probabilities of the F value including the independent variable, for the significance probability of the F value’s entry and the significance probability of the F value’s removal.

Regarding the derived final quantification I model, the significance of the model itself was examined by conducting a significance test (variance analysis). A significance test was also carried out on the regression coefficient included in the final model. Lastly, the fitness of the estimation model was derived through a collinearity test of the independent variable included in the final model.

This study has shown that the indirect method incorrectly estimates EMR, which is a key component in estimating design population for infrastructural projects. Underestimating the design population can lead to insufficient water demand plans, which can cause unbalanced water welfare issues. Meanwhile, excessive water demand planning reduces the operational efficiency of limited water resources and causes waste at the national level.

To overcome this challenge, two new models were developed using quantification analysis. The models were developed for both counties and cities, after identifying the major factors influencing EM.

The major findings of this study were:

However, as described previously, this research has the following limitations. First, a relatively small number of samples were used for model development. Second, 11 influential factors were considered in this research; however, various influential factors need to be considered additionally.

Consequently, future medium and long-term studies should aim to cover more counties and cities in order to comprehensively identify important trends and significant factors that affect EMR. Such results would be useful for both governments and the private sector when planning public infrastructural projects.