• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.456-460
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• 2005

The purpose of this study is to develop a estimation model of migration with only population data using the cohort-survival model which has been used for forecasting future population. The fluctuation of population can be bisected to the natural change which can be occurred by birth and death and the social change which means migration. The factors of the social change are usually very important for establishing rural policies. However, researches using migration data has limitations because the usage of them are restricted. For verifying a estimation model of migration, comparing estimated population in 2000 year and migration quantity between 1996 and 2000 of 25 gu with real values, using population data and death ratio from 1995 to 2000 of the 25 gu in Seoul. Result shows a reliable data that R-square of forecating population model is 0.9755 and migration is 0.9180. So these model are worth to estimate a population and migration quantity to restricted migration data.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.347-355
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• 2020

An Isolation-with-Migration (IM) model is used to estimate extant population sizes, the splitting time of populations split away from their common ancestral populations, and migration rates between the extant populations. An evolutionary model such as IM models is estimated by analyzing DNA sequences sampled from the extant populations in the model. When a true model includes an unsampled 'ghost' population without data, the unsampled population is often ignored from the evolutionary model to infer. In this paper, we conduct a simulation study to investigate the effect of an unsampled population on the estimation of the size of the sampled population. When there exists an unsampled population that shares migrations with the sampled population, the size estimation of the sampled population was biased. However, the size estimation was improved if an evolutionary model, including the unsampled population, was estimated.

• Journal of the Economic Geographical Society of Korea
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• v.18 no.1
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• pp.76-86
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• 2015

In this study, we presented a method for estimating the potential population damage of the Seoul Nowon-gu area in the event of a terrorist using a vehicle improvised explosive devices (IED). Using the object-based building extraction method with orthophoto image, the area of the apartment has been determined, and the apartment's height and level were estimated based on the elevation data. Using the population estimation method based on total floor area of building, each apartment resident population was estimated, and then potential population damage at the time of terrorist attacks was estimated around the subway station through a scenario analysis. Terrorism damage using IED depends on the type of vehicle greatly because of the amount loadable explosives. Therefore, potential population damage was calculated based on the type of vehicle. In the results, the maximum potential damage population during terrorist attacks has been estimated to occur around Madeul station, Nowon-gu. The method used in this study can be used various population estimation research and disaster damage estimation.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.97-115
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• 2023

In recent years, the structure of the population has been changing rapidly, with a declining birthrate and aging population, and the inequality of population distribution is expanding. At this point, changes in population estimation methods are required, and more accurate estimates are needed at the subregional level. This study aims to estimate the population in 2040 at the 500m grid level by applying an explainable machine learning to Busan in order to respond to this need for a change in population estimation method. Comparing the results of population estimation by applying the explainable machine learning and the cohort component method, we found that the machine learning produces lower errors and is more applicable to estimating areas with large population changes. This is because machine learning can account for a combination of variables that are likely to affect demographic change. Overestimated population values in a declining population period are likely to cause problems in urban planning, such as inefficiency of investment and overinvestment in certain sectors, resulting in a decrease in quality in other sectors. Underestimated population values can also accelerate the shrinkage of cities and reduce the quality of life, so there is a need to develop appropriate population estimation methods and alternatives.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.1 no.1
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• pp.83-104
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• 1997

This paper studies parameter estimation for a first-order hyperbolic integro-differential equation modelling one-sex population dynamics. A second-order finite difference scheme is used to estimate parameters such as the age-specific death-rate and the age-specific fertility from fully discrete observations on the population. The function space parameter estimation convergence of this scheme is proved. Also, numerical simulations are performed.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.407-416
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• 2001

Sample surveys are usually designed and analyzed to produce estimates for a large area or populations. Therefore, for the small area estimations, sample sizes are often not large enough to give adequate precision. Several small area estimation methods were proposed in recent years concerning with sample sizes. Here, we will compare simple Bayesian approach with Bayesian prediction for small area estimation based on linear regression model. The performance of the proposed method was evaluated through unemployment population data form Economic Active Population(EAP) Survey.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.181-195
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• 2019

This study aims to analyze the spatial context by analyzing the flow characteristics of the walking tour course and visualizing effectively using the floating population data constructed through the communication data. The floating population data refinement algorithm was developed for estimation flow population along the road and the floating population data for each walking tour courses was constructed. In order to adopt the algorithm for forming suitable for the analysis of the walking tour courses, the estimation of floating population considering the area of the road and the estimation of floating population considering the value of floating population around the road were compared. As a result, the estimation of floating population considering ambient the values of flow population was adopted, which is more appropriate to apply analysis method due to the relatively consistent data. Then, a datamining algorithm for walking tour course was constructed according to the characteristics of the floating population data, the absence of missing values. Finally, this study analyzed the flow characteristics and spatial patterns of 18 walking trails in Seoul through the floating population data according to walking tour course. To do this, the kernel density analysis and the Getis-Ord $G^*_i$ statistical hotspot analysis were applied to visualize the main characteristics of each walking tour course.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.11-22
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• 2007

This paper is to discuss analytical techniques to estimate demand sizes and volumes that determine optimal locations for multiple facilities for a given services. While demand size estimation is a core part of location modeling to enhance solution quality and practical applicability, the estimation method has been used in limited and restrict parts such as a single population centroid in a given larger census boundary area or small theoretical application experiments(e.s. census track and enumeration district). Therefore, this paper strives to develop an analytical estimation method of demand size that converts area based demand data to point based population weighted centroids. This method is free to spatial boundary units and more robust to estimate accurate demand volumes regardless of geographic boundaries. To improve the estimation accuracy, this paper uses house weighted value to the population centroid calculation process. Then the population weighted centroids are converted to individual demand points on a grid formated surface area. In turn, the population weighted centroids, demand points and network distance measures are operated into location-allocation models to examine their roles to enhance solution quality and applicability of GIS location models. Finally, this paper demonstrates the robustness of the weighted estimation method with the application of location-allocation models.

• Journal of the Korean Statistical Society
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• v.7 no.2
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• pp.131-142
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• 1978

This study aims to find out a suitable mathematical models for the estimation of population size and improve it for the estimation of social increase of population at urban areas. This study shows that Model (I) is obtained by the generalization of Kabak's Wild Life Management Model together with some other useful results as follows: a) By the transition matrix P, it is known that the interregional migrations have shown greater rise than those of five years ago. b) The invariant population vector $\alpha$ predicts that the Kyonggi area will have a share of 48%, the Choongcheong area of 10%, the Honam area of 12%, and the Youngnam area of 17% of the total population of Korea. c) The estimated population of the Special City of Seoul (Metropolitan) will be above ten millon in 1983. d) The estimated optmum population of Korea will be 53,850,000 in 2000 A.D.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.47-55
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• 2001

Usually estimating the means is used for statistical inference. However depending the purpose of survey, sometimes totals will give the better and more meaningful in statistical inference than the means. Here in this study, we dealt with the unemployment population of small areas with using 4 different small area estimation methods: Direct, Synthetic, Composite, Bayes estimation. For all the estimates considered in this study, the average of absolute bias and men square error were obtained in the Monte Carlo Study which was simulated using data from 1998 Economic Active Population Survey in Korea.