• Title, Summary, Keyword: population model

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A Study on the Application of Building Population Weighting to ERAM Model Based on GIS Data (GIS 데이터에 기반한 건물인구 가중치 적용 ERAM 모델에 관한 연구)

  • Mun, Sunghoon;Piao, Gensong;Choi, Jaepil
    • Journal of the Architectural Institute of Korea Planning & Design
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    • v.35 no.1
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    • pp.47-54
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    • 2019
  • This study proposes a new ERAM model with building population weighting. Previous studies of applying weightings on ERAM model on the scale of urban space were focused on the relationship between the street and the human behavior. However, this study focuses on the influences that buildings give to human behavior and develops a building population weighted ERAM model. This research starts by analyzing ERAM model to its basic compositions, which are adjacency matrix and row vector. It applies building population weighting to the row vector, while previous studies put weightings in the adjacency matrix. Building population weighted ERAM model calculates the building population weighting based on GIS data, which provides objective and massive data of buildings in the urban scale. For the verification of the model, Insa-dong and Myeong-dong were analyzed with both ERAM model and building population weighted ERAM model. The results were analyzed through the correlation test with actual pedestrian population data of the two districts. As a result, the explanation ability of building population weighted ERAM model for the pedestrian population turned out to be higher than the ERAM model. Since building population weighted ERAM model has the structure that can be combined with other weighted ERAM models, it is expected to develop a multi-weighted ERAM model with better explanation ability as a further study.

Development of a Rural Population Model Considering Shift-Share Effects in Cohort-Survival Method (집단생잔모델에 변화할당효과를 고려한 농촌지역 인구모델의 개발)

  • Jung, Nam-Su;Lee, Haeng-Woo
    • Journal of Korean Society of Rural Planning
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    • v.12 no.3
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    • pp.39-42
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    • 2006
  • The purpose of this study is to develop rural population model adapting cohort survival method with sift-share effects. Administrative district in this study is below Myun: about 2,000 population. Population data of rural area in 1990, 1995, and 2000 by age cohort were selected for applying developed model. Damping coefficient from population data was calculated as 7% and results applying this coefficient in rural population data below the error from 12% to 1.06%. In detail, most of cohorts fitted with developed model except from 15 to 29 age groups. Application result of small population area; DaesulMyun revealed that main factor of population change is not natural change but migration.

Effect of an unsampled population on the estimation of a population size (집단 크기 추정에 대한 미표본 집단의 영향)

  • Chung, Yujin
    • 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.

Development a Estimate Model of Migration Using Cohort-Survival Model (집단 생잔 모형을 이용한 인구이동모델 개발)

  • Han, Yi-Cheol;Lee, Jeong-Jae;Jung, Nam-Su
    • Proceedings of the Korean Society of Agricultural Engineers Conference
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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.

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A Comparative Study on the Spatial Statistical Models for the Estimation of Population Distribution

  • Oh, Doo-Ri;Hwang, Chul Sue
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.33 no.3
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    • pp.145-153
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    • 2015
  • This study aims to accurately estimate population distribution more specifically than administrative unites using a RK (Regression-Kriging) model. The RK model is the areal interpolation technique that involves linear regression and the Kriging model. In order to estimate a population’s distribution using a sample region, four different models were used, namely; a regression model, RK model, OK (Ordinary Kriging) model and CK (Co-Kriging) model. The results were then compared with each other. Evaluation of the accuracy and validity of evaluation analysis results were the basis RMSE (Root Mean Square Error), MAE (Mean Absolute Error), G statistic and correlation coefficient (ρ). In the sample regions, every statistic value of the RK model showed better results than other models. The results of this comparative study will be useful to estimate a population distribution of the metropolitan areas with high population density

Population Distribution Estimation Using Regression-Kriging Model (Regression-Kriging 모형을 이용한 인구분포 추정에 관한 연구)

  • Kim, Byeong-Sun;Ku, Cha-Yong;Choi, Jin-Mu
    • Journal of the Korean Geographical Society
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    • v.45 no.6
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    • pp.806-819
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    • 2010
  • Population data has been essential and fundamental in spatial analysis and commonly aggregated into political boundaries. A conventional method for population distribution estimation was a regression model with land use data, but the estimation process has limitation because of spatial autocorrelation of the population data. This study aimed to improve the accuracy of population distribution estimation by adopting a Regression-Kriging method, namely RK Model, which combines a regression model with Kriging for the residuals. RK Model was applied to a part of Seoul metropolitan area to estimate population distribution based on the residential zones. Comparative results of regression model and RK model using RMSE, MAE, and G statistics revealed that RK model could substantially improve the accuracy of population distribution. It is expected that RK model could be adopted actively for further population distribution estimation.

Macro-Level Accident Prediction Model using Mobile Phone Data (이동통신 자료를 활용한 거시적 교통사고 예측 모형 개발)

  • Kwak, Ho-Chan;Song, Ji Young;Lee, In Mook;Lee, Jun
    • Journal of the Korean Society of Safety
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    • v.33 no.4
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    • pp.98-104
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    • 2018
  • Macroscopic accident analyses have been conducted to incorporate transportation safety into long-term transportation planning. In macro-level accident prediction model, exposure variable(e.g. a settled population) have been used as fundamental explanatory variable under the concept that each trip will be subjected to a probable risk of accident. However, a settled population may be embedded error by exclusion of active population concept. The objective of this research study is to develop macro-level accident prediction model using floating population variable(concept of including a settled population and active population) collected from mobile phone data. The concept of accident prediction models is introduced utilizing exposure variable as explanatory variable in a generalized linear regression with assumption of a negative binomial error structure. The goodness of fit of model using floating population variable is compared with that of the each models using population and the number of household variables. Also, log transformation models are additionally developed to improve the goodness of fit. The results show that the log transformation model using floating population variable is useful for capturing the relationships between accident and exposure variable and generally perform better than the models using other existing exposure variables. The developed model using floating population variable can be used to guide transportation safety policy decision makers to allocate resources more efficiently for the regions(or zones) with higher risk and improve urban transportation safety in transportation planning step.

A Model to Estimate Population by Sex, Age and District Based on Fuzzy Theory

  • Pak. Pyong-Sik;Kim, Gwan
    • 제어로봇시스템학회:학술대회논문집
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    • pp.42.1-42
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    • 2002
  • A model to predict population by sex, age and district over a long-range period is proposed based on fuzzy theories. First, a fuzzy model is described. Second, a method to estimate the social increase by sex and age in each district is proposed based on a fuzzy clustering method for dealing with long-range socioeconomic changes in population migration. By the proposed methods, it became possible to predict the population by sex, age and district over a long-range period. Third, the structure and characteristics of the three models of employment model, time distance model, and land use model constructed to predict various socioeconomic indicators, which are require...

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Machine Learning Based Architecture and Urban Data Analysis - Construction of Floating Population Model Using Deep Learning - (머신러닝을 통한 건축 도시 데이터 분석의 기초적 연구 - 딥러닝을 이용한 유동인구 모델 구축 -)

  • Shin, Dong-Youn
    • Journal of KIBIM
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    • v.9 no.1
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    • pp.22-31
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    • 2019
  • In this paper, we construct a prototype model for city data prediction by using time series data of floating population, and use machine learning to analyze urban data of complex structure. A correlation prediction model was constructed using three of the 10 data (total flow population, male flow population, and Monday flow population), and the result was compared with the actual data. The results of the accuracy were evaluated. The results of this study show that the predicted model of the floating population predicts the correlation between the predicted floating population and the current state of commerce. It is expected that it will help efficient and objective design in the planning stages of architecture, landscape, and urban areas such as tree environment design and layout of trails. Also, it is expected that the dynamic population prediction using multivariate time series data and collected location data will be able to perform integrated simulation with time series data of various fields.

Model Validation Methods of Population Pharmacokinetic Models (집단 약동학 모형을 위한 모형 진단과 적합도 검정에 대한 고찰)

  • Lee, Eun-Kyung
    • The Korean Journal of Applied Statistics
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    • v.25 no.1
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    • pp.139-152
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    • 2012
  • The result of the analysis of a population pharmacokinetic model can directly influence the decision of the dose level applied to the targeted patients. Therefore the validation procedure of the final model is very important in this area. This paper reviews the validation methods of population pharmacokinetic models from a statistical viewpoint. In addition, the whole procedure of the analysis of population pharmacokinetics, from the base model to the final model (that includes various validation procedures for the final model) is tested with real clinical data.