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題名 人口流動模型的距離效應之探討
A distance-based modification of spatial interaction model in modelling population movement
作者 梁穎誼
Leong, Yin Yee
貢獻者 余清祥
梁穎誼
Leong, Yin Yee
關鍵詞 空間互動模型
人口流動
轉折點模型
中間障礙因素
距離遞減函數
Spatial interaction model
Population movement
Change-point analysis
Intervening obstacles
Distance decay function
日期 2017
上傳時間 3-七月-2017 14:34:43 (UTC+8)
摘要 人口流動具有各種型態。其中包含了遷移、移動、以及通勤人口。在宏觀模型框架下，空間互動模型(簡稱SIM)對於測量人口流動扮演了重要的角色。距離遞減效應為空間互動模型中重要的因子。該效應描述了人口流動的頻率會隨著移動距離而逐漸下降。然而，從實證上，本研究發現人口流動與移動距離的函數，並非在距離上保有恆定的關係。
      在本文中，我們提出了對此非恆定的距離遞減效應之修正方法。本修正法運用了轉折點模型的特點，套入了空間互動模型的距離函數上。本文首先運用了電腦模擬驗證了此方法的穩定性與有效性。接下來，研究將此方法應用在兩個人口流動資料。第一個是從台灣健保資料庫觀察出的民眾就醫地變化。健保資料庫包含了總人口的5%抽樣資料。由於在抽樣上瑕疵不大，因此健保抽樣資料具有了一定的代表性。第二個資料則是英國統計局所提供的人口遷移普查資料。在這兩個資料上，我們發現本研究所提修正法，相較於傳統的空間互動模型具有更好的模型配適表現。此改善程度在非都市地區尤其更為明顯。
Population movement encompasses various forms, such as migration, mobility, and commuting. Spatial Interaction Model (SIM) serves as an important tool to calibrate these movements in the sense of macro modelling. One of the important features of this model is that the number of migrants often decays with the distance. However, we found that this is not always the case in practice and the decay pattern may change with distance.
     
     In this study, we propose a distanced-based modification to the SIM, via applying the techniques of change-point problem to construct distance functional form. Computer simulation is illustrated to validate the method and the empirical analysis of flow data from Taiwan’s National Health Insurance Research Database (NHIRD), and also England & Wales internal migration data also provides sound evidences to support the proposed approach. Note that the flow data from the NHIRD consists of a sample of about one million people and can be treated as a fine sample representative of Taiwan’s whole population (about 23 million people). Our results show that the modified approach is more adequate than the traditional SIM, especially for describing the movements of suburban areas in Taiwan.
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描述 博士
國立政治大學
統計學系
100354501
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100354501
資料類型 thesis
dc.contributor.advisor 余清祥zh_TW
dc.contributor.author (作者) 梁穎誼zh_TW
dc.contributor.author (作者) Leong, Yin Yeeen_US
dc.creator (作者) 梁穎誼zh_TW
dc.creator (作者) Leong, Yin Yeeen_US
dc.date (日期) 2017en_US
dc.date.accessioned 3-七月-2017 14:34:43 (UTC+8)-
dc.date.available 3-七月-2017 14:34:43 (UTC+8)-
dc.date.issued (上傳時間) 3-七月-2017 14:34:43 (UTC+8)-
dc.identifier (其他 識別碼) G0100354501en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/110648-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 100354501zh_TW
dc.description.abstract (摘要) 人口流動具有各種型態。其中包含了遷移、移動、以及通勤人口。在宏觀模型框架下，空間互動模型(簡稱SIM)對於測量人口流動扮演了重要的角色。距離遞減效應為空間互動模型中重要的因子。該效應描述了人口流動的頻率會隨著移動距離而逐漸下降。然而，從實證上，本研究發現人口流動與移動距離的函數，並非在距離上保有恆定的關係。
      在本文中，我們提出了對此非恆定的距離遞減效應之修正方法。本修正法運用了轉折點模型的特點，套入了空間互動模型的距離函數上。本文首先運用了電腦模擬驗證了此方法的穩定性與有效性。接下來，研究將此方法應用在兩個人口流動資料。第一個是從台灣健保資料庫觀察出的民眾就醫地變化。健保資料庫包含了總人口的5%抽樣資料。由於在抽樣上瑕疵不大，因此健保抽樣資料具有了一定的代表性。第二個資料則是英國統計局所提供的人口遷移普查資料。在這兩個資料上，我們發現本研究所提修正法，相較於傳統的空間互動模型具有更好的模型配適表現。此改善程度在非都市地區尤其更為明顯。		zh_TW
dc.description.abstract (摘要) Population movement encompasses various forms, such as migration, mobility, and commuting. Spatial Interaction Model (SIM) serves as an important tool to calibrate these movements in the sense of macro modelling. One of the important features of this model is that the number of migrants often decays with the distance. However, we found that this is not always the case in practice and the decay pattern may change with distance.
     
     In this study, we propose a distanced-based modification to the SIM, via applying the techniques of change-point problem to construct distance functional form. Computer simulation is illustrated to validate the method and the empirical analysis of flow data from Taiwan’s National Health Insurance Research Database (NHIRD), and also England & Wales internal migration data also provides sound evidences to support the proposed approach. Note that the flow data from the NHIRD consists of a sample of about one million people and can be treated as a fine sample representative of Taiwan’s whole population (about 23 million people). Our results show that the modified approach is more adequate than the traditional SIM, especially for describing the movements of suburban areas in Taiwan.		en_US
dc.description.tableofcontents 1. Introduction 7
     2. Population movement and literature reviews 10
     2.1 Briefing on population movement 10
     2.2 Migration data 14
     2.3 Review on migration theories 16
     2.3.1 Newtonian gravity model and Push-Pull theory 18
     2.3.2 Neo-Classical approach 20
     2.3.3 New economics of migration 21
     2.3.4 Dual labor market theory 22
     2.3.5 World systems theory 25
     2.4 Definition of SIM in this study and recent development
      26
     2.5 Other flow models 32
     3. Changing point in distance decay function 36
     4. Change-point modelling approach to distance-decay effect 45
     5. Simulation studies 54
     5.1 Type I Error and Power 54
     5.2 Accuracy and Precision 58
     6. Empirical studies 65
     6.1 Change of locations of hospital visit based on Taiwan NHRID 66
     6.1.1 NHIRD data and data extraction 66
     6.1.2 Model validation 73
     6.2 England & Wales migration data 79
     7. Conclusion and discussions 83
     Reference 87		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100354501en_US
dc.subject (關鍵詞) 空間互動模型zh_TW
dc.subject (關鍵詞) 人口流動zh_TW
dc.subject (關鍵詞) 轉折點模型zh_TW
dc.subject (關鍵詞) 中間障礙因素zh_TW
dc.subject (關鍵詞) 距離遞減函數zh_TW
dc.subject (關鍵詞) Spatial interaction modelen_US
dc.subject (關鍵詞) Population movementen_US
dc.subject (關鍵詞) Change-point analysisen_US
dc.subject (關鍵詞) Intervening obstaclesen_US
dc.subject (關鍵詞) Distance decay functionen_US
dc.title (題名) 人口流動模型的距離效應之探討zh_TW
dc.title (題名) A distance-based modification of spatial interaction model in modelling population movementen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] Abel, G.J. (2010). Estimation of international migration flow tables in Europe. Journal of the Royal Statistical Society Series A, 173(4): 797–825.
     [2] Abel, G.J. (2013). Estimating global migration flow tables using place of birth data. Demographic Research, 28(18): 505-546.
     [3] Abel, G.J. (2013). Estimating global migration flow tables using place of birth data. Demographic Research, 28: 505-546.
     [4] Alecke, B., Huber, P., and Untiedt, G. (2001). What difference a constant makes? How predictable are international migration flows? Migration policies and EU enlargement. The case of central and eastern Europe: 63–78.
     [5] Bai, J. (1997). Estimation of a Change Point in Multiple Regression Models. The Review of Economics and Statistics, LXXIX(4)
     [6] Bauer, T and Klaus F.Z. (1999). Assessment of possible migration pressure and its labor market impact following EU enlargement to Central and Eastern Europe. A study for the Department of Education and Employment, UK. IZA Research Report No.3, July.
     [7] Bell, M., Charles-Edwards, E., Ueffing, P., Stillwell, J., Kupiszewski, M., and Kupiszewska, D. (2015). Internal migration and development: comparing migration intensities around the world. Population and Development Review, 41(1): 33-58.
     [8] Bijak, J. (2010). Forecasting International Migration in Europe: A Bayesian View. The Springer Series on Demographic Methods and Population Analysis Series. Berlin: Springer.
     [9] Black, R., Bennett S.R., Thomas S.M., and Beddington J.R. (2011). Climate change: Migration as adaptation. Nature, 478(7370):447–449
     [10] Center for Migration Studies. (1996). Population Mobility: A Theoretical Exploration. Center for Migration Studies special issues, 13(3); 9-17.
     [11] Chakraborty, M.A., Beamonte. A., Gelfand. A.E., Alonso M.P., Gargallo, P., and Salvador, M. (2013) . Spatial interaction models with individual-level data for explaining labor flows and developing local labor markets. Computational and Statistics & Data Analysis 58: 292–307.
     [12] Claydon, K. (2012). A Global Model of Human Migration, CASA Working Paper 186, UCL, Centre for Advanced Spatial Analysis, London.
     Retreive from https://www.bartlett.ucl.ac.uk/casa/pdf/paper186
     [13] de Vries, J.J., Nijkamp, P. and Rietveld, P. (2009). Exponential or power distance-decay for commuting? An alternative specification. Environment and Planning A, 41, 461–480
     [14] Dennett, A., and Wilson, A. (2013). A multi-level spatial interaction modelling framework for estimating interregional migration in Europe. Environment and Planning A, 45: 1491–1507.
     [15] Dickinson, R.E. (1934). Markets and Market Areas in East Anglia. Economic Geography, 10: 172-82.
     [16] Drobne, S., and Lakner, M. (2014). Which Distance-decay function for migration and which one for commuting? A case study of Slovenia. Croatian Operational Research Review, (5):2
     [17] Evans, A.W., (1970). Some properties of trip distribution methods, Transportation Research, 4: 19-36.
     [18] Fik, T. and Mulligan, G. (1990). Spatial flows and competing central places: towards a general theory of hierarchical interaction. Environ Planning A, 22, 527–559.
     [19] Fischer, M.M., and Jinfeng, W. (2011). Spatial Data Analysis. Models, Methods and Techniques. Heidelberg, Dordrecht, London, New York: Springer.
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