請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/34551完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蘇明道 | |
| dc.contributor.author | Mei-Chun Lin | en |
| dc.contributor.author | 林美君 | zh_TW |
| dc.date.accessioned | 2021-06-13T06:14:42Z | - |
| dc.date.available | 2011-07-27 | |
| dc.date.copyright | 2011-07-27 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-07-26 | |
| dc.identifier.citation | 1.Balk, D. and G. Yetman (2004). The Global Distribution of Population Evaluating the gains in resolution refinement, Center for International Earth Science Information Network, Columbia University.
2.Bivand, R. (1998). A Review of Spatial Statistical Techniques for Location Studies. CEPR symposium. Sweden: 1-21. 3.Bracken, I. (1991). ' A Surface Model Approach to Small Area Population Estimation.' The Town Planning Review 62: 225-237 4.Bracken, I. and D. Martin (1989). 'The generation of spatial population distributions from census centroid data.' Environment and Planning A 21(4): 537 - 543 5.Burrough, P. A. (1986). Principles of geographical information systems for land resources assessment New York Oxford 6.Chainey, S., S. Reid, et al. (2002). When is a hotspot a hotspot? A procedure for creating statistically robust hotspot maps of crime Socio-economic Applications of Geographic Information Science Innovations in GIS - 9. Kidner, D. B., G. Higgs and S. White. London, New York, Taylor & Francis: 21-36. 7.Chang, L. F., C. H. Lin, et al. (2008). 'Application of Geographic Weighted Regression to establish flood damage functions reflecting spatial variation.' Water SA 34: 209-216. 8.Chiang, J.-L. and K.-S. Cheng (2004). 'Remote Sensing Image Classification Using Indicator Kriging.' Journal of Photogrammetry and Remote Sensing 9: 43-58. 9.Cohen, J. (1960). ' A Coefficient of Agreement for Nominal Scales ' Educational and Psychological Measurement 20: 37-46. 10.Deichmann, U., D. Balk, et al. (2001). Transforming Population Data for Interdisciplinary Usages: From census to grid. 11.Dobson, J. E., E. A. Bright, et al. (2000). 'LandScan: A Global Population Database for Estimating Populations at Risk ' Photogrammetric Engineering & Remote Sensing 66(7): 849-857. 12.Donnay, J. P. and D. J. Unwin (2001). Modelling geographical distributions in urban areas. Remote sensing and urban analysis. J. P. Donnay, M. J. Barnsley and P. C. Longley. London, Taylor & Francis: 205-224. 13.Eck, J. E., S. Chainey, et al. (2005). Mapping Crime: Understanding Hot Spots National Institute of Justice, US. 14.Eicher, C. L. and C. A. Brewer (2001). 'Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation.' Cartography and Geographic Information Science 28(2): 125-138. 15.Everitt, B. (1980). Cluster analysis. New York, Halsted Press. 16.Fisher, P. F. and M. Langford (1996). 'Modeling sensitivity to accuracy in classified imagery: A study of areal interpolation by dasymetric mapping.' Professional Geographer 48(3): 299-309. 17.Fisher, P. F. and M. Langford (1996). 'Modeling the errors in areal interpolation between zonal systems by Monte Carlo Simulation.' Environment and Planning A 27(2): 211-224. 18.Flowerdew, R. and M. Green (1992). 'Developments in Areal Interpolation Methods and GIS ' The Annals of Regional Science 26(1): 67-78. 19.Garb, J. L., R. G. Cromley, et al. (2007). 'Estimating Populations at Risk for Disaster Preparedness and Response.' Journal of Homeland Security and Emergency Management 4(1): 1-17. 20.Goodchild, M. F., L. Anselin, et al. (1993). 'A framework for the areal interpolation of socioeconomic data.' Environment and Planning A 25(3): 383-397. 21.Goodchild, M. F. and N. S. Lam (1980). 'Areal Interpolation: A Variant of The Traditional Spatial Problem.' Geo-processing 1: 297-312. 22.Green, N. E. (1956). 'Aerial Photographic Analysis of Residential Neighborhoods: An Evaluation of Data Accuracy ' Social Forces 35: 142-147. 23.Gregory, I. N. and P. S. Ell (2005). 'Breaking the boundaries: geographical approaches to integrating 200 years of the census.' Journal of the Royal Statistical Society Series A (Statistics in Society) 168(2): 419-437. 24.Harris, R., P. Sleight, et al. (2005). Geodemographics, GIS, and Neighborhood Targeting, Wiley. 25.Harvey, J. T. (2000). 'Small Aera Population Estimation Using Statellite Imagery.' Statistics in Transition 4: 611-633. 26.Harvey, J. T. (2002). 'Estimating census district populations from satellite imagery: some approaches and limitations ' International Journal of Remote Sensing 23(10): 2071-2095 27.Hay, S. I., A. M. Noor, et al. (2005). 'The accuracy of human population maps for public health application.' Tropical Medicine and International Health 10(10): 1073-1086. 28.Holt, J. B., C. P. Lo, et al. (2004). 'Dasymetric Estimation of Population Density and Areal Interpolation of Census Data.' Cartography and Geographic Information Science 31(2): 103-121. 29.Horton, M. S. (2008). People and place : a covenant ecclesiology. Louisville, Ky., Westminster John Knox Press. 30.Katajisto, J. and A. Moilanena (2006). 'Kernel-based home range method for data with irregular sampling intervals.' Ecological modelling 194(4): 405-413. 31.Keping, C., M. John, et al. (2004). 'Defining area at risk and its effect in catastrophe loss estimation: a dasymetric mapping approach.' Applied Geography(24 ): 97-117. 32.Kevin, H. and M. Harold (2005). 'A Comparative Analysis of Areal Interpolation Methods ' Cartography and Geographic Information Science 32(4): 411-423. 33.Kevin J. Hawley, B. S. (2005). A Comparative Analysis of Areal Interpolation Methods, Ohio State University. 34.Kim, H. and X. Yao (2010). 'Pycnophylactic interpolation revisited: integration with the dasymetric-mapping method ' International Journal of Remote Sensing 31(21): 5657 - 5671 35.Kraus, S. P., L. W. Senger, et al. (1974). 'Estimating Population from Photographically Determined Residential Land Use Types ' Remote Sensing of Environment 3(1): 35-42. 36.Lam, N. S. (1983). 'Spatial Interpolation Methods: A Review.' The American Cartographer(2): 129-149. 37.Langford, M. (2003). Refining Methods for Dasymetric Mapping Using Satellite Remote Sensing. Remotely-Sensed Cities V. Mesev. London, Taylor & Francis: 137-156. 38.Langford, M. (2006). 'Obtaining population estimates in non-census reporting zones: An evaluation of the 3-class dasymetric method.' Computers, Environment and Urban Systems 30: 161-180. 39.Langford, M. and G. Higgs (2006 ). 'Measuring Potential Access to Primary Healthcare Services: The Influence of Alternative Spatial Representations of Population.' The Professional Geographer 58(3): 294. 40.Langford, M., D. J. Maguire, et al. (1991). The Areal Interpolation Problem: Estimating Population Using Remote Sensing in a GIS Framework. Handling Geographical Information: Methodology and Potential Applications. I. Masser and M. Blakemore. New York, Wiley: 55-77. 41.Langford, M. and D. J. Unwin (1994). 'Generating and mapping population density surface within a grographical information system.' The Cartographic Journal 31(1): 21-26. 42.Liu, X. H., P. C. Kyriakidis, et al. (2008). 'Population-density estimation using regression and area-to-point residual kriging ' International Journal of Geographical Information Science 22(4): 431-447. 43.Lo, C. P. (2003). Zone-Based Estimation of Population and Housing Units from Satellite-Generated Land Use. Remotely sensed cities. V. Mesev. London ; New York, Taylor & Francis: 157-180. 44.Maantay, J. A., A. R. Maroko, et al. (2007). 'Mapping Population Distribution in the Urban Environment: The Cadastral-based Expert Dasymetric System (CEDS) ' Cartography and Geographic Information Science 34: 77-102. 45.Martin, D. (2002). Output Areas for 2001. The census data system. P. Rees, D. Martin and P. Williamson, Wiley. 46.McCullagh, M. J. (2006). Detecting Hotspots in Time and Space ISG 47.McLafferty, S., D, D.Williamson, et al. (2000). Identifying crime hot spots using kernel smoothing. Analyzing Crime Patterns: Frontiers of Practice. P. G. Goldsmith, J. H. McGuire, Mollenkopf and T. A. Ross. Thousand Oaks, Calif., Sage: 77-85. 48.Mennis, J. (2003 ). 'Generating Surface Models of Population Using Dasymetric Mapping ' The Professional Geographer 55(1 ): 31-42. 49.Mennis, J. and T. Hultgren (2006). 'Intelligent Dasymetric Mapping and Its Application to Areal Interpolation.' Cartography and Geographic Information Science 33: 179-194. 50.Mennis, J. and T. Hultgren (2006). Intelligent Dasymetric Mapping and its Comparison to Other Areal Interpolation Techniques. AutoCarto 2006 symposium Vancouver, WA. 51.Monserud, R. A. and R. Leemans (1992). 'Comparing global vegetation maps with the Kappa statistic ' Ecological Modelling 62(4): 275-293 52.Newling, B. E. (1965). 'Urban Growth and Spatial Structure-Mathematical Models and Empirical Evidence ' Annals of the Association of American Geographers 554(4): 637-637. 53.Openshaw, S. (1984). 'The modifiable areal unit problem.' Concepts and Techniques in Modern Geography 38(41). 54.Pan, Z.-q. and G.-h. Liu (2002). 'The Research Progress of Areal Interpolation.' Progress In Geography 2002(02): 146-152. 55.Paul, B. K. (1993). 'Choropleth Map Review:A Class Exercise.' Journal of Geography 92(5): 227-230. 56.Ratcliffe, J. H. (2005). 'Detecting Spatial Movement of Intra-Region Crime Patterns Over Time.' Journal of Quantitative Criminology 21: 103-123. 57.Reibel, M. and M. E. Bufalino (2005). 'Street weighted interpolation techniques for demographic count estimation in incompatible zone systems.' Environment and Planning A 37: 127-139. 58.Relbel, M. and A. Agrawal (2005). Land Use Weighted Areal Interpolation. GIS Planet 2005 International Conference. Estoril, Portugal. 59.Shuo-sheng, W. (2006). Incorporating GIS Building Data and Census Housing Statistics for Sub-Block Population Estimation. 2006 Summer Assembly UNIVERSITY CONSORTIUM FOR GEOGRAPHIC INFORMATION SCIENCE. 60.Shuo-sheng, W., Q. Xiaomin, et al. (2005 ). 'Population Estimation Methods in GIS and Remote Sensing: A Review.' GIScience and Remote Sensing 42(1): 58-74. 61.Silverman, B. W. (1986). Density estimation for statistics and data analysis. London, Chapman and Hall. 62.Su, M. D., M. C. Lin, et al. (2010). 'Multi-layer multi-class dasymetric mapping to estimate population distribution.' Science of the Total Environment 408(20): 4807-4816. 63.Tapp, A. F. (2010). 'Areal Interpolation and Dasymetric Mapping Methods Using Local Ancillary Data Sources.' Cartography and Geographic Information Science 37: 215-228. 64.Tiebei, L. and C. Jonathan (2010). Testing dasymetric techniques to spatially disaggregate regional population forecasts for South East Queensland, Urban Research Program 1-22. 65.Tobler, W. R. (1979). 'Smooth Pycnophylactic Interpolation for Geographical Regions.' Journal of the American Statistical Association 74(367): 519-530. 66.Unwin, D. J. (1996 ). 'GIS, spatial analysis and spatial statistics.' Progress in Human Geography 20(4): 540-551. 67.Weber, C. (1994). Per-zone Classification of Urban Land Use Cover for Urban Population Estimation. Environmental Remote Sensing from Regional to Global Scales. G. M. Foody and P. J. Curran. New York, Wiley: 142-148. 68.Webster, C. J. (1996). 'Population and Dwelling Unit Estimation from Space.' Third World Planning Review 18(2): 155-176. 69.Weichselbaum, J., F. P.-. Monteferri, et al. (2005). Sharpening Census Information in GIS to Meet Real-World Conditions – The Case for Earth Observation. Sustainable Development and Planning II. W. I. o. T. C.A. BREBBIA, UK and A. KUNGOLOS, University of Thessaly, Greece 143. 70.Wikipedia (2010). Choropleth map. Wikipedia Encyclopedia. 71.Worton, B. J. (1989). 'Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies.' Ecology 70: 164-168. 72.Wright, J. K. (1936). 'A Method of Mapping Densities of Population: With Cape Cod as an Example.' Geographical Review 26: 103-110. 73.Wu, S. s., X. Qiu, et al. (2005). 'Population Estimation Methods in GIS and Remote Sensing: A Review ' GIScience & Remote Sensing 4: 80-96. 74.Xie, Y. (1995 ). 'The Overlaid Network Algoithms for Areal Interpolation Problem.' Computers, environment and urban systems 19: 287-306. 75.Yew, Y., R. M. Smith, et al. (1997). 'Remodelling Census Population with Spatial Informationfrom Landsat TM imagery.' Computers Environment and Urban Systems 21 245-258. 76.Yuan, Y., R. M. Smith, et al. (1997). 'Remodeling census population with spatial information from LandSat TM imagery ' Computers, Environment and Urban Systems 21(3-4): 245-258. 77.內政部 (1997). '國土利用現況調查報告書.' 78.內政部國土測繪中心 (2008). 96年國土利用調查成果推廣教育訓練講義, 內政部國土測繪中心. 79.台灣地理資訊學會 (2008 ). 國土資訊系統統計區建置計畫整體規劃暨試作. 台北, 內政部統計處. 80.交通部運輸研究所 (2008). '交通路網數值地圖使用說明手冊.' 81.行政院主計處 (2002). 中華民國八十九年臺閩地區戶口及住宅普查綜合報告. 台北, 行政院主計處. 82.周學政 (2005). 社經人文地理資料庫整合應用. 台北, 台北市政府民政局. 83.林建元和李萬凱 (2005). '國土計畫與GIS的運用.' 國土資訊系統通訊 55. 84.許閔婷 (2009). 環境資源及社經資料網格地理資料庫之建置. 生物環境系統工程學系, 臺灣大學. 碩士. 85.郭芳慈 (2005). 水田對週邊環境空氣溫度之影響評估. 生物環境系統工程學系. 台北, 國立臺灣大學. 碩士. 86.廖浩鈞 (2006). 以QuickBird 高解析度遙測影像以分類法判釋南二高邊坡保護工程之研究. 資源工程工程學系. 台南, 國立成功大學. 碩士. 87.臺北市政府. (2009). '臺北市地理區位.' from http://edb.epa.gov.tw/localenvdb/TaipeiCity/first.asp?admip=TaipeiCity&item=location&theme=blue. 88.臺北市政府都市發展局 (2007). '全市發展狀況.' http://www.udd.taipei.gov.tw/planweb/FiRoger/Whole_City.htm. 89.臺北縣民政局 (2007). '歷年人口統計.' http://www.ris.tpc.gov.tw/_file/1392/SG/24964/38064.html. 90.臺北縣政府資訊服務站. (2009). '臺北縣地理區位.' from http://edb.epa.gov.tw/localenvdb/TaipeiCounty/first.asp?admip=TaipeiCounty&item=location&theme=blue. 91.劉亞蘭 (2008). 濁水溪南岸民居類型與地理條件關連性之研究. 空間設計系. 雲林縣, 國立雲林科技大學. 碩士. 92.謝心怡 (2007). 多層多類別之人口地理分布模式. 生物環境系統工程學系, 國立臺灣大學. 碩士. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/34551 | - |
| dc.description.abstract | 詳細正確的人口資料與人口的空間分布是相關決策的重要依據，但因涉及隱私保護因素，一般多將人口資料加總統計後再以行政區界發布，易產生分區單元不夠細緻、空間型態扭曲或因行政邊界變動無法進行跨時間的時序性分析等問題。且於災害發生時，因受災範圍與行政單元邊界不一致或橫跨數個行政區，不易得知正確的受災人數，雖可用面積內差法推估受災人數，但實際人口分布與面積內差下的區域之均勻分布假設有所矛盾，故如何合理重分配加總的人口資料是極為重要的課題。
本研究所建立之多層多類分區密度(multi-layer multi-class dasymetric model, MLMCD)的人口重分配模式，是以加總的人口資料為基礎，重建人口空間分布趨勢，可以合理將大區域（如縣市）加總之人口資料重分配至小網格單元。MLMCD是根據輔助資料進行分層與分類，層間具有從屬關係，層內有類別之分類，需要以社經資料決定各類別之人口重分配權重。研究中以臺北縣、市為例，將縣市的總人口重新分配至網格，利用統計區分類系統中的村里、最小統計區與網格之空間單元進行誤差比較，除了以統計的絕對平均誤差(mean absolute percentage error, MAPE)評估人口重分配的正確性外，進一步利用誤差矩陣(error matrix)及Kappa指標比較人口重分配後的空間分布與原始資料的空間分布是否一致，用來評估MLMCD人口重分配之正確性。 研究顯示面積內差法與MLMCD的人口推估結果，在山區呈現被高估現象，平地區域為低估，但MLMCD之高估與低估幅度較小並具有逐層改善誤差特性存在。以誤差的統計指標而言，由第零層至第三層均有逐層改善的結果：MAPE值分別由0.99降至0. 13(以網格單元為比較基準)；0.866降至0. 583(以最小統計區單元為比較基準)；0.809大幅降至0.458(以村里單元為比較基準)。有關空間分布型態之差異分析上，Kappa值由0.351提升至0.814（以網格為比較基準）;由0.669提升至0.888(以最小統計區為比較基準)，顯示以MLMCD進行人口重分配的結果與原資料之空間分布型態具有一致性。 | zh_TW |
| dc.description.abstract | Detailed and correct spatial distributions of population are the foundation for sound regional planning and management decisions. Population data are usually disseminated in aggregated form for confidentiality concerns. However, this approach of spatial aggregation may distort the original spatial pattern from the modified areal unit problem (MAUP). And the frequent changes of boundaries over time may make the across temporal analysis impossible. It is difficult to estimate population at risk in disaster because boundary of the disaster area may not coincide with the population aggregation units. Although population at risk may be estimated using areal interpolation method, errors may arise from unreasonable assumption of uniform distribution in the aggregation area. Effective algorithms to disaggregate the aggregated population into smaller spatial units get more and more important.
A Multi-Layer Multi-Class Daymetric (MLMCD) model was developed in this study to reconstruct spatial distributions from spatially aggregated population data. Ancillary data, such as remote sensing imageries, census, land use, traffic network and other infrastructure were used to disaggregate the aggregated population data into smaller grids. These disaggregated grid data were then summed up to different spatial levels for error comparisons. Mean Absolute Percentage Error (MAPE) was used to examine the effectiveness of the proposed MLMCD model in this population disaggregation process. Error matrix and Kappa Index as in remote sensing were used to compare the spatial distribution pattern using hotspot analysis. From the case study in Taipei metropolitan area, the results show the error is decreased as the layer increased and more ancillary data were used. The MAPE are significantly improved from layer 0 to layer 3. MAPE decreased from 0.99 to 0.13 (compared at grid level), from 0.866 to 0.583 (compared at census tract level) and from 0.809 to 0.458 (compared at Li administration level). Besides, the increases of Kappa indices from 0.351 to 0.814 (at grid level) and from 0.669 to 0.888 (at census tract level) shows that the proposed MLMCD model effectively preserve the spatial distribution characteristics of population in the disaggregation process. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T06:14:42Z (GMT). No. of bitstreams: 1 ntu-100-F90622002-1.pdf: 9808088 bytes, checksum: 9fd475f52e630fb1501fd435c5cfb5d4 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 目 錄
謝 誌 I 摘 要 II Abstract IV 目 錄 VI 圖 目 錄 IX 表 目 錄 XI 第一章 前言 1 1.1研究動機 1 1.2 研究目的 2 1.3 研究流程 3 第二章 文獻回顧 5 2.1點或面為基礎之內差法 5 2.1-1點為基礎的內差法 5 2.1-2面積內差法 6 2.2分區密度法 9 2.2-1二元分區密度法 10 2.2-2影像分區密度法 12 2.2-3多類別權重分區密度法 12 2.2-4街道權重法 14 2.3網格式的人口推估法與人口資料庫 17 2.3-1 Pycnophylatic Interpolation法 17 2.3-2 GPW 19 2.3-3 LandScan 23 2.4統計模型 24 2.5 誤差分析 25 2.5-1統計面向 25 2.5-2空間面向 27 2.6小結 31 第三章 研究方法 33 3.1 多層多類分區密度模式 33 3.2誤差比較 37 第四章 研究區域 38 4.1 地理環境 38 4.2地理空間單元 39 4.2-1統計區分類系統 39 4.2-2網格單元 42 4.3人口之統計資料 43 4.4人口重分配之輔助資料 44 4.4-1衛星影像 45 4.4-2普查統計 46 4.4-3國土利用調查 48 4-4.4交通路網數值圖 54 第五章 權重與實例結果 57 5.1人口重分配權重訂定 57 5.1-1 第零層與第一層權重 57 5.1-2 第二層權重 58 5.1-3 第三層權重 61 5.2各層的人口重分配結果 65 第六章 討論 70 6.1統計誤差 70 6.2空間分布型態之比較 77 6.2-1誤差百分比之空間分布 77 6.2-2熱源之空間分布 80 6.3空間分布趨勢之重要性 88 第七章 結論與建議 91 7.1 結論 91 7.2 建議 93 參考文獻 94 圖 目 錄 圖1-1研究流程圖 4 圖2-1 面積內差法示意圖 7 圖2-2 人口面量圖 8 圖2-3 人口分區密度圖 9 圖2-4 Cape Code的人口分區密度圖 11 圖2-5多類別人口推估示意圖 14 圖2-6街道權重法示意圖 15 圖2-7 Pycnophylactic 內差示意圖 18 圖2-8 不同Pycnophylactic 內差次數的結果圖 19 圖2-9 GPW推估2015年之人口密度 22 圖2-10 白天/夜晚之人口分布圖 23 圖2-11 絕對誤差百分比示意圖 26 圖2-12 建立密度圖之示意 28 圖2-13 犯罪的熱源空間分布圖 28 圖3-1 MLMCD概念示意圖 34 圖3-2 MLMCD模式之分層與分類 35 圖4-1 研究區域分布圖 39 圖4-2 統計區分類系統之架構 40 圖4-3統計區系統之空間單元 (壯圍鄉為例) 41 圖4-4研究區域之網格單元(20×20公尺) 42 圖4-5 人口資料加總至20×20米網格之空間分布圖 43 圖4-6 行政層級空間分布圖 44 圖4-7 衛星影像圖 45 圖4-8 臺北市與臺北縣之國土利用調查分布圖 53 圖4-9 研究區域之交通路網數值圖 56 圖5-1 有人居住區與國土利用調查資料空間疊合(例) 58 圖5-2 國道/快速道路之匝道口可及性分布 62 圖5-3 捷運之可及性分布 63 圖5-4 省縣至市區道路的長度與節點 63 圖5-5 研究區域之交通可及性 64 圖5-6 第零層人口分布趨勢面 65 圖5-7 SPOT影像判識之建物之分布圖 66 圖5-8 第一層人口分布趨勢面 67 圖5-9 第二層人口分布趨勢面 68 圖5-10 第三層人口分布趨勢面 69 圖6-1 網格為基礎的人口數與重分配之各層人口 72 圖6-2 最小統計區人口密度與各層結果彙整至最小統計區之人口密度圖 73 圖6-3 村里人口密度與各層結果彙整至村里之人口密度圖 74 圖6-4 MAPE與層別關係 76 圖6-5 以網格為空間單元的誤差百分比空間分布圖 77 圖6-6 以最小統計區為基礎的誤差百分比空間分布圖 78 圖6-7 以村里為基礎的誤差百分比空間分布圖 79 圖6-8 人口密度分布圖與人口熱源區 81 圖6-9 各層重分配人口與網格人口之熱源比較 82 圖6-10 最小統計區的各層重分配人口與網格人口之熱源比較 85 圖6-11 不同空間單元之加總人口重分配所產生的熱源與與網格人口熱源之比較 89 表 目 錄 表2-1 GPWv1-v3比較 20 表2-2 GPW空間輸出單元 22 表2-3誤差矩陣 29 表2-4 Kappa一致性分類表 30 表2-5 人口推估法綜整表 31 表2-6人口推估文獻彙整表 32 表4-1台閩地區戶數、人口數及平均戶量－按縣市別分 46 表4-2臺閩地區農林漁牧普查統計摘要 47 表4-3 第二次(2008年)國土利用調查分類表 49 表4-4 臺北市與臺北縣之國土利用調查統計 53 表4-5 交通路網數值地圖資料 54 表4-6 路網代碼與層級說明 55 表5-1 有人居住區之土地利用統計 59 表5-2 農林漁牧統計查詢表 59 表5-3 各層的單一網格(20×20米)內之推估人口數 69 表6-1 各層在不同空間單元之RMSE 71 表6-2 各層在不同空間單元之MAPE 75 表6-3 第零層熱源區與網格人口熱源區之誤差矩陣 82 表6-4 第一層至第三層熱源區與網格人口熱源區之誤差矩陣(網格) 83 表6-5 第零層至第三層熱源區與網格人口熱源區之誤差矩陣(最小統計區) 86 表6-6 不同空間單元之誤差分析結果 89 | |
| dc.language.iso | zh-TW | |
| dc.subject | 最小統計區 | zh_TW |
| dc.subject | 多層多類分區密度模式 | zh_TW |
| dc.subject | 重分配 | zh_TW |
| dc.subject | 網格 | zh_TW |
| dc.subject | 人口 | zh_TW |
| dc.subject | 誤差 | zh_TW |
| dc.subject | error | en |
| dc.subject | disaggregate | en |
| dc.subject | multi-layer multi-class dasymetric model (MLMCD) | en |
| dc.subject | grid | en |
| dc.subject | population | en |
| dc.title | 多層多類分區密度之空間人口重分布模式 | zh_TW |
| dc.title | A Multi-Layer and Multi-Class Dasymetric Model for Reconstructing Spatial Population Distribution | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 范毅軍,周天穎,黃文政,蔡博文,鄭欽龍,童慶斌 | |
| dc.subject.keyword | 人口,重分配,多層多類分區密度模式,網格,最小統計區,誤差, | zh_TW |
| dc.subject.keyword | population,disaggregate,multi-layer multi-class dasymetric model (MLMCD),grid,error, | en |
| dc.relation.page | 99 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-07-26 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物環境系統工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-100-1.pdf 未授權公開取用 | 9.58 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。