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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 溫在弘 | zh_TW |
| dc.contributor.advisor | Tzai-Hung Wen | en |
| dc.contributor.author | 陳立恆 | zh_TW |
| dc.contributor.author | Li-Heng Chen | en |
| dc.date.accessioned | 2023-02-01T17:04:13Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-02-01 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2023-01-16 | - |
| dc.identifier.citation | Gardy, M. A., & Bradley, J. (2019). Hypothesis Test for the Modifiable Areal Unit Problem.
Tuson, M., Yap, M., Kok, M. R., Boruff, B., Murray, K., Vickery, A., ... & Whyatt, D. (2020). Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps. International journal of health geographics, 19(1), 1-18. Jelinski, D. E., & Wu, J. (1996). The modifiable areal unit problem and implications for landscape ecology. Landscape ecology, 11(3), 129-140. Gehlke, C. E., & Biehl, K. (1934). Certain effects of grouping upon the size of the correlation coefficient in census tract material. Journal of the American Statistical Association, 29(185A), 169-170. Openshaw, S. (1984). Ecological fallacies and the analysis of areal census data. Environment and planning A, 16(1), 17-31. Duque, J. C., Laniado, H., & Polo, A. (2018). S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem. PloS one, 13(11), e0207377. Openshow, S. (1979). A million or so correlation coefficients, three experiments on the modifiable areal unit problem. Statistical applications in the spatial science, 127-144. Dark, S. J., & Bram, D. (2007). The modifiable areal unit problem (MAUP) in physical geography. Progress in Physical Geography, 31(5), 471-479. Lee, S. I., Lee, M., Chun, Y., & Griffith, D. A. (2019). Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation: A simulation study. International Journal of Geographical Information Science, 33(6), 1135-1154. Swift, A., Liu, L., & Uber, J. (2008). Reducing MAUP bias of correlation statistics between water quality and GI illness. Computers, Environment and Urban Systems, 32(2), 134-148. Arbia, G., & Petrarca, F. (2011). Effects of MAUP on spatial econometric models. Letters in Spatial and Resource Sciences, 4(3), 173-185. Openshaw, S. (1977). A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modelling. Transactions of the institute of british geographers, 459-472. Clark, A., & Scott, D. (2014). Understanding the impact of the modifiable areal unit problem on the relationship between active travel and the built environment. Urban Studies, 51(2), 284-299. Aydin, O., Janikas, M. V., Assunção, R. M., & Lee, T. H. (2021). A quantitative comparison of regionalization methods. International Journal of Geographical Information Science, 35(11), 2287-2315. Assunção, R. M., Neves, M. C., Câmara, G., & da Costa Freitas, C. (2006). Efficient regionalization techniques for socio‐economic geographical units using minimum spanning trees. International Journal of Geographical Information Science, 20(7), 797-811. Guo, D. (2008). Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). International Journal of Geographical Information Science, 22(7), 801-823. Xiao, J. (2021). Spatial aggregation entropy: a heterogeneity and uncertainty metric of spatial aggregation. Annals of the American Association of Geographers, 111(4), 1236-1252. Fotheringham, A. S., & Sachdeva, M. (2022). Scale and local modeling: new perspectives on the modifiable areal unit problem and Simpson’s paradox. Journal of Geographical Systems, 1-25. Kim, K. Y. (2011). Effects of the modifiable areal unit problem (MAUP) on a spatial interaction model. Journal of the Korean Geographical Society, 46(2), 197-211. Cliff, A. D., & Haggett, P. (1970). On the efficiency of alternative aggregations in region-building problems. Environment and Planning A, 2(3), 285-294. Dao, T. H. D., & Thill, J. C. (2018). Detecting attribute-based homogeneous patches using spatial clustering: a comparison test. In Information Fusion and Intelligent Geographic Information Systems (IF&IGIS'17) (pp. 37-54). Springer, Cham. Openshaw, S., & Rao, L. (1995). Algorithms for reengineering 1991 Census geography. Environment and planning A, 27(3), 425-446. Bian, L., & Butler, R. (1999). Comparing effects of aggregation methods on statistical and spatial properties of simulated spatial data. Photogrammetric engineering and remote sensing, 65, 73-84. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83249 | - |
| dc.description.abstract | 空間分析中常利用聚合資料的方法來去除資料的不確定性並做後續的分析，然而聚合的結果不同進而導致空間現象在統計解讀的不同，其為Modifiable area unit problem (MAUP) 中分區效果的影響。如何控制MAUP儼然成為重要議題。近年來，研究指出Spatial aggregation entropy (SAE) 可做為衡量MAUP的統計量。然而，目前SAE多用於檢視區域分區受MAUP影響的結果而未應用於最適分群方法，且SAE對於相同變異不同平均的組別，有估計上的差異。因此本研究欲將以SAE進行改善後，做為目標函數並提出新的演算法 Searching Neighbors and Aggregating algorithm (SNA)，目的是在給定分組數下計算最小異質性損失量的空間劃分。為了衡量演算法的表現，本研究模擬12種不同空間型態之資料與臺灣中部鄉鎮區域之收入平均與變異係數，應用於不同演算法中，討論其整體分組與各組組內均質程度以評估演算法的表現。結果顯示，SNA的表現與自相關的程度有關。在高度自相關時，SNA整體表現較其他演算法都來的好，中度自相關時，該演算法在分組數小於6時表現較佳。而低度自相關時該演算法則表現較差。本研究以改良後的SAE作為目標函數控制MAUP的效果，與過往分群方法比較，討論分群結果在統計量上的表現，並提供單變量在特定尺度下，最小異質性損失量的空間劃分結果。 | zh_TW |
| dc.description.abstract | Aggregation is typically used to smooth the noise in data for spatial analysis. Different aggregating methods may lead to various zoning schemes, and the statistical outcome may differ owing to the zoning effect of the modifiable area unit problem (MAUP). The spatial aggregation entropy (SAE) method has been proposed to measure the MAUP effect. However, SAE is used to verify the MAUP effect using aggregated data and has not been applied for optimal zoning. This study aims to correct SAE and takes it as the objective function to propose a new algorithm, Searching Neighbors and Aggregating (SNA). The performance of this algorithm is tested via various aggregating method using 12 types of spatial pattern "single-core" hypothetical data and individual income tax data of middle Taiwan. The results show that SNA's performance is related to spatial autocorrelation (SA). Specifically, SNA's performance is the best when SA is high (0.9), or SA is approximately 0.5 and the number of groups is less than six. Overall, this study corrects the SAE formula and provides a new algorithm that returns the optimal zoning, thereby preserving most spatial heterogeneity at a specific scale. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-02-01T17:04:13Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-02-01T17:04:13Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 中文摘要 I
Abstract II Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Purpose 5 Chapter 2 Literature Review 6 2.1 The Measurement of MAUP 6 2.2 Spatial Optimal Regionalization Algorithm 8 2.2.1 Iterative-Based Algorithm 9 2.2.2 Graph-Based Algorithm 11 2.3 Summary 13 Chapter 3 Data and Methods 15 3.1 Searching Neighbors and Aggregating Algorithm 15 3.1.1 Spatial Aggregation Entropy 15 3.1.2 Modified Spatial Aggregation Entropy 18 3.1.3 SNA Procedure 20 3.2 Experiment Data 30 3.2.1 Hypothetical Data 30 3.2.2 Real Case Example 34 3.3 Research Framework 37 Chapter 4 Results 39 4.1 Comparison between MSAE and SAE 39 4.2 Comparison of Experiment Data 42 4.2.1 Performance in Total Zonal Error 42 4.2.2 Performance with Different Areas (Total Zonal Error) 46 4.2.3 Performance on Variance Zonal Error 48 4.2.4 Performance with Different Areas (Variance of Zonal Error) 51 4.2.5 Real Case Example 53 Chapter 5 Discussion 59 5.1 Performance on Total Zonal Error 59 5.2 Performance on Variance of Zonal Error 62 5.3 Performance with Real Data 63 5.4 Suggestions 64 5.5 Limitation 65 Chapter 6 Conclusion 66 Chapter 7 Reference 67 Chapter 8 Appendix 70 | - |
| dc.language.iso | en | - |
| dc.subject | 分區效果 | zh_TW |
| dc.subject | 空間數據挖掘 | zh_TW |
| dc.subject | MAUP | zh_TW |
| dc.subject | 空間劃分 | zh_TW |
| dc.subject | 空間分群演算法 | zh_TW |
| dc.subject | Segregation | en |
| dc.subject | MAUP | en |
| dc.subject | Regionalization | en |
| dc.subject | Spatial cluster algorithm | en |
| dc.subject | spatial data mining | en |
| dc.title | 最小化空間異質性損失之分群演算法 | zh_TW |
| dc.title | An Optimal Zoning Algorithm For Preserving of Spatial Heterogeneity | en |
| dc.title.alternative | An Optimal Zoning Algorithm For Preserving of Spatial Heterogeneity | - |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 鄭克聲 | zh_TW |
| dc.contributor.coadvisor | Ke-Sheng Cheng | en |
| dc.contributor.oralexamcommittee | 林楨家;余清祥 | zh_TW |
| dc.contributor.oralexamcommittee | Jen-Jia Lin;Qing-Xiang Yu | en |
| dc.subject.keyword | MAUP,分區效果,空間分群演算法,空間劃分,空間數據挖掘, | zh_TW |
| dc.subject.keyword | MAUP,Regionalization,Spatial cluster algorithm,spatial data mining,Segregation, | en |
| dc.relation.page | 73 | - |
| dc.identifier.doi | 10.6342/NTU202300092 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-01-17 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 地理環境資源學系 | - |
| 顯示於系所單位： | 地理環境資源學系 | |
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