請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 溫在弘 | |
| dc.contributor.author | Meng-Ping Haung | en |
| dc.contributor.author | 黃夢萍 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:06:35Z | - |
| dc.date.available | 2020-08-28 | |
| dc.date.copyright | 2019-08-28 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-19 | |
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Reilly WJ (1931) The law of retail gravitation. New York: Knickerbocker Press. Huff, D. L. (1964). Defining and estimating a trading area. Journal of marketing, 28(3), 34-38.. Brunner, J. A., & Mason, J. L. (1968). The influence of driving time upon shopping center preference. Journal of Marketing, 32(2), 57-61. Dramowicz, E. (2005). Retail trade area analysis using the Huff model. Directions Magazine, Jul, 2. Baray, J., & Cliquet, G. (2007). Delineating store trade areas through morphological analysis. European Journal of Operational Research, 182(2), 886-898. Gauri, D. K., Pauler, J. G., & Trivedi, M. (2009). Benchmarking performance in retail chains: an integrated approach. Marketing Science, 28(3), 502-515. Cui, C., Wang, J., Pu, Y., Ma, J., & Chen, G. (2012). GIS-based method of delimitating trade area for retail chains. International Journal of Geographical Information Science, 26(10), 1863-1879. Hanna, R., Kreindler, G., & Olken, B. A. (2017). Citywide effects of high-occupancy vehicle restrictions: Evidence from “three-in-one” in Jakarta. Science, 357(6346), 89-93. Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home‐range studies. Ecology, 70(1), 164-168. Getz, W. M., Fortmann-Roe, S., Cross, P. C., Lyons, A. J., Ryan, S. J., & Wilmers, C. C. (2007). LoCoH: nonparameteric kernel methods for constructing home ranges and utilization distributions. PloS one, 2(2), e207. Trevor, H., Robert, T., & JH, F. (2009). The elements of statistical learning: data mining, inference, and prediction. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer. Fonti, V., & Belitser, E. (2017). Feature selection using lasso. VU Amsterdam Research Paper in Business Analytics. CSIS - Center for Spatial Information Science, The University of Tokyo. About People Flow Data. Retrieved July 8, 2019, from https://pflow.csis.u-tokyo.ac.jp/data-service/pflow-data/ Mark Li., & Zhou Bailiang. (2016). Discover the action around you with the updated Google Maps. Retrieved July 8, 2019, from https://blog.google/products/maps/discover-action-around-you-with-updated/ | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73599 | - |
| dc.description.abstract | 本研究以東京都的商圈分析做為案例研究，提出一個使用人口移動資料在空間中劃分研究單位，並且標記單位特徵的過程。研究方法使用核密度估計(Kernel Density Estimation, KDE)以及階層式分群方法(Hierarchical Clustering)逐步劃分研究單位、伽瑪混合模型(Gamma Mixture Model) 標記單位特徵。此過程得到的結果，再利用邏輯斯迴歸模型(Logistic Regression Model)解釋影響單位特徵表現的重要變數。研究結果顯示在東京都的重要幹線包含:中央線快速(JC)、武藏野線(JM)以及京葉線(JE)附近被標記為非在地商圈。此類型商圈吸引較多四十歲以上的職業人口和女性，且除了步行以外，使用各項運輸工具的比例皆較高。本研究使用新提出的方法和移動資料劃分出的研究單位，避免了主觀決定單位尺度的方式。 | zh_TW |
| dc.description.abstract | The study takes a trading area analysis for Tokyo city as a case study to propose a process for the study area to group into each unit and then label the characteristics. We used Kernel Density Estimation (KDE) and Hierarchical Clustering for the grouping phase and Gamma Mixture Model for labeling. After the process, we built the Logistic Regression model to describe the difference between a local and non-local shopping region in Tokyo city. The results of the study show that the non-local shopping regions are surrounding by several railway lines include Chūō Rapid Line (JC), Musashino Line (JM), and Keiyo Line (JE). This type of shopping region attracts more employed people with age over 40 and women. In addition to walking, the proportion of other modes of transport used is higher than in the local shopping regions. This study proposed the new method for grouping the mobility patterns of customers to delineate the boundary of the shopping districts. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:06:35Z (GMT). No. of bitstreams: 1 ntu-108-R06h41009-1.pdf: 3551824 bytes, checksum: b32301ac8d1c53eeadb0b48c40f8d1a3 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 謝 辭 i
摘 要 ii Abstract iii 第一章 緒論 1 第二章 文獻回顧 2 第三章 模型 4 3.1 階層式分群方法(Hierarchical Clustering) 4 3.2 混合模型(Mixture Model) 6 3.3 邏輯斯迴歸(Logistic Regression) 7 第四章 研究方法 8 4.1 研究架構 8 4.2 研究流程 8 4.3 資料處理 10 4.4 建立商圈分析單位 11 4.5 Mixture models建構移動距離混合模型 15 4.6 Logistic Regression探討商圈在地與否的特徵差異 17 第五章 研究結果 19 5.1 商圈單位建立 19 5.2 在地商圈與非在地商圈 22 第六章 討論與結論 31 參考文獻 32 | |
| dc.language.iso | zh-TW | |
| dc.subject | 核密度估計 | zh_TW |
| dc.subject | 商圈分析 | zh_TW |
| dc.subject | 伽瑪混合模型 | zh_TW |
| dc.subject | 階層式分群方法 | zh_TW |
| dc.subject | 邏輯斯迴歸模型 | zh_TW |
| dc.subject | Trading Area Analysis | en |
| dc.subject | Hierarchical Clustering | en |
| dc.subject | Kernel Density Estimation | en |
| dc.subject | Gamma Mixture Model | en |
| dc.subject | Logistic Regression Model | en |
| dc.title | 考慮人口移動相似性的分群：以東京都的商圈分析為例 | zh_TW |
| dc.title | Grouping with the Similarity of Human Movements: A Case Study of Trading Area Analysis in Tokyo City | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 任立中,林楨家 | |
| dc.subject.keyword | 商圈分析,核密度估計,階層式分群方法,伽瑪混合模型,邏輯斯迴歸模型, | zh_TW |
| dc.subject.keyword | Trading Area Analysis,Kernel Density Estimation,Hierarchical Clustering,Gamma Mixture Model,Logistic Regression Model, | en |
| dc.relation.page | 33 | |
| dc.identifier.doi | 10.6342/NTU201904021 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-19 | |
| dc.contributor.author-college | 共同教育中心 | zh_TW |
| dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
| 顯示於系所單位： | 統計碩士學位學程 | |
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