自Jane Jacobs於1961年提出「城市活力」的概念，如何創建有品質的城市空間與優良的生活品質成為學者們所關注的主題，換言之，城市活力即是在討論城市本身的環境如何影響到城市生活—滿足多樣的人類活動需求，從而創建良好的生活品質。在全球各大城市以追求永續發展的模式下，城市的活力可以作為判斷城市是否具有一定吸引力以及能在城市功能維度健全的依據，成為研究者探討城市永續性發展不可或缺的重要指標。
而建成環境的設計作為都市規劃者介入地區活力的重要工具，因此有必要理解建成環境對於城市活力的影響機制並作調整，另一方面，面對現今多變的都市環境，許多議題無法再由過去耗時費力的一手調查資料著手，要能夠及時掌握城市的發展動態而做出迅速且適當的反應。因此，本研究以臺北捷運淡水信義線為研究範圍，利用餐飲業POI資料來衡量城市活力，並以線性迴歸模型分析建成環境之密度、多樣性、設施品質與地景特性對城市活力的影響。
彙整研究成果發現，城市活力的空間分布與實際發展情況吻合，城市活力之高點多位於現今之商業繁榮中心區，如中山雙連站、信義安和站與國父紀念館站，是合適的衡量指標。進一步，本研究以多元線性迴歸模式分析建成環境與城市活力之關係，並根據城市活力可能會在空間以及時間分布上呈現差異的特性，將樣本以建成環境差異、時間態樣差異區分討論，結果顯示以時間態樣區分的結果有較好的預測能力。而在建成環境之密度面向，對於促進城市活力有很好的幫助，越密集的街區與建物使用，有促進城市活力作用；而在多樣性面向，沿街的商業使用比例越高，對促進活力有幫助，優勢度結果也顯示，越高強度的單一土地使用，對於活力有負面的影響；在地景面向，舒適的綠蔭與良好的街道圍閉空間，對於促進城市活力有積極作用；在設施品質面向，由於研究結果沒有明顯的模式，因此其對於促進城市活力的作用仍有待查驗。
本研究透過新興大數據來衡量城市活力，檢視建成環境在促進活力的影響成效，並理解如何運用新型態的資料來幫助規劃政策，期能對未來活力營造提供更多元的參考依據。

Urban vitality is discussing how the environment of the city itself affects urban life—satisfying the needs of diverse human activities to create a good quality of life is an indispensable factor for the sustainable development of cities, so how to create quality urban space and richness Activities become an important theme
In this study, the catering POI data is used to measure the urban vitality and the linear regression model is used to analyze the impact of the built environment on the urban vitality. The results show that the spatial distribution of the urban vitality measured by POI data is consistent with the actual development situation, which is a suitable measurement indicator. Furthermore, from the perspective of the impact of the built environment, density and diversity are important elements, and urban vitality is heterogeneous in space and time.
This research uses new types of data to measure urban vitality, examining the impact of the built environment in promoting vitality. It will provide more diversity references for future planning guideline and vitality creation.

一、英文文獻
1. Ameli, S. H., Hamidi, S., Garfinkel-Castro, A., & Ewing, R. (2015). Do Better Urban Design Qualities Lead to More Walking in Salt Lake City, Utah? Journal of urban design, 20(3), 393-410.
2. Batty, M. (2016). Empty buildings, shrinking cities and ghost towns. Environment and Planning B: Planning and Design, 43(1), 3-6.
3. Bivina, G. R., Gupta, A., & Parida, M. (2019). Influence of microscale environmental factors on perceived walk accessibility to metro stations. Transportation Research Part D: Transport and Environment, 67, 142-155.
4. boyd, d., & Crawford, K. (2012). CRITICAL QUESTIONS FOR BIG DATA. Information, Communication & Society, 15(5), 662-679.
5. Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199-219.
6. Delclòs-Alió, X., & Miralles-Guasch, C. (2018). Looking at Barcelona through Jane Jacobs’s eyes: Mapping the basic conditions for urban vitality in a Mediterranean conurbation. Land Use Policy, 75, 505-517.
7. Dovey, K., & Pafka, E. (2014). The urban density assemblage: Modelling multiple measures. URBAN DESIGN International, 19(1), 66-76.
8. Ewing, R., & Cervero, R. (2001). Travel and the Built Environment: A Synthesis. Transportation Research Record, 1780(1), 87-114.
9. Ewing, R., & Cervero, R. (2010). Travel and the Built Environment. Journal of the American Planning Association, 76(3), 265-294.
10. Ewing, R., & Hamidi, S. (2014). Measuring urban sprawl and validating sprawl measures. Washington, DC: National Institutes of Health and Smart Growth America.
11. Ewing, R., & Handy, S. (2009). Measuring the Unmeasurable: Urban Design Qualities Related to Walkability. Journal of urban design, 14(1), 65-84.
12. Fuentes, L., Miralles-Guasch, C., Truffello, R., Delclòs-Alió, X., Flores, M., & Rodríguez, S. (2020). Santiago de Chile through the Eyes of Jane Jacobs. Analysis of the Conditions for Urban Vitality in a Latin American Metropolis. Land, 9(12), 498.
13. García-Palomares, J. C., Salas-Olmedo, M. H., Moya-Gómez, B., Condeço-Melhorado, A., & Gutiérrez, J. (2018). City dynamics through Twitter: Relationships between land use and spatiotemporal demographics. Cities, 72, 310-319.
14. Gehl, J. (2011). Life between buildings: using public space. Island press.
15. Grant, J. (2002). Mixed Use in Theory and Practice: Canadian Experience with Implementing a Planning Principle. Journal of the American Planning Association, 68(1), 71-84.
16. Guo, X., Chen, H., & Yang, X. (2021). An Evaluation of Street Dynamic Vitality and Its Influential Factors Based on Multi-Source Big Data. ISPRS International Journal of Geo-Information, 10(3), 143.
17. Handy, S. L., Boarnet, M. G., Ewing, R., & Killingsworth, R. E. (2002). How the built environment affects physical activity: Views from urban planning. American Journal of Preventive Medicine, 23(2, Supplement 1), 64-73.
18. He, Q., He, W., Song, Y., Wu, J., Yin, C., & Mou, Y. (2018). The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data’. Land Use Policy, 78, 726-738.
19. Hox, J. J., Moerbeek, M., & Van de Schoot, R. (2017). Multilevel analysis: Techniques and applications. Routledge.
20. Jacobs-Crisioni, C., Rietveld, P., Koomen, E., & Tranos, E. (2014). Evaluating the Impact of Land-Use Density and Mix on Spatiotemporal Urban Activity Patterns: An Exploratory Study Using Mobile Phone Data. Environment and Planning A: Economy and Space, 46(11), 2769-2785.
21. Jacobs, J. (1961). The death and life of great American cities.
22. Jiang, S., Alves, A., Rodrigues, F., Ferreira, J., & Pereira, F. C. (2015). Mining point-of-interest data from social networks for urban land use classification and disaggregation. Computers, Environment and Urban Systems, 53, 36-46.
23. Jin, X., Long, Y., Sun, W., Lu, Y., Yang, X., & Tang, J. (2017). Evaluating cities' vitality and identifying ghost cities in China with emerging geographical data. Cities, 63, 98-109.
24. Kang, C.-D. (2015). The effects of spatial accessibility and centrality to land use on walking in Seoul, Korea. Cities, 46, 94-103.
25. Kang, C.-D. (2017). Measuring the effects of street network configurations on walking in Seoul, Korea. Cities, 71, 30-40.
26. Kang, C.-D. (2020). Effects of the Human and Built Environment on Neighborhood Vitality: Evidence from Seoul, Korea, Using Mobile Phone Data. Journal of Urban Planning and Development, 146(4), 05020024.
27. Kang, C., Fan, D., & Jiao, H. (2020). Validating activity, time, and space diversity as essential components of urban vitality. Environment and Planning B: Urban Analytics and City Science, 2399808320919771.
28. Kim, S., Park, S., & Lee, J. S. (2014). Meso- or micro-scale? Environmental factors influencing pedestrian satisfaction. Transportation Research Part D: Transport and Environment, 30, 10-20.
29. Kim, Y.-L. (2018). Seoul's Wi-Fi hotspots: Wi-Fi access points as an indicator of urban vitality. Computers, Environment and Urban Systems, 72, 13-24.
30. Lan, F., Gong, X., Da, H., & Wen, H. (2020). How do population inflow and social infrastructure affect urban vitality? Evidence from 35 large- and medium-sized cities in China. Cities, 100, 102454.
31. Lee, C., & Moudon, A. V. (2006). The 3Ds+R: Quantifying land use and urban form correlates of walking. Transportation Research Part D: Transport and Environment, 11(3), 204-215.
32. Li, S., Wu, C., Lin, Y., Li, Z., & Du, Q. (2020). Urban Morphology Promotes Urban Vibrancy from the Spatiotemporal and Synergetic Perspectives: A Case Study Using Multisource Data in Shenzhen, China. Sustainability, 12(12).
33. Liu, J., Li, J., Li, W., & Wu, J. (2016). Rethinking big data: A review on the data quality and usage issues. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 134-142.
34. Liu, S., Zhang, L., Long, Y., Long, Y., & Xu, M. (2020). A New Urban Vitality Analysis and Evaluation Framework Based on Human Activity Modeling Using Multi-Source Big Data. ISPRS International Journal of Geo-Information, 9(11).
35. Long, Y., & Huang, C. C. (2017). Does block size matter? The impact of urban design on economic vitality for Chinese cities. Environment and Planning B: Urban Analytics and City Science, 46(3), 406-422.
36. Lu, S., Shi, C., & Yang, X. (2019). Impacts of Built Environment on Urban Vitality: Regression Analyses of Beijing and Chengdu, China. International journal of environmental research and public health, 16(23).
37. Lu, Y. (2019). Using Google Street View to investigate the association between street greenery and physical activity. Landscape and Urban Planning, 191, 103435.
38. Lynch, K. (1982). A Theory of Good City Form. Mit Press,, C1981 1982.
39. Maas, P. R. (1984). Towards a theory of urban vitality University of British Columbia].
40. Malizia, E., & Motoyama, Y. (2016). The Economic Development–Vibrant Center Connection: Tracking High-Growth Firms in the DC Region. The Professional Geographer, 68(3), 349-355.
41. Martín, A., Julián, A. B. A., & Cos-Gayón, F. (2019). Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain). Cities, 86, 37-50.
42. McCluskey, J. (1979). Road form and townscape / Jim McCluskey. Architectural Press.
43. Meng, Y., & Xing, H. (2019). Exploring the relationship between landscape characteristics and urban vibrancy: A case study using morphology and review data. Cities, 95, 102389.
44. Montgomery, J. (1998). Making a city: Urbanity, vitality and urban design. Journal of urban design, 3(1), 93-116.
45. Moughtin, C., & Mertens, M. (2003). Urban Design: Street and Square. Architectural Press.
46. Nicodemus, A. G. (2013). Fuzzy vibrancy: Creative placemaking as ascendant US cultural policy. Cultural Trends, 22(3-4), 213-222.
47. Niu, H., & Silva, E. A. (2020). Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods. Journal of Urban Planning and Development, 146(2), 04020007.
48. Niu, H., & Silva Elisabete, A. (2020). Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods. Journal of Urban Planning and Development, 146(2), 04020007.
49. Park, K., Ewing, R., Sabouri, S., & Larsen, J. (2019). Street life and the built environment in an auto-oriented US region. Cities, 88, 243-251.
50. Saelens, B. E., & Handy, S. L. (2008). Built environment correlates of walking: a review. Medicine and Science in Sports and Exercise, 40(7 Suppl), S550-S566.
51. Sulis, P., Manley, E., Zhong, C., & Batty, M. (2018). Using mobility data as proxy for measuring urban vitality. Journal of Spatial Information Science, 2018.
52. Sung, H.-G., Go, D.-H., & Choi, C. G. (2013). Evidence of Jacobs’s street life in the great Seoul city: Identifying the association of physical environment with walking activity on streets. Cities, 35, 164-173.
53. Sung, H., & Lee, S. (2015). Residential built environment and walking activity: Empirical evidence of Jane Jacobs’ urban vitality. Transportation Research Part D: Transport and Environment, 41, 318-329.
54. Talen, E., & Koschinsky, J. (2013). The Walkable Neighborhood: A Literature Review. Int. J. Sustain. Land Use Urban Plan., 1, 42-63.
55. Tang, J., & Long, Y. (2019). Measuring visual quality of street space and its temporal variation: Methodology and its application in the Hutong area in Beijing. Landscape and Urban Planning, 191, 103436.
56. Tang, L., Lin, Y., Li, S., Li, S., Li, J., Ren, F., & Wu, C. (2018). Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data. Sustainability, 10(12), 4565.
57. Tribby, C. P., Miller, H. J., Brown, B. B., Werner, C. M., & Smith, K. R. (2016). Analyzing walking route choice through built environments using random forests and discrete choice techniques. Environment and Planning B: Urban Analytics and City Science, 44(6), 1145-1167.
58. Tu, W., Zhu, T., Xia, J., Zhou, Y., Lai, Y., Jiang, J., & Li, Q. (2020). Portraying the spatial dynamics of urban vibrancy using multisource urban big data. Computers, Environment and Urban Systems, 80, 101428.
59. Wu, C., Ye, X., Ren, F., & Du, Q. (2018). Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China. Cities, 77, 104-116.
60. Wu, J., Ta, N., Song, Y., Lin, J., & Chai, Y. (2018). Urban form breeds neighborhood vibrancy: A case study using a GPS-based activity survey in suburban Beijing. Cities, 74, 100-108.
61. Wu, L., Zhi, Y., Sui, Z., & Liu, Y. (2014). Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data. PLOS ONE, 9(5), e97010.
62. Xia, C., Yeh, A. G.-O., & Zhang, A. (2020). Analyzing spatial relationships between urban land use intensity and urban vitality at street block level: A case study of five Chinese megacities. Landscape and Urban Planning, 193, 103669.
63. Xu, X., Xu, X., Guan, P., Ren, Y., Wang, W., & Xu, N. (2018). The Cause and Evolution of Urban Street Vitality under the Time Dimension: Nine Cases of Streets in Nanjing City, China. Sustainability, 10(8).
64. Ye, Y., Li, D., & Liu, X. (2018). How block density and typology affect urban vitality: an exploratory analysis in Shenzhen, China. Urban Geography, 39(4), 631-652.
65. Yue, W., Chen, Y., Thy, P. T. M., Fan, P., Liu, Y., & Zhang, W. (2021). Identifying urban vitality in metropolitan areas of developing countries from a comparative perspective: Ho Chi Minh City versus Shanghai. Sustainable Cities and Society, 65, 102609.
66. Yue, W., Chen, Y., Zhang, Q., & Liu, Y. (2019). Spatial Explicit Assessment of Urban Vitality Using Multi-Source Data: A Case of Shanghai, China. Sustainability, 11(3).
67. Yue, Y., Zhuang, Y., Yeh, A. G. O., Xie, J.-Y., Ma, C.-L., & Li, Q.-Q. (2017). Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy. International Journal of Geographical Information Science, 31(4), 658-675.
68. Zarin, S. Z., Niroomand, M., & Heidari, A. A. (2015). Physical and Social Aspects of Vitality Case Study: Traditional Street and Modern Street in Tehran. Procedia - Social and Behavioral Sciences, 170, 659-668.
69. Zeng, C., Song, Y., He, Q., & Shen, F. (2018). Spatially explicit assessment on urban vitality: Case studies in Chicago and Wuhan. Sustainable Cities and Society, 40, 296-306.
70. Zhang, A., Li, W., Wu, J., Lin, J., Chu, J., & Xia, C. (2020). How can the urban landscape affect urban vitality at the street block level? A case study of 15 metropolises in China. Environment and Planning B Planning and Design.
71. Zhang, L., Zhang, R., & Yin, B. (2021). The impact of the built-up environment of streets on pedestrian activities in the historical area. Alexandria Engineering Journal, 60(1), 285-300.
72. Zhang, Z., Xiao, Y., Luo, X., & Zhou, M. (2020). Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps-based analysis of Wuhan, China. Growth and Change, 51(1), 505-529.
73. Zhen, F., Cao, Y., Qin, X., & Wang, B. (2017). Delineation of an urban agglomeration boundary based on Sina Weibo microblog ‘check-in’ data: A case study of the Yangtze River Delta. Cities, 60, 180-191.
二、中文文獻
1. 白仁德、劉人華. (2014)。大眾運輸導向建成環境特性對捷運運量影響之研究－以臺北捷運為實證對象。建築與規劃學報，15(2&3)，111-127。
2. 李舒媛(2018) .。以悠遊卡大數據探討YouBike租賃及轉乘捷運之使用者行為。淡江大學運輸管理學系運輸科學系碩士論文。
3. 葉佳靈(2011)。都市商業街道之型態研究。國立成功大學都市計劃系碩士論文。
4. 葉奕新(2017)。臺北捷運系統之人潮移動分析。中國統計學報，55(2)，69-95。
5. 鄭雨桐(2016)。建成環境對公共自行車使用之影響。國立臺灣大學地理環境資源學系碩士論文。
6. 郭建佑（2006）。台北市的街道生產及空間經驗之研究—以忠孝復興商圈、天母商圈、南京西路商圈為例。淡江大學建築學系碩士論文。
7. 王晉元、盧宗成、李晟豪、陳其華、吳東凌、陳翔捷(2019)。手機信令資料探勘於改善觀光旅客公共運輸服務之研究－以花蓮縣臺灣好行路線為例。運輸計劃季刊，48(2)，105-131。
8. 內政部(2021)。電信信令人口統計。