都會區域空間結構的組成是由人類活動於一定範圍內所形成的空間互動關係，在經歷外部環境長期不斷變化的影響下，從而形成穩定的城際生活圈。過去研究常基於二元論將區域劃分為城市或鄉村，除了容易造成城與鄉產生社會、經濟、文化、基礎設施等方面的隔閡與差異外，基於城鄉界線已逐漸模糊，城市與鄉村從空間上很難進行界定與劃分。因此，由城際間的人口活動來界定都會區域下的空間互動關係，透過大數據揭示人口流動的強度與特徵，識別都會區域下的空間結構與活動類型，用以劃定城際生活圈的空間範圍與活動分佈。而考量到COVID-19事件對互動關係造成關鍵性的影響，遂而成為探究區域尺度下空間互動關係之契機，透過比較疫情爆發前後的空間結構與活動特徵，以釐清城際生活圈的結構穩定性與活動必要性。
基於城市與鄉村的互動關係已漸趨於整合，本研究之目的為解析城鄉空間互動關係，並選擇北部都會區域為實證地區，以解析城際生活圈之組成與活動特徵，以解析城際生活圈之組成與活動特徵。研究架構上，以高速公路交流道旅次起迄數據資料為基礎，運用Louvain社群檢測分析來識別車流旅次流向和強度，以劃定不同的生活圈範圍，並以交流道出入口分時流量以及尖峰時段活動特徵進行k-means集群分析，分析各生活圈下的活動特徵與分佈，掌握各生活圈內不同的空間互動關係。最後，透過比較疫情爆發前後生活圈差異及其在網絡中所扮演的角色、定位的差異和變化，以解釋城鄉空間互動下之實質關係。
研究結果顯示，本研究之分析架構將北部都會區域劃分為3個生活圈與10種活動類型：生活圈包含北北基桃宜多核心區域綜合生活圈、北桃竹苗區域住工生活圈及竹竹桃單核心地區工作導向生活圈；生活圈內部的活動類型則可分為居住導向型、工作導向型、住工混合型、住工偏居住型、住工偏工作型、綜合型、綜合偏居住型、綜合偏工作型、核心綜合型及其他型。另外，本研究證實疫情爆發後生活圈的空間結構產生階段性的影響與調整，可以發現北北基桃宜及北桃竹苗生活圈的空間結構集中化且橫跨範圍減少，但新增了2個空間結構零散、跨越多個縣市的生活圈，空間結構僅發生小幅度變化則表示互動關係穩定。而活動類型則大多僅剩居住及工作類型存在，從而反映出生活圈的必要性關係。透過城鄉空間互動關係的釐清，可協助規劃者從城鄉整合思維進行區域規劃外，以提供在我國各級國土空間計畫策略研擬或資源投入順序之參考。

The integration of urban and rural areas has resulted in the creation of metropolitan areas, which symbolize the connection between urban and rural regions. The development of big data has allowed for the use of population data to identify the spatial structures and activity characteristics of intercity living spheres. This understanding is crucial for examining the interaction between urban and rural areas.
The purpose of this study is to analyze the spatial interaction between urban and rural areas, with a specific focus on the northern metropolitan area as the empirical area. The study will analyze both pre- and post-outbreak periods for research purposes. The research data will be collected from Electronic Toll Collection big data and analyzed using the Louvain community detection and k-means clustering techniques. This analysis will help identify living spheres and identify patterns of internal activity. This study will compare the spatial structures and activity characteristics pre- and post-outbreak.
The research findings suggest that the northern metropolitan area can be divided into three distinct living spheres, each characterized by ten types of activities. These activities encompass residential living, work, and various other aspects. After the outbreak, it was observed that living spheres became more centralized, and their spatial coverage decreased. The activities within these living spheres primarily focused on essential tasks, such as residential and work-related activities. This information is valuable for regional planners when prioritizing resource allocation in regional planning.

外文參考文獻
Adam, A., Delvenne, J. C., & Thomas, I. (2018). Detecting communities with the multi-scale Louvain method: robustness test on the metropolitan area of Brussels [Article]. Journal of Geographical Systems, 20(4), 363-386. https://doi.org/10.1007/s10109-018-0279-0
Afsar, R. (1999). Rural-urban dichotomy and convergence: emerging realities in Bangladesh [Article]. Environment and Urbanization, 11(1), 235-246. https://doi.org/10.1177/095624789901100106
Alexander, J. T., Andersen, R., Cookson, P. W., Edin, K., Fisher, J., Grusky, D. B., . . . Varner, C. (2017). A Qualitative Census of Rural and Urban Poverty [Article]. Annals of the American Academy of Political and Social Science, 672(1), 143-161. https://doi.org/10.1177/0002716217714156
Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots [Article]. Accident Analysis and Prevention, 41(3), 359-364. https://doi.org/10.1016/j.aap.2008.12.014
Aslam, N. S., Ibrahim, M. R., Cheng, T., Chen, H. F., & Zhang, Y. (2021). ActivityNET: Neural networks to predict public transport trip purposes from individual smart card data and POIs [Article]. Geo-Spatial Information Science, 24(4), 711-721. https://doi.org/10.1080/10095020.2021.1985943
Bae, S. H., Halperin, D., West, J. D., Rosvall, M., & Howe, B. (2017). Scalable and Efficient Flow-Based Community Detection for Large-Scale Graph Analysis [Article]. Acm Transactions on Knowledge Discovery from Data, 11(3), 30, Article 32. https://doi.org/10.1145/2992785
Baffoe, G. (2020). Rural-urban studies: A macro analyses of the scholarship terrain [Article]. Habitat International, 98, 11, Article 102156. https://doi.org/10.1016/j.habitatint.2020.102156
Bao, H. Q., Ming, D. P., Guo, Y., Zhang, K., Zhou, K. Q., & Du, S. G. (2020). DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data [Article]. Remote Sensing, 12(7), 26, Article 1088. https://doi.org/10.3390/rs12071088
Batabyal, A., Higano, Y., & Nijkamp, P. (2020). Introduction to "Rural-Urban
Dichotomies and Spatial Development in
Asia" [Article]. Rochester Institute of Technology.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks [Article]. Journal of Statistical Mechanics-Theory and Experiment, 12, Article P10008. https://doi.org/10.1088/1742-5468/2008/10/p10008
Boratinskii, V., Loopmans, M., & Osaragi, T. (2023). Reshuffling city life: spatial and functional dynamics of urban activity in Tokyo during COVID-19. International Journal of Urban Sciences. https://doi.org/10.1080/12265934.2023.2172065
Borsdorf, A. (2022). Postsuburban sprawl in European Cities Challenges for European urban policy and research in the 21st century [Review]. Urbanization and the Global Environment, 181-198.
Carolina, P. C., Ine, V., Philippe, A. J., Sjoerdje, V. H., Ricardo, R. B., Claudio, B., . . . Carlo, L. (2022). Urban-rural interactions and their territorial disparities. European Commission.
Cui, H., Wu, L., Hu, S., Lu, R., & Wang, S. (2020). Recognition of Urban Functions and Mixed Use Based on Residents’ Movement and Topic Generation Model: The Case of Wuhan, China. Remote Sensing, 12(18), 2889. https://doi.org/10.3390/rs12182889
Dandekar, H. C., & Hibbard, M. (2016). Rural issues in urban planning: current trends and reflections. International Planning Studies, 21(3), 225-229. https://doi.org/10.1080/13563475.2016.1185007
De Araujo, A., Marcos Do Valle, J., & Cacho, N. (2013, 2020). Geographic Feature Engineering with Points-of-Interest from OpenStreetMap.
De Araujo, A., Marcos Do Valle, J., & Cacho, N. (2020, 2020). Geographic Feature Engineering with Points-of-Interest from OpenStreetMap.
Delgado-Viñas, C., & Gómez-Moreno, M.-L. (2022). The Interaction between Urban and Rural Areas: An Updated Paradigmatic, Methodological and Bibliographic Review. Land, 11(8), 1298. https://doi.org/10.3390/land11081298
Doernberg, A., & Weith, T. (2021). Urban-Rural Interrelations—A Challenge for Sustainable Land Management. In (pp. 101-124). Springer International Publishing. https://doi.org/10.1007/978-3-030-50841-8_6
Dominguez, A., Sierra, H. E., & Ballesteros, N. C. (2021). Regional Spatial Structure and Land Use: Evidence from Bogota and 17 Municipalities [Article]. Land, 10(9), 22, Article 908. https://doi.org/10.3390/land10090908
Dong, Q. F., Chen, D. K., & Wang, T. (2019). Urban community structure detection based on the OD of traffic analysis zones [Article]. Modern Physics Letters B, 33(13), 13, Article 1950164. https://doi.org/10.1142/s0217984919501641
Dong, R. X., & Yan, F. Y. (2021). Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China [Article]. Land, 10(11), 20, Article 1144. https://doi.org/10.3390/land10111144
Duvernoy, I., Zambon, I., Sateriano, A., & Salvati, L. (2018). Pictures from the other side of the fringe: Urban growth and peri-urban agriculture in a post-industrial city (Toulouse, France) [Article]. Journal of Rural Studies, 57, 25-35. https://doi.org/10.1016/j.jrurstud.2017.10.007
Esch, T., Marconcini, M., Marmanis, D., Zeidler, J., Elsayed, S., Metz, A., . . . Dech, S. (2014). Dimensioning urbanization - An advanced procedure for characterizing human settlement properties and patterns using spatial network analysis [Review]. Applied Geography, 55, 212-228. https://doi.org/10.1016/j.apgeog.2014.09.009
Fu, C., Tu, X. Q., & Huang, A. (2021). Identification and Characterization of Production-Living-Ecological Space in a Central Urban Area Based on POI Data: A Case Study for Wuhan, China [Article]. Sustainability, 13(14), 25, Article 7691. https://doi.org/10.3390/su13147691
Fung, P. K. (2019). InfoFlow: A Distributed Algorithm to Detect Communities According to the Map Equation [Article]. Big Data and Cognitive Computing, 3(3), 11, Article 42. https://doi.org/10.3390/bdcc3030042
Furno, A., Faouzi, N.-E. E., Sharma, R., & Zimeo, E. (2021). Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks. Plos One, 16(3), e0248764. https://doi.org/10.1371/journal.pone.0248764
Gajic, A., Krunic, N., & Protic, B. (2021). Classification of Rural Areas in Serbia: Framework and Implications for Spatial Planning [Article]. Sustainability, 13(4), 15, Article 1596. https://doi.org/10.3390/su13041596
Gao, S., Liu, Y., Wang, Y. L., & Ma, X. J. (2013). Discovering Spatial Interaction Communities from Mobile Phone Data [Article]. Transactions in Gis, 17(3), 463-481. https://doi.org/10.1111/tgis.12042
Gebre, T., & Gebremedhin, B. (2019). The mutual benefits of promoting rural-urban interdependence through linked ecosystem services [Article]. Global Ecology and Conservation, 20, 14, Article e00707. https://doi.org/10.1016/j.gecco.2019.e00707
Gould, W. T. S. (1982). Rural-Urban Interaction in the Third World. JOURNAL ARTICLE, Vol. 14, No. 4 (1982), p. 334 (1 page)(Area). https://doi.org/10.2307/20001852
Guirao, B., Campa, J. L., & Casado-Sanz, N. (2018). Labour mobility between cities and metropolitan integration: The role of high speed rail commuting in Spain [Article]. Cities, 78, 140-154. https://doi.org/10.1016/j.cities.2018.02.008
Haghbayan, S. A., Geroliminis, N., & Akbarzadeh, M. (2021). Community detection in large scale congested urban road networks. Plos One, 16(11), Article e0260201. https://doi.org/10.1371/journal.pone.0260201
HaifengNiu, & A.Silva, E. (2021). Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London. Computers, Environment and Urban Systems, Volume 88.
Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns [Article]. Cartography and Geographic Information Science, 41(3), 260-271. https://doi.org/10.1080/15230406.2014.890072
He, Q. S., He, W. S., Song, Y., Wu, J. Y., Yin, C. H., & Mou, Y. C. (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. https://doi.org/10.1016/j.landusepol.2018.07.020
He, Y. H., Zhou, G. H., Tang, C. L., Fan, S. G., & Guo, X. S. (2019). The spatial organization pattern of urban-rural integration in urban agglomerations in China: An agglomeration-diffusion analysis of the population and firms [Article]. Habitat International, 87, 54-65. https://doi.org/10.1016/j.habitatint.2019.04.003
Hu, Y. F., & Han, Y. Q. (2019). Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone [Article]. Sustainability, 11(5), 15, Article 1385. https://doi.org/10.3390/su11051385
Huang, J., Levinson, D., Wang, J. O., & Jin, H. T. (2019). Job-worker spatial dynamics in Beijing: Insights from Smart Card Data [Article]. Cities, 86, 83-93. https://doi.org/10.1016/j.cities.2018.11.021
Huang, W. J. (2022). Repositioning the rural in city-regionalism through a locally based functional analysis [Article]. Regional Studies, 56(5), 770-781. https://doi.org/10.1080/00343404.2021.1932791
Jana Zscheischler, T. W., Christian Strauß, Nadin Gaasch, Klaus Müller, Institut für Sozioökonomie am Leibniz-Zentrum für Agrarlandschaftsforschung. (2012). Urban-rurale Verflechtungen - Analytische Zugänge und Governance-Diskurs.
Jang, W., & Yao, X. B. (2011). Interpolating Spatial Interaction Data [Article]. Transactions in Gis, 15(4), 541-555. https://doi.org/10.1111/j.1467-9671.2011.01273.x
Jiaqi, C. (2021). Measurement and Characteristics of Employment Centers in Shenzhen: A Study Using Enterprise Survey Data in 2017. Journal of Landscape Research, 13(3), 51-56,62. https://doi.org/https://doi.org/10.16785/j.issn1943-989x.2021.3.012
Ke, W. Q., Chen, W., & Yu, Z. Y. (2017). Uncovering Spatial Structures of Regional City Networks from Expressway Traffic Flow Data: A Case Study from Jiangsu Province, China [Article]. Sustainability, 9(9), 16, Article 1541. https://doi.org/10.3390/su9091541
Kong, X. J., Xia, F., Ma, K., Li, J. X., & Yang, Q. Y. (2019). Discovering Transit-Oriented Development Regions of Megacities Using Heterogeneous Urban Data [Article]. Ieee Transactions on Computational Social Systems, 6(5), 943-955. https://doi.org/10.1109/tcss.2019.2919960
Lange, A., Piorr, A., Siebert, R., & Zasada, I. (2013). Spatial differentiation of farm diversification: How rural attractiveness and vicinity to cities determine farm households' response to the CAP [Article]. Land Use Policy, 31, 136-144. https://doi.org/10.1016/j.landusepol.2012.02.010
Leisz, S. J., Rounds, E., An, N. T., Yen, N. T. B., Bang, T. N., Douangphachanh, S., & Ninchaleune, B. (2016). Telecouplings in the East-West Economic Corridor within Borders and Across [Article]. Remote Sensing, 8(12), 30, Article 1012. https://doi.org/10.3390/rs8121012
Li, J., Zheng, P. Q., & Zhang, W. N. (2020). Identifying the spatial distribution of public transportation trips by node and community characteristics [Article]. Transportation Planning and Technology, 43(3), 325-340. https://doi.org/10.1080/03081060.2020.1735776
Li, W., Zhou, M., & Dong, H. R. (2020). Classifications of Stations in Urban Rail Transit based on the Two-step Cluster. Intelligent Automation and Soft Computing, 26(3), 531-538. https://doi.org/10.32604/iasc.2020.013930
Li, Y. H., Westlund, H., & Liu, Y. S. (2019). Why some rural areas decline while some others not: An overview of rural evolution in the world [Article]. Journal of Rural Studies, 68, 135-143. https://doi.org/10.1016/j.jrurstud.2019.03.003
Lichter, D. T., & Brown, D. L. (2011). Rural America in an Urban Society: Changing Spatial and Social Boundaries. In K. S. Cook & D. S. Massey (Eds.), Annual Review of Sociology, Vol 37 (Vol. 37, pp. 565-592). Annual Reviews. https://doi.org/10.1146/annurev-soc-081309-150208
Lichter, D. T., & Ziliak, J. P. (2017). The Rural-Urban Interface: New Patterns of Spatial Interdependence and Inequality in America [Article]. Annals of the American Academy of Political and Social Science, 672(1), 6-25. https://doi.org/10.1177/0002716217714180
Lin, R. (2021). 城市新興數據、空間策略與城市治理的探索. 建築師.
Liu, C., Chen, L., Yuan, Q., Wu, H. B., & Huang, W. (2022). Revealing Dynamic Spatial Structures of Urban Mobility Networks and the Underlying Evolutionary Patterns [Article]. Isprs International Journal of Geo-Information, 11(4), 23, Article 237. https://doi.org/10.3390/ijgi11040237
Liu, K., Murayama, Y., & Ichinose, T. (2020). Using a new approach for revealing the spatiotemporal patterns of functional urban polycentricity: A case study in the Tokyo metropolitan area [Article]. Sustainable Cities and Society, 59, 16, Article 102176. https://doi.org/10.1016/j.scs.2020.102176
Liu, X., Jiao, P. F., Yuan, N., & Wang, W. J. (2016). Identification of multi-attribute functional urban areas under a perspective of community detection: A case study [Article]. Physica a-Statistical Mechanics and Its Applications, 462, 827-836. https://doi.org/10.1016/j.physa.2016.06.106
Lu, C., Pang, M., Zhang, Y., Li, H., Lu, C., Tang, X., & Cheng, W. (2020). Mapping Urban Spatial Structure Based on POI (Point of Interest) Data: A Case Study of the Central City of Lanzhou, China. Isprs International Journal of Geo-Information, 9(2), 92. https://doi.org/10.3390/ijgi9020092
Lu, Y. G., & Giuliano, G. (2023). Where do people meet? Time-series clustering for social interaction levels in daily-life spaces during the COVID-19 pandemic. Cities, 137, Article 104298. https://doi.org/10.1016/j.cities.2023.104298
Ma, X. L., Liu, C. C., Wen, H. M., Wang, Y. P., & Wu, Y. J. (2017). Understanding commuting patterns using transit smart card data [Article]. Journal of Transport Geography, 58, 135-145. https://doi.org/10.1016/j.jtrangeo.2016.12.001
Marafon, G. J., Fortes, G. B., & Seabra, R. (2021). COUNTRY-CITY AND RURAL-URBAN RELATIONS IN THE TWENTIETH-FIRST CENTURY [Article]. Geo Uerj(38), 21, Article e57686. https://doi.org/10.12957/geouerj.2021.57686
Ndegwa, E. (2004). The concept, practice and related issues of the urban-rural linkage method. n Proceedings of the International Conference on Improving Urban-Rural Links and Coordinating Economic Development.
Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks [Article]. Physical Review E, 69(2), 15, Article 026113. https://doi.org/10.1103/PhysRevE.69.026113
OECD. (2013). Rural-urban partnerships: An integrated approach to economic development.
Ouwehand, W. M., van Oort, F. G., & Cortinovis, N. (2022). Spatial structure and productivity in European regions [Article]. Regional Studies, 56(1), 48-62. https://doi.org/10.1080/00343404.2021.1950912
Ovaska, U., Vihinen, H., Oostindie, H., Farinos, J., Hrabar, M., Kilis, E., . . . Vulto, H. (2021). Network Governance Arrangements and Rural-Urban Synergy [Article]. Sustainability, 13(5), 16, Article 2952. https://doi.org/10.3390/su13052952
Pagliacci, F. (2017). MEASURING EU URBAN-RURAL CONTINUUM THROUGH FUZZY LOGIC [Article]. Tijdschrift Voor Economische En Sociale Geografie, 108(2), 157-174. https://doi.org/10.1111/tesg.12201
Peters, D. J. (2020). Community Susceptibility and Resiliency to COVID-19 Across the Rural-Urban Continuum in the United States. Journal of Rural Health, 36(3), 446-456. https://doi.org/10.1111/jrh.12477
Preston, D. A. (1975). Rural-Urban and Inter-Settlement Interaction: Theory and Analytical Structure. Area, Vol. 7, No. 3 (1975), pp. 171-174 (4 pages).
Qiao, T., Shan, W., Yu, G., & Liu, C. (2018). A Novel Entropy-Based Centrality Approach for Identifying Vital Nodes in Weighted Networks. Entropy, 20(4), 261. https://doi.org/10.3390/e20040261
Rae, A. (2009). From spatial interaction data to spatial interaction information? Geovisualisation and spatial structures of migration from the 2001 UK census. Computers, Environment and Urban Systems, 33(3), 161-178.
Rao, C. J., & Gao, Y. (2022). Evaluation Mechanism Design for the Development Level of Urban-Rural Integration Based on an Improved TOPSIS Method. Mathematics, 10(3), Article 380. https://doi.org/10.3390/math10030380
Ratti, C. (2015). Special Interview With Carlo Ratti On Senseable City Lab MIT. United States Architecture News.
Rita, L. (2020). Louvain Algorithm.
Rocchini. (2012). A random geometric graph, one of the simplest models of spatial network.
Rodríguez, H. (2016). Comparison of bus network structures versus urban dispersion : a monocentric analytical approach : evidences from Barcelona's bus network. Computer Science.
Roy, J. R., & Thill, J. C. (2004). Spatial interaction modelling. Papers in Regional Science, 83(1), 339-361. https://doi.org/10.1007/s10110-003-0189-4
Rozenblat, C. (2020). Extending the concept of city for delineating large urban regions (LUR) for the cities of the world.
Sari Aslam, N., Zhu, D., Cheng, T., Ibrahim, M. R., & Zhang, Y. (2021). Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London. Annals of GIS, 27(1), 29-41. https://doi.org/10.1080/19475683.2020.1783359
Seto, K. C., Reenberg, A., Boone, C. G., Fragkias, M., Haase, D., Langanke, T., . . . Simon, D. (2012). Urban land teleconnections and sustainability [Article]. Proceedings of the National Academy of Sciences of the United States of America, 109(20), 7687-7692. https://doi.org/10.1073/pnas.1117622109
Shi, Y., & Yang, J. (2018). Application of Big Data in the Study of Urban Spatial Structures. In (pp. 255-272). Springer International Publishing. https://doi.org/10.1007/978-3-319-51929-6_14
Smith, G., Archer, R., Nandwani, D., & Li, J. W. (2018). Impacts of urbanization: diversity and the symbiotic relationships of rural, urban, and spaces in-between [Article]. International Journal of Sustainable Development and World Ecology, 25(3), 276-289. https://doi.org/10.1080/13504509.2017.1383321
Stead, D. (2002). Urban-Rural Relationships in the West of England. Built Environment, Vol. 28, No. 4, Urban-Rural Relationships (2002), pp. 299-310 (12 pages).
Stewart, C. T. (1958). THE URBAN-RURAL DICHOTOMY - CONCEPTS AND USES [Article]. American Journal of Sociology, 64(2), 152-158. https://doi.org/10.1086/222422
Tacoli, C. (1998). Rural-urban interactions: a guide to the literature [Review]. Environment and Urbanization, 10(1), 147-166. https://doi.org/10.1177/095624789801000105
Tao, R., Gong, Z., Ma, Q., & Thill, J.-C. (2020). Boosting Computational Effectiveness in Big Spatial Flow Data Analysis with Intelligent Data Reduction. Isprs International Journal of Geo-Information, 9(5), 299. https://doi.org/10.3390/ijgi9050299
Toger, M., Kourtit, K., Nijkamp, P., & Osth, J. (2021). Mobility during the COVID-19 Pandemic: A Data-Driven Time-Geographic Analysis of Health-Induced Mobility Changes. Sustainability, 13(7), Article 4027. https://doi.org/10.3390/su13074027
Traag, V. A., Waltman, L., & Van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-41695-z
Tu, W., Cao, J. Z., Yue, Y., Shaw, S. L., Zhou, M., Wang, Z. S., . . . Li, Q. Q. (2017). Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns [Article]. International Journal of Geographical Information Science, 31(12), 2331-2358. https://doi.org/10.1080/13658816.2017.1356464
Urso, G. (2021). Metropolisation and the challenge of rural-urban dichotomies [Article]. Urban Geography, 42(1), 37-57. https://doi.org/10.1080/02723638.2020.1760536
van Vliet, J., Birch-Thomsen, T., Gallardo, M., Hemerijckx, L. M., Hersperger, A. M., Li, M. M., . . . van Rompaey, A. (2020). Bridging the rural-urban dichotomy in land use science [Article]. Journal of Land Use Science, 15(5), 585-591. https://doi.org/10.1080/1747423x.2020.1829120
Vilcek, A. (2014). Deep Learning with K-Means Applied to Community Detection in Networks. Computer Science.
Vizzari, M., & Sigura, M. (2015). Landscape sequences along the urban-rural-natural gradient: A novel geospatial approach for identification and analysis [Article]. Landscape and Urban Planning, 140, 42-55. https://doi.org/10.1016/j.landurbplan.2015.04.001
Wang, P., Qi, M. N., Liang, Y. J., Ling, X. B., & Song, Y. (2019). Examining the Relationship between Environmentally Friendly Land Use and Rural Revitalization Using a Coupling Analysis: A Case Study of Hainan Province, China [Article]. Sustainability, 11(22), 19, Article 6266. https://doi.org/10.3390/su11226266
Wang, Y., Chen, X. H., Sun, P. J., Liu, H., & He, J. X. (2021). Spatial-temporal Evolution of the Urban-rural Coordination Relationship in Northeast China in 1990-2018 [Article]. Chinese Geographical Science, 31(3), 429-443. https://doi.org/10.1007/s11769-021-1202-z
Wang, Z., Ma, D., Sun, D., & Zhang, J. (2021). Identification and analysis of urban functional area in Hangzhou based on OSM and POI data. Plos One, 16(5), e0251988. https://doi.org/10.1371/journal.pone.0251988
Wataru Nakanishi, H. Y., Daisuke Fukuda. (2018). Feature Extraction of Inter-Region Travel Pattern Using Random Matrix Theory and Mobile Phone Location Data. Transportation Research Procedia.
Wei, S., & Wang, L. (2021). Community detection, road importance assessment, and urban function pattern recognition: a big data approach [Article; Early Access]. Journal of Spatial Science, 21. https://doi.org/10.1080/14498596.2021.1936669
Wu, C., Smith, D., & Wang, M. S. (2021). Simulating the urban spatial structure with spatial interaction: A case study of urban polycentricity under different scenarios [Article]. Computers Environment and Urban Systems, 89, 13, Article 101677. https://doi.org/10.1016/j.compenvurbsys.2021.101677
Xu, D. D., Zhang, X. D., Zhang, X. H., & Yu, Y. G. (2022). Type Identification of Land Use in Metro Station Area Based on Spatial-Temporal Features Extraction of Human Activities. Sustainability, 14(20), Article 13122. https://doi.org/10.3390/su142013122
Yang, X. P., Fang, Z. X., Yin, L., Li, J. Y., Zhou, Y., & Lu, S. W. (2018). Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China [Article]. Sustainability, 10(5), 14, Article 1435. https://doi.org/10.3390/su10051435
Yap, M., Luo, D., Cats, O., Van Oort, N., & Hoogendoorn, S. (2019). Where shall we sync? Clustering passenger flows to identify urban public transport hubs and their key synchronization priorities. Transportation Research Part C: Emerging Technologies, 98, 433-448. https://doi.org/10.1016/j.trc.2018.12.013
Ye, C. (2021). Rural–Urban Relations: Concepts, Methods and Theories. In (pp. 25-55). Springer Singapore. https://doi.org/10.1007/978-981-16-1201-5_2
Yinv, W. (2015, 2015). A Large Scale Social Network Community Detection Method Based on Probability Model.
Yuan, N. J., Zheng, Y., & Xie, X. (2018). Discovering Functional Zones in a City Using Human Movements and Points of Interest. In J. C. Thill (Ed.), Spatial Analysis and Location Modeling in Urban and Regional Systems (pp. 33-62). Springer-Verlag Berlin. https://doi.org/10.1007/978-3-642-37896-6_3
Zasada, I. (2011). Multifunctional peri-urban agriculture-A review of societal demands and the provision of goods and services by farming [Review]. Land Use Policy, 28(4), 639-648. https://doi.org/10.1016/j.landusepol.2011.01.008
Zhang, Y. L., Xia, X. W., Xu, X., Yu, F., Wu, H. R., Yu, Y., & Wei, B. (2020). Robust Hierarchical Overlapping Community Detection With Personalized PageRank [Article]. Ieee Access, 8, 102867-102882. https://doi.org/10.1109/access.2020.2998860
Zhang, Y. R., Marshall, S., & Manley, E. (2021). Understanding the roles of rail stations: Insights from network approaches in the London metropolitan area [Article]. Journal of Transport Geography, 94, 11, Article 103110. https://doi.org/10.1016/j.jtrangeo.2021.103110
Zhen, Q. (2019). Quantitative Identification of Urban Functional Areas in Downtown Area of Changchun Based on POI Data. IOP Conference Series: Earth and Environmental Science, 330(5), 052001. https://doi.org/10.1088/1755-1315/330/5/052001
Zhong, B. L., Liu, T. B., Chiu, H. F. K., Chan, S. S. M., Hu, C. Y., Hu, X. F., . . . Caine, E. D. (2013). Prevalence of psychological symptoms in contemporary Chinese rural-to-urban migrant workers: an exploratory meta-analysis of observational studies using the SCL-90-R [Article]. Social Psychiatry and Psychiatric Epidemiology, 48(10), 1569-1581. https://doi.org/10.1007/s00127-013-0672-4
Zhong, C., Arisona, S. M., Huang, X. F., Batty, M., & Schmitt, G. (2014). Detecting the dynamics of urban structure through spatial network analysis [Article]. International Journal of Geographical Information Science, 28(11), 2178-2199. https://doi.org/10.1080/13658816.2014.914521
Zhong, C., Manley, E., Arisona, S. M., Batty, M., & Schmitt, G. (2015). Measuring variability of mobility patterns from multiday smart-card data [Article; Proceedings Paper]. Journal of Computational Science, 9, 125-130. https://doi.org/10.1016/j.jocs.2015.04.021
Zhong, Y. F., Zhu, Q. Q., & Zhang, L. P. (2015). Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery [Article]. Ieee Transactions on Geoscience and Remote Sensing, 53(11), 6207-6222. https://doi.org/10.1109/tgrs.2015.2435801
Zhou, C. S., He, X., Wu, R. N., & Zhang, G. J. (2022). Using Food Delivery Data to Identify Urban -Rural Areas: A Case Study of Guangzhou, China [Article]. Frontiers in Earth Science, 10, 11, Article 860361. https://doi.org/10.3389/feart.2022.860361
Zhou, J. Z., Hou, Q. H., & Dong, W. T. (2019). Spatial Characteristics of Population Activities in Suburban Villages Based on Cellphone Signaling Analysis [Article]. Sustainability, 11(7), 19, Article 2159. https://doi.org/10.3390/su11072159
Zhou, J. Z., Hou, Q. H., Fan, X. Y., & Du, Y. (2019). Village-Town System in Suburban Areas Based on Cellphone Signaling Mining and Network Hierarchy Structure Analysis [Article]. Ieee Access, 7, 128579-128592. https://doi.org/10.1109/access.2019.2939504
Zhou, M., Yue, Y., Li, Q. Q., & Wang, D. G. (2016). Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach [Article]. Isprs International Journal of Geo-Information, 5(12), 21, Article 240. https://doi.org/10.3390/ijgi5120240
Zhu, C. M., Zhang, X. L., Wang, K., Yuan, S. F., Yang, L. X., & Skitmore, M. (2020). Urban-rural construction land transition and its coupling relationship with population flow in China's urban agglomeration region [Article]. Cities, 101, 10, Article 102701. https://doi.org/10.1016/j.cities.2020.102701
 
中文參考文獻
介姿淇. (2022). 以ETC交通資料探討臺灣都會通勤圈住業失衡現象 國立臺灣師範大學]. 台北市. https://hdl.handle.net/11296/b2y7g4
內政部營建署城鄉發展分署. (2008). 北臺都會區域計畫 2030(草案).
尹芹, Qin, Y., 孟斌, 张丽英, Bin, M., & Liying, Z. (2016). 基于客流特征的北京地铁站点类型识别. 地理科学进展, 35(1), 126-134. https://doi.org/10.18306/dlkxjz.2016.01.014
李俊昇. (1991). 都會區域空間結構發展分析--應用Q-analysis方法 國立交通大學]. 新竹市. https://hdl.handle.net/11296/e4x92s
李琦華, & 林峰田. (2008). 台灣聚落的空間結構與社會脈絡研究 [A Study on Spatial Structure and Social Culture of Taiwanese Settlements]. 建築學報(65), 45-62. https://doi.org/10.6377/ja.200809.0045
杨静，吴可，张红亮，代盛旭，王亦乐. (2021). 基于土地利用及客流特征的地铁车站分类. 交通运输系统工程与信息.
秦萧, 甄峰, & 熊丽芳. (2014). 大数据时代城市时空间行为研究方法. 地理科学进展.
馬小毅, 劉. 陳. (2013). 城市軌道交通網絡化客流特徵及成長規律——基於北京滬綜深城市軌道交通網絡客流數據分析. 城市交通.
陳建忠. (1980). 都會空間結構變遷之研究 文化大學]. 台北市. https://hdl.handle.net/11296/tqvk62
楊仁豪. (2005). 地理空間結構變遷下台灣行政區劃調整之研究 國立政治大學]. 台北市. https://hdl.handle.net/11296/682vja
楊鑫. (2020). 臺灣鄉村地區發展特性分類之研究—以雲林及嘉義縣為例 國立成功大學]. 台南市. https://hdl.handle.net/11296/a9qmea
劉可泰. (1977). 都會區空間結構模型之研究-以台北都會區為例 國立中興大學]. 台中市. https://hdl.handle.net/11296/g7mfxp
歐政煌. (2017). 以城鄉比較探討世代交流與共同住宅之研究 國立臺北科技大學]. 台北市. https://hdl.handle.net/11296/pc6tfm
謝宏昌. (2007). 規劃“都會區域”：一個概念的考察 [Metropolitan-Regional Plan: An Appraisal of the Concept's Career]. 都市與計劃, 34(3), 273-291. https://doi.org/10.6128/cp.34.3.273
顏郁慈. (2022). 探討多元⼤數據下的都市空間結構 - 以臺北市為例 國立成功大學]. 台南市. https://hdl.handle.net/11296/k8dnpk