The multiplex relations between cities: a lexicon-based approach to detect urban systems

REFERENCES

• An, D., Tong, X., Liu, K., & Chan, E. (2019). Understanding the impact of built environment on metro ridership using open source in Shanghai. Cities, 93, 177–187. https://doi.org/10.1016/j.cities.2019.05.013
   Web of Science ®Google Scholar
• Baeza-Yates, R., Blanco, R., & Castellanos, M. (2016). Web text mining. In R. Mitkov (Ed.), The Oxford handbook of computational linguistics (2nd ed., pp. 1013–1030). Oxford University Press.
   Google Scholar
• Baker, P., Gabrielatos, C., Khosravinik, M., Krzyżanowski, M., McEnery, T., & Wodak, R. (2008). A useful methodological synergy? Combining critical discourse analysis and corpus linguistics to examine discourses of refugees and asylum seekers in the UK press. Discourse and Society, 19(3), 273–306. https://doi.org/10.1177/0957926508088962
   Web of Science ®Google Scholar
• Baker, P., & Vessey, R. (2018). A corpus-driven comparison of English and French Islamist extremist texts. International Journal of Corpus Linguistics, 23(3), 255–278. https://doi.org/10.1075/ijcl.17108.bak
   Web of Science ®Google Scholar
• Baxter, L., & Montgomery, B. (1996). Relating: Dialogues and dialectics. Guilford.
   Google Scholar
• Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3(Feb), 1137–1155. https://www.iro.umontreal.ca/~vincentp/Publications/lm_jmlr.pdf.
   Google Scholar
• Berroir, S., Cattan, N., Dobruszkes, F., Guérois, M., Paulus, F., & Vacchiani-Marcuzzo, C. (2017). Les systèmes urbains français: Une approche relationnelle. Cybergeo: European Journal of Geography, Document, 807. https://doi.org/10.4000/cybergeo.35587
   Google Scholar
• Biber, D., Johansson, S., Leech, G., Conrad, S., Finegan, E., & Quirk, R. (1999). Longman grammar of spoken and written English (Vol 2). Longman.
   Google Scholar
• Burger, M., de Goei, B., Van der Laan, L., & Huisman, F. (2011). Heterogeneous development of metropolitan spatial structure: Evidence from commuting patterns in English and Welsh city-regions, 1981–2001. Cities, 28(2), 160–170. https://doi.org/10.1016/j.cities.2010.11.006
   Web of Science ®Google Scholar
• Burger, M., Meijers, E., & Van Oort, F. (2014a). Multiple perspectives on functional coherence: Heterogeneity and multiplexity in the Randstad. Tijdschrift voor Economische en Sociale Geografie, 105(4), 444–464. https://doi.org/10.1111/tesg.12061
   Web of Science ®Google Scholar
• Burger, M., Van der Knaap, B., & Wall, R. (2014b). Polycentricity and the multiplexity of urban networks. European Planning Studies, 22(4), 816–840. https://doi.org/10.1080/09654313.2013.771619
   Web of Science ®Google Scholar
• Callon, M., Courtial, J., Turner, W., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235. https://doi.org/10.1177/053901883022002003
   Web of Science ®Google Scholar
• Cao, Z., Peng, Z., & Derudder, B. (2021). Interurban scientific collaboration networks across Chinese city-regions. Environment and Planning A: Economy and Space, 53(1), 6–8. https://doi.org/10.1177/0308518X20938381
   Web of Science ®Google Scholar
• Capello, R. (2000). The city network paradigm: Measuring urban network externalities. Urban Studies, 37(11), 1925–1945. https://doi.org/10.1080/713707232
   Web of Science ®Google Scholar
• Cardoso, R., & Meijers, E. (2016). Contrasts between first-tier and second-tier cities in Europe: A functional perspective. European Planning Studies, 24(5), 996–1015. https://doi.org/10.1080/09654313.2015.1120708
   Web of Science ®Google Scholar
• Chew, C., & Eysenbach, G. (2010). Pandemics in the age of twitter: Content analysis of tweets during the 2009 H1N1 outbreak. PloS ONE, 5(11), e14118. https://doi.org/10.1371/journal.pone.0014118
   PubMed Web of Science ®Google Scholar
• Culpeper, J. (2009). Keyness: Words, parts-of-speech and semantic categories in the character-talk of Shakespeare’s Romeo and Juliet. International Journal of Corpus Linguistics, 14(1), 29–59. https://doi.org/10.1075/ijcl.14.1.03cul
   Web of Science ®Google Scholar
• Davoudi, S. (2008). Conceptions of the city-region: A critical review. Proceedings of the Institution of Civil Engineers – Urban Design and Planning, 161(2), 51–60. https://doi.org/10.1680/udap.2008.161.2.51
   Google Scholar
• Derudder, B., & Neal, Z. (2018). Uncovering links between urban studies and network science. Networks and Spatial Economics, 18(3), 441–446. https://doi.org/10.1007/s11067-019-09453-w
   Web of Science ®Google Scholar
• Derudder, B., & Taylor, P. J. (2018). Central flow theory: Comparative connectivities in the world-city network. Regional Studies, 52(8), 1029–1040. https://doi.org/10.1080/00343404.2017.1330538
   Web of Science ®Google Scholar
• Derudder, B., Taylor, P., Witlox, F., & Catalano, G. (2003). Hierarchical tendencies and regional patterns in the world city network: A global urban analysis of 234 cities. Regional Studies, 37(9), 875–886. https://doi.org/10.1080/0034340032000143887
   Web of Science ®Google Scholar
• Derudder, B., & Witlox, F. (2005). An appraisal of the use of airline data in assessing the world city network: A research note on data. Urban Studies, 42(13), 2371–2388. https://doi.org/10.1080/00420980500379503
   Web of Science ®Google Scholar
• Devriendt, L., Boulton, A., Brunn, S., Derudder, B., & Witlox, F. (2011). Searching for cyberspace: The position of major cities in the information age. Journal of Urban Technology, 18(1), 73–92. https://doi.org/10.1080/10630732.2011.578410
   Web of Science ®Google Scholar
• Devriendt, L., Derudder, B., & Witlox, F. (2008). Cyberplace and cyberspace: Two approaches to analyzing digital intercity linkages. Journal of Urban Technology, 15(2), 5–32. https://doi.org/10.1080/10630730802401926
   Web of Science ®Google Scholar
• Fanelli, D., Costas, R., & Larivière, V. (2015). Misconduct policies, academic culture and career stage, not gender or pressures to publish, affect scientific integrity. PloS ONE, 10(6), e0127556. https://doi.org/10.1371/journal.pone.0127556
   PubMed Web of Science ®Google Scholar
• Gregory, I., Cooper, D., Hardie, A., & Rayson, P. (2015). Spatializing and analyzing digital texts: Corpora, GIS and places. Spatial Narratives and Deep Maps, 150–178. https://e-space.mmu.ac.uk/579357/. https://doi.org/10.2307/j.ctt1zxxzr2.11
   Google Scholar
• Griffin, L. (1993). Narrative, event-structure analysis, and causal interpretation in historical sociology. American Journal of Sociology, 98(5), 1094–1133. https://doi.org/10.1086/230140
   Web of Science ®Google Scholar
• Guo, J., Shu, H., & Li, P. (2007). Context effects in lexical ambiguity processing in Chinese: A meta-analysis. Journal of Cognitive Science, 8(1), 85–101. https://doi.org/10.17791/jcs.2007.8.1.85
   Google Scholar
• Hidalgo, C., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26), 10570–10575. https://doi.org/10.1073/pnas.090094310
   PubMed Web of Science ®Google Scholar
• Hsieh, H., & Shannon, S. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687
   PubMed Web of Science ®Google Scholar
• Hu, X., Wang, C., Wu, J., & Stanley, H. (2020). Understanding interurban networks from a multiplexity perspective. Cities, 99, 102625. https://doi.org/10.1016/j.cities.2020.102625
   Web of Science ®Google Scholar
• Hu, Y., Ye, X., & Shaw, S. (2017). Extracting and analyzing semantic relatedness between cities using news articles. International Journal of Geographical Information Science, 31(12), 2427–2451. https://doi.org/10.1080/13658816.2017.1367797
   Web of Science ®Google Scholar
• Hunston, S. (2002). Pattern grammar, language teaching, and linguistic variation. Using Corpora to Explore Linguistic Variation 9, 167–183. https://doi.org/10.1075/scl.9.11hun
   Google Scholar
• Janc, K. (2015). Geography of hyperlinks – spatial dimensions of local government websites. European Planning Studies, 23(5), 1019–1037. https://doi.org/10.1080/09654313.2014.889090
   Web of Science ®Google Scholar
• Järv, O., Ahas, R., Saluveer, E., Derudder, B., & Witlox, F. (2012). Mobile phones in a traffic flow: A geographical perspective to evening rush hour traffic analysis using call detail records. PloS ONE, 7(11), e49171. https://doi.org/10.1371/journal.pone.0049171
   PubMed Web of Science ®Google Scholar
• Jiang, S., Alves, A., Rodrigues, F., Ferreira, J., & Pereira, F. (2015). Mining point-of-interest data from social networks for urban land use classification and disaggregation. Computers, Environment and Urban Systems, 53, 36–46. https://doi.org/10.1016/j.compenvurbsys.2014.12.001
   Web of Science ®Google Scholar
• Jiang, X., Yang, Y., Wang, S., Wang, M., & Yang, Y. (2017). Spatial structure of Chinese intercity network based on the data of listed companies. City Planning Review, 41(6), 18–26. https://doi.org/10.3390/land11060823
   Google Scholar
• Lao, X., Zhang, X., Shen, T., & Skitmore, M. (2016). Comparing China's city transportation and economic networks. Cities, 53, 43–50. https://doi.org/10.1016/j.cities.2016.01.006
   Web of Science ®Google Scholar
• Li, Y., & Wu, F. (2012). The transformation of regional governance in China: The rescaling of statehood. Progress in Planning, 78(2), 55–99. https://doi.org/10.1016/j.progress.2012.03.001
   Web of Science ®Google Scholar
• Liu, S., Wan, Y., & Zhang, A. (2020). Does China’s high-speed rail development lead to regional disparities? A network perspective. Transportation Research Part A: Policy and Practice, 138, 299–321. https://doi.org/10.1016/j.tra.2020.06.010
   PubMed Web of Science ®Google Scholar
• Liu, X., Derudder, B., & Wu, K. (2016). Measuring polycentric urban development in China: An intercity transportation network perspective. Regional Studies, 50(8), 1302–1315. https://doi.org/10.1080/00343404.2015.1004535
   Web of Science ®Google Scholar
• Liu, Y., Wang, F., Kang, C., Gao, Y., & Lu, Y. (2014). Analyzing relatedness by toponym Co-occurrences on Web pages. Transactions in GIS, 18(1), 89–107. https://doi.org/10.1111/tgis.12023
   Web of Science ®Google Scholar
• Lucas, M. (1999). Context effects in lexical access: A meta-analysis. Memory & Cognition, 27(3), 385–398. https://doi.org/10.3758/BF03211535
   PubMed Web of Science ®Google Scholar
• Ma, L. (2005). Urban administrative restructuring, changing scale relations and local economic development in China. Political Geography, 24(4), 477–497. https://doi.org/10.1016/j.polgeo.2004.10.005
   Web of Science ®Google Scholar
• Meijers, E., Burger, M., & Hoogerbrugge, M. (2016). Borrowing size in networks of cities: City size, network connectivity and metropolitan functions in Europe. Papers in Regional Science, 95(1), 181–198. https://doi.org/10.1111/pirs.12181
   Web of Science ®Google Scholar
• Meijers, E., & Peris, A. (2019). Using toponym co-occurrences to measure relationships between places: Review, application and evaluation. International Journal of Urban Sciences, 23(2), 246–268. https://doi.org/10.1080/12265934.2018.1497526
   Web of Science ®Google Scholar
• Mello, R. (2002). Collocation analysis: A method for conceptualizing and understanding narrative data. Qualitative Research, 2(2), 231–243. https://doi.org/10.1177/146879410200200206
   Google Scholar
• Neal, Z. (2012). The connected city: How networks are shaping the modern metropolis. Routledge.
   Google Scholar
• Pan, F., Bi, W., Liu, X., & Sigler, T. (2020). Exploring financial centre networks through inter-urban collaboration in high-end financial transactions in China. Regional Studies, 54(2), 162–172. https://doi.org/10.1080/00343404.2018.1475728
   Web of Science ®Google Scholar
• Peris, A., Meijers, E., & Van Ham, M. (2018). The evolution of the systems of cities literature since 1995: Schools of thought and their interaction. Networks and Spatial Economics, 18(3), 533–554. https://doi.org/10.1007/s11067-018-9410-5
   Web of Science ®Google Scholar
• Porter, C., Atkinson, P., & Gregory, I. (2015). Geographical text analysis: A new approach to understanding nineteenth-century mortality. Health & Place, 36, 25–34. https://doi.org/10.1016/j.healthplace.2015.08.010
   PubMed Web of Science ®Google Scholar
• Rosengren, K. (1981). Advances in content analysis. Sage.
   Google Scholar
• Salvini, M., & Fabrikant, S. (2016). Spatialization of user-generated content to uncover the multirelational world city network. Environment and Planning B: Planning and Design, 43(1), 228–248. https://doi.org/10.1177/0265813515603868
   Google Scholar
• Schuckert, M., Liu, X., & Law, R. (2015). Hospitality and tourism online reviews: Recent trends and future directions. Journal of Travel & Tourism Marketing, 32(5), 608–621. https://doi.org/10.1080/10548408.2014.933154
   Web of Science ®Google Scholar
• Scott, M. (1999). WordSmith tools help manual, Version 3.0.
   Google Scholar
• Sinclair, J., & Sinclair, L. (1991). Corpus, concordance, collocation. Oxford University Press.
   Google Scholar
• Song, J., Zhang, H., & Dong, W. (2016). A review of emerging trends in global PPP research: Analysis and visualization. Scientometrics, 107(3), 1111–1147. https://doi.org/10.1007/s11192-016-1918-1
   Web of Science ®Google Scholar
• Song, Y., Shi, S., Li, J., & Zhang, H. (2018). Directional skip-gram: Explicitly distinguishing left and right context for word embeddings. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 2, 175–180. https://doi.org/10.18653/v1/N18-2028
   Google Scholar
• Taylor, P., Ni, P., Derudder, B., Hoyler, M., Huang, J., & Witlox, F. (2012). Global urban analysis: A survey of cities in globalization. Routledge.
   Google Scholar
• Tobler, W., & Wineburg, S. (1971). A Cappadocian speculation. Nature, 231(5297), 39–41. https://doi.org/10.1038/231039a0
   PubMed Web of Science ®Google Scholar
• Tongjing, W., Yin, Z., Bao, Z., & Meijers, E. (2022). Dataset of intercity relationships between 293 Chinese cities extracted and classified on the basis of toponym co-occurrences on Common Crawl. https://www.researchgate.net/publication/362952059_Dataset_of_intercity_relationships_between_293_Chinese_cities_extracted_and_classified_on_the_basis_of_toponym_co-occurrences_on_Common_Crawl
   Google Scholar
• Vaughan, L., & You, J. (2010). Word co-occurrences on webpages as a measure of the relatedness of organizations: A new webometrics concept. Journal of Informetrics, 4(4), 483–491. https://doi.org/10.1016/j.joi.2010.04.005
   Web of Science ®Google Scholar
• Watts, D. (2004). The “new” science of networks. Annual Review of Sociology, 30(1), 243–270. https://doi.org/10.1146/annurev.soc.30.020404.104342
   Web of Science ®Google Scholar
• Yang, H., Dijst, M., Witte, P., Van Ginkel, H., & Wang, J. (2019). Comparing passenger flow and time schedule data to analyse high-speed railways and urban networks in China. Urban Studies, 56(6), 1267–1287. https://doi.org/10.1177/0042098018761498
   Web of Science ®Google Scholar
• Zhang, J., & Peck, J. (2016). Variegated capitalism, Chinese style: Regional models, multi-scalar constructions. Regional Studies, 50(1), 52–78. https://doi.org/10.1080/00343404.2013.856514
   Web of Science ®Google Scholar
• Zhang, W., Derudder, B., Wang, J., & Witlox, F. (2020). An analysis of the determinants of the multiplex urban networks in the Yangtze River Delta. Tijdschrift Voor Economische en Sociale Geografie, 111(2), 117–133. https://doi.org/10.1111/tesg.12361
   Web of Science ®Google Scholar
• Zhong, X., Liu, J., Gao, Y., & Wu, L. (2017). Analysis of co-occurrence toponyms in web pages based on complex networks. Physica A: Statistical Mechanics and its Applications, 466, 462–475. https://doi.org/10.1016/j.physa.2016.09.024
   Web of Science ®Google Scholar