The Urban Facilities Before and After the COVID-19 Pandemic: Spatial Association Patterns Mining in Wuhan, China
Tóm tắt
The COVID-19 pandemic has resulted in significant global impacts on human society. With the evolution of cities during this period, urban facilities can rise and fall, showing different spatial patterns and function structures in local areas. Analyzing the change of urban facilities before and after the pandemic can assist to understand the impact of the COVID-19 on cities, which is especially important for the Wuhan city. In this paper, we first characterized the “birth”, “death” and “retention” of urban facilities through matching POIs (Points of Interest) before and after the pandemic, and then examined their spatial distribution patterns across the city. In addition, spatial association patterns mining was applied to explore the interactions between the change of urban facilities and the environmental factors. The results revealed that the catering service has decreased the most in the wake of the pandemic, while accommodation service has grown the most. The state changes of catering service, living service and shopping service frequently appear together in the data mining results. During the pandemic, these types of urban facility changes are closely linked to each other. Many regional urban functions have also changed due to the epidemic, which are most obvious in the community. This update can facilitate the management of gated communities after the pandemic.
Tài liệu tham khảo
An, N., Zheng, Z., Chen, C., Yang, X., & Lin, M. (2022). Mapping a country image from Global News reports about COVID-19 pandemic. Applied Spatial Analysis and Policy. https://doi.org/10.1007/s12061-022-09498-4
Anselin, L. (2010). Local indicators of spatial association—lisa. Geographical Analysis, 27(2), 93–115.
Backer, J. A., Klinkenberg, D., & Wallinga, J. (2020). Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20–28 January 2020. Eurosurveillance, 25(5). https://doi.org/10.2807/1560-7917.ES.2020.25.5.2000062
Bates, A. E., Primack, R. B., Moraga, P., & Duarte, C. M. (2020). COVID-19 pandemic and associated lockdown as a “Global Human Confinement Experiment” to investigate biodiversity conservation. Biological Conservation, 248, 108665.
Borgelt, C. (2005). An implementation of the FP-growth algorithm. Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, 1–5. https://doi.org/10.1145/1133905.1133907
Calafiore, A., Macdonald, J. L., & Singleton, A. (2022). Decomposing the temporal signature of Nitrogen Dioxide declines during the COVID-19 pandemic in UK Urban Areas. Applied Spatial Analysis and Policy, 15(4), 1167–1191. https://doi.org/10.1007/s12061-022-09438-2
CGTN. (2020). Wuhan’s war on COVID-19. Retrieved June 15, 2022, from http://english.scio.gov.cn/videos/2020-02/28/content_75754291.htm
Connolly, C., Keil, R., & Ali, S. H. (2021). Extended urbanisation and the spatialities of infectious disease: Demographic change, infrastructure and governance. Urban Studies, 58(2), 245–263.
Corlett, R. T., Primack, R. B., Devictor, V., Maas, B., Goswami, V. R., Bates, A. E., Koh, L. P., Regan, T. J., Loyola, R., Pakeman, R. J., Cumming, G. S., Pidgeon, A., Johns, D., & Roth, R. (2020). Impacts of the coronavirus pandemic on biodiversity conservation. Biological Conservation, 246, 108571.
de Oliveira, L. A., & de Aguiar Arantes, R. (2020) Neighborhood effects and urban inequalities: The impact of Covid-19 on the periphery of Salvador, Brazil. City & Society (Washington, DC), 32(1).
Dong, L., Ratti, C., & Zheng, S. (2019). Predicting neighborhoods’ socioeconomic attributes using restaurant data. Proceedings of the National Academy of Sciences of the United States of America, 116(31), 15447–15452.
Dzúrová, D., & Květoň, V. (2021). How health capabilities and government restrictions affect the COVID-19 pandemic: Cross-country differences in Europe. Applied Geography, 135, 102551.
Fisher, D. P. (1997). Location, location, location: Ensuring a franchisee’s success. Hospitality Review, 15(1), 4. Available at: https://paperity.org/p/82355038/location-location-location-ensuring-a-franchisees-success
Freeman, J., & Hannan, M. T. (1987). The population ecology of restaurants revisited - reply. American Journal of Sociology, 92(5), 1214–1220
Geng, L., Chen, X., & Liang, Y. (2018). The location of retail stores and street centrality in guangzhou, china. Applied Geography, 100, 12–20.
Hamidi, S., Sabouri, S., & Ewing, R. (2020). Does density aggravate the COVID-19 pandemic? Journal of the American Planning Association, 86(4), 495–509. https://doi.org/10.1080/01944363.2020.1777891
Jung, S. S., & Jang, S. (2018). To cluster or not to cluster?: Understanding geographic clustering by restaurant segment. International Journal of Hospitality Management, 77, 448–457. https://doi.org/10.1016/j.ijhm.2018.08.008
Kang, Y., Wang, Y., Xia, Z., Chi, J., & Wei, Z. (2018). Identification and classification of Wuhan urban districts based on POI. Journal of Geomatics, 43(1), 81–85.
Kang, D., Choi, H., Kim, J. H., & Choi, J. (2020). Spatial epidemic dynamics of the covid-19 outbreak in china. International Journal of Infectious Diseases, 94, 96–102. https://doi.org/10.1016/j.ijid.2020.03.076
Krösche, J., & Boll, S. (2005). The xPOI Concept. In T. Strang & C. Linnhoff-Popien (Eds.), Location- and Context-Awareness (pp. 113–119). Springer. https://doi.org/10.1007/11426646_11
Krzysztofik, R., Kantor-Pietraga, I., & Spórna, T. (2020). Spatial and functional dimensions of the COVID-19 epidemic in Poland. Eurasian Geography and Economics, 61(4–5), 573–586. https://doi.org/10.1080/15387216.2020.1783337
Le Quéré, C., Jackson, R. B., Jones, M. W., Smith, A. J. P., Abernethy, S., Andrew, R. M., De-Gol, A. J., Willis, D. R., Shan, Y., Canadell, J. G., Friedlingstein, P., Creutzig, F., & Peters, G. P. (2020). Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement. Nature Climate Change, 10(7), 647–653.
Li, G., Jin, F., Chen, Y., Jiao, J., & Liu, S. (2017). Location characteristics and differentiation mechanism of logistics nodes and logistics enterprises based on points of interest (POI): A case study of Beijing. Journal of Geographical Sciences, 27(7), 879–896. https://doi.org/10.1007/s11442-017-1411-7
Lin, Z., Li, G., Mehmood, M. S., Nie, Q., & Zheng, Z. (2023). Spatial analysis and optimization of self-pickup points of a new retail model in the post-epidemic era: The case of community-group-buying in Xi’an City. Computational Urban Science, 3(1), 13. https://doi.org/10.1007/s43762-023-00089-8
Liu, Y., Liu, X., Gao, S., Gong, L., & Shi, L. (2015). Social sensing: a new approach to understanding our socioeconomic environments. Annals of the American Association of Geographers, 105(3), 1–19.
Meltzer, R., & Capperis, S. (2017). Neighbourhood differences in retail turnover: Evidence from New York City. Urban Studies, Urban Studies Journal Limited, 54(13), 3022–3057.
Mitchell, W., & Watts, M. (2010). Identifying functional regions in Australia using hierarchical aggregation techniques. Geographical Research, 48(1), 24–41.
Newling, B. E., & Forrester, J. W. (1970). Urban dynamics / jay w. forrester. Geographical Review, 60(3), 455.
Olvera, J., & Sutton, S. (2020). An organizational ecology approach to new food marts in New York City neighbourhoods. International Journal of Urban Sciences, 25, 252–271.
Parsa, H. G., Van, D., Smith, S. R., Parsa, R. A., & Bujisic, M. (2015). Why restaurants fail? Part iv: The relationship between restaurant failures and demographic factors. Cornell Hospitality Quarterly, 56(1), 80–90.
Patino, J. E., Duque, J. C., Pardo-Pascual, J. E., & Ruiz, L. A. (2014). Using remote sensing to assess the relationship between crime and the urban layout. Applied Geography, 55, 48–60.
Pearson, R. M., Sievers, M., McClure, E. C., Turschwell, M. P., & Connolly, R. M. (2020). COVID-19 recovery can benefit biodiversity. Science, 368(6493), 838–839.
Rutynskyi, M., & Kushniruk, H. (2020). The impact of quarantine due to COVID-19 pandemic on the tourism industry in Lviv (Ukraine). Problems and Perspectives in Management, 18(2), 194–205. https://doi.org/10.21511/ppm.18(2).2020.17
Rutz, C., Loretto, M. C., Bates, A. E., Davidson, S. C., Duarte, C. M., Jetz, W., Johnson, M., Kato, A., Kays, R., Mueller, T., Primack, R. B., Ropert-Coudert, Y., Tucker, M. A., Wikelski, M., & Cagnacci, F. (2020). COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife. Nature Ecology and Evolution, 4(9), 1156–1159.
Wang, B. H., Yun-Chao, W. U., & Huang, X. C. (2012). Urban Land-use classification using high Resolution Remote Sensing Data. Remote Sensing Information, 27(122), 113–117.
Wang, F., Yuan, Z., & Chen, T. (1992). A study on the functional organization of Henan’s urban system. Acta Geographica Sinica, 47(3), 274–283.
Wang, X. (2020). Based on the application of the ARIMA Model in the cumulative number of confirmed cases of COVID-19 in the United States. Statistics and Applications, 09(6), 979–987.
Wen, L., Yang, C., Liao, X., Zhang, Y., & Cai, Z. (2021). Investigation of PM2.5 pollution during COVID-19 pandemic in Guangzhou, China. Journal of Environmental Sciences (China), 115, 443–452. https://doi.org/10.1016/j.jes.2021.07.009
Wrigley, N., Warm, D., Margetts, B., & Whelan, A. (2002). Assessing the impact of improved retail access on diet in a ‘food desert’: A preliminary report. Urban Studies, 39(11), 2061–2082.
Xie, W., Mou, Y., & Song, J. S. (2020) Based on the application of the ARIMA model in the cumulative number of confirmed cases of COVID-19 in the United States. Statistics and Application, 09(06), 979–987. https://doi.org/10.12677/SA.2020.96103
Xu, R., Yue, W., Wei, F., Yang, G., He, T., & Pan, K. (2022). Density pattern of functional facilities and its responses to urban development, especially in polycentric cities. Sustainable Cities and Society, 76, 103526.
Yuan, F., Wei, Y. D., & Wu, J. (2020). Amenity effects of urban facilities on housing prices in china: Accessibility, scarcity, and urban spaces. Cities, 96, 102433.
Yuan, J., Zheng, Y., & Xie, X. (2012). Discovering regions of different functions in a city using human mobility and POIs. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 186–194). https://doi.org/10.1145/2339530.2339561
Yu, W. (2016). Spatial co-location pattern mining for location-based services in road networks. Expert Systems with Applications, 46, 324–335. https://doi.org/10.1016/j.eswa.2015.10.010
Yu, W., Ai, T., & Shao, S. (2015). The analysis and delimitation of Central Business District using network kernel density estimation. Journal of Transport Geography, 45, 32–47. https://doi.org/10.1016/j.jtrangeo.2015.04.008
Zhang, X., & Yilun, X. U. (2009). Study on the distribution in space of urban caterings and its influencing factors: A case study of nanjing. Tropical Geography.
Zhou, J., Wu, J., & Ma, H. (2021). Abrupt changes, institutional reactions, and adaptive behaviors: An exploratory study of COVID-19 and related events’ impacts on Hong Kong’s metro riders. Applied Geography, 134, 102504.