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Analyses of Metro Station Service Area in Shanghai Downtown Based on Traffic Networks

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Abstract

Service area research is one of the pivotal topics in Urban Geography. This article first put forward a model of urban population estimation. And on the basis we measured the size and distribution of population in downtown Shanghai, China. The population model was confirmed well by the traditional survey model. Then we extracted a 1-month actual-time data set contains geo-location by collecting in Sina Weibo data, and generated Voronoi diagram by these data which denoted the service patches. We assigned population to each patch. Second part, we proposed a shortest distance algorithm, a minimum time algorithm and an improved p-median algorithm, took advantages of these three methods to divide the service area of metro stations based on patches. Subsequently, we computed the service population in each service area. Last, we took metro line 1 and 2 as examples to research the relationship among 3 location-allocation methods in detail. The results showed that: The spatial distribution of population of the core city in Shanghai emerged a descending trend from center to periphery clearly. All indicators (including area, population, distance and time) in central city within inner ring road have changed little compared with the region between inner and outer ring road. Yet the improved p-median algorithm has a certain effect of optimization. It presented a scientific and rational travel scheme for citizens cost smallest price to select better starting metro station. The study results should contribute to theoretical and technical support for location-allocation of public service facilities.

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References

  • Applebaum, W. (1968). The analog method for estimating Potential Store Sales. In C. Kornblau (Ed.), Guide to store location research (pp. 232–243). Reading: Addison-Wesley Publishing Company.

    Google Scholar 

  • Applebaum, W., & Green, H. L. (1974). Determining store trade areas. In R. Ferber (Ed.), Handbook of marketing research (pp. 4313–4323). New York: Mc-Graw Hill.

    Google Scholar 

  • Brunner, J. A., & Mason, J. L. (1968). The influence of driving time upon shopping center preference. Journal of Marketing, 32(2), 57–61.

    Article  Google Scholar 

  • Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility: user movement in location-based social networks. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. New York: ACM, pp. 1082–1090.

  • Christaller, W. (1966). Central places in Southern Germany. Englewood Cliffs: Prentice Hall Inc.

    Google Scholar 

  • Ghosh, A., & McLafferty, S. L. (1987). Location strategies for retail and service firms. New York: Free Press.

    Google Scholar 

  • Hartigan, J. A. (1975). Clustering algorithms (pp. 8–10). New York: Wiley.

    Google Scholar 

  • Indriasari, V., Mahmud, A. R., Ahmad, N., & Shariff, A. R. M. (2010). Maximal service area problem for optimal siting of emergency facilities. International Journal of Geographical Information Science, 24(2), 213–230.

    Article  Google Scholar 

  • Jin, C., Huang, Z., Xu, J., & Gu, Q. (2013a). Evaluation of travel convenience for residents in the Yangtze River Delta, China. Asia Pacific Journal of Tourism Research, 18(8), 865–879.

    Article  Google Scholar 

  • Jin, C., Xu, J., Lu, Y., & Huang, Z. (2013b). The impact of Chinese Shanghai–Nanjing high-speed rail on regional accessibility. Geografisk Tidsskrift—Danish Journal of Geography, 113(2), 133–145.

    Article  Google Scholar 

  • Kim, H., & Nam, J. (2013). The size of the station influence area in Seoul, Korea: based on the survey of users of seven stations. International Journal of Urban Sciences, 17(3), 331–349.

    Article  Google Scholar 

  • Lee, S., Yi, C., & Hong, S.-P. (2013). Urban structural hierarchy and the relationship between the ridership of the Seoul Metropolitan Subway and the land-use pattern of the station areas. Cities, 35, 69–77.

    Article  Google Scholar 

  • Li, X., Shi, X., He, J. Q., & Liu, X. P. (2011). Coupling simulation and optimization to solve planning problems in a fast developing area. Annals of the Association of American Geographers, 101(5), 1032–1048.

    Article  Google Scholar 

  • Liang, S. (2009). Research on the urban influence domains in China. International Journal of Geographical Information Science, 23(12), 1527–1539.

    Article  Google Scholar 

  • Liu, X., Ou, J., Li, X., & Ai, B. (2013). Combining system dynamics and hybrid particle swarm optimization for land use allocation. Ecological Modelling, 257, 11–24.

    Article  Google Scholar 

  • Liu, Y., Sui, Z., Kang, C., & Gao, Y. (2014). Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS ONE, 9(1), 1–11.

    Google Scholar 

  • Mestre, A. M., Oliveira, M. D., & Barbosa-Póvoa, A. P. (2015). Location–allocation approaches for hospital network planning under uncertainty. European Journal of Operational Research, 240(3), 791–806.

    Article  Google Scholar 

  • Ortigosa, J., & Menendez, M. (2014). The relationship between urban structure and traffic demand. Cities, 36, 18–27.

    Article  Google Scholar 

  • Rapid transit (2014-12-18) http://en.wikipedia.org/wiki/Rapid_transit.

  • Tsekeris, T., & Geroliminis, N. (2013). City size, network structure and traffic congestion. Journal of Urban Economics, 76, 1–14.

    Article  Google Scholar 

  • Urban rail transit in China (2014-12-18) http://en.wikipedia.org/wiki/Urban_rail_transit_in_China.

  • Vidyarthi, N., & Jayaswal, S. (2014). Efficient solution of a class of location–allocation problems with stochastic demand and congestion. Computers & Operations Research, 48, 20–30.

    Article  Google Scholar 

  • 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), 1–13.

    Google Scholar 

  • Yu, B., Shu, S., Liu, H., Song, W., Wu, J., Wang, L., & Chen, Z. (2014). Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China. International Journal of Geographical Information Science, 28(11), 1–28.

    Article  Google Scholar 

  • Zeev, S., Meidad, K., & Avinoam, M. (2014). A multi-spatial scale approach to urban sustainability: An illustration of the domestic and global hinterlands of the city of Beer-Sheva. Land Use Policy, 41, 498–505.

    Article  Google Scholar 

  • Zhang, L., & Lu, Y. (2007). Regional accessibility of land traffic network in the Yangtze River Delta. Journal of Geographical Sciences, 3, 351–364.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Geographic Sciences, East China Normal University, No. 500 Dongchuan Road, Shanghai, 200241, China

    Zujing Wang

  2. College of Geography and Environment Science, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Peiji Shi

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  1. Zujing Wang
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  2. Peiji Shi
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Correspondence to Zujing Wang.

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Wang, Z., Shi, P. Analyses of Metro Station Service Area in Shanghai Downtown Based on Traffic Networks. J Indian Soc Remote Sens 45, 337–352 (2017). https://doi.org/10.1007/s12524-016-0595-0

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  • DOI: https://doi.org/10.1007/s12524-016-0595-0

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