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Population Allocation at the Building level for Micro-level Urban Simulation: A Case of Jeonju, Korea

  • Kim, Dohyung (Department of Urban and Regional Planning, California State Polytechnic University) ;
  • Cho, Dongin (MUREPA Korea)
  • 투고 : 2020.08.22
  • 심사 : 2020.08.29
  • 발행 : 2020.08.31

초록

It is important for urban planners and policy makers to understand complex, diverse urban demands and social structure, but this is not easy due to lack of data that represents the dynamics of residents at micro-geographical level. This paper explores how to create population data at at a micro-level by allocating population data to building. It attempted to allocate population data stored in a grid layer (100 meters by 100 meters) into a building footprint layer that represents the appearance of physical buildings. For the allocation, this paper describes a systemic approach that classifies grid cells into five prototypical patterns based on the composition of residential building types in a grid cell. This approach enhances allocation accuracy by accommodating heterogeneity of urban space rather than relying on the assumption of uniform spatial homogeneity of populations within an aerial unit. Unlike the methods that disaggregate population data to the parcel, this approach is more applicable to Asian cities where large multifamily residential parcels are common. However, it should be noted that this paper does not demonstrate the validity of the allocated population since there is a lack of the actual data available to be compared with the current estimated population. In the case of water and electricity, the data is already attached to an individual address, and hence, it can be considered to the purpose of the validation for the allocation. By doing so, it will be possible to identify innovative methods that create a population distribution dataset representing the comprehensive and dynamic nature of the population at the micro geographical level.

키워드

참고문헌

  1. Alberti, M. (2016). Cities That Think like Planets: Complexity, Resilience, and Innovation in Hybrid Ecosystems. Seattle: University of Washington Press.
  2. Bai, X. 2003. The process and mechanism of urban environmental change: An evolutionary view. International Journal of Environment and Pollution, 19(5), 528-541. https://doi.org/10.1504/IJEP.2003.004319
  3. Beckman, R. J., Baggerly, K. A. and McKay, M. D. (1996). Creating synthetic baseline populations. Transportation Research Part A: Policy and Practice, 30, 415-429. https://doi.org/10.1016/0965-8564(96)00004-3
  4. Chandrasekar, P. (2015). Big data and transport modelling: opportunities and challenges. International Journal of Applied Engineering Research, 10 (17), 38038-38044.
  5. Guo, J. Y. and Bhat, C. R. (2007). Population synthesis for microsimulating travel behavior. Transportation Research Record: Journal of the Transportation Research Board, 2014, 92-101. https://doi.org/10.3141/2014-12
  6. Kim, D. (2010). Place type-oriented land-use simulation for land use/transportation decision making. Proceedings of ESRI International User Conference, July 12-16, San Diego, CA.
  7. Kim, D. (2012). Land use scenario simulation based on urban typologies. Korean Local Administration Review, 9 (2), 103-120.
  8. Kim, D. Pedestrian and bicycle volume data collection using drone technology. Journal of Urban Technology, 27(2), 45-60. https://doi.org/10.1080/10630732.2020.1715158
  9. Kim, D. Hu, H., and Choi, S. (2012). Micro-level land-use simulation for estimating demands on urban infrastructure. Proceedings of ESRI International User Conference, July 23-27, San Diego, CA.
  10. Kim, D. and Chanchlani, H.M. (2018). Influence of the built environment on the spatiotemporal variation of pedestrian collision in Seoul: An application of big data. Transportation research record: Journal of the Transportation Research Board, 2672 (35), 90-100.
  11. Lenormanda, M. and Deffuant, G. (2013). Generating a synthetic population of individuals in households: Sample-free vs sample-based methods. Journal of Artificial Societies and Social Simulation, 16 (4), 12. https://doi.org/10.18564/jasss.2319
  12. Mennis J. (2009). Dasymetric mapping for estimating population in small areas. Geographiy Compass, 3(2), 727-45. https://doi.org/10.1111/j.1749-8198.2009.00220.x
  13. Michanowicz, D.R., Williams, S.R., Buonocore, J.J., Rowland, S.T., Konschnik, K.E., Goho, S.A., and Bernstein, A.S. (2019). Population allocation at the housing unit level: estimates around underground natural gas storage wells in PA, OH, NY, WV, MI, and CA, Environmental Health, 18 (58).
  14. Mohammadian, A. and Zhang, Y. (2008). Microsimulation of household travel survey data. Proceedings of Transportation Research Board 87th Annual Meeting, January 13-17, Washington, D.C.
  15. Polsky, C., Grove, J.M., Knudson, C. Groffman, P.M., Bettez, N., Cavender-Bares, J., et al. (2014). Assessing the homogenization of urban land management with an application to U.S. residential lawn care. Proceedings of the National Academy of Sciences of the United States of America, 111(12): 4432-4437. https://doi.org/10.1073/pnas.1323995111
  16. United Nations (U.N.). (2019). World Urbanization Prospects: The 2018 Revision. New York, NY: United Nations.
  17. U.S. Census. (1994). Geographic Areas Reference Manual. Washington D.C.: U.S. Census.

피인용 문헌

  1. Relationship between Public Service Satisfaction and Intention of Continuous Residence of Younger Generations in Rural Areas: The Case of Jeonbuk, Korea vol.10, pp.11, 2020, https://doi.org/10.3390/land10111203