IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2011.06851.html
   My bibliography  Save this paper

Population synthesis for urban resident modeling using deep generative models

Author

Listed:
  • Martin Johnsen
  • Oliver Brandt
  • Sergio Garrido
  • Francisco C. Pereira

Abstract

The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned on aspects such as dwelling typology, price, location, and floor level. This paper presents a Machine Learning based method to model the population distribution of upcoming developments of new buildings within larger neighborhood/condo settings. We use a real data set from Ecopark Township, a real estate development project in Hanoi, Vietnam, where we study two machine learning algorithms from the deep generative models literature to create a population of synthetic agents: Conditional Variational Auto-Encoder (CVAE) and Conditional Generative Adversarial Networks (CGAN). A large experimental study was performed, showing that the CVAE outperforms both the empirical distribution, a non-trivial baseline model, and the CGAN in estimating the population distribution of new real estate development projects.

Suggested Citation

  • Martin Johnsen & Oliver Brandt & Sergio Garrido & Francisco C. Pereira, 2020. "Population synthesis for urban resident modeling using deep generative models," Papers 2011.06851, arXiv.org.
    • Handle: RePEc:arx:papers:2011.06851
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2011.06851
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cathal O'Donoghue & Karyn Morrissey & John Lennon, 2014. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 26-75.
    2. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.
    3. Asim Roy & Shiban Qureshi & Kartikeya Pande & Divitha Nair & Kartik Gairola & Pooja Jain & Suraj Singh & Kirti Sharma & Akshay Jagadale & Yi-Yang Lin & Shashank Sharma & Ramya Gotety & Yuexin Zhang & , 2019. "Performance Comparison of Machine Learning Platforms," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 207-225, April.
    4. Farooq, Bilal & Bierlaire, Michel & Hurtubia, Ricardo & Flötteröd, Gunnar, 2013. "Simulation based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 243-263.
    5. Steven C. Deller & Tsung-Hsiu (Sue) Tsai & David W. Marcouiller & Donald B.K. English, 2001. "The Role of Amenities and Quality of Life In Rural Economic Growth," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(2), pages 352-365.
    6. Jaehyung An & Alexey Mikhaylov & Nikita Moiseev, 2019. "Oil Price Predictors: Machine Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 1-6.
    7. Ismaïl Saadi & Hamed Eftekhar & Jacques Teller & Mario Cools, 2018. "Investigating scalability in population synthesis: a comparative approach," Transportation Planning and Technology, Taylor & Francis Journals, vol. 41(7), pages 724-735, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jian Liu & Xiaosu Ma & Yi Zhu & Jing Li & Zong He & Sheng Ye, 2021. "Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service," Sustainability, MDPI, vol. 13(3), pages 1-16, February.
    2. Sun, Lijun & Erath, Alexander & Cai, Ming, 2018. "A hierarchical mixture modeling framework for population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 199-212.
    3. Mohamed Khachman & Catherine Morency & Francesco Ciari, 2024. "Integrated multiresolution framework for spatialized population synthesis," Transportation, Springer, vol. 51(3), pages 823-852, June.
    4. Nicholas Fournier & Eleni Christofa & Arun Prakash Akkinepally & Carlos Lima Azevedo, 2021. "Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method," Transportation, Springer, vol. 48(2), pages 1061-1087, April.
    5. Abdoul Razac Sané & Pierre-Olivier Vandanjon & Rachid Belaroussi & Pierre Hankach, 2025. "A comprehensive investigation of variational auto-encoders for population synthesis," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-34, February.
    6. Stanislav S. Borysov & Jeppe Rich, 2021. "Introducing synthetic pseudo panels: application to transport behaviour dynamics," Transportation, Springer, vol. 48(5), pages 2493-2520, October.
    7. Yu Han & Changjie Chen & Zhong-Ren Peng & Pallab Mozumder, 2022. "Evaluating impacts of coastal flooding on the transportation system using an activity-based travel demand model: a case study in Miami-Dade County, FL," Transportation, Springer, vol. 49(1), pages 163-184, February.
    8. Anura Amarasinghe & Gerard D'Souza & Cheryl Brown & Tatiana Borisova, 2006. "A Spatial Analysis of Obesity in West Virginia," Working Papers Working Paper 2006-13, Regional Research Institute, West Virginia University.
    9. repec:rri:wpaper:200803 is not listed on IDEAS
    10. Schultz, Michael & Rosenow, Judith & Olive, Xavier, 2022. "Data-driven airport management enabled by operational milestones derived from ADS-B messages," Journal of Air Transport Management, Elsevier, vol. 99(C).
    11. Daniel C. Monchuk & John A. Miranowski & Dermot J. Hayes & Bruce A. Babcock, 2007. "An Analysis of Regional Economic Growth in the U.S. Midwest," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 29(1), pages 17-39.
    12. Shichao Yuan & Xizhuo Wang, 2024. "Increase or Reduce: How Does Rural Infrastructure Investment Affect Villagers’ Income?," Agriculture, MDPI, vol. 14(12), pages 1-21, December.
    13. Artur Meynkhard, 2020. "Long-Term Prospects for the Development Energy Complex of Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 224-232.
    14. repec:prg:jnlpep:v:preprint:id:658:p:1-18 is not listed on IDEAS
    15. Emilio Colombo & Alessandra Michelangeli & Luca Stanca, 2014. "La Dolce Vita : Hedonic Estimates of Quality of Life in Italian Cities," Regional Studies, Taylor & Francis Journals, vol. 48(8), pages 1404-1418, August.
    16. Kurt Paulsen, 2014. "Geography, policy or market? New evidence on the measurement and causes of sprawl (and infill) in US metropolitan regions," Urban Studies, Urban Studies Journal Limited, vol. 51(12), pages 2629-2645, September.
    17. Jae Hong Kim & Geoffrey J. D. Hewings, 2012. "An Application of the Disequilibrium Adjustment Framework to Small Area Forecasting and Impact Analysis," Advances in Spatial Science, in: Esteban Fernández Vázquez & Fernando Rubiera Morollón (ed.), Defining the Spatial Scale in Modern Regional Analysis, edition 127, chapter 0, pages 139-155, Springer.
    18. Andrés Rodríguez-Pose & Tobias D. Ketterer, 2012. "Do Local Amenities Affect The Appeal Of Regions In Europe For Migrants?," Journal of Regional Science, Wiley Blackwell, vol. 52(4), pages 535-561, October.
    19. repec:rri:wpaper:200613 is not listed on IDEAS
    20. Felix N. Fernando & Dennis R. Cooley, 2016. "An Oil Boom’s Effect on Quality of Life (QoL): Lessons from Western North Dakota," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 11(4), pages 1083-1115, December.
    21. Melstrom, Richard & Lupi, Frank, 2012. "Using a Control Function to Resolve the Travel Cost Endogeneity Problem in Recreation Demand Models," MPRA Paper 48036, University Library of Munich, Germany, revised May 2013.
    22. Mikhail Bondarev, 2020. "Energy Consumption of Bitcoin Mining," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 525-529.
    23. Robert Skikiewicz & Krzysztof Blonski, 2018. "Economic Sentiment Level versus the Quality of Life in European Union Member States," Prague Economic Papers, Prague University of Economics and Business, vol. 2018(4), pages 379-396.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2011.06851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.