IDEAS home Printed from https://ideas.repec.org/a/ijm/journl/v10y2017i1p167-200.html
   My bibliography  Save this article

A Fine Grained Hybrid Spatial Microsimulation Technique for Generating Detailed Synthetic Individuals from Multiple Data Sources: An Application To Walking And Cycling

Author

Listed:
  • Ian Philips

    (Institute for Transport Studies, University of Leeds LS2 9JT, UK)

  • Graham Clarke

    (School of Geography, University of Leeds LS2 9JT, UK)

  • David Watling

    (Institute for Transport Studies, University of Leeds LS2 9JT, UK)

Abstract

We propose a hybrid static spatial microsimulation technique that combines simulated annealing and synthetic reconstruction (Monte-Carlo sampling), in order to generate a synthetic population of individuals as part of a model-based policy indicator. We focus on the following case: (i) the model must produce outputs at a fine spatial resolution; (ii) the individuals have many attributes the majority of which are found in an available micro-data survey, though some attributes are missing and need to be added from other sources. The hybrid method proposed uses simulated annealing to simulate the majority of the required attributes, and Monte-Carlo sampling to add the missing attributes. Our paper expands the range of techniques which could produce this type of model. We test the hybrid technique on a UK example estimating the capability of individuals to make journeys by walking and cycling, in order to produce a novel indicator of resilience to the disruption of fuel availability. Additionally, the staged approach means that the intermediate steps in the spatial microsimulation modelling process generate data on bicycle availability and the need to escort children during commuting that are useful in their own right.

Suggested Citation

  • Ian Philips & Graham Clarke & David Watling, 2017. "A Fine Grained Hybrid Spatial Microsimulation Technique for Generating Detailed Synthetic Individuals from Multiple Data Sources: An Application To Walking And Cycling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 167-200.
    • Handle: RePEc:ijm:journl:v10:y:2017:i:1:p:167-200
    as

    Download full text from publisher

    File URL: http://www.microsimulation.org/IJM/V10_1/IJM_2017_10_1_6.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jones, Tim, 2012. "Getting the British back on bicycles—The effects of urban traffic-free paths on everyday cycling," Transport Policy, Elsevier, vol. 20(C), pages 138-149.
    2. Nam Huynh & Johan Barthelemy & Pascal Perez, 2016. "A Heuristic Combinatorial Optimisation Approach to Synthesising a Population for Agent Based Modelling Purposes," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-11.
    3. Natasha Stout & Sue Goldie, 2008. "Keeping the noise down: common random numbers for disease simulation modeling," Health Care Management Science, Springer, vol. 11(4), pages 399-406, December.
    4. Azizur Rahman & Ann Harding & Robert Tanton & Shuangzhe Liu, 2010. "Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation," International Journal of Microsimulation, International Microsimulation Association, vol. 3(2), pages 3-22.
    5. Floriana Gargiulo & Sônia Ternes & Sylvie Huet & Guillaume Deffuant, 2010. "An Iterative Approach for Generating Statistically Realistic Populations of Households," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    6. Rathi, Ajay K., 1992. "The use of common random numbers to reduce the variance in network simulation of traffic," Transportation Research Part B: Methodological, Elsevier, vol. 26(5), pages 357-363, October.
    7. John Stillwell & Oliver Duke-Williams, 2003. "A New Web-Based Interface to British Census of Population Origin–Destination Statistics," Environment and Planning A, , vol. 35(1), pages 113-132, January.
    8. Kirk Harland & Alison Heppenstall & Dianna Smith & Mark Birkin, 2012. "Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-1.
    9. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    10. 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.
    11. S Openshaw, 1984. "Ecological Fallacies and the Analysis of Areal Census Data," Environment and Planning A, , vol. 16(1), pages 17-31, January.
    12. Robert Tanton, 2014. "A Review of Spatial Microsimulation Methods," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 4-25.
    13. Iacono, Michael & Krizek, Kevin J. & El-Geneidy, Ahmed, 2010. "Measuring non-motorized accessibility: issues, alternatives, and execution," Journal of Transport Geography, Elsevier, vol. 18(1), pages 133-140.
    14. Susan Handy & Bert van Wee & Maarten Kroesen, 2014. "Promoting Cycling for Transport: Research Needs and Challenges," Transport Reviews, Taylor & Francis Journals, vol. 34(1), pages 4-24, January.
    15. Riyana Miranti & Rebecca Cassells & Yogi Vidyattama & Justine Mc Namara, 2015. "Measuring Small Area Inequality Using Spatial Microsimulation: Lessons Learned from Australia," International Journal of Microsimulation, International Microsimulation Association, vol. 8(2), pages 152-175.
    16. Dianna M Smith & Graham P Clarke & Kirk Harland, 2009. "Improving the Synthetic Data Generation Process in Spatial Microsimulation Models," Environment and Planning A, , vol. 41(5), pages 1251-1268, May.
    17. Sharyn Lymer & Laurie Brown & Ann Harding & Mandy Yap, 2009. "Predicting the need for aged care services at the small area level: the CAREMOD spatial microsimulation model," International Journal of Microsimulation, International Microsimulation Association, vol. 2(2), pages 27-42.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    2. Milne, Dave & Watling, David, 2019. "Big data and understanding change in the context of planning transport systems," Journal of Transport Geography, Elsevier, vol. 76(C), pages 235-244.
    3. Dianna M. Smith & Alison Heppenstall & Monique Campbell, 2021. "Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health," J, MDPI, vol. 4(2), pages 1-11, June.
    4. Ali Soltani & Andrew Allan & Masoud Javadpoor & Jaswanth Lella, 2022. "Space Syntax in Analysing Bicycle Commuting Routes in Inner Metropolitan Adelaide," Sustainability, MDPI, vol. 14(6), pages 1-13, March.
    5. Philips, Ian & Anable, Jillian & Chatterton, Tim, 2022. "E-bikes and their capability to reduce car CO2 emissions," Transport Policy, Elsevier, vol. 116(C), pages 11-23.

    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. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    2. Robert Tanton & Paul Williamson & Ann Harding, 2014. "Comparing Two Methods of Reweighting a Survey File to Small Area Data," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 76-99.
    3. Suesse Thomas & Namazi-Rad Mohammad-Reza & Mokhtarian Payam & Barthélemy Johan, 2017. "Estimating Cross-Classified Population Counts of Multidimensional Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1021-1050, December.
    4. Philips, Ian & Anable, Jillian & Chatterton, Tim, 2022. "E-bikes and their capability to reduce car CO2 emissions," Transport Policy, Elsevier, vol. 116(C), pages 11-23.
    5. Robert Tanton, 2018. "Spatial Microsimulation: Developments and Potential Future Directions," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 143-161.
    6. Lovelace, Robin & Ballas, Dimitris & Watson, Matt, 2014. "A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels," Journal of Transport Geography, Elsevier, vol. 34(C), pages 282-296.
    7. Ma, Lu & Srinivasan, Sivaramakrishnan, 2016. "An empirical assessment of factors affecting the accuracy of target-year synthetic populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 247-264.
    8. Xueying Wu & Yi Lu & Yaoyu Lin & Yiyang Yang, 2019. "Measuring the Destination Accessibility of Cycling Transfer Trips in Metro Station Areas: A Big Data Approach," IJERPH, MDPI, vol. 16(15), pages 1-16, July.
    9. Jan Pablo Burgard & Joscha Krause & Simon Schmaus, 2019. "Estimation of Regional Transition Probabilities for Spatial Dynamic Microsimulations from Survey Data Lacking in Regional Detail," Research Papers in Economics 2019-12, University of Trier, Department of Economics.
    10. M. Esteban Muñoz H. & Irene Peters, 2014. "Constructing an Urban Microsimulation Model to Assess the Influence of Demographics on Heat Consumption," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 127-157.
    11. Ricardo Crespo & Ignacio Hernandez, 2020. "On the spatially explicit Gini coefficient: the case study of Chile—a high-income developing country," Letters in Spatial and Resource Sciences, Springer, vol. 13(1), pages 37-47, April.
    12. M. Esteban Muñoz H. & Ivan Dochev & Hannes Seller & Irene Peters, 2016. "Constructing a Synthetic City for Estimating Spatially Disaggregated Heat Demand," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 66-88.
    13. Huang, Charlotte & Elsland, Rainer, 2019. "A survey-based approach to estimate residential electricity consumption at municipal level in Germany," Working Papers "Sustainability and Innovation" S10/2019, Fraunhofer Institute for Systems and Innovation Research (ISI).
    14. 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.
    15. Yogi Vidyattama & Riyana Miranti & Justine McNamara & Robert Tanton & Ann Harding, 2013. "The Challenges of Combining Two Databases in Small-Area Estimation: An Example Using Spatial Microsimulation of Child Poverty," Environment and Planning A, , vol. 45(2), pages 344-361, February.
    16. Jason Hawkins & Khandker Nurul Habib, 2023. "A multi-source data fusion framework for joint population, expenditure, and time use synthesis," Transportation, Springer, vol. 50(4), pages 1323-1346, August.
    17. Burgard, Jan Pablo & Krause, Joscha & Schmaus, Simon, 2021. "Estimation of regional transition probabilities for spatial dynamic microsimulations from survey data lacking in regional detail," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    18. Willberg, Elias S & Tenkanen, Henrikki & Poom, Age & Salonen, Maria & Toivonen, Tuuli, 2021. "Comparing spatial data sources for cycling studies – a review," SocArXiv ruy3j, Center for Open Science.
    19. Lansley, Guy & Longley, Paul, 2016. "Deriving age and gender from forenames for consumer analytics," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 271-278.
    20. Koglin, Till, 2015. "Organisation does matter – planning for cycling in Stockholm and Copenhagen," Transport Policy, Elsevier, vol. 39(C), pages 55-62.
    21. Jie Gao & Dick Ettema & Marco Helbich & Carlijn B. M. Kamphuis, 2019. "Travel mode attitudes, urban context, and demographics: do they interact differently for bicycle commuting and cycling for other purposes?," Transportation, Springer, vol. 46(6), pages 2441-2463, December.

    More about this item

    Keywords

    Spatial microsimulation; walking; cycling; transport resilience;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

    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:ijm:journl:v10:y:2017:i:1:p:167-200. 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: Jinjing Li (email available below). General contact details of provider: http://www.microsimulation.org/ijm/ .

    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.