IDEAS home Printed from https://ideas.repec.org/a/dem/demres/v29y2013i22.html

Validation of spatially allocated small area estimates for 1880 Census demography

Author

Listed:
  • Matt Ruther

    (University of Colorado Boulder)

  • Nicholas Nagle

    (University of Tennessee)

  • Galen Maclaurin

    (University of Colorado Boulder)

  • Stefan Leyk

    (University of Colorado Boulder)

  • Barbara Buttenfield

    (University of Colorado Boulder)

Abstract

Objective: This paper details the validation of a methodology which spatially allocates Census microdata to census tracts, based on known, aggregate tract population distributions. To protect confidentiality, public-use microdata contain no spatial identifiers other than the code indicating the Public Use Microdata Area (PUMA) in which the individual or household is located. Confirmatory information including the location of microdata households can only be obtained in a Census Research Data Center (CRDC). Due to restrictions in place at CRDCs, a systematic procedure for validating the spatial allocation methodology needs to be implemented prior to accessing CRDC data. Methods: This study demonstrates and evaluates such an approach, using historical census data for which a 100% count of the full population is available at a fine spatial resolution. The approach described allows for testing of the behavior of a maximum entropy imputation and spatial allocation model under different specifications. The imputation and allocation is performed using a microdata sample of records drawn from the full 1880 Census enumeration and synthetic summary files created from the same source. The results of the allocation are then validated against the actual values from the 100% count of 1880. Results: The results indicate that the validation procedure provides useful statistics, allowing an in-depth evaluation of the household allocation and identifying optimal configurations for model parameterization. This provides important insights as to how to design a validation procedure at a CRDC for spatial allocations using contemporary census data.

Suggested Citation

  • Matt Ruther & Nicholas Nagle & Galen Maclaurin & Stefan Leyk & Barbara Buttenfield, 2013. "Validation of spatially allocated small area estimates for 1880 Census demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(22), pages 579-616.
    • Handle: RePEc:dem:demres:v:29:y:2013:i:22
    DOI: 10.4054/DemRes.2013.29.22
    as

    Download full text from publisher

    File URL: https://www.demographic-research.org/volumes/vol29/22/29-22.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.4054/DemRes.2013.29.22?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Robert Tanton & Yogi Vidyattama & Binod Nepal & Justine McNamara, 2011. "Small area estimation using a reweighting algorithm," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 931-951, October.
    2. Renato Assunção & Carl Schmertmann & Joseph Potter & Suzana Cavenaghi, 2005. "Empirical bayes estimation of demographic schedules for small areas," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 537-558, August.
    3. Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
    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. Xu, Hai-Yan & Kuo, Shyh-Hao & Li, Guoqi & Legara, Erika Fille T. & Zhao, Daxuan & Monterola, Christopher P., 2016. "Generalized Cross Entropy Method for estimating joint distribution from incomplete information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 162-172.

    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. Vo, Khoa D. & Kim, Eui-Jin & Bansal, Prateek, 2025. "A novel data fusion method to leverage passively-collected mobility data in generating spatially-heterogeneous synthetic population," Transportation Research Part B: Methodological, Elsevier, vol. 191(C).
    2. Maheshwar Rao & Robert Tanton & Yogi Vidyattama, 2013. "‘A Systems Approach to Analyse the Impacts of Water Policy Reform in the Murray-Darling Basin: a conceptual and an analytical framework’," NATSEM Working Paper Series 13/22, University of Canberra, National Centre for Social and Economic Modelling.
    3. Frederik Priem & Philip Stessens & Frank Canters, 2020. "Microsimulation of Residential Activity for Alternative Urban Development Scenarios: A Case Study on Brussels and Flemish Brabant," Sustainability, MDPI, vol. 12(6), pages 1-28, March.
    4. Susan M. Rogers & James Rineer & Matthew D. Scruggs & William D. Wheaton & Phillip C. Cooley & Douglas J. Roberts & Diane K. Wagener, 2014. "A Geospatial Dynamic Microsimulation Model for Household Population Projections," International Journal of Microsimulation, International Microsimulation Association, vol. 7(2), pages 119-146.
    5. Sullivan, James L. & Novak, David C., 2024. "A method for evaluating accessibility in transportation problems considering social vulnerability," European Journal of Operational Research, Elsevier, vol. 317(3), pages 646-659.
    6. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    7. 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.
    8. repec:osf:osfxxx:jk67t_v1 is not listed on IDEAS
    9. Loughrey, Jason & O’Donoghue, Cathal & Meredith, David & Murphy, Ger & Shanahan, Ultan & Miller, Corina, "undated". "The Local Impact of Cattle Farming," 166th Seminar, August 30-31, 2018, Galway, West of Ireland 276231, European Association of Agricultural Economists.
    10. Templ, Matthias & Meindl, Bernhard & Kowarik, Alexander & Dupriez, Olivier, 2017. "Simulation of Synthetic Complex Data: The R Package simPop," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i10).
    11. Chen, Zhiwei & Li, Xiaopeng, 2021. "Unobserved heterogeneity in transportation equity analysis: Evidence from a bike-sharing system in southern Tampa," Journal of Transport Geography, Elsevier, vol. 91(C).
    12. Evangelos Panos & Stavroula Margelou, 2019. "Long-Term Solar Photovoltaics Penetration in Single- and Two-Family Houses in Switzerland," Energies, MDPI, vol. 12(13), pages 1-33, June.
    13. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    14. Marcia Castro, 2007. "Spatial Demography: An Opportunity to Improve Policy Making at Diverse Decision Levels," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(5), pages 477-509, December.
    15. Johan Barthelemy & Philippe L. Toint, 2013. "Synthetic Population Generation Without a Sample," Transportation Science, INFORMS, vol. 47(2), pages 266-279, May.
    16. Fenton, Alex, 2013. "Small-area measures of income poverty," LSE Research Online Documents on Economics 58053, London School of Economics and Political Science, LSE Library.
    17. Roszka Wojciech, 2019. "Spatial Microsimulation Of Personal Income In Poland At The Level Of Subregions," Statistics in Transition New Series, Statistics Poland, vol. 20(3), pages 133-153, September.
    18. Dietmar Göhlich & Kai Nagel & Anne Magdalene Syré & Alexander Grahle & Kai Martins-Turner & Ricardo Ewert & Ricardo Miranda Jahn & Dominic Jefferies, 2021. "Integrated Approach for the Assessment of Strategies for the Decarbonization of Urban Traffic," Sustainability, MDPI, vol. 13(2), pages 1-31, January.
    19. repec:cep:sticas:/173 is not listed on IDEAS
    20. Stefano Guarino & Enrico Mastrostefano & Massimo Bernaschi & Alessandro Celestini & Marco Cianfriglia & Davide Torre & Lena Rebecca Zastrow, 2021. "Inferring Urban Social Networks from Publicly Available Data," Future Internet, MDPI, vol. 13(5), pages 1-45, April.
    21. Zou, Tianqi & Aemmer, Zack & MacKenzie, Don & Laberteaux, Ken, 2022. "A framework for estimating commute accessibility and adoption of ridehailing services under functional improvements from vehicle automation," Journal of Transport Geography, Elsevier, vol. 102(C).
    22. 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.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

    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:dem:demres:v:29:y:2013:i:22. 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: Editorial Office (email available below). General contact details of provider: https://www.demogr.mpg.de/ .

    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.