IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v45y2018i6p1122-1141.html
   My bibliography  Save this article

Modelling residential segregation as unevenness and clustering: A multilevel modelling approach incorporating spatial dependence and tackling the MAUP

Author

Listed:
  • Kelvyn Jones
  • David Manley
  • Ron Johnston
  • Dewi Owen

Abstract

Traditional studies of residential segregation use a descriptive index approach with predefined spatial units to report the degree of neighbourhood differentiation. We develop a model-based approach which explicitly includes spatial effects at multiple scales, recognising the complexity of the urban environment while simultaneously distinguishing segregation at each scale net of all other scales. Moreover, this model distinguishes segregation as unevenness and as spatial clustering in the presence of stochastic variation. The modelling approach, unlike traditional index approaches, allows hypothesis evaluation concerning alternative scales and zonation through an accompanying badness-of-fit measure. Ultimately, this permits the identification of the scale and zonation regime where the spatial patterns come into focus thereby directly tackling the modifiable areal unit problem. The model is applied to Indian ethnicity in Leicester, UK, finding segregation as unevenness and as spatial clustering at multiple scales.

Suggested Citation

  • Kelvyn Jones & David Manley & Ron Johnston & Dewi Owen, 2018. "Modelling residential segregation as unevenness and clustering: A multilevel modelling approach incorporating spatial dependence and tackling the MAUP," Environment and Planning B, , vol. 45(6), pages 1122-1141, November.
  • Handle: RePEc:sae:envirb:v:45:y:2018:i:6:p:1122-1141
    DOI: 10.1177/2399808318782703
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2399808318782703
    Download Restriction: no

    File URL: https://libkey.io/10.1177/2399808318782703?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. W. J. Browne & S. V. Subramanian & K. Jones & H. Goldstein, 2005. "Variance partitioning in multilevel logistic models that exhibit overdispersion," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 599-613, July.
    2. C G Amrhein, 1995. "Searching for the Elusive Aggregation Effect: Evidence from Statistical Simulations," Environment and Planning A, , vol. 27(1), pages 105-119, January.
    3. Tomoki Nakaya, 2000. "An Information Statistical Approach to the Modifiable Areal Unit Problem in Incidence Rate Maps," Environment and Planning A, , vol. 32(1), pages 91-109, January.
    4. Kelvyn Jones & Ron Johnston & James Forrest & Chris Charlton & David Manley, 2018. "Ethnic and class residential segregation: exploring their intersection – a multilevel analysis of ancestry and occupational class in Sydney," Urban Studies, Urban Studies Journal Limited, vol. 55(6), pages 1163-1184, May.
    5. Angelo Mazza & Antonio Punzo, 2015. "On the Upward Bias of the Dissimilarity Index and Its Corrections," Sociological Methods & Research, , vol. 44(1), pages 80-107, February.
    6. Wenquan Zhang & John R. Logan, 2016. "Global Neighborhoods: Beyond the Multiethnic Metropolis," Demography, Springer;Population Association of America (PAA), vol. 53(6), pages 1933-1953, December.
    7. Propper, Carol & Jones, Kelvyn & Bolster, Anne & Burgess, Simon & Johnston, Ron & Sarker, Rebecca, 2005. "Local neighbourhood and mental health: Evidence from the UK," Social Science & Medicine, Elsevier, vol. 61(10), pages 2065-2083, November.
    8. Carrington, William J & Troske, Kenneth R, 1997. "On Measuring Segregation in Samples with Small Units," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 402-409, October.
    9. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Linde, 2014. "The deviance information criterion: 12 years on," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 485-493, June.
    10. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    11. Henk Elffers, 2003. "Analysing neighbourhood influence in criminology," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(3), pages 347-367, August.
    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. Stepinski, Tomasz & Dmowska, Anna, 2019. "Imperfect melting pot – analysis of changes in diversity and segregation of US urban census tracts in the period of 1990-2010," SocArXiv uqj8x, Center for Open Science.
    2. Alexis Comber & Paul Harris, 2022. "The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions," Land, MDPI, vol. 11(3), pages 1-17, March.
    3. Matthew Quick & Nick Revington, 2022. "Exploring the global and local patterns of income segregation in Toronto, Canada: A multilevel multigroup modeling approach," Environment and Planning B, , vol. 49(2), pages 637-653, February.
    4. Samuel H Langton & Reka Solymosi, 2021. "Cartograms, hexograms and regular grids: Minimising misrepresentation in spatial data visualisations," Environment and Planning B, , vol. 48(2), pages 348-357, February.

    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. Kelvyn Jones & Ron Johnston & David Manley & Dewi Owen & Chris Charlton, 2015. "Ethnic Residential Segregation: A Multilevel, Multigroup, Multiscale Approach Exemplified by London in 2011," Demography, Springer;Population Association of America (PAA), vol. 52(6), pages 1995-2019, December.
    2. Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
    3. Angelo Mazza, 2017. "Dealing With The Bias Of The Dissimilarity Index Of Segregation1," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 71(2), pages 11-20, April-Jun.
    4. Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
    5. Coral Río & Olga Alonso-Villar, 2022. "On Measuring Segregation in a Multigroup Context: Standardized Versus Unstandardized Indices," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(2), pages 633-659, September.
    6. Shuhui Guo & Lihua Xiong & Jie Chen & Shenglian Guo & Jun Xia & Ling Zeng & Chong-Yu Xu, 2023. "Nonstationary Regional Flood Frequency Analysis Based on the Bayesian Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 659-681, January.
    7. Griffith, Gareth J. & Jones, Kelvyn, 2019. "Understanding the population structure of the GHQ-12: Methodological considerations in dimensionally complex measurement outcomes," Social Science & Medicine, Elsevier, vol. 243(C).
    8. Hazelton, Martin L. & Parry, Katharina, 2016. "Statistical methods for comparison of day-to-day traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 22-34.
    9. Muhammed Semakula & Franco̧is Niragire & Christel Faes, 2020. "Bayesian spatio-temporal modeling of malaria risk in Rwanda," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
    10. Fabian Krüger & Sebastian Lerch & Thordis Thorarinsdottir & Tilmann Gneiting, 2021. "Predictive Inference Based on Markov Chain Monte Carlo Output," International Statistical Review, International Statistical Institute, vol. 89(2), pages 274-301, August.
    11. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Integrated Deviance Information Criterion for Latent Variable Models," Economics and Statistics Working Papers 6-2018, Singapore Management University, School of Economics.
    12. Merlo, Juan & Ohlsson, Henrik & Chaix, Basile & Lichtenstein, Paul & Kawachi, Ichiro & Subramanian, S.V., 2013. "Revisiting causal neighborhood effects on individual ischemic heart disease risk: A quasi-experimental multilevel analysis among Swedish siblings," Social Science & Medicine, Elsevier, vol. 76(C), pages 39-46.
    13. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
    14. Yang, Kai & Yu, Xinyang & Zhang, Qingqing & Dong, Xiaogang, 2022. "On MCMC sampling in self-exciting integer-valued threshold time series models," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    15. Yaojun Zhang & Lanpeng Ji & Georgios Aivaliotis & Charles Taylor, 2023. "Bayesian CART models for insurance claims frequency," Papers 2303.01923, arXiv.org, revised Dec 2023.
    16. Bresson Georges & Chaturvedi Anoop & Rahman Mohammad Arshad & Shalabh, 2021. "Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation," The International Journal of Biostatistics, De Gruyter, vol. 17(1), pages 75-97, May.
    17. Juan Merlo & Philippe Wagner & Nermin Ghith & George Leckie, 2016. "An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
    18. Kelvyn Jones & Dewi Owen & Ron Johnston & James Forrest & David Manley, 2015. "Modelling the occupational assimilation of immigrants by ancestry, age group and generational differences in Australia: a random effects approach to a large table of counts," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2595-2615, November.
    19. Pedro Saramago & Karl Claxton & Nicky J. Welton & Marta Soares, 2020. "Bayesian econometric modelling of observational data for cost‐effectiveness analysis: establishing the value of negative pressure wound therapy in the healing of open surgical wounds," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1575-1593, October.
    20. George Leckie & Rebecca Pillinger & Kelvyn Jones & Harvey Goldstein, 2012. "Multilevel Modeling of Social Segregation," Journal of Educational and Behavioral Statistics, , vol. 37(1), pages 3-30, February.

    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:sae:envirb:v:45:y:2018:i:6:p:1122-1141. 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: SAGE Publications (email available below). General contact details of provider: .

    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.