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Fuzzy Bayesian inference for mapping vague and place-based regions: a case study of sectarian territory

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Fuzzy Bayesian inference for mapping vague and place-based regions: a case study of sectarian territory. / Huck, Jonny; Whyatt, Duncan; Davies, Gemma et al.
In: International Journal of Geographical Information Science, Vol. 37, No. 8, 31.08.2023, p. 1765-1786.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Huck, J, Whyatt, D, Davies, G, Dixon, J, Sturgeon, B, Hocking, B, Tredoux, C & Bryan, D 2023, 'Fuzzy Bayesian inference for mapping vague and place-based regions: a case study of sectarian territory', International Journal of Geographical Information Science, vol. 37, no. 8, pp. 1765-1786. https://doi.org/10.1080/13658816.2023.2229894

APA

Huck, J., Whyatt, D., Davies, G., Dixon, J., Sturgeon, B., Hocking, B., Tredoux, C., & Bryan, D. (2023). Fuzzy Bayesian inference for mapping vague and place-based regions: a case study of sectarian territory. International Journal of Geographical Information Science, 37(8), 1765-1786. https://doi.org/10.1080/13658816.2023.2229894

Vancouver

Huck J, Whyatt D, Davies G, Dixon J, Sturgeon B, Hocking B et al. Fuzzy Bayesian inference for mapping vague and place-based regions: a case study of sectarian territory. International Journal of Geographical Information Science. 2023 Aug 31;37(8):1765-1786. Epub 2023 Jul 6. doi: 10.1080/13658816.2023.2229894

Author

Huck, Jonny ; Whyatt, Duncan ; Davies, Gemma et al. / Fuzzy Bayesian inference for mapping vague and place-based regions : a case study of sectarian territory. In: International Journal of Geographical Information Science. 2023 ; Vol. 37, No. 8. pp. 1765-1786.

Bibtex

@article{f60ac028aad747c1a8948e8e0cc8ba0f,
title = "Fuzzy Bayesian inference for mapping vague and place-based regions: a case study of sectarian territory",
abstract = "The problem of mapping regions with socially-derived boundaries has been a topic of discussion in the GIS literature for many years. Fuzzy approaches have frequently been suggested as solutions, but none have been adopted. This is likely due to difficulties associated with determining suitable membership functions, which are often as arbitrary as the crisp boundaries that they seek to replace. This paper presents a novel approach to fuzzy geographical modelling that replaces the membership function with a possibility distribution that is estimated using Bayesian inference. In this method, data from multiple sources are combined to estimate the degree to which a given location is a member of a given set and the level of uncertainty associated with that estimate. The Fuzzy Bayesian Inference approach is demonstrated through a case study in which census data are combined with perceptual and behavioural evidence to model the territory of two segregated groups (Catholics and Protestants) in Belfast, Northern Ireland, UK. This novel method provides a robust empirical basis for the use of fuzzy models in GIS, and therefore has applications for mapping a range of socially-derived and otherwise vague boundaries.",
keywords = "Fuzzy, Vague, Place, Segregation, Territory",
author = "Jonny Huck and Duncan Whyatt and Gemma Davies and John Dixon and Brendan Sturgeon and Bree Hocking and Colin Tredoux and Dominic Bryan",
year = "2023",
month = aug,
day = "31",
doi = "10.1080/13658816.2023.2229894",
language = "English",
volume = "37",
pages = "1765--1786",
journal = "International Journal of Geographical Information Science",
issn = "1365-8816",
publisher = "Taylor and Francis Ltd.",
number = "8",

}

RIS

TY - JOUR

T1 - Fuzzy Bayesian inference for mapping vague and place-based regions

T2 - a case study of sectarian territory

AU - Huck, Jonny

AU - Whyatt, Duncan

AU - Davies, Gemma

AU - Dixon, John

AU - Sturgeon, Brendan

AU - Hocking, Bree

AU - Tredoux, Colin

AU - Bryan, Dominic

PY - 2023/8/31

Y1 - 2023/8/31

N2 - The problem of mapping regions with socially-derived boundaries has been a topic of discussion in the GIS literature for many years. Fuzzy approaches have frequently been suggested as solutions, but none have been adopted. This is likely due to difficulties associated with determining suitable membership functions, which are often as arbitrary as the crisp boundaries that they seek to replace. This paper presents a novel approach to fuzzy geographical modelling that replaces the membership function with a possibility distribution that is estimated using Bayesian inference. In this method, data from multiple sources are combined to estimate the degree to which a given location is a member of a given set and the level of uncertainty associated with that estimate. The Fuzzy Bayesian Inference approach is demonstrated through a case study in which census data are combined with perceptual and behavioural evidence to model the territory of two segregated groups (Catholics and Protestants) in Belfast, Northern Ireland, UK. This novel method provides a robust empirical basis for the use of fuzzy models in GIS, and therefore has applications for mapping a range of socially-derived and otherwise vague boundaries.

AB - The problem of mapping regions with socially-derived boundaries has been a topic of discussion in the GIS literature for many years. Fuzzy approaches have frequently been suggested as solutions, but none have been adopted. This is likely due to difficulties associated with determining suitable membership functions, which are often as arbitrary as the crisp boundaries that they seek to replace. This paper presents a novel approach to fuzzy geographical modelling that replaces the membership function with a possibility distribution that is estimated using Bayesian inference. In this method, data from multiple sources are combined to estimate the degree to which a given location is a member of a given set and the level of uncertainty associated with that estimate. The Fuzzy Bayesian Inference approach is demonstrated through a case study in which census data are combined with perceptual and behavioural evidence to model the territory of two segregated groups (Catholics and Protestants) in Belfast, Northern Ireland, UK. This novel method provides a robust empirical basis for the use of fuzzy models in GIS, and therefore has applications for mapping a range of socially-derived and otherwise vague boundaries.

KW - Fuzzy

KW - Vague

KW - Place

KW - Segregation

KW - Territory

U2 - 10.1080/13658816.2023.2229894

DO - 10.1080/13658816.2023.2229894

M3 - Journal article

VL - 37

SP - 1765

EP - 1786

JO - International Journal of Geographical Information Science

JF - International Journal of Geographical Information Science

SN - 1365-8816

IS - 8

ER -