<div class="csl-bib-body">
<div class="csl-entry">Shita, M. W., Zewale, H. L., & Navratil, G. (2026). Spatial Determinants of Urbanisation in Debre Markos, Ethiopia: Modelling Building Footprint. In <i>REAL CORP 2026: Everybody plans ... sometimes - cherish heritage, plan now, create a better future! : proceedings of 31st International Conference on Urban Planning, Regional Development and Information Society</i> (pp. 829–838).</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/227712
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dc.description.abstract
Urbanization puts pressure on socio-economic and environmental aspects worldwide. Spatial factors play a pivotal role in driving this urbanization; for instance, geographical features, proximity to socio-economic services, and government zoning regulations are spatial determinants discussed in the literature. The purpose of this study is to understand the spatial determinants of urbanization based on building footprints. The Google Open Building Dataset has been used for retrieving building footprints. Additionally, 26 dependent variables were collected from various sources. Road networks were extracted from OSMnx, and geographical data were collected from Google Earth Engine. Points of interest for proximity estimation were gathered from the Debre Markos municipality, and some socio-economic data were collected through a survey of 385 respondents, which were then interpolated to the entire area. The independent variables are categorized as geographical, proximity, socio-economic, and governmental regulation factors. About 25,000 training samples were extracted from each variable to train the models. Two methods were employed in this research: the binary logistic regression and the machine-learning model of XGBoost. Binary logistic regression was employed for its interpretability, while XGBoost was employed for its superior data management and prediction accuracy. According to the results of the area under the curve (AUC) for accuracy measurement, logistic regression achieved 0.73, and XGBoost achieved 0.82. However, the data fit the model in both cases. Distance from road, building height zone, road network density, and slope are among the top factors determining urban building footprint. This implies that the likelihood of building has increased near roads. The results of building-height zoning show that local government regulations affect the likelihood of building, and a model result on slope also indicates that topography is a significant determinant of urbanization.
en
dc.language.iso
en
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dc.subject
XGBoost
en
dc.subject
logistic regression
en
dc.subject
urban expansion
en
dc.subject
building presence
en
dc.subject
urban modeling
en
dc.title
Spatial Determinants of Urbanisation in Debre Markos, Ethiopia: Modelling Building Footprint
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
TU Wien, Austria
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dc.contributor.affiliation
TU Wien, Austria
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dc.relation.isbn
978-3-9504945-5-6
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dc.description.startpage
829
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dc.description.endpage
838
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
REAL CORP 2026: Everybody plans ... sometimes - cherish heritage, plan now, create a better future! : proceedings of 31st International Conference on Urban Planning, Regional Development and Information Society
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tuw.peerreviewed
true
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tuw.researchTopic.id
C4
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tuw.researchTopic.id
C6
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tuw.researchTopic.name
Mathematical and Algorithmic Foundations
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.value
20
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tuw.researchTopic.value
80
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tuw.publication.orgunit
E120-02 - Forschungsbereich Geoinformation
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dc.description.numberOfPages
10
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tuw.author.orcid
0000-0002-4309-2641
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tuw.author.orcid
0009-0004-7638-9806
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tuw.author.orcid
0000-0002-2978-5724
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tuw.event.name
31. Internationale Konferenz zu Stadtplanung, Regionalentwicklung und Informationsgesellschaft (REAL CORP 2026)