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Determining Factors for Slum Growth with Predictive Data Mining Methods

Friesen, John ; Rausch, Lea ; Pelz, Peter F. ; Fürnkranz, Johannes (2023)
Determining Factors for Slum Growth with Predictive Data Mining Methods.
In: Urban Science, 2018, 2 (3)
doi: 10.26083/tuprints-00016703
Article, Secondary publication, Publisher's Version

Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

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Item Type: Article
Type of entry: Secondary publication
Title: Determining Factors for Slum Growth with Predictive Data Mining Methods
Language: English
Date: 20 November 2023
Place of Publication: Darmstadt
Year of primary publication: 2018
Place of primary publication: Basel
Publisher: MDPI
Journal or Publication Title: Urban Science
Volume of the journal: 2
Issue Number: 3
Collation: 19 Seiten
DOI: 10.26083/tuprints-00016703
Corresponding Links:
Origin: Secondary publication DeepGreen

Currently, more than half of the world’s population lives in cities. Out of these more than four billion people, almost one quarter live in slums or informal settlements. In order to improve living conditions and provide possible solutions for the major problems in slums (e.g., insufficient infrastructure), it is important to understand the current situation of this form of settlement and its development. There are many different models that attempt to simulate the development of slums. In this paper, we present data mining models that correlate information about the temporal development of slums with other economic, ecologic, and demographic factors in order to identify dependencies. Different learning algorithms, such as decision rules and decision trees, are used to learn descriptive models for slum development from data, and the results are evaluated with commonly used attribute evaluation methods known from data mining. The results confirm various previously made statements about slum development in a quantitative way, such as the fact that slum development is very strongly linked to the demographic development of a country. Applying the introduced classification models to the most recent data for different regions, it can be shown that the slum development in Africa is expected to be above average.

Uncontrolled Keywords: slums, informal settlements, data mining, slum development
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-167038
Additional Information:

This article belongs to the Special Issue Urban Modeling and Simulation

Classification DDC: 000 Generalities, computers, information > 004 Computer science
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institute for Fluid Systems (FST) (since 01.10.2006)
20 Department of Computer Science > Knowl­edge En­gi­neer­ing
Date Deposited: 20 Nov 2023 15:00
Last Modified: 29 Nov 2023 15:22
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/16703
PPN: 513551328
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