Next Article in Journal
Extension of Space Syntax Methods to Generic Urban Variables
Next Article in Special Issue
Urban Science: Putting the “Smart” in Smart Cities
Previous Article in Journal
Who Is At Risk of Migrating? Developing Synthetic Populations to Produce Efficient Domestic Migration Rates Using the American Community Survey
Previous Article in Special Issue
Projecting Land-Use and Land Cover Change in a Subtropical Urban Watershed

Determining Factors for Slum Growth with Predictive Data Mining Methods

Chair of Fluid Systems, Technische Universität Darmstadt, Otto-Berndt-Straße 2, D-64287 Darmstadt, Germany
Knowledge Engineering Group, Technische Universität Darmstadt, Hochschulstrasse 10, D-64289 Darmstadt, Germany
Author to whom correspondence should be addressed.
Urban Sci. 2018, 2(3), 81;
Received: 2 July 2018 / Revised: 22 August 2018 / Accepted: 28 August 2018 / Published: 29 August 2018
(This article belongs to the Special Issue Urban Modeling and Simulation)
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. View Full-Text
Keywords: slums; informal settlements; data mining; slum development slums; informal settlements; data mining; slum development
Show Figures

Figure 1

MDPI and ACS Style

Friesen, J.; Rausch, L.; Pelz, P.F.; Fürnkranz, J. Determining Factors for Slum Growth with Predictive Data Mining Methods. Urban Sci. 2018, 2, 81.

AMA Style

Friesen J, Rausch L, Pelz PF, Fürnkranz J. Determining Factors for Slum Growth with Predictive Data Mining Methods. Urban Science. 2018; 2(3):81.

Chicago/Turabian Style

Friesen, John, Lea Rausch, Peter F. Pelz, and Johannes Fürnkranz. 2018. "Determining Factors for Slum Growth with Predictive Data Mining Methods" Urban Science 2, no. 3: 81.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop