Next Article in Journal
Then and Now: A Comparative Historical Toponomastics Analysis of Station Names in 2 of Singapore’s Mass Rapid Transit (MRT) Lines
Previous Article in Journal
Culture-Led Urban Development vs. Capital-Led Colonization of Urban Space: Savamala—End of Story?
Article

Spatial Factor—Using a Random Forest Classification Model to Measure an Internationally Comparable Urbanity Index

1
BMW AG, Petuelring 130, 80788 Munich, Germany
2
Institute for Transport Studies, Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131 Karlsruhe, Germany
*
Authors to whom correspondence should be addressed.
Urban Sci. 2020, 4(3), 36; https://doi.org/10.3390/urbansci4030036
Received: 6 July 2020 / Revised: 3 August 2020 / Accepted: 6 August 2020 / Published: 10 August 2020
Travel behavior can be determined by its spatial context. If there are many shops and restaurants in close proximity, various activities can be done by walking or cycling, and a car is not needed. It is also more difficult (e.g., parking space, traffic jams) to use a car in high-density areas. Overall, travel behavior and dependencies on travel behavior are influenced by urbanity. These relationships have so far only been examined very selectively (e.g., at city level) and not in international comparison. In this study we define an Urbanity Index (UI) at zip code level, which considers factors influencing mobility, international comparability, reproducibility as well as practical application and the development of a scalable methodology. In order to describe urbanity, data were collected regarding spatial structure, population, land use, and public transport. We developed the UI using a supervised machine learning technique which divides zip codes into four area types: (1) super-urban, (2) urban, (3) suburban/small town, (4) rural. To train the model, the perception from experts in known zip codes concerning urbanity and mobility was set as ground truth. With the UI, it is possible to compare countries (Germany and France) with a uniform definition and comparable datasets. View Full-Text
Keywords: urbanization; travel behavior; urbanity index; random forest; urban forms at zip code level; France; Germany urbanization; travel behavior; urbanity index; random forest; urban forms at zip code level; France; Germany
Show Figures

Figure 1

MDPI and ACS Style

Niklas, U.; von Behren, S.; Soylu, T.; Kopp, J.; Chlond, B.; Vortisch, P. Spatial Factor—Using a Random Forest Classification Model to Measure an Internationally Comparable Urbanity Index. Urban Sci. 2020, 4, 36. https://doi.org/10.3390/urbansci4030036

AMA Style

Niklas U, von Behren S, Soylu T, Kopp J, Chlond B, Vortisch P. Spatial Factor—Using a Random Forest Classification Model to Measure an Internationally Comparable Urbanity Index. Urban Science. 2020; 4(3):36. https://doi.org/10.3390/urbansci4030036

Chicago/Turabian Style

Niklas, Ulrich, Sascha von Behren, Tamer Soylu, Johanna Kopp, Bastian Chlond, and Peter Vortisch. 2020. "Spatial Factor—Using a Random Forest Classification Model to Measure an Internationally Comparable Urbanity Index" Urban Science 4, no. 3: 36. https://doi.org/10.3390/urbansci4030036

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

1
Back to TopTop