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

Niklas, Ulrich; Behren, Sascha von; Soylu, Tamer; Kopp, Johanna; Chlond, Bastian; Vortisch, Peter


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. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000122670
Veröffentlicht am 17.08.2020
DOI: 10.3390/urbansci4030036
Zitationen: 11
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Verkehrswesen (IFV)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2413-8851
KITopen-ID: 1000122670
Erschienen in Urban science
Verlag MDPI
Band 4
Heft 3
Seiten 36
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 10.08.2020
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