Analyzing the Supply and Detecting Spatial Patterns of Urban Green Spaces via Optimization

  • Johannes OehrleinEmail author
  • Benjamin Niedermann
  • Jan-Henrik Haunert
Original Article


Green spaces in urban areas offer great possibilities of recreation, provided that they are easily accessible. Therefore, an ideal city should offer large green spaces close to where its residents live. Although there are several measures for the assessment of urban green spaces, the existing measures usually focus either on the total size of all green spaces or on their accessibility. Hence, in this paper, we present a new methodology for assessing green-space provision and accessibility in an integrated way. The core of our methodology is an algorithm based on linear programming that computes an optimal assignment between residential areas and green spaces. In a basic setting, it assigns green spaces of a prescribed size exclusively to each resident, such that an objective function that, in particular, considers the average distance between residents and assigned green spaces is optimized. We contribute a detailed presentation on how to engineer an assignment-based method, such that it yields plausible results (e.g., by considering distances in the road network) and becomes efficient enough for the analysis of large metropolitan areas (e.g., we were able to process an instance of Berlin with about 130,000 polygons representing green spaces, 18,000 polygons representing residential areas, and 6 million road segments). Furthermore, we show that the optimal assignments resulting from our method enable a subsequent analysis that reveals both interesting global properties of a city as well as spatial patterns. For example, our method allows us to identify neighbourhoods with a shortage of green spaces, which will help spatial planners in their decision-making.


Urban green Transportation problem Maximum flow Linear program Cluster analysis 


Analyse des Angebots an und Detektion räumlicher Muster von städtischen Grünflächen. Grünflächen in städtischen Gebieten bieten große Erholungsmöglichkeiten, sofern sie leicht zugänglich sind. Daher sollte eine ideale Stadt große Grünflächen in der Nähe der Wohnungen ihrer Bewohner bieten. Obwohl es mehrere Maße zur Bewertung städtischer Grünflächen gibt, konzentrieren sich die bestehenden Maße in der Regel entweder auf die Gesamtgröße aller Grünflächen oder auf ihre Zugänglichkeit. In diesem Artikel stellen wir daher eine neue Methode zur integrierten Bewertung der Versorgung und Zugänglichkeit von Grünflächen vor. Der Kern unserer Methodik ist ein Algorithmus, der auf linearer Programmierung basiert und eine optimale Zuordnung zwischen Wohngebieten und Grünflächen berechnet. In seiner Grundeinstellung weist er jedem Bewohner exklusiv Grünflächen einer vorgegebenen Größe zu, so dass eine mathematische Zielfunktion optimiert wird, die insbesondere den durchschnittlichen Abstand zwischen Bewohnern und zugewiesenen Grünflächen berücksichtigt. In einer ausführlichen Diskussion zeigen wir, wie diese zuweisungsbasierte Methode so in der Praxis umgesetzt werden kann, dass sie plausible Ergebnisse liefert (z.B. durch Berücksichtigung von Entfernungen im Straßennetz) und effizient genug für die Analyse großer Ballungsräume ist. Zum Beispiel sind wir in der Lage, eine Instanz von Berlin mit etwa 130.000 Polygonen für Grünflächen, 18.000 Polygonen für Wohngebiete und 6 Millionen Straßensegmenten zu verarbeiten. Darüber hinaus zeigen wir, dass die optimalen Zuordnungen, die sich aus unserer Methode ergeben, eine nachfolgende Analyse ermöglichen, die sowohl interessante globale Eigenschaften einer Stadt als auch räumliche Muster aufdeckt. Unsere Methode ermöglicht es uns beispielsweise, Nachbarschaften mit einem Mangel an Grünflächen zu identifizieren, was Raumplanern bei ihrer Entscheidungsfindung hilft.


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Copyright information

© Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2019

Authors and Affiliations

  1. 1.Institute of Geodesy and GeoinformationUniversity of BonnBonnGermany

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