A Visual Analytics Approach for Assessing Pedestrian Friendliness of Urban Environments

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


The availability of efficient transportation facilities is vital to the function and development of modern cities. Promoting walking is crucial for supporting livable communities and cities. Assessing the quality of pedestrian facilities and constructing appropriate pedestrian walking facilities are important tasks in public city planning. Additionally, walking facilities in a community affect commercial activities including private investment decisions such as those of retailers. However, analyzing what we call pedestrian friendliness in an urban environment involves multiple data perspectives, such as street networks, land use, and other multivariate observation measurements, and consequently poses significant challenges. In this study, we investigate the effect of urban environment properties on pedestrian movement in different locations in the metropolitan region of Tel Aviv. The first urban area we investigated was the inner city of the Tel Aviv metropolitan region, one of the central regions in Tel Aviv, a city that serves many non-local residents. For simplicity, we refer to this area as Tel Aviv. We also investigated Bat Yam, a small city, whose residents use many of the services of Tel Aviv. We apply an improved tool for visual analysis of the correlation between multiple independent and one dependent variable in geographical context. We use the tool to investigate the effect of functional and topological properties on the volume of pedestrian movement. The results of our study indicate that these two urban areas differ greatly. The urban area of Tel Aviv has much more correspondence and interdependency among the functional and topological properties of the urban environment that might influence pedestrian movement. We also found that the pedestrian movements as well as the related urban environment properties in this region are distributed geographically in a more equal and organized form.


Street Network Urban Environment Street Segment Metropolitan Region Street Connectivity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Sebastian Bremm and Tatiana von Landesberger of TU Darmstadt for fruitful discussions on the topic as part of a working group meeting of the DFG SPP 1335 on Scalable Visual Analytics.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.University of KonstanzKonstanzGermany
  2. 2.Tel Aviv UniversityTel AvivIsrael
  3. 3.IBM Research LabHaifaIsrael

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