Walkability for Different Urban Granularities

Peer-reviewed
Conference Proceedings
The positive effects of low-intensity physical activity are widely acknowledged and in this context walking is often promoted as an active form of transport. Under the concept of walkability the role of the built …
Author

Hollenstein, D., and Bleisch, S.

Published

2016

Doi

[pdf]

Abstract

The positive effects of low-intensity physical activity are widely acknowledged and in this context walking is often promoted as an active form of transport. Under the concept of walkability the role of the built environment in encouraging walking is investigated. For that purpose, walkability is quantified area-wise by measuring a varying set of built environment attributes. In purely GIS-based approaches to studying walkability, indices are generally built using existing and easily accessible data. These include street network design, population density, land use mix, and access to destinations. Access to destinations is usually estimated using either a fixed radius, or distances in the street network. In this paper, two approaches to approximate a footpath network are presented. The two footpath networks were built making different assumptions regarding the walkability of different street types with respect to more or less restrictive safety preferences. Information on sidewalk presence, pedestrian crossings, and traffic restrictions were used to build both networks. The first network comprises car traffic free areas only. The second network includes streets with low speed limits that have no sidewalks. Both networks are compared to the more commonly used street network in an access-to-distance analysis. The results suggest that for the generally highly walkable study area, access to destination mostly depends on destination density within the defined walkable distance. However, on single street segments access to destinations is diminished when only car traffic free spaces are assumed to be walkable.

Figures

Access-to-destination maps (number of accessible amenities within 400m) calculated based on footpath network 1 comprising car traffic free paths only (left); based on footpath network 2 built from walkability rated streets (middle); and based on unrated street network (right). (Data Sources: Amt für Geoinformation Kanton Solothurn 2016; Direktion für Öffentliche Sicherheit, Stadt Olten)

Footpath network 1 (left) comprising car traffic free paths only; footpath network 2 built from walkability rated streets (middle, the colour coding is explained in Table 1); and the unrated street network (right). (Data sources: Amt für Geoinformation Kanton Solothurn 2016; Direktion Öffentliche Sicherheit, Stadt Olten)

Study Perimeter with various amenities (red dots). (Data Sources: Amt für Geoinformation Kanton Solothurn 2016, OpenStreetMap Contributors 2016, Yellow Pages http://yellow.local.ch/ and Post website https://www.post.ch)

Study Perimeter with test locations A and B. (Data Sources: Amt für Geoinformation Kanton Solothurn 2016)

Cost distance maps for location A (top row) on footpath network 1 (left), on footpath network 2 (middle), on street network (right). Cost distance maps for location B (bottom row) on footpath network 1 (left), on footpath network 2 (middle), on street network (right). (Data Sources: Amt für Geoinformation Kanton Solothurn 2016; Direktion für Öffentliche Sicherheit, Stadt Olten).

BibTeX

@article{hollenstein_walkabilityDiffUrbanGranularities_2016,
 abstract = {The positive effects of low-intensity physical activity are widely acknowledged and in this context walking is often promoted as an active form of transport. Under the concept of walkability the role of the built environment in encouraging walking is investigated. For that purpose, walkability is quantified area-wise by measuring a varying set of built environment attributes. In purely GIS-based approaches to studying walkability, indices are generally built using existing and easily accessible data. These include street network design, population density, land use mix, and access to destinations. Access to destinations is usually estimated using either a fixed radius, or distances in the street network. In this paper, two approaches to approximate a footpath network are presented. The two footpath networks were built making different assumptions regarding the walkability of different street types with respect to more or less restrictive safety preferences. Information on sidewalk presence, pedestrian crossings, and traffic restrictions were used to build both networks. The first network comprises car traffic free areas only. The second network includes streets with low speed limits that have no sidewalks. Both networks are compared to the more commonly used street network in an access-to-distance analysis. The results suggest that for the generally highly walkable study area, access to destination mostly depends on destination density within the defined walkable distance. However, on single street segments access to destinations is diminished when only car traffic free spaces are assumed to be walkable.},
 author = {Hollenstein, Daria and Bleisch, Susanne},
 doi = {10.5194/isprsarchives-XLI-B2-703-2016},
 journal = {ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
 pages = {703--708},
 title = {{Walkability} {for} {Different} {Urban} {Granularities}},
 url = {http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/703/2016/isprs-archives-XLI-B2-703-2016.pdf},
 volume = {XLI-B2},
 year = {2016}
}