Geographic regions for assessing built environmental correlates with walking trips: A comparison using different metrics and model designs

Tribby C.P., Miller H.J., Brown B.B., Smith K.R. and Werner C.M. “Geographic regions for assessing built environmental correlates with walking trips: A comparison using different metrics and model designs, Health and Place, 45, 1-9.

Highlights

  • We assess walking with audit and perceived built environment measures.
  • Spatial measures are walking activity spaces and self-defined neighborhoods.
  • Findings indicate that environmental measures have preferred spatial extents.
  • Researchers need to consider varying spatial measures to assess walking correlates

Abstract

There is growing international evidence that supportive built environments encourage active travel such as walking. An unsettled question is the role of geographic regions for analyzing the relationship between the built environment and active travel. This paper examines the geographic region question by assessing walking trip models that use two different regions: walking activity spaces and self-defined neighborhoods. We also use two types of built environment metrics, perceived and audit data, and two types of study design, cross-sectional and longitudinal, to assess these regions. We find that the built environment associations with walking are dependent on the type of metric and the type of model. Audit measures summarized within walking activity spaces better explain walking trips compared to audit measures within self-defined neighborhoods. Perceived measures summarized within self-defined neighborhoods have mixed results. Finally, results differ based on study design. This suggests that results may not be comparable among different regions, metrics and designs; researchers need to consider carefully these choices when assessing active travel correlates.

Big Data for healthy cities

Miller H.J. and Tolle K. (2016), “Big Data for healthy cities: Using location-aware technologies, open data and 3D urban models to design healthier built environments,” Built Environment, 42, 441-456.

Abstract:  A healthy city is a built environment that encourages physical, mental and social wellbeing. Few neighbourhoods and communities in the United States and increasingly elsewhere in the world are healthy places. A major factor is changes in built environments and lifestyles that have not only eliminated physical activity from daily lives but can also make physical activity unpleasant, unhealthy and unsafe. The development and deployment of sensors connected to location-aware technologies are improving the scientific understanding of built environment characteristics that facilitate healthy and safe physical activity. This paper argues that integrating data from these with new sources of urban data can allow for deeper understanding of the intricate relationships between individuals, environments and healthy places. We discuss the need for an integrated, ecological approach to understanding healthy places and the role of location aware technologies, open data and 3D urban models in facilitating this approach. We also identify major challenges to this approach, including privacy protection.

Big Data and the City: special issue of Built Environment

Special Issue of Built Environment on ‘Big Data and the City’; Volume 42, Number 3, Autumn 2016

http://www.ingentaconnect.com/content/alex/benv

  • Editorial: Big Data, Cities and Herodotus – Batty, Michael
  • Big Data and the City – Batty, Michael
  • From Origins to Destinations: The Past, Present and Future of Visualizing Flow Maps – Claudel, Matthew; Nagel, Till; Ratti, Carlo
  • Towards a Better Understanding of Cities Using Mobility Data – Lenormand, Maxime; Ramasco, José J.
  • Finding Pearls in London’s Oysters – Reades, Jonathan; Zhong, Chen; Manley, ED; Milton, Richard; Batty, Michael
  • A Classification of Multidimensional Open Data for Urban Morphology – Alexiou, Alexandros; Singleton, Alex; Longley, Paul A.
  • User-Generated Big Data and Urban Morphology – Crooks, A.T.; Croitoru, A.; Jenkins, A.; Mahabir, R.; Agouris, P.; Stefanidis, A.
  • Sensing Spatiotemporal Patterns in Urban Areas: Analytics and Visualizations Using the Integrated Multimedia City Data Platform – Thakuriah, Piyushimita; Sila-Nowicka, Katarzyna; Paule, Jorge Gonzalez
  • Playful Cities: Crowdsourcing Urban Happiness with Web Games – Quercia, Daniele
  • Big Data for Healthy Cities: Using Location-Aware Technologies, Open Data and 3D Urban Models to Design Healthier Built Environments – Miller, Harvey J.; Tolle, Kristin
  • Improving the Veracity of Open and Real-Time Urban Data – Mcardle, Gavin; Kitchin, Rob
  • Wise Cities: ‘Old’ Big Data and ‘Slow’ Real Time – Carrera, Fabio
  • Collecting and Visualizing Real-Time Urban Data through City Dashboards –Gray, Steven; O’Brien, Oliver; Hügel, Stephan