1. Creating a Walkability Data Set and Prediction Map Using the Walk ScoreTM Algorithm
John Whalen, M.A.
Department of Geography
The State University of New York at Buffalo
February 25th, 2012

2. Definition of Walkability
- How conducive is the built environment to personal vehicle independence for everyday activities? - Influenced by numerous aspects, such as density, land use, street connectivity, etc. - Many proposed methods to quantify

3. Walkability Has Been Linked To
- Increased physical activity - Lower likelihood of obesity - Less fossil-fuel consumption from cars - Less air pollution from cars - Increased property values

4. Walk Score
Gives any location a score from based on the variety and proximity of nearby commercial facilities Looks for closest facilities in five categories: education, retail, food, recreation, and entertainment.

6. Transit Score
score rating public transportation access - Based on proximity to transit stops, type of transportation and frequency of stops. - Available in about 150 cities

7. Walk Score - Pros
- Free to use - International scale (US, Canada, UK, Ireland, Australia, New Zealand) - Uses a dynamic data set - Eliminates the necessity to gather data sets from many different agencies

8. Walk Score - Limitations
- Straight-line (as the crow flies) distances - Natural barriers/hindrances are disregarded (i.e. bodies of water, slope, weather, etc) - Assumes existence of pedestrian paths - Public Transit not considered - Source data concerns

9. Validation for Research Purposes
Carr LJ, Dunsiger SI, Marcus BH. (2010). Walk score™ as a global estimate of neighborhood walkability. American Journal of Preventive Medicine. 39(5): Carr LJ, Dunsiger SI, Marcus BH. (2011) Validation of Walk Score for estimating access to walkable amenities. British Journal of Sports Medicine. 45(14): Duncan DT, Aldstadt J, Whalen J, Melly SJ, and Gortmaker SL. (2011). Validation of Walk Score® for Estimating Neighborhood Walkability: An Analysis of Four US Metropolitan Areas. International Journal of Environmental Research and Public Health. 8(11): Duncan DT, Aldstadt J, Whalen J, and Melly SJ. (in press). Validation of Walk Scores and Transit Scores for Estimating Neighborhood Walkability and Transit Availability: A Small- Area Analysis. GeoJournal. DOI: /s

10. Walk Score API
- Application Programming Interface (API) - Interface created in R to query Walk Score API with lat/long coordinates, returns Walk and Transit Scores - Greatly accelerates mass-data collection - Available from CRAN – “walkscoreAPI”

11. Walk Score Prediction Map
Heat map to see spatial patterns Walk Score and Transit Score on city-wide scale. Sample area – Buffalo, NY

12. Sampling
Lat/Long coordinates of each Census Block centroid found Used as input parameters for API calls Uploaded to ArcMap as points

13. Interpolation
Ordinary kriging – no trend removal, Gaussian model 6 Neighbors, at least 3 included

14. Buffalo NY
Walk Score
prediction map

15. Buffalo NY
Transit Score
prediction map

16. Buffalo NY
Walk Score +
Transit Score

17. Works Cited
Lo, R. H. (2009). "Walkability: What is it?" Journal of Urbanism: International Research on Placemaking and Urban Sustainability 2(2): Frank, L. D. and P. Engelke (2005). "Multiple Impacts of the Built Environment on Public Health: Walkable Places and the Exposure to Air Pollution." International Regional Science Review 28(2): Owen, N., E. Leslie, et al. (2000). "Environmental Determinants of Physical Activity and Sedentary Behavior." Excercise & Sport Sciences Reviews 28(4): Pivo, G. and J. Fisher (2009). "Effects of Walkability on Property Values and Investment Returns." Working Paper. Front Seat (2010). Walk Score Methodology. Seattle, WA, Front Seat. R Development Core Team (2010). R: A Language and Environment for Statistical Computing., R Foundation for Statistical Computing, Vienna, Austria. Cressie, N. (1990). "The origins of kriging." Mathematical Geology 22(3): Whalen, J. (2011). "WalkscoreAPI Walk Score and Transit Score API." R Package version 1.0.