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

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

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

Walk Score

www.walkscore.comGives any location a score from 0-100 based on

the variety and proximity of nearby commercial facilities

Looks for closest facilities in five categories: education, retail, food, recreation, and entertainment.

Transit Score

- 0-100 score rating public transportation access- Based on proximity to transit stops, type of

transportation and frequency of stops.- Available in about 150 cities

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

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

Validation for Research PurposesCarr LJ, Dunsiger SI, Marcus BH. (2010). Walk score™ as a global estimate of

neighborhoodwalkability. American Journal of Preventive Medicine. 39(5):460-3.

Carr LJ, Dunsiger SI, Marcus BH. (2011) Validation of Walk Score for estimating access to

walkable amenities. British Journal of Sports Medicine. 45(14):1144-8.

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): 4160-

4179.

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: 10.1007/s10708-011-9444-4

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”

Walk Score Prediction Map

Heat map to see spatial patterns Walk Score and Transit Score on city-wide scale.

Sample area – Buffalo, NY

Sampling

Lat/Long coordinates of each Census Block centroid found

Used as input parameters for API calls

Uploaded to ArcMap as points

InterpolationOrdinary kriging – no trend removal, Gaussian

model6 Neighbors, at least 3 included

Buffalo NY

Walk Score prediction map

Buffalo NY

Transit Score prediction map

Buffalo NY

Walk Score +Transit Score

Works CitedLo, R. H. (2009). "Walkability: What is it?" Journal of Urbanism:

International Research on Placemaking and Urban Sustainability 2(2): 145-166.

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): 193-216.

Owen, N., E. Leslie, et al. (2000). "Environmental Determinants of Physical Activity and Sedentary Behavior." Excercise & Sport Sciences Reviews 28(4): 153-158.

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): 193-216.

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): 239-252.

Whalen, J. (2011). "WalkscoreAPI Walk Score and Transit Score API." R Package version 1.0.