How walkable is your neighborhood? Assessing walkability around T-stops outside the city of Boston

Conclusions

Methodology

Findings

Limitations

Background

Data Sources

First, roads one mile within each T-

stop were selected from the

MassGIS Mass Department of

Transportation (DOT) Roads dataset

using the “Select by Location” tool.

T-stops in Boston and Brookline

were excluded by deselecting roads

with a city code of “35” or “46” .

The remaining roads were exported

into a new file and the attributes ta-

ble was imported into SAS. SAS

was used to create scores for various

components within the dataset to

compute a walkability score. Any

road with missing data was excluded

from the total score.

Right side walk and left side walk

were both categorized as follows: 0,

greater than 0 but less than five feet

wide, equal to five feet wide, and

greater than five feet wide, and as-

signed a score of 0, 1, 2, or 3, respec-

tively. Road type was categorized us-

ing the Functional Classification as

follows: Interstate, Urban or Rural

Principal Arterial, Rural Minor Arte-

rial; Urban Minor Arterial or Rural

Major Collector; Urban Collector or

Rural Minor Collector; and Local,

and assigned a score of 0, 1, 2, or 3,

respectively. Right Shoulder Type

was categorized as having a shoulder

or no shoulder, and assigned a score

of 0 or 3 respectively. Left shoulder

type could not be used, as 85% of the

variable was missing. Terrain was

categorized as Mountainous, Rolling,

and Level, and scored as 0, 1, and 2,

respectively. Finally, curbs were

classified as no curb or median only,

left or right side only, or all sides, and

scored as 0, 1, and 2, respectively.

The scores from each component were

averaged together. The new variables

created were then imported back to

ArcGIS and joined to the MassDOT

Roads shape file. Then, a total score

for each T-stop was created by select-

ing the T-stop of interest, using the

“Select by Location” tool to select

roads within 1 mile of the selected T-

stop, and summing the scores for all

roads selected. The average score per

road for each T-stop was calculated by

dividing the total score by the count of

roads. Finally, the percent of missing

data was calculated.

Table 1 presents the T-stop, train line,

Walkability Score, number of observa-

tions, the percent missing, and the walka-

bility per road. Many T-stops had over

fifty percent of data missing (Braintree,

Rivreside, Woodland, Waban, Chestnut

Hill). According the data and scoring

method, the areas with the highest walka-

bility per count were along the red line,

and included Porter Square, Davis

Square, Harvard Square, and Kendall/

MIT. These areas also tended to have the

lowest percentages of missing data. The

areas with the lowest walkability per

count were Riverside, Chestnut Hill,

Woodland, and Waban. These areas also

tended to have the highest percentages of

missing data. It is unknown how the miss-

ing would have affected the direction of

the scores. For illustrative purposes,

each map shows each score component

comparing the Alewife stop to the Davis

Square stop. Based on the data and on

the scoring system, it could be concluded

that Davis Square is more walkable than

Alewife.

Walkability was only assessed using

one data source, the MassGIS

MassDOT Roads dataset. Each vector

was assumed to be homogenous.

Sidewalks were only included if asso-

ciated with a road in the dataset.

Other potential contributors to walka-

bility, such as distance to businesses,

lighting, land use diversity, and crime

rates were not included. Results have

not been validated against other walk-

ability scores or against surveys

measuring perception of walkability.

Of the variables of interest, the da-

taset had a high proportion missing,

ranging between 18% and 59%.

Walkability can be defined as the

overall walking conditions and indi-

viduals who live in more walkable

areas tend to report being healthier

(Oishi et al). Walkability is especially

important in New England, as heavy

snow can narrow sidewalks.

Past walk scores have included resi-

dential density score, street connec-

tivity score, and a land use mix score

(Sundquist et al). These calculated

scores can validated by comparing to

surveys measuring perception of

walkability, or by accelerometer use.

The purpose of this project was to de-

velop a measure of walkability using

variables considered to reflect

walkability in the MassGIS

MassDOT Roads dataset and compare

scores among various T-stops outside

the city of Boston.

MassGIS Data – Massachusetts Department of Transportation

(MassDOT) Roads, June 2014; MassDOT and MassGIS,

published by MassGIS, accessed November 6, 2015.

MassGIS Data – Rapid Transit, September 2014, Central Trans-

portation Planning Staff and MassGIS. Accessed November

6, 2015.

MassDEP Hydrography (1:25,000), March 2010, Massachusetts

Department of Environmental Protection and U.S. Geologi-

cal Survey (USGS)

Protected and Recreational OpenSpace, May 2015, Executive

Office of Energy and Environmental Affairs.

MassGIS Data – Community Boundaries (Towns), February

2014, USGS, Massachusetts Dept. of Public Works, and

MassGIS.

Oishi S, Saeki M, Axt J.Are People Living in Walkable Areas Healthier and More Satisfied with Life? Appl Psy-

chol Health Well Being. 2015 Nov; 7(3):365-86.

Sundquist, K., Eriksson, U., Kawakami, N., Skog, L., Ohlsson, H., Arvidsson, D. Neighborhood walkability,

physical activity, and walking behavior: The Swedish Neighborhood and Physical Activity (SNAP) study.

Cartographer: Tamar Roomian

Date: December 22, 2015

Course: Fundamentals of GIS for Food, Agriculture, and

Environmental Applications

Lecturer: Paul B. Cote

School: Gerald J. and Dorothy R. Friedman School of Nutrition

Science and Policy

Map displayed on 1:60,000 scale Map displayed on 1:25,000 scale

Map displayed on 1:25,000 scale

Map displayed on 1:25,000 scale Map displayed on 1:25,000 scale

Map displayed on 1:25,000 scale

Map displayed on 1:25,000 scale

While a score was created to compare

walkability around T-stops outside the

Boston area, given the high proportion

of missing data, results should be inter-

preted with caution. Future work may

incorporate other contributors to

walkability, such as proximity to busi-

nesses, lighting, land use diversity, and

crime rates. Future work may also vali-

date against other walkability scores,

surveys measuring perception of walka-

bility, or accelerometer use.

Table 1. Walkability Score and Walkability Score Per Road

T-stop Line Walkability Score Count % Missing Walkability/Count

Porter Red 2916 1526 17.60 1.911

Davis Square Red 2681 1406 20.25 1.907

Central Red 2350 1235 21.59 1.903

Malden Center Orange 2163 1149 28.46 1.883

Harvard Red 2250 1207 21.01 1.864

Kendall/MIT Red 1398 756 27.03 1.849

Oak Grove Orange 1452 786 45.87 1.847

Sullivan Square Orange 1124 617 34.92 1.822

Assembly Orange 1056 595 42.06 1.775

Alewife Red 1496 849 27.25 1.762

Revere Beach Blue 916 525 40.27 1.745

Wonderland Blue 919 533 37.88 1.724

Quincy Center Red 1304 757 32.41 1.723

Wellington Orange 896 523 44.06 1.713

Beachmont Blue 552 326 46.47 1.693

Newton Highlands Green 1143 679 40.75 1.683

Eliot Green 1028 617 42.71 1.666

North Quincy Red 1183 715 23.37 1.655

Braintree Red 531 324 60.77 1.639

Quincy Adams Red 907 557 43.79 1.628

Wollaston Red 1492 924 18.30 1.615

Newton Centre Green 1098 682 40.64 1.610

Waban Green 687 435 52.15 1.579

Woodland Green 611 402 54.78 1.520

Chestnut Hill Green 398 277 57.77 1.437

Riverside Green 554 404 58.61 1.371