Using LEHD Origin-Destination Data to Measure Commuting Distance
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Transcript Using LEHD Origin-Destination Data to Measure Commuting Distance
Using LEHD Origin-Destination Data
to Measure Commuting Distance
James Palma
Maryland State Data Center
Maryland Department of Planning
301 West Preston Street, Suite 702
Baltimore, Maryland 20201
September 20, 2010
Presented at the 2010 APDU Conference
Washington, D.C.
Smart Growth
“Smart growth” policies:
Desire to locate workers near their workplaces
Reducing commuting reduces greenhouse gas emissions
Compact development conserves land
Lack tools to measure policy success or failure
Priority Funding Areas (PFAs)
Maryland’s “Priority Funding Areas”:
Were created by the 1997 Priority Funding Areas Act
Direct state investment into “existing communities and places
where local governments want State investment to support
future growth.”
Consist of:
every municipality, as they existed in 1997;
areas inside the Washington Beltway and the Baltimore Beltway;
areas already designated as enterprise zones, neighborhood
revitalization areas, heritage areas and existing industrial land;
Areas designated by local governments for future industrial,
commercial, or residential growth.
Lack of Adequate Data
Few data sources allow widescale measurement of
commuting distance.
Decennial Census and ACS:
Measure time, not distance
Time is affected by traffic congestion and travel mode
Travel surveys
Lack geographic specificity
Have small sample sizes
Are not updated on a regular basis
LEHD Data
Tracks origins and destinations of workers
Uses a reasonably small geography (blocks)
Separates workers into three:
Age groups
Income groups
Industry categories
Based on a large dataset with near-national coverage
Tracks commuting patterns over time, is updated
frequently
LEHD Data Limitations
Suppression of small areas for origins and
destinations
Synthetic data to protect confidentiality
Lack of data on non-QCEW employment and sole
proprietors
Lack of federal employment data
Important for Maryland
Lack of data for Washington, D.C.
Soon to be rectified
Method
Calculate geographic centroid of each block
Use coordinates of each origin and destination
centroid in formula to create a “distance matrix”
Convert results to your favorite measurement system
Feed results into your favorite statistical processing
program (I used R)
Spherical Law of Cosines
Simple formula:
d = acos(sin(lat1)*sin(lat2)+cos(lat1) *cos(lat2)*cos(long2−long1))*R
Where:
d = distance
lat is latitude in radians
long is longitude in radians
R is the mean radius of the Earth (6,371 km)
Accurate down to one meter (with limitations)
For workers who live and work in same block:
Area
Distance used is radius of area of block: r
http://www.movable-type.co.uk/scripts/latlong.html. Graphic sourced from http://en.wikipedia.org/wiki/Spherical_law_of_cosines.
Data Files Used for Analysis
All Jobs files (JA), both Main (In-state commuting) and
Aux (In-commuting for out-state residents) for:
Maryland
JA Aux files only for bordering states (others ignored):
Delaware
Pennsylvania
Virginia
West Virginia
TIGER 2009 files for Census 2000 Blocks
DBF files from ESRI shapefiles imported into MS-Access
Each DBF saved as two tables (workplace and residence) for ease of
processing
One file from each state above, all appended together
Data Processing Steps
Extract all Maryland origin and destination data from AUX
files, append to MD Main file
Append all DBF block files together
Convert decimal degree coordinates for block centroids to
radians for work and home block tables
Use block area to calculate “radius” value to use as block-
internal commuting distance
Join work and home block tables to O-D files
Test for O-D in same block, apply proper formula
Distance is “radius” for O-D in same block
Spherical law of cosines formula for O-D in different blocks
Results
Works
Lives
In PFA
In PFA
In PFA
Outside PFA (In
MD)
In PFA
Outstate
Outside PFA (In
MD)
Outside PFA (In
MD)
Outside PFA (In
MD)
Distance
(mi)
Average
(mi)
15.2
339,460
13.3%
7,829,454
23.1
195,270
7.6%
7,595,898
38.9
In PFA
96,396
3.8%
1,872,985
19.4
Outside PFA (In
MD)
52,024
2.0%
740,847
14.2
Outstate
16,129
0.6%
670,058
41.5
140,650
5.5%
5,126,210
36.4
35,507
1.4%
1,405,926
39.6
100.0% 50,762,759
19.8
In PFA
Outstate
Outside PFA (In
MD)
1,684,407
Percentage
of Workers
65.8% 25,521,380
Outstate
Total
Total
Workers
2,559,843
Results
Percentage of Workers Living and Working In and Out of PFA
9%
4%
Works In PFA, Lives In
PFA
Works In PFA, Lives
Outside PFA (All)
21%
66%
Works Outside PFA (All),
Lives In PFA
Works Outside PFA (All),
Lives Outside PFA (All)
Results
Average Commute Distance in Maryland, 2008
35
30
Distance in Miles
25
20
15
10
5
0
Works In PFA, Lives Works In PFA, Lives Works Outside PFA Works Outside PFA
In PFA
Outside PFA (All) (All), Lives In PFA (All), Lives Outside
PFA (All)
Overall Average
LEHD Analysis Limitations
Not measuring commutes, but distance to workplace (really,
payroll processing location)
Not actual distance, but centroid-to-centroid distance
Some blocks are larger than others, a problem when calculating
distance matrices
Formula result is air distance only, does not take road system
into account
Some commute lengths are very long, implying that workers do
not actually work at their “workplace”
Though extreme commuting may be an issue, telecommuting is more likely
Usefulness of Analysis
Already used to compare commutes by workers
residing inside and outside Priority Funding Areas
(PFAs)
Can also be used to track transit-friendly commutes
Other data layers can be added for further analysis:
Housing price data
Demographics
Development trends and patterns
Etc.
Near-nationwide LEHD coverage allows
comparisons to other areas
Next Steps
Weight centroids based on property parcel location
May create more accurate distances, esp. in larger blocks
Calculate distance on road network for sample of
origins and destinations
Create a multiplier to adjust “air distance” to road distance
Experiment with different job categories:
Primary
Private
More research on extreme commuting vs. data
anomalies
Questions?