1. Integrating Online Geocoding Sources into Web-Based Household Travel Surveys

2. Introduction
Most samples in Household Travel Surveys (HTS) complete via web
Geocoding is an important element in HTS collection
Online geocoding services are commonly used in web-based instruments
Google Maps (geocoding) + Google Places (point of interest – POI)
Bing Maps
General assumption that these services are equivalent to traditional desktop GIS geocoding
Including offsetting addresses from centerline…
Intro / background / objective

3. Household Travel Survey User Needs
Origin
Destination

4. The Typical Travel Survey Geocoding Process
Web survey instrument uses Google APIs for geocoding locations
Searches done using geocoding and POI services
Real-time response
Familiar user interface (for many)
High Precision
Participant’s encouraged to provide nearest cross-streets or better
Ability to drag location marker and click on map
Quality-control
Automated checks verify minimum geocode type (address, intersection, POI)
Consistency in travel reviewed using speed checks
Regular client deliverables and review
Discuss typical front end and back end processes for acquiring places, geocoding locations, and quality-control

5. 2015 MDOT Statewide (MTC III) + SEMCOG HTS
Address-based sample
Invitations delivered by USPS
Focus on web self-completion (MITravelCounts.com)
WebGeoSurvey + TripBuilder Web (online instrument)
Telephone reporting and support available via toll-free number
Statewide Survey - additional interest from SEMCOG region
3rd Iteration (previous efforts in 2004 and 2009)
Very peculiar geocoding requirements in RFP
SEMCOG addresses had to be matched to a point address file
Minimum MDOT Statewide model network offsets (25’)
Discuss size and significance of MI travel survey, the multiple interests/parties/GIS-groups (and the effects that had on our own review and processes)

6. Study Area
MDOT
SEMCOG
Continue discussion from previous slide

7. SEMCOG Point Address Matching
Loaded point addresses into PostgreSQL table (~1.7 million)
Created indexed geometries using original coordinates
Exposed geocoding service via a web-service to online tools
Developed match query in PostgreSQL that matched online geocodes to point addresses in post-processing
Used built-in address parsing and spatial data extensions
Matched using location and address components (fuzzy street name match and number within 225’)
Used functions in the tiger and postgis extensions of PostgreSQL

8. MDOT Model Network Distance Requirement
Compared geocode locations against MDOT network to look for cases where the geocode fell closer than 25’ of a link
Our expectation was that most cases would fall around this distance or, at least at some consistent offset to the correct side of the street segments which we could then extend to meet minimum distance requirements
Not exactly what we found…

9. Pilot Study Results
Geocode Distance to MDOT Statewide Model Network

10. Network distances – What we found (1/4)
Google Maps does not really offset results from the centerline for address matches that don’t use parcel data
The geocode_type we were saving did not include enough information to determine if the coordinates were offset from centerline.
A new variable called location_type was added
rooftop*
range_interpolated
approximate
geometric_center
The process used by online instruments tools to augment POI results with address components involved re-geocoding them, which replaced the original coordinates with the ones returned by the Google geocoder.

11. Network distances – What we found (2/4)
Out of about 3,000 geocodes in the pilot, close to 400 fell within 25’ of a link (excludes home locations and ”intersection” geocodes)
The data on the SEMCOG region was not as affected because we were already matching geocodes to point addresses
What was Google doing?
We re-ran the delivery addresses that were not home nor intersections through the Google geocoder to get its indicator of location_type (not captured in the pilot).
This data element told us how the coordinate was obtained.
Not available on POI results, only geocode results.
Distances to resulting coordinates were then measured against the MDOT network.
For comparison, we ran those same addresses through the Bing Maps geocoder service.
Now let’s look at some pretty charts…

12. Network distances – What we found (3/4)
Google distance to MDOT Network

13. Network distances – What we found (4/4)
Bing distance to MDOT Network

14. Post-Pilot Process Improvements (1/2)
Changed online tools so that original offset coordinates from POI results were preserved.
Added geometry location type to saved geocode attributes.

15. Post-Pilot Process Improvements (2/2)
Created new process that re-geocoded Google results using Bing
Target location type of “range_interpolated”
Also re-geocoded locations that are not “intersection” nor home that fall within 25’ of the MDOT road network
Only replaced geocodes with Bing results if they fell close to the original (within 75’)
Added checks that identified cases for review using ancillary data sources
Expected only a small percentage (~5%) of Bing re-geocoded coordinates may need manual review and adjustment.
And now, more pretty charts…
Google
Bing
Analyst

16. Main Study Adjustments - Examples

17. Main Study Adjustments - Examples
Discuss the pilot effort and results with graphics

18. Network-aware Geocode Auditing

19. Discuss the pilot effort and results with graphics

20. Discuss the pilot effort and results with graphics

21. Discuss the pilot effort and results with graphics

22. Final Remarks Using PostgreSQL made it easy to automate geocode checks
Address parsing (tiger extension)
Fuzzy string matching (
Spatial matching to point addresses
Distance to network
There is a need to check Google’s returned location types
If it interpolated coordinates along its centerlines it likely did not offset them
Combining the strengths of multiple geocoding sources in order to maximize
The final break down of geocodes was:
Google Rooftop: 17,462
Google POI: 77,878
Bing Re-geocode: 2,819
Analyst review: 2,835
Discuss closing observations, impressions, conclusions