data collection and quality assurance
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Data Collectionand Quality Assurance
Ron Howard Jr., EIEnvironmental
Coordinator
Russell Koenig, PS, EISurveyor
DLZ Ohio, Inc.2008 Ohio GIS
ConferenceSeptember 10-12, 2008
Crowne Plaza North Hotel
Columbus, Ohio
QUALITY ASSURANCE
Refers to planned and systematic processes that
provide confidence of a product’s or service’s
effectiveness.
Original Hand
WrittenDocument
. Drawbacks • Difficult to
read• Not structured• Must be
retyped• Open to
interpretation
Palm Database Document
Benefits• Standardized
responses.• Easy sorting
and manipulation of data.
• Provide client with a document designed around their changing needs.
Palm Database Document
Benefits cont.• Check
boxes and drop down menus save time.
• Once entered document is complete.
• Greater quality final product.
Palm Database Document
Drawbacks• Special
Training.• Greater
learning curve for users.
• Field crew’s level of detail is visibly displayed whether good or bad.
Hours / Job Palm Hand Written Position Hourly RateField Work 267 481 Field Tech 21.00$ Retyping hand written forms 240 Administrative assistant 16.00$ Inserting forms into books 40 Intern 12.00$ Database development 40 Programmer 35.00$
Palm Hand Written SavingsTotal Hours 307 761 60%Total Cost 7,007$ 14,419$ 51%
Comparison and Savings between Electronic and Hand Written Data
Collection.Parcels / Day Palm Hand WrittenField Work 45 25Retyping hand written forms 0 50Inserting forms into books 0 300
Total Parcels
1502
Related Benefits1. Less people handling information relates to a higher quality product.2. Realistic deliverable 1 cd -vs- 13 3-inch binders3. Ability to sort specific information due to standardized inputs.4. Ability to generate reports relative to each client's particular need.5. Ability to attach a photo with pertinent data.6. Improved ability to check work for accuracy.7. Database development was more extensive in this case because it was our 1st project.8. Newer databases need approx 50% less work.
Note: Now that the client has increased ability to work with the data they will ask for extra database items. Therefore, don't reduce programmer time.
Brain power has to rise in proportion to the reduction in time, operational effort, and
muscle power.Joseph V.R. Paiva, PH.D., P.S.
Case Study: I-70/71
Existing Utility Location– Subsurface Utility Engineering (SUE)– Sewerage/Drainage
Project Approach
Combine SurveyData with GIS DataCollection methodsto produce a finalproduct withmaximum quality inminimal time.
707802.19, 1873544.62
987.12
70-71 MANHOLE INVENTORY
NAVD881000 STORM MH
1
2
24” RCP
1000 STORM MH
8.10 1001
09-11-08
707802.19, 1873544.62 RH/RK
CONC
987.12
STEEL
GOOD
70-71 MANHOLE INVENTORY
NOTRASH & SILT
24” RCP 8.42
NAVD88N
24” NE RCP
1000 STORM MH
8.10 1001
DIR
09-11-08
707802.19, 1873544.62 RH/RK
CONC
987.12
STEEL
GOOD
70-71 MANHOLE INVENTORY
NOTRASH & SILT
24” S RCP 8.42
NAVD881
2
N
GIS Data Collection Advantages
• Upload coordinates for navigation• Customized drop-down menus• No lost, damaged, or illegible forms• Downloadable information
GIS-Grade GPS
• Fast and effective– Navigation– Inventory
• Low accuracy– 30 feet un-processed– 1-3 feet post-processed
(using CORS)
Survey-Grade GPS
• Static (~0.03’)– Must Post-Process
Survey-Grade GPS
• Static (~0.03’)– Must Post-Process
• RTK (~0.10’)– Base Station
& Radio Waves– No Post-Processing
Survey-Grade GPS
• Static (~0.03’)– Must Post-Process
• RTK (~0.10’)– Base Station
& Radio Waves– No Post-Processing
• VRS (~0.10’)– CORS Network– “On the fly” results
Step 1: Survey• Search for approximately 1900 structures
– 770 located previously during SUE work• Obtain accurate coordinates
Step 2: Report• Structure details & condition• Pipe information
– Direction, size, material, depth, connection• GIS-Grade GPS coordinate
• Sewer Database spreadsheet (Excel)– Survey coordinates and GIS report data
• Compare GIS and survey coordinates• Calculate pipe invert elevations
Step 3: Assemble
Step 4: QC
• Database analysis– Identify missing or conflicting information
• Existing records– City of Columbus Sewer Atlas– Original construction and as-built drawings
• Additional field work– Stakeout, locate, and report missing
structures
Step 5: Deliver• The final report included
– 1458 located structures– 3177 reported inverts
• Bound book– Database was easy to format
• Organized into 3 sections– Structure Location– Structure Information– Pipe Information
Structure Location
Structure Information
Pipe Information
Additional Request – Pipe Drawing
• Create invert coordinates• Place all start & stop points in order• Add point number & survey linework codes
– BL*P = Begin Line “Pipe”– EL*P = End Line “Pipe”
• Run through drafting software– Lines were automatic and at true elevation
Pipe Point FilePOINT NORTHING EASTING ELEV
CODE30000 711769.45 1827261.58 696.27
BL*P130001 711778.66 1827277.52 696.27
EL*P130002 711769.45 1827261.58 696.24
BL*P230003 711778.29 1827093.73 695.56
EL*P230004 711798.41 1827232.98 710.24
BL*P330005 711792.29 1827244.84 709.55
EL*P330006 711792.94 1827113.96 0.00
BL*P430007 711792.29 1827244.84 708.36
EL*P430008 711792.94 1827113.96 704.57
BL*P530009 711787.09 1827114.39 704.45
EL*P530010 711787.09 1827114.39 702.07
BL*P630011 711786.62 1827092.33 701.14
EL*P630012 711787.09 1827114.39 704.29
BL*P730013 711424.05 1827187.41 703.43
EL*P7
Highlights
• Minimal field time• Structure & report verification• Automated calculations• No transposing errors• Easily formatted report
The Future• Innovative process and
experience– More jobs of this type at very
low cost• Enhanced Sewer Database
spreadsheet– Automated calculations and
error messages– Faster and better
THANKS!Ron Howard Jr., EI
Environmental Coordinator
Russell Koenig, PS, EISurveyor
DLZ Ohio, Inc.6121 huntley RoadColumbus, Ohio 43229614-888-0040
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