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GIS Research Needs. Strategic Planning. Crystal Ball Metaphor GIS Research Committee wants us to GAZE INTO THE FUTURE Anticipate and plan for new technologies and applications Strategic Planning Anticipate and plan for growing, decreasing, or changing travel demands - PowerPoint PPT Presentation
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Wende Mix, Buffalo State College
GIS Research Needs
Strategic Planning
Wende Mix, Buffalo State College
Crystal Ball Metaphor
GIS Research Committee wants us to GAZE INTO THE FUTURE
Anticipate and plan for new technologies and applications
Strategic Planning
Anticipate and plan for growing, decreasing, or changing travel demands
Forecast infrastructure needs plan operations, address practices and policies
Wende Mix, Buffalo State College
Crystal Ball
Metaphor
Simulation/ scenariosCause and effect relationshipsTrends (historical data)Spatial AnalysisPredictionGraphical Output
TransportationStrategic Planning
Wende Mix, Buffalo State College
Wende Mix, Buffalo State College
• GIS-T Research VisionBack to the future
• GIS-T Research Mission– Encourage and champion research, – training, and – information dissemination and sharing
Strategic Planning
Wende Mix, Buffalo State College
Critical Issues• DOTs, MPOs, & other agencies have
spent over a decade amassing huge amounts of very detailed spatial data and building Linear Referencing Systems (LRS)
• Planners use vast amounts of demographic and socio-economic data
• Data models mostly center on Census geography and transportation analysis zones (TAZs)
• What about parcels, individual locations (GPS)?
• What about neighborhoods, planning communities?
Wende Mix, Buffalo State College
Critical Issues• New data sources
– American Community Survey (ACS)– Establishment data (LEHD)
• Visualization, data quality, documentation of uncertainty (accuracy)
• ACS 5 year average data• Estimates have upper and lower bounds
How do we visually communicate that some tracts, TAZs, etc have values that are not statistically significantly different?
• Tract A has 120 (+ 10) households with 0 vehicles (110, 130)
• Tract B has 95 (+ 15) households with 0 vehicles (80, 110)
• Class ranges areO-5051-100101-150151-200201 +
Wende Mix, Buffalo State College
Critical Issues• New data sources
– American Community Survey (ACS)– Establishment data (LEHD)
• Visualization, data quality, documentation of uncertainty (accuracy)
• Does establishment data accurately represent where workers work?
– Headquarters, administrative offices, multi-units– Workers from out of state– Workers who work out of state
• Can parameters be established that characterize the accuracy of aggregate workplace locations from establishment (or Census) data?
Wende Mix, Buffalo State College
Critical Issues• Geocoded data
• Visualization, data quality, documentation of uncertainty (accuracy)
• How accurate is it?
• How can it be improved?
• How do we document its quality?
Wende Mix, Buffalo State College
Air photos, parcels, TIGER
All projected to State Plane, NAD 83 (feet), NYS West
Wende Mix, Buffalo State College
Street Centerline Model• Model of last resort!• Fraught with positional and representational
inconsistencies– E.g. No addresses on east side of street– Addresses don’t exist along entire range (continuum)– Nodes (beginning/ending) location and parcel locations
don’t coincide– Databases inaccurately represent jurisdictional
boundaries• Search algorithms rely heavily on accurate zip
code and jurisdiction data.• More effective for navigational purposes than
representing land use or reflecting human perception
Wende Mix, Buffalo State College
Address data
• How good is it?– Train people to collect better data– Train people to use GIS capabilities to QC the data
• Consider the source– Crime locations
• From police records
– Real estate transactions• Deeds of records (County clerk’s office)
– Travel Survey Data!!!!!!!!!!!!!!!!• Many sources of error
• Document the accuracy (Methods?)
Wende Mix, Buffalo State College
Original Crime DatasetJan – July 2005
Buffalo, NY37487 Records
Unique Crime Calls21764 records
Locations with a street address18545 records (85%)
Locations with Intersection/place name3219 records (15%)
Locations withStreet name in Parcel database
18181 records (98%)
Locations withoutStreet name in Parcel database364 records (2%)
Batch match toParcel database
13722 records (75%)
No match toParcel database
4459 records (25%)
Batch match toStreetmap database4087 records (92%)
Interactive match toStreetmap database
372 records (8%)
Batch match toStreetmap database2582 records (80%)
Interactive match toStreetmap database637 records (20%)
Geocoding Accuracy SummaryMost accurate level possible – 16495 (76%)Including secondary batch match – 20582 (95%)Need manual intervention – 1182 (5%)
Batch match toStreetmap database191 records (52%)
Interactive match toStreetmap database173 records (48%)
Documenting AccuracyUsing Two Tiered Geocoding
Wende Mix, Buffalo State College
GBNRTC Household Travel Survey 2002Buffalo, NY
15969 Location Records
Reported City = Buffalo3947 records (25%)
Location Type = Home1033 records (26%)
Location Type = Work827 records (21%)
Zip code in Buffalo784 records (76%)
Zip code not in Buffalo249 records (24%)
No Street Address4 records (0.5%)
Street Name inParcel database574 records (73%)
Geocoding Accuracy SummaryHome Addresses - BuffaloMost accurate level possible – 830 (80%)Including secondary batch match – 994 (96%)Need manual intervention – 35 (4%)
Batch match toStreetmap
193 records (94%)(5 in Buffalo)
Location Type = School205 records (5%)
Location Type = Trip End1882 records (48%)
Street Name not inParcel database206 records (26%)
Street Name inParcel database52 records (21%)
Street Name not inParcel database197 records (79%)
Batch match toParcel database445 records (78%)
No match toParcel database129 records (22%)
Batch match toParcel database
7 records (13%)
No match toParcel database45 records (87%)
Manual Intervention
13 records (6%)
Batch match toStreetmap
122 records (95%)(40 in Buffalo)
Manual Intervention7 records (5%)
Batch match toStreetmap
185 records (94%)
Manual Intervention
12 records (6%)
Batch match toStreetmap
42 records (93%)
Manual Intervention3 records (7%)
Wende Mix, Buffalo State College
Wende Mix, Buffalo State College
Bad Data makes Bad Models
• Focus on data quality– Preventing reporting
errors– Finding and correcting
errors– Documenting accuracy– Understanding error
propagation through models