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GIS Research Needs

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

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GIS Research Needs

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  1. GIS Research Needs Strategic Planning

  2. 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

  3. Crystal Ball Metaphor Transportation Strategic Planning Simulation/ scenarios Cause and effect relationships Trends (historical data) Spatial Analysis Prediction Graphical Output

  4. Strategic Planning • GIS-T Research Vision Back to the future • GIS-T Research Mission • Encourage and champion research, • training, and • information dissemination and sharing

  5. 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?

  6. 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 are O-50 51-100 101-150 151-200 201 +

  7. 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?

  8. 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?

  9. Air photos, parcels, TIGER All projected to State Plane, NAD 83 (feet), NYS West

  10. 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

  11. 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?)

  12. Documenting Accuracy Using Two Tiered Geocoding Original Crime Dataset Jan – July 2005 Buffalo, NY 37487 Records Unique Crime Calls 21764 records Locations with a street address 18545 records (85%) Locations with Intersection/place name 3219 records (15%) Locations with Street name in Parcel database 18181 records (98%) Locations without Street name in Parcel database 364 records (2%) Batch match to Streetmap database 2582 records (80%) Interactive match to Streetmap database 637 records (20%) Batch match to Streetmap database 191 records (52%) Interactive match to Streetmap database 173 records (48%) Batch match to Parcel database 13722 records (75%) No match to Parcel database 4459 records (25%) Geocoding Accuracy Summary Most accurate level possible – 16495 (76%) Including secondary batch match – 20582 (95%) Need manual intervention – 1182 (5%) Batch match to Streetmap database 4087 records (92%) Interactive match to Streetmap database 372 records (8%)

  13. GBNRTC Household Travel Survey 2002 Buffalo, NY 15969 Location Records Geocoding Accuracy Summary Home Addresses - Buffalo Most accurate level possible – 830 (80%) Including secondary batch match – 994 (96%) Need manual intervention – 35 (4%) Reported City = Buffalo 3947 records (25%) Location Type = Home 1033 records (26%) Location Type = School 205 records (5%) Location Type = Work 827 records (21%) Location Type = Trip End 1882 records (48%) Zip code in Buffalo 784 records (76%) Zip code not in Buffalo 249 records (24%) Street Name in Parcel database 52 records (21%) Street Name not in Parcel database 197 records (79%) No Street Address 4 records (0.5%) Street Name in Parcel database 574 records (73%) Street Name not in Parcel database 206 records (26%) Batch match to Parcel database 7 records (13%) No match to Parcel database 45 records (87%) Batch match to Parcel database 445 records (78%) No match to Parcel database 129 records (22%) Batch match to Streetmap 42 records (93%) Manual Intervention 3 records (7%) Batch match to Streetmap 122 records (95%) (40 in Buffalo) Manual Intervention 7 records (5%) Batch match to Streetmap 193 records (94%) (5 in Buffalo) Manual Intervention 13 records (6%) Batch match to Streetmap 185 records (94%) Manual Intervention 12 records (6%)

  14. Bad Data makes Bad Models • Focus on data quality • Preventing reporting errors • Finding and correcting errors • Documenting accuracy • Understanding error propagation through models

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