1. Wende Mix, Buffalo State College 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. Wende Mix, Buffalo State College Crystal Ball Metaphor Simulation/ scenarios Cause and effect relationships Trends (historical data) Spatial Analysis Prediction Graphical Output Transportation Strategic Planning

4. Wende Mix, Buffalo State College

5. GIS-T Research Vision Back to the future GIS-T Research Mission –E–Encourage and champion research, –t–training, and –i–information dissemination and sharing Strategic Planning

6. 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?

7. Wende Mix, Buffalo State College Critical Issues New data sources –A–American Community Survey (ACS) –E–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 +

8. Wende Mix, Buffalo State College Critical Issues New data sources –A–American Community Survey (ACS) –E–Establishment data (LEHD) Visualization, data quality, documentation of uncertainty (accuracy) Does establishment data accurately represent where workers work? –H–Headquarters, administrative offices, multi-units –W–Workers from out of state –W–Workers who work out of state Can parameters be established that characterize the accuracy of aggregate workplace locations from establishment (or Census) data?

9. 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?

10. Wende Mix, Buffalo State College Air photos, parcels, TIGER All projected to State Plane, NAD 83 (feet), NYS West

11. 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

12. 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?)

13. Wende Mix, Buffalo State College 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 Parcel database 13722 records (75%) No match to Parcel database 4459 records (25%) Batch match to Streetmap database 4087 records (92%) Interactive match to Streetmap database 372 records (8%) Batch match to Streetmap database 2582 records (80%) Interactive match to Streetmap database 637 records (20%) 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 191 records (52%) Interactive match to Streetmap database 173 records (48%) Documenting Accuracy Using Two Tiered Geocoding

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

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