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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

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Gis research needs

GIS Research Needs

Strategic Planning


Gis research needs

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


Gis research needs

Crystal

Ball

Metaphor

Transportation

Strategic Planning

Simulation/ scenarios

Cause and effect relationships

Trends (historical data)

Spatial Analysis

Prediction

Graphical Output


Strategic planning

Strategic Planning

  • GIS-T Research Vision

    Back to the future

  • GIS-T Research Mission

    • Encourage and champion research,

    • training, and

    • information dissemination and sharing


Critical issues

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?


Critical issues1

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 +


Critical issues2

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?


Critical issues3

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?


Air photos parcels tiger

Air photos, parcels, TIGER

All projected to State Plane, NAD 83 (feet), NYS West


Street centerline model

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


Address data

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?)


Gis research needs

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%)


Gis research needs

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%)


Bad data makes bad models

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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