Error propagation models dlto examine the effects of geocoding quality on spatial analysis of individual level datasets

Transcription

1 Error propagation models dlto examine the effects of geocoding quality on spatial analysis of individual level datasets P.A. Zandbergen 1, T.C. Hart 2, K.E. Lenzer 1 and M.E. Camponovo 1 1 Department tof Geography, University it of New Mexico 2 Department of Criminal Justice, University of Nevada Las Vegas

2 Outline Quality of geocoding Error propagation modeling Model specific for geocoding Three case studies

3 Quality Metrics of Geocoding Positional accuracy Distance between geocoded and true location Completeness Match rate (%) Repeatability /replicability Agreement across reference data, algorithms Usefulness Quality metrics vs. intended use

4 Typical Error Distribution Cumulative Freq quency (%) N =104,865 school children in Orange County, FL ,000 Positional Error (m) Zandbergen, P.A Influence of geocoding quality on environmental exposure assessment of children living near high traffic roads. BMC Public Health, 7:37.

5 Typical Errors Statistic Value (m) Min 1 Max 32,356 Mean 66 SD 435 Median th % th % th % 373 N 104,865 Expected Normal Log-normalized Q-Q plot Observed Value Based on the 90 th percentile, typical street geocoding does not meet the accuracy standards for a 1:100,000 scale map based on National Map Accuracy Standards! Zandbergen, P.A Influence of geocoding quality on environmental exposure assessment of children living near high traffic roads. BMC Public Health, 7:37.

6 Actual Building vs. Street Geocoded

7 It Gets Messy

8 Positional Accuracy of Geocoding 1. Varies strongly across urban rural gradients 2. Direction not uniform but follows reference data 3. Distribution does not follow a normal distribution 4. Spatial autocorrelated with reference data This complicates error propagation modeling using traditional Monte Carlo simulation

9 Error term Original DEM Error DEM + Stream Probability Streams

10 Error Propagation Model Focus on positional accuracy Reference scenario based on address points

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12 Case study study Example 1. Point in raster overlay 2. Kernel density hotspots 3. Point in polygon overlay

13 Case study study 1: Point in raster Overlay Study areas: Clay County, MN; Jackson County, OR; New Hannover County, NC; San Francisco County, CA; Travis County, TX; and Washington, DC 2006 National Land Cover Data Original classes Reclassified into urban, suburban, rural Sample of 1,000 residential address points per study area Geocoded using StreetMap USA 2009 Error matrices for land cover associations

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15 Geocoded Locations (StreetMap USA) open water developed, open space developed, low intensity developed, medium i t it developed, high intensity barren land (rock/sand/clay) deciduous forest evergreen forest m ixed forest shrub/scrub gr assland/her pasture/hay cultivated crops woody wetlan to tal baceous ds open water 1 1 developed, open space developed, low intensity developed, medium intensity developed, high intensity barren land (rock/sand/clay) 0 deciduous forest 2 2 evergreen forest mixed forest shrub/scrub grassland/herbaceous pasture/hay cultivated crops woody wetlands total Reference Locations

16 Land Cover Results Clay, MN Jackson, OR New Hannover, NC San Fran., CA Travis, TX Wash., DC Pop. density (#/ sq. mile) , ,379.4 Detailed land cover accuracy (%) unweighted kappa weighted kappa Urban/suburban/rural accuracy (%) unweighted kappa weighted kappa

17 Case study study 2: Kernel Density Hotspots Study area: Charlotte, NC Crime data: Assaults for 2008 Geocoding techniques: Address points Local Street Centerlines TeleAtlas StreetMap USA 2009 Kernel density hotspot analysis: 100, 200, 300, 400 and 500 m Boundary error analysis

18 Measurement of Hotspot Boundary Error Reference Hotspot Street Geocoded Comparison FN A FP Boundary Error = (FP + FN) / A A = Agreement FN = False Negative FP = False Positive

19 Kernel Geocoding Method Area of Area of False Area of False (FN+FP)/A bandwidth Agreement (A) (km2) Negative (FN) (km2) Positive (FP) (km2) 100 m Local Street Centerlines TeleAtlas StreetMap USA m Local Street Centerlines TeleAtlas StreetMap USA m Local Street Centerlines TeleAtlas StreetMap USA m Local Street Centerlines TeleAtlas StreetMap USA m Local Street Centerlines TeleAtlas StreetMap USA

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21 Case study study 3: Point in Polygon Polygon Study area: Orange County, FL Datasets: enrollment records of public schools (n= 120,222) Geocoding techniques: Address points Local Street Centerlines TIGER 2000 Point in polygon overlay: Elementary, middle and high schools % incorrectly assigned Results split by race/ethnicity

22 Point in Polygon Polygon Results % Error Asian Black Hispanic White Total Elementary schools ( n= 2,021) (n = 18,330) (n = 18,218) (n = 21,350) (n = 59,919) Street Centerlines TIGER Middle schools (n = 1,023 ) (n = 8,464) (n = 8,104) (n = 10,938) (n = 28,529) Street Centerlines TIGER High schools (n = 1,395) (n = 8,501) (n = 7,997) (n = 13,781) (n = 31,674) Street Centerlines TIGER

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24 Summary Results Case study 1: Land cover associations iti Substantial errors 5 to 50% Varies with land cover heterogeneity Case study 2: Kernel density hotspots Substantial errors at large scales Bandwidth key parameter Limited effect of geocoding type Case study 3: Point in polygon Moderate errors few > 5% Varies with polygon on size Strong effect of geocoding type

25 Error Propagation Model Strengths: th No assumptions about the nature of positional error Relatively simple, no complex statistical characterizations Applies to any spatial analytical technique Does not require validation of geocoding accuracy Assumptions/limitations: Requires high quality address point data Records with very poor address quality excluded Doesnot address issues of geocoding completeness Does not incorporate errors in other inputs

26 Funding Support National Science Foundation National Institute of Environmental Health Sciences RWJF Center for Health Policy

27 Contact Paul Zandbergen University of New Mexico