Multiple Representations of Geospatial Data: A Cartographic Search for the Holy Grail?

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Bertram Walton
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edu 13 May 2011 Digital Landscape Model Cartographic Processing Digital Cartographic Model Map / Atlas Products DLM 1km resolution DCM_2Million 10M World Wall Map 2M Federal Lands

1 Multiple Representations of Geospatial Data: A Cartographic Search for the Holy Grail? Barbara P. Buttenfield University of Colorado Boulder Research Faculty Affiliate, USGS-CEGIS babs@colorado.edu 13 May 2011 Digital Landscape Model Cartographic Processing Digital Cartographic Model Map / Atlas Products DLM 1km resolution DCM_2Million 10M World Wall Map 2M Federal Lands DCM_250K LoD 250K Road Map 150K Land Cover Analysis DLM 90m resolution DCM_100K 100K Topo 70K Infrastructure Development DCM_50K LoD 50K Transrpt. Routing DLM 5m 10m resolution DCM_Topo24K 24K DOQ 24K Hillshade 24K USGS Topo Special Thanks to Charlie Frye and Aileen Buckley ESRI DCM_10K Local government maps Conference, Denver CO, May

2 Do we need multiple versions? For some data layers, a single version may suffice Placenames, some attributes Revision information Important boundary and geodetic control For other data layers, challenges persist Terrain, image base, hydrography Roads, settlement, land cover and derivatives Scale sensitivities vary among data layers What about intermediate scale LoDs? Processing smaller scale versions of scale sensitive layers to fill in the scale gaps between capture databases possible? 1. For analysis, digital features must reflect processes which are evident within a finite range of scales 2. Place matters. Evidence of spatial process varies from one location to another so processing must be tailored to local landscape characteristics. These two premises drive efforts to create a multi-scale version of NHD data, usable from high-res (24k) to National-Atlas-res (1m-2m or smaller scales). Conference, Denver CO, May

3 Constraints Generalization must preserve: Connectivity (topology) of hydrographic network Full stream reaches Complete feature codes and attribution Local variations that typify physiographic regimes (landscape types) Local stream channel densities Local textures (channel shape and sinuosity) Generalization Framework Objective: Fully automated generalization to support multiplescale versions of USGS National Hydrography Dataset (NHD) Enrichment (NHD schema) Classify Catchments Land Types Upstream Drainage Area Density Partitions Low High Generalization Stratified pruning (Radical Law) Differential simplification Multiscale symbolization Assessment Cartographic Evaluation Generalization metrics Cross-Validation w/ other basins Special Thanks to Larry Stanislawski USGS Correct centerline gaps Products LoD data layers The Processing National Map toolbox User Conference Graphic Denver Products Colorado Dissemination with The National Map Conference, Denver CO, May

Prune and Simplify by Density Partition To preserve evidence of valid

Pruning and Simplification Density Partitions Low 13 High May 2011 Prune

density partitions Pruning and Simplification Density Partitions Low High

4 Prune and Simplify by Density Partition To preserve evidence of valid spatial process Four subbasins in Iowa spanning glacial till and a lake bed Pruning and Simplification Density Partitions Low 13 High May 2011 Prune and Simplify by Density Partition New Jersey Case Study 12 subbasins, 4 density partitions Pruning and Simplification Density Partitions Low High Special Thanks to Ellen Finelli and Ariel Doumbouya USGSI Conference, Denver CO, May

5 Why Simplify by Density Partition? Special Thanks to Ellen Finelli and Ariel Doumbouya USGSI Prune to Homogenize Inconsistent Compilation Alabama Pruning Density Partitions Low High Pruning Software: Larry Stanislawski Processing: Chris Anderson-Tarver, CU ArcMap Display: Andy Stauffer, PSU Conference, Denver CO, May

Prune to Homogenize Inconsistent Compilation Alabama 03150201 Pruning Density Partitions Low High Pruning Software: Larry Stanislawski Processing: Chris Anderson-Tarver, CU ArcMap Display: Andy

6 Prune to Homogenize Inconsistent Compilation Alabama Pruning Density Partitions Low High Pruning Software: Larry Stanislawski Processing: Chris Anderson-Tarver, CU ArcMap Display: Andy Stauffer, PSU Defining Landscape Types Tailor generalization sequences to preserve distinctive characteristics on two basic factors: Precipitation (humid, dry) Terrain (flat, hilly, mountainous) Physical divisions of the coterminous United States (Fenneman and Johnson, 1946) ROCKY MOUNTAIN PACIFIC SYSTEM MOUNTAIN SYSTEM INTERMONTANE PLATEAUS INTERIOR PLAINS LAURENTIAN UPLAND INTERIOR HIGHLANDS APPALACHIAN HIGHLANDS Simplification Classify Land Types ATLANTIC PLAIN 0 1,000 2,000 KM Conference, Denver CO, May

Defining Landscape Types Classify coterminous US on five variables for 5km grid

Slope Runoff (mm/year) Standard Deviation of Elevation Simplification Classify

coterminous US on 7 variables for 5km grid Average Elevation Drainage Density

Bedrock Density Area of Inland Surface Water Landscape Types Simplification

7 Defining Landscape Types Classify coterminous US on five variables for 5km grid overlaid on 3 arcsec DEMs Average Elevation Drainage Density Estimates Average Slope Runoff (mm/year) Standard Deviation of Elevation Simplification Classify Land Types Maximum likelihood classification Defining Landscape Types Classify coterminous US on 7 variables for 5km grid Average Elevation Drainage Density Estimates Average Slope Runoff (mm/year) Standard Deviation of Elevation Add: Bedrock Density Area of Inland Surface Water Landscape Types Simplification Classify Land Types Maximum likelihood classification Conference, Denver CO, May

Tailor generalization sequences to preserve distinctive characteristics on landscape factors: Humid-Dry Flat-Hilly-Mountainous Generalization Strategy Subbasin Name and Location NHD

10290107 Humid Hilly D Lower Prairie Dog Town Fork Red River, Texas 11120105 Dry Hilly E Piceance-Yellow Creeks, Colorado 14050006 Dry Mountainous Minimize the number of customized

8 Tailor generalization sequences to preserve distinctive characteristics on landscape factors: Humid-Dry Flat-Hilly-Mountainous Generalization Strategy Subbasin Name and Location NHD subbasin Regime A South Branch Potomac River, West Virginia B Upper Suwannee River, Florida-Georgia Humid Mountainous Humid Flat C Pomme De Terre River, Missouri Humid Hilly D Lower Prairie Dog Town Fork Red River, Texas Dry Hilly E Piceance-Yellow Creeks, Colorado Dry Mountainous Minimize the number of customized solutions F Lower Beaver River, Utah Dry Flat K Lower Cimarron River, Oklahoma Humid Hilly West Virginia K High-res NHD 50K LoD 100K Med-res NHD Conference, Denver CO, May

9 Missouri K High Res NHD 50K LoD 100K Med Res NHD Texas Generalization challenge: delineating the primary flowline and distinguishing it from the stream braid ongoing work High-Res NHD 50K LoD 100K Med Res NHD Conference, Denver CO, May

Automatic Centerline Delineation (aka Dealing with Divergence ) Select on Artificial Path Intersect flowlines with Hydro Areas and Waterbodies Flow Hierarchy Trace through upstream nodes Correct 13

10 Automatic Centerline Delineation (aka Dealing with Divergence ) Select on Artificial Path Intersect flowlines with Hydro Areas and Waterbodies Flow Hierarchy Trace through upstream nodes Correct 13 May 2011 centerline gaps Processing Summary for 50K LoDs Minimize the number of customized solutions Humid Dry Flat Hilly Mtn Flat Hilly Mtn FL GA MO W Va UT TX CO Density Stratification Ctrline Delineation Contained Topological Contained and Contained Contained Intersect Contained Relation Intersect Flow Hierarchy no; select on GNIS name no but deal w/ braids no; select on GNIS name Continuity check No gaps Yes No gaps Simplify (m) Polys Lines 100c 75f 100c 75f 100c 75f Smooth (Poly Bdy) PAEK 250 Conference, Denver CO, May

Validation FL-GA: Original 24K hydro compared to 50K LoD Map

11 Assessment Three Types of Assessment Cartographic Validation Metric Validation Cross-Validation 13 May 2011 Cartographic Validation FL-GA: Original 24K hydro compared to 50K LoD Map Design by Cindy Brewer PSU Conference, Denver CO, May

by Cindy Brewer PSU Conference, Denver CO, May 2011

12 Cartographic Validation FL-GA: 50K LoD displayed at 100K Map Design by Cindy Brewer PSU Cartographic Validation FL-GA: 50K LoD displayed at 200K Map Design by Cindy Brewer PSU Conference, Denver CO, May

Metric Validation Conflation of 50K LoD against benchmark (100k NHD)

length of features common to both Omissions = length of features present

features present in the LoD but not in the 100K benchmark Similar metric

de Terre, MO Subbasin 10290107 Coefficient of Line Correspondence CLC =

13 Metric Validation Conflation of 50K LoD against benchmark (100k NHD) Coefficient of Line Correspondence (CLC) CLC = M / (O+C+M) Matches = length of features common to both Omissions = length of features present in 100K benchmark but omitted from the LoD Comissions = length of features present in the LoD but not in the 100K benchmark Similar metric for polygons: Coefficient of Area Correspondence (CAC) Assessment: Pomme de Terre, MO Subbasin Coefficient of Line Correspondence CLC = Coefficient of Area Correspondence CAC = Conference, Denver CO, May

14 Cross-validation: Lower Cimarron, OK Subbasin Processed using Humid Hilly geoprocessing tool 24K High Res NHD 50K LoD 100K Med Res NHD Assessment: Lower Cimarron, OK Subbasin Coefficient of Line Correspondence CLC=0.830 Coefficient of Area Correspondence CAC=0.623 Conference, Denver CO, May

15 NHD Generalization Toolbox Summary: Prospects for The National Map And what about The Holy Grail? How much of the multiple representation data processing and generalization can be automated? 13 May 2011 Conference, Denver CO, May

Place Still Matters: Generalizing the NHD by Local Terrain and Climate

Place Still Matters: Generalizing the NHD by Local Terrain and Climate Barbara Buttenfield, Geography, CU-Boulder Cynthia Brewer, Geography, Penn State E. Lynn Usery, USGS-CEGIS Research Assistants: Chris