Establishing Classification and Hierarchy in Populated Place Labeling for Multiscale Mapping for The National Map
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1 Establishing Classification and Hierarchy in Populated Place Labeling for Multiscale Mapping for The National Map Wesley Stroh, Stephen Butzler and Cynthia Brewer Gould Center, Geography Pennsylvania State University
2 Portion of USGS ScaleMaster work (24K, 50K, 100K )
3 CO no hierarchy K: With our basic hierarchy and with no place hierarchy
4 CO no hierarchy K: With our basic hierarchy and with no place hierarchy
5 Background Point vs Polygon The National Map data, portal and mapping tool GNIS point data is names theme Limited for label positioning Limits use of attributes for hierarchy An alternative U.S. Census Bureau polygons GNIS place points (many) come from census geography Distinguish between places and economic places Class census attribute data for additional hierarchy Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
6 Background GNIS * Geographic Names Information Service (GNIS) Maintained by USGS Board on Geographic Names (BGN) GNIS Feature Class Codes (broadest definitions of place) Populated Place subjective definition of place Civil incorporated places (administrative) Census census-defined places (statistical) Locale historic human activity (not active) Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
7 Census Data Types * 2009 TIGER/Line Shapefiles (geography) Incorporated Places Census Designated Places (CDPs) Minor Civil Divisions (MCDs), no CCDs used Economic Places (NOTE: 2007 Economic Census definitions of Economic Place include all places with EITHER 5000 residents OR 5000 employees) 2002 Survey of Business Owners (attributes) part of Economic Census (every 5 years) provides data on employment numbers (used to create our classifications) place-level data for the 2007 SBO was not yet available at time of analysis NationalMapping.us AutoCarto, November 2010
8 Cases and Methods * Pilot tests: Colorado (Wes) and Pennsylvania (Steve) Distinct geographies of place MCDs prevalent in Pennsylvania (eastern U.S.) PA denser, CO less dense Initial (coarse) hierarchy of TIGER/Line polygons ranked by FIPS 55 1 st C1-6 Incorporated municipalities 2 nd U1/U2 CDPs 3 rd T1 MCDs (eastern US only; e.g., townships) Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
9 Additional Hierarchy: Build Pittsburgh 1 st : Incorporated Place (C5) and Economic Place Pittsburgh
10 1 st : Incorporated Place (C5) and Economic Place 2 nd : Incorporated Place (C5) Pittsburgh
11 1 st : Incorporated Place (C5) and Economic Place 2 nd : Incorporated Place (C5) 3 nd : CDP (U1/2) / MCD (T) and Economic Place Pittsburgh
12 1 st : Incorporated Place (C5) and Economic Place 2 nd : Incorporated Place (C5) 3 nd : CDP (U1/2) / MCD (T) and Economic Place 4 th : MCD (T) and Economic Place Pittsburgh
13 1 st : Incorporated Place (C5) and Economic Place 2 nd : Incorporated Place (C5) 3 nd : CDP (U1/2) / MCD (T) and Economic Place 4 th : MCD (T) and Economic Place 5 th : CDP (U1/2)/MCD (T) Pittsburgh
14 1 st : Incorporated Place (C5) and Economic Place 2 nd : Incorporated Place (C5) 3 nd : CDP (U1/2) / MCD (T) and Economic Place 4 th : MCD (T) and Economic Place 5 th : CDP (U1/2)/MCD (T) 6 th : MCD (T) Pittsburgh
15 Census Class Codes (FIPS 55) Class codes further define census polygons: Class C incorporated places Class M military places Class P pop. place = incorp. place Class T MCD (e.g. township) Class U CDP Note: We ranked MCDs coextensive with CDPs (T/U) higher than sole MCDs (T). NationalMapping.us Pittsburgh
16 Why TIGER/Line Shapefiles? * Polygon label placement more dynamic labels reposition in parts of polygons with zoom and pan GNIS points provide single centroid Additional place point for adjacent topo quad, but limited use in seamless context However, GNIS points do provide an FID which could link back to census data NationalMapping.us AutoCarto, November 2010
17 Other Possible Attributes Additional hierarchy with census data In pilot, used SBO Number of Employees but there are other indicators For places: Total population For economic places: Total number of firms Total revenue Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
18 Processing for three layers Repeating geometry Unique geometries Slivers Inc. pl/cdp Econ. pl MCD
19 Sub-county polygons most comprehensive (MCD layer) Collapse place and Economic Place polygons to points Identify which places do not have a polygon at sub-county level Inc. pl/cdp Econ. pl MCD
20 Strip out identified unique-geometry place points Spatial Join the remaining place and Economic Place point attributes to sub-county polygons Create separate polygon layer of unique-geometry places Then establish hierarchy of places Inc. Pl/CDP Ec. Pl MCD
21 PA Place Hierarchy Pittsburgh West Mifflin R1 C T U Economic Place CDP Incorp. Place MCD (e.g. township) Incorporated and Economic place CO Place Hierarchy Denver Aurora Bethel Park Englewood Mount Oliver R3 Incorporated place BowMar Penn Hills R2 Economic place, CDP and MCD Columbine Cranberry R4 Economic place and MCD Reserve R5 MCD and CDP Roxborough Park Mount Pleasant R6 MCD Gastonville R7 + unique (dashed outline) CDP Shadyside U4 and U6 GNIS points (no polygons) Sullivan
22 144K PA Best design 144
23 289K PA Best design 288
24 578K PA Best design 577
25 1.2M PA Best design 1 mill-ish All R1, R3
26 2.3M Top 4 of 5 classes in R1 PA Best design 2 mill-ish
27 PA Place Hierarchy Pittsburgh West Mifflin R1 C T U Incorporated Economic Place MCD CDP CO Place Hierarchy Denver Aurora Bethel Park Englewood Mount Oliver Penn Hills R3 R2 BowMar Columbine Cranberry R4 Reserve R5 Roxborough Park Mount Pleasant Gastonville R6 R7 + unique dashed outline Shadyside U4 and U6 GNIS points (no polygons) Sullivan
28 Places with No Census Attributes At large scales add GNIS point data of U4/U6 Populated Places
29 Evaluating Our Results 1. Are labels placed on maps? 2. Does the additional label hierarchy provide value? We compared our results to The National Map Viewer and Google maps at three scales Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
30 Pittsburgh at 1:144,448 using our place classing with The National Map Viewer and Google maps comparisons
31 Replace w Steve s best Yes different sets of other features labeled and different label sizes among compared maps interfere with precise comparisons this is pilot work Pittsburgh at 1:288,895 using our place classing with The National Map Viewer and Google maps comparisons
32 Replace w Steve s best Pittsburgh at 1:577,791 using our place classing with The National Map Viewer and Google maps comparisons
33 Google/Bing scales for cached map tiles in WGS 1984 Web Mercator projection 1:1, K 1:2, K 1:4, K 1:9, K 1:18, K 1:36, K 1:72, K 1:144, K 1:288, K 1:577, K 1:1,155, M 1:2,311, M 1:4,622, M 1:9,244, M 1:18,489, M 1:36,978, M 1:73,957, M 1:147,914, M 1:295,828, M 1:591,657, M Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
34 289K Evaluation: Label Inventory Process PSU 6 pages of placenames TNM Viewer Google map
35 Comparison among PSU, Google, TNM maps High category, ordered by number employees: Dark cells indicate placed labels and light cells for unplaced labels AutoCarto, November 2010
36 Unplaced Label Performance Yellow cells highlight map with fewest unplaced labels, by scale and label group Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
37 Labels placed on all three maps Incorporated and economic place MCD, CDP and economic place Incorporated place Other MCD/CDP 144K Same labels on maps by PSU, Google and TNM Viewer at three scales 289K R1 R2 R3 R4/5/6 578K number of labels Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
38 Labels compared to PSU map Incorporated and economic place MCD, CDP and economic place Incorporated place Other MCD/CDP 144K (on right) PSU map did better than Google and/or TNM Viewer (on left) PSU map did worse than Google and/or TNM Viewer Incorp. places not on PSU map 289K 578K Many more township names on PSU map number of labels Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
39 Labels unplaced on all maps Incorporated and economic place MCD, CDP and economic place Incorporated place Other MCD/CDP 144K Labels not on maps by PSU, Google and TNM Viewer at three scales 289K 589K number of labels Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
40 Next steps * Address the U4/U6s Subjective places (e.g. neighborhood points) No attribute data Surface analysis of block group level data perhaps Residential population Number of employees (using LED) Overlay U4/U6 points using surface hotspots to rank Use ranks for label hierarchy or pruning decisions And make maps Stroh, Butzler, Brewer NationalMapping.us AutoCarto, November 2010
41 Acknowledgements USGS Center of Excellence for Geospatial Information Science (CEGIS), 2007 to present Resources Project resources: ScaleMaster.org Gould Center: NationalMapping.us CEGIS-USGS: cegis.usgs.gov Thanks: Babs Buttenfield CU-Boulder Lynn Usery Larry Stanislawski USGS-CEGIS Charlie Frye ESRI Stroh, Butzler, Brewer AutoCarto, November 2010
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