ScaleMaster characterization of types of design change through scale for mapping from multi-resolution databases

Transcription

1 ScaleMaster characterization of types of design change through scale for mapping from multi-resolution databases Cynthia A. Brewer, Pennsylvania State University Jessica Acosta, Penn State ESRI Barbara P. Buttenfield, University of Colorado Boulder Charlie Frye and Aileen Buckley, ESRI Redlands a.k.a. Cindy, Jess, babs, Charlie, Aileen

2 ScaleMaster Understand workload balance between display change (select, eliminate, symbolize) geometry change (simplify, aggregate, displace, collapse ) Complement to European MRDB research reviewed in Brewer & Buttenfield (2007) in CaGIS

3 ScaleMaster Understand workload balance using databases with different resolutions through a continuous range of scales for varied map purposes (topographic, zoning, soils, population) Return to diagram details later

4 Project context Ada County government (Boise Idaho) ESRI clean an existing 1:5,000 base map database topographic and special purpose map products mapping scales 5K and smaller 1:5K and 1:1M map segments

5 Project context Work that happens at CU-Boulder geodatabases for ScaleMaster edit, clean and attribute Ada County data create smaller scale geodatabases (Python, ModelBuilder) akin to LoDs (level-of-detail preprocessed databases) See Buttenfield & Frye paper in ICC2007 Proceedings

6 Map examples at 1:5K, 15K, 30K 100K, 200K, 700K

7 Example topo map at 1:30K from 1:5K data RA Jess Acosta s map sets: Designed for onscreen display Prepared using ArcGIS 9.2 and Maplex (no convert to annotation)

8 Different map purposes with same base data Topo Zoning 1:30K maps from 1:5K data

9 Different map purposes with same base data Topo Zoning Soils 1:30K maps from 1:5K data

10 Different map purposes with same base data Population density Soils Zoning Topo

11

12 ScaleMaster topo Horizontal axis is log of scale 1:5K to 1:1M Feature types grouped along vertical axis (hues) Low tech Excel page

13 ScaleMaster topo symbols wide bars labels thinner bars Bars extend through range that symbols/labels for feature are present

14 ScaleMaster topo Geometry change marked by lightness change features off map

15

16 14 types of design decisions for scale change code description s size change* c color change* p pattern change (e.g., dash, crosshatch)* t transparency change* l modify label appearance (e.g., bold, italic, character spacing, leading) i improve label positions in relation to nearby features or labels (e.g., overrun, remove duplicates, feature weighting) o on/off for aspect of symbol or label (e.g. remove outlines for areas with feature type still present) * visual variables

17 14 types of design decisions for scale change r reclassify features by attribute (e.g. fewer categories) f filter by threshold on feature attribute (e.g., filter on size to remove small parks) e eliminate layer or eliminate by feature type (e.g., eliminate intermittent streams) a add layer or add by feature type (e.g., add labels for physiographic features classed as large) x change layer order in TOC (e.g., roads moved from beneath transparent area to above) R use Representations tools (e.g., set endings of dashes with full pattern) G geometry change (e.g., new data set or new layer with generalized features)

18 Procedure: Design individual maps working randomly through scales, starting as new project on each, with same geodatabase, then examine designs in series procedure Jess designs: 5K, 10K, 12K, 15K, 20K, 25K, 30K, 40K, 50K, 60K, 70K, 80K, 90K, 100K, 120K, 140K, 175K, 185K, 210K, 250K, 350K, 400K, 500K, 600K, 700K, 800K, 900K, 1M

19 Procedure: Design individual maps working randomly through scales, starting as new project on each, with same geodatabase, then examine designs in series 1 st 2 nd Jess designs: 5K, 10K, 12K, 15K, 20K, 25K, 30K, 40K, 50K, 60K, 70K, 80K, 90K, 100K, 120K, 140K, 175K, 185K, 210K, 250K, 350K, 400K, 500K, 600K, 6 700K, 800K, 900K, 1M th 4 th 3 rd 5 th etc.

20 Total 122 map projects

21

22 ScaleBrewer 5K 30K 210K 500K

23

24

25 Topo Four map purposes at 1:150K Soils Population Zoning

26 Small foursome Topo Population Soils Zoning

27 Small foursome Topo Population Soils Zoning

28 Small foursome Topo Population Soils Zoning

29 Small foursome Topo Population Soils Zoning

30 histogram Number of changes Topo other changes Characterize frequency of changes for all four map series K- 10K Scale ranges M elimination size label position

31 histogram % changes no mapping K- 10K M Total # Type of map Topo Zoning Soils Pop'n Percent of total changes by scale range

32 Potential as a GIS map design tool ScaleBrewer? ScaleBrewer 5K 30K 210K 500K

33 Potential as a GIS map design tool ScaleBrewer? ScaleBrewer 5K 30K 210K 500K Click here to see styled feature set: e.g., roads with line styles by class

34 Click here to see processing detail: e.g., aggregation tolerances in LoD Potential as a GIS map design tool ScaleBrewer? ScaleBrewer 5K 30K 210K 500K

35 Potential as a GIS map design tool ScaleBrewer? ScaleBrewer Select to deliver: - Table of specs -.style file,.lyr file - OGC SLD spec 5K 30K 210K 500K

36 Summary Workload balance between geometry changes (simplify, displace, collapse, aggregate ) display changes (symbolize, select and eliminate) You can design through an entire scale range with few data captures key geometry pairs (e.g., centerlines that correspond to polygons) few LoDs for sensitive feature types (e.g., hydrography)

37 Next steps How general is ScaleMaster? Examine distributions of types of change through scale assist design decisions assist data download choices Establish effective workload that minimizes geometry change reduce layer integration burdens reduce processing time different (cheaper) worker skills

38 Next steps How general is ScaleMaster? Determine where preprocessed LoDs and new data captures most needed reduce workload/cost assist decision to incorporate others existing datasets (are data close enough to be considered redundant scale?)

39 Regular research meetings Kiyoshi, babs, Charlie, Aileen, Clint, Jess December 2006 in Redlands

40 ESRI PSU CU babs, Jess, Aileen, Charlie March 2007 at Penn State

41 Student acknowledgements Research Assistants/Interns, 2005 to 2007: PSU: Jessica Acosta, Mamata Akella, Zachary Tardivo CU: Torrin Hultgren, Kiyoshi Yamashita, Anna Harman Applied Cartographic Design, PSU undergrads, Spring 2006: Steve Cline, Rosie Daley, Emily Dux, Derek Foll, Katie Holmes, Allen Huber, Alex Krishchyunas, Adam Naito, Joe Palchak, Ed Petronsky, Ryan Stoyek, Matt Sudac, Jessica Acosta (grad) Cartography Seminar, PSU graduate students, Fall 2005: Tanuka Bhowmick, Steve Gardner, Adrienne Gruver, Tania del Mar López Marerro, Sohyun Park, Anthony Robinson, Steve Weaver

42 ESRI acknowledgements Funding from ESRI: Professional Services Agreements 2003B6345, 2003B6346, 2003B6347, and 2006B2964 ScaleMaster Excel file and paper posted on Mappingcenter: blogs.esri.com/support/blogs/mappingcenter/ archive/2007/08/22/scalemaster.aspx