Data Modeling for Large Scale Maps and Map Production

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Kerry Young
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1 Slide 1 Data Modeling for Large Scale Maps and Map Production Charlie Frye, ESRI Redlands Aileen Buckley, PhD, ESRI Redlands Cartographic Research and Special Projects Group Abstract: In this session, we focus on the requirements for modeling data to produce large scale maps. Defining an appropriate data model for map compilation and production assures consistent and appropriate maps for local, regional and municipal resource management, as well as the more recently required monitoring and management of information for homeland security. Topics include: using high resolution imagery to derive cartographic data, such as physiography and hydrography; deriving cultural cartographic features from parcel and other municipal data; defining the semantic models for data at various scales; and procedures for extracting smaller scale data from larger scale data. These techniques and concepts are applied in particular to the use of local scale GIS data to make high quality cartographic products that meet the various needs of its users.

2 Slide 2 Session Overview Show examples that highlight How to model cartographic information in GIS A systematic approach to modeling data facilitates map production How to solve common cartographic design problems, particularly for large scale maps The methodologies for creating cartographic data What should be common techniques for modeling cartographic information in a GIS database One of our main goals is to develop methods for automating map production in GIS through effective data management

3 Slide 3 Who We Are and What We Do Cartographic Research and Special Projects Group Find and document / publish best practices in GIS- based cartography Importance of this session Lack of a common model for maps in the U.S. (in particular) at scales larger than 1:24,000 The volume of local scale data is very high, but is it usable for mapping? Lots of assumptions like, it should just work Could you make your maps with your neighbor s s data? What if you had to go to the USGS (TNM) to get yours or your neighbor s s data? Before we go off half-cocked down that path, the real point is, is that all other things being equal, is the data you are publishing (to The National Map) good enough for somebody else to make a good map? The reason we re here is to show you what we d hope our neighbors have for data.

4 Slide 4 What is Large Scale? 1:5,000 (at or near) Insets maps likely to happen at 1:1200 to ~1:2000 What activities are supported by maps at this scale? Public Local event management Security Planning and decision making Private LBS support High quality navigation systems Poll on who s (categorically) attending.

5 Slide 5 1:5,000

6 Slide 6 1: 25,000

7 Slide 7 1:100,000

8 Slide 8 Examples for Today Cultural Buildings & Structures Cultural Areas Place Names Administrative Boundaries Names Transportation Centerlines vs. Polygons Terrain Data Sources: LIDAR to DEMs Hillshading Contours Hydrography Data Sources Ortho Imagery Warn that there s lots of material and that we re sacrificing some good presentation strategies in favor of providing content on the CD.

9 Slide 9 Cultural: Buildings and Structures As scale increases more information can be shown On general reference maps 25K base map model had 43 building types A few are symbolized uniquely and very few have names 5K base map model has 194 building types Most are symbolized by a major type category and labeled by name Over 50% of non-residential buildings have names On special purpose maps (e.g., Natural Disaster Response & Management) Specific information about buildings or about a specific class of buildings may be needed One issue here that is not obvious is that many of the 43 types of buildings on the 25K maps should be differentiated on the maps, and currently they are not on USGS maps. For instance the locations of police and fire stations, paramedics, hospitals, etc We used Google Maps to find many of the names, searching by business type and by location

10 Slide 10 Cultural: Major Building Types (1:5,000) Major types of buildings (194) General Case (37) Commercial (26) Agricultural (6) Educational (14) Industrial/Utility (22) Governmental (35) Military (14) Residential (11) Religious (13) Healthcare (16)

11 Slide 11 Building Labels (1:5,000)

12 Slide 12 Buildings Label Manager

13 Slide 13 Buildings: Label Manager Class SQL

14 Slide 14 Cultural: Labeling Buildings (1:5,000) Goal: Balance building size with label size Large buildings (> 20,000 ft2) Stack, Reduce Font Size, Overrun (24pts), May not place outside, Position = Horizontal Smaller buildings (8,000-20,000 ft2) Stack Reduce font size, overrun(24pts), May place outside, Has leader line symbol, Position = Horizontal Smallest buildings (< 8,000 ft2) Stack Reduce font size, overrun(24pts), May place outside, Has leader line symbol, Position = Offset Horizontal University of Boise Example; not all buildings are completely contained. Intersects may save more time. Naming then becomes an issue. BSU -?????? for buildings outside and no BSU for buildings inside.

15 Slide 15 Cultural: Labeling Buildings (1:5,000) Building complexes Attribute containing T or F for building inside complex Automated based on select by location and calculate Label classes for buildings in complexes have lower priority ranking (below below complex labels) Important buildings Always labeled, in separate classes and given highest priority Manual based on local knowledge

16 Slide 16 Cultural Areas U.S. mapping tradition for land cover is safe, but at expense of useful content Land cover does not always sufficiently express land use Intended land cover/use like zoning is misleading on reference maps Robustly classified cultural areas fill this gap ESRI 1:5,000 scale model has 8 major classes and 177 specific types USGS model has less than 20 that are drawn on maps About 80 exist, but for the most part are not used

17 Slide 17 Cultural Areas: Major Types (1:5,000) Major types of cultural areas (177) Agricultural (16) Archeological (6) Building Complexes (33) Natural Resources (33) Utility/Industrial (23) Recreation (45) Religious (6 conservative effort) Special Areas (15)

18 Slide 18 Cultural: Place Names Centrally managed normalized names table Sets organizational standard for names Joined to features using Name_ID field Exported to Cartographic Data Cartographic representations of names Names are joined and result is exported to become a cartographic dataset LabelStr field: Contains string representation for maps Example: Interstate 84 is shown with just 84 Example: Sasquatch Mountain is abbreviated as Sasquatch Mtn FeatType field: Used in SQL Query Option to set up Label Classes, and is basis for text symbol

19 Slide 19 Demo Names Show BuildRelationships.mxd. Have ArcCatalog open with the PGDM_DCM directory showing. This is a demo of the address data model and it is used here to show a centrally managed names database that allows you to get the name for your maps from different points of entry. BuildRelationships.mxd What you are seeing here is cultural areas, parcels, buildings and address points. I ll use the identify tool and I ll move the window separator so we can see the left side a little better. If I identify on one of these larger buildings, we can take a look at some of its attributes. I ll click on some of the drop downs and you can see that: has street contains the street name is named shows you that it is actually encoded now to carry some of the cartographic attributes in the database Some of the other feature attributes can also be used for naming. Now let s look at ArcCatalog. Here we ll look at the Ada_TopoBase geodatabase this is a database we are using from Ada County, Idaho to test some fo thee methods and to further develop the data model. I ll use the Preview Geography window to show you Feature Names this has 794 items, and Street Names this has over 7300 items. But the point is that these are the centrally managed names database that are used to label the features on our map. And the Feature Names really isn t all that long at least for Ada County!

21 Slide 20 Cultural Features: Labeling Even features that don t t have names often get labels Label by feature type Make sure feature type descriptions are Map-worthy Shortcut is to add these descriptions to the LabelStr field when no name exists Label placement follows similar placement strategy as large buildings

22 Slide 21 Cultural Features: Symbology Three Layers (based on CFT_ID field) 1. Cultural Overlays: Tracks, ball fields, ball courts, etc. 2. Building Complexes (complexes can fall inside of other cultural areas 3. All other cultural Areas Aileen Demo this with TestMap_5K_D_Series.mxd

23 Slide 22 Boundaries Representation Typically GIS representation is polygon Cartographic line work is more effective based on lines Labeling should be based on polygons using Maplex Labeling based on size Large Areas: using boundary placement option Small Areas: place label inside area Tiny Areas: place label outside and use leader line

24 Slide 23 Boundaries: Labeling

25 Slide 24 1:5,000 Map Labeling Parameters for Boundaries Large areas SQL Query: [Shape_Area]] > (Units = feet) Boundary Label Placement Make sure May Place Label Outside option is Off Stack Labels is Off Small areas SQL Query: [Shape_Area]] > AND [Shape_Area[ Shape_Area] ] < (Units = feet) Horizontal Label Placement Make sure May Place Label Outside option is Off Stack Labels is On Tiny areas SQL Query: [Shape_Area]] < (Units = feet) Horizontal Label Placement Make sure May Place Label Outside option is On Stack labels is On

26 Slide 25 Producing Boundary Lines Polygon to Line tool (not Feature to Line) Produces lines that are similar in character to what the ArcInfo Coverage stored in line feature classes Includes left and right FID from the original polygons

27 Slide 26 Transportation (1:5,000) Centerlines: Labels Curb Lines/Pavement Polygons: Stronger cartographic representation through ~1:30,000 Centerlines still used for labels though lines are not drawn Producing polygons using Feature to Polygon tool is not perfect and requires some hand editing Would ideally be created when curb lines are captured Susceptible to topological inconsistencies in curb lines

28 Slide 27 Transportation: Road Polygons

29 Slide 28 Transportation: Labeling Streets and Highways For Highway Shield Symbols Use marker symbols based on ESRI Shields font Jim Mossman s ddvcoyote Application for the ultimate level of graphical quality

30 Slide 29 Transportation: Labeling Streets and Highways (1:5,000) Highways (shields) Placement = Horizontal Repeat Labels ~5,000 ft, Remove Duplicates ~4,000 ft Streets (inside street polygons using centerlines) Placement = Curved Stacking = No, Street Placement = No Repeat Labels ~4,000 ft, Remove Duplicates ~1,200 ft SQL Query: [Shape_Length[ Shape_Length] ] > 120 (units = feet) Street (spurs / cul de sacs) Placement = Straight Stacking = Yes, Street Placement = No, Overrun = 16 pts, Font Reduction = Yes SQL Query: [Shape_Length[ Shape_Length] ] <= 120 (units = feet)

31 Slide 30

32 Slide 31 Terrain: Elevation Model Data Sources 1:5,000 2m pixel bare earth DEM from LIDAR 2-10 foot contour intervals 1:25,000 10m (1/3 Arc Second) pixel DEM (barely adequate*) foot contour intervals 1:100,000 30m (1 Arc Second) pixel DEM (barely adequate*) foot contour intervals *Use Bilinear Interpolation resampling method

33 Slide 32 Raster Layer Interpolation Method

34 Slide 33 Terrain: Hillshading 1:5,000: Default Hillshade tool works well 1:25,000: DEM Quality can be an issue* Use Cartographic Hillshading techniques** 1:100,000 DEM Quality Cartographic Hillshading techniques** *Smooth your DEM using: Neighborhood Statistics Tool, Circle, Mean, r = 6 (henceforth referred to as Smoothed DEM ** models > base map data model > Hillshade tools

35 Slide 34 LIDAR-based Hillshade (1:5,000)

36 Slide 35 Default Hillshade

37 Slide 36 Default with Tweak

38 Slide 37 Cartographic Hillshading Technique

39 Slide 38 Terrain: Contour Lines There are no USGS contour lines for 1:5,000 maps Default contours in GIS lack Supplementary contours (in flat areas) Carrying contours (in steep areas) Adequate drawing performance Depression contours (more depressions at 1:5,000) Index contour identification Good cartographically placed labels

40 Slide 39 Terrain: Slow Contour Lines Use the smoothed DEM from hillshade work Result from Contour Tool is excessively dense Generalize using Douglas-Peucker Advanced mode field calculator Dim pcurve as IPolyCurve Dim pgeom as IGeometry Dim pc as Integer Dim i as Integer Set pcurve = [Shape] if not pcurve.isempty then pcurve.generalize(200) Generalize method uses Douglas- Peucker end if Set pgeom = pcurve

41 Slide 40 Terrain: Slow Contour Lines Steps 1. Start Editing 2. Zoom in on a portion of an undulating contour line 3. Select it using the Edit Tool 4. Set the Edit Task to Modify Feature (this will show the vertexes and you ll see how dense 5. Use the Measure tool to measure the distance (curve radius between 3 vertexes you want to use to maintain your shape) 6. Now set your zoom level to be your map s s scale 7. Use the above field calculator on the shape field to calculate a new shape for the selected feature. The number you got from the measure tool is what you should put into the generalize command s s parameter (200 is used above). On 25K contours when units are Decimal Degrees I used , when units are feet, I used 4 feet. 8. If it s s too much, click UNDO and try again until you get a good thinning of points without changing your shape (as you don t want topological inconsistencies to arise from this process) 9. Once you find a good radius value stop editing and do not save your edits 10. Use the field calculator again on your shape field to generalize all the shapes

42 Slide 41 Zoom to Area with Curves

43 Slide 42 Isolate Typical Curve to be Maintained

44 Slide 43 Start Editing & Select Contour Line Distance for Generalize Method

45 Slide 44 Calculate Shape Field

46 Slide 45 Set Generalization Level

47 Slide 46 Review Result Undo/Repeat until Happy

48 Slide 47 Terrain: Contour Line Symbol Width Using Smoothed DEM Create Slope GRID (Use Percent Rise Option) Reclass Slope Grid into 4 Classes < 3,250,000 (flat areas; consider making supplemental contours) 3,250,000-15,000,000 (general case) 15,000,000 to 24,000,000 (fairly steep) > 24,000,000 (very steep) Convert reclassified slope to polygons Use Identity tool with slope polygons on contour lines Use the Unique Values multiple fields symbology method, include your identity field which is effectively the slope category Set the symbol width of the contours in the higher slope categories to be narrower by 20%-25% 25% than the neighboring lesser slope category

49 Slide 48 Width Narrows in Steeper Regions

50 Slide 49 Terrain: Supplemental Contours Example: Adding 5 5 supplemental contours to a contour line data set with a 10 contour interval 1. Create 10 contour dataset with a base contour of 0 (zero) 2. Create another contour 10 contour line dataset with a base contour of 5 3. Identity using polygons from previous slide (see supplemental contour line reference) 4. Select and delete contour lines whose identity is not in the flattest class 5. Select and delete short supplemental contours (may need to use Multipart to Singlepart tool first).

51 Slide 50 Supplemental Contours

52 Slide 51 Terrain: Contour Line Labeling Use Maplex with the Street Placement option Use Repeat Labels option Use Remove Duplicates option (75-80% of distance specified in Repeat Labels option) Create masks: use the Feature Outline Masks tool (in Cartography Tools toolbox) Use Masks with Advanced Drawing Options to mask contour lines (right click in TOC on data frame)

53 Slide 52

54 Slide 53 Terrain: Index Contours Add Short Integer Field called IndexYN Calculated using Dim k As String k = Right( Str( ( [CONTOUR] ),2 ) Dim d As Integer d = val(k) p = d mod 25 if p > 1 then p = 0 else p = 1 endif

55 Slide 54 Hydrography SCALE RANGE OF USE 1:1000 1:2?,000 1:25,000 to 1:100,000 1:100,000 to? DATA SOURCE Local data captured at ~1:500 NHD High Resolution NHD Medium Resolution Lessons Learned At smaller scales, NHD data required finer line weights too avoid looking unrefined/inappropriate Selected subset of larger local scale data with slight simplification of geometry would be much better at 1:25,000 than the NHD High Resolution

56 Slide 55 1:5,000 Hydro (matches (matches orthoimagery)

57 Slide 56 NHD High-Resolution

58 Slide 57 NHD Medium Resolution

59 Slide 58 Ortho Imagery SCALE 1:5000 ORTHO IMAGE RESOLUTION 0.3 Meter Pixel PRINTED OUTPUT RESOLUTION 600 DPI 1: Meter Pixel 600 DPI 1: Meter Pixel 300 DPI 1:100,000 2 Meter Pixel 600 DPI

60 Slide 59 Thank you! -> Base Map Data Model

Designing GIS Databases to Support Mapping and Map Production Charlie Frye, ESRI Redlands Aileen Buckley, ESRI Redlands

Designing GIS Databases to Support Mapping and Map Production Charlie Frye, ESRI Redlands Aileen Buckley, ESRI Redlands 1 Designing GIS Databases to Support Mapping and Map Production Charlie Frye, ESRI