Down in the Trenches

Automating Label Placement in Dense Utility Maps

Jill Phelps Kern

3

• Definition

• Literature review

• Research problem

• Approach and timeline

The Map Label Placement Problem

4

Problem Definition

The Map Label Placement Problem

Placing map feature labels

• legibly

• without overlap (features / other labels)

• maintaining visual association of labels with their features

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Densely Labeled Maps

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Densely Labeled Maps

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Literature Review Themes

Label Placement

• rules

• quality metrics

• algorithms

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Label Placement Rules

• Area features

• Point features

• Line features

Label placement most difficult

Label placement least constrained

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W i s s o t a R. W i s s o t a R.

Label Placement Rules

Potomo

Franklin CountyDavis County

Lakeview

Menemsha

Lake WinnipesaukePotomo

Lakeview

Sources: Imhof (1962, 1975); Wood (2000)

10

Label Placement Quality Metrics

• Aesthetics

• Label visibility

• Feature visibility

• Association

R i ve r

R i v e r

City City

ATownBTown

ATown

BTown

PeakPeak Peak

Based on Van Dijk et al. (1999)

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Label Placement Quality Metrics

• Aesthetics 5 of 20 papers reviewed

• Label visibility 20

• Feature visibility 10

• Association 11

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Automating Label Placement

• Area features

• Point features

• Line features

Label placement most difficult

Label placement least constrained

Frequent research target for label placement automation

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Automating Label Placement

• Area features

• Point feature label placement

• Line features

models

algorithms

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Automated Point Feature Label Placement Models

1

3

2

4

Discrete label position priorities: Yoeli (1972)

6 5

8

7

Slider model: Van Kreveld et al. (1999)

Continuous circumferential movement: Hirsch (1982), Kameda & Imai (2003)

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Automated Point Feature Label Placement Algorithms

Local Search

Global Optimization

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Automated Point Feature Label Placement Algorithms

Local Search

• Rule-based exhaustive search

• Gradient descent

Global Optimization

• Force-directed

• Simulated annealing

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Exhaustive Search

Rule

Rule

Rule

…

• Place labels according to rules until violation• Backtrack and adjust to maximize number of labels placed

x

Local Search Algorithms

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Local Search Algorithms

• Develop initial label placement• Compute overlap vectors to guide

next movement• Iterate From Hirsch (1982), p. 13

Gradient Descent

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Local Search Algorithms

• Develop initial label placement• Compute overlap vectors to guide

next movement• Iterate• Can cycle between

local minima (a) and (b)

without finding

preferred placement (c)

From Christensen et al. (1995), p. 213

(a) (b)

(c)

From Hirsch (1982), p. 13

Gradient Descent

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Automated Point Feature Label Placement Algorithms

Local Search

• Rule-based exhaustive search

• Gradient descent

Global Optimization

• Force-directed

• Simulated annealing

21

From Stadler et al. (2006), p. 211

Global Optimization Algorithms

Force-Directed

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Global Optimization Algorithms

Based on Zoraster (1997) and Christensen et al. (1995)

Simulated Annealing

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Automated Label Placement Software

Yoelipriorities

Slidermodels

Simulatedannealing

Iteration andbacktracking

Optimization

Imhof (and others’)labeling rules

Force-directedmethods

Label / featurevisibility

Association

Aesthetics

9.2

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Project Objectives

Evaluate the automated labeling capabilities of current GIS software when applied to dense maps

Identify factors which necessitate

manual label placement

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Project Context

Town of Concord Sewer Map Book

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Point feature: Sewer manholeAttributes: Facility ID, station number,

rim elevation, invert elevation

Sewer Infrastructure Features

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Sewer Infrastructure Features

Line feature: Sewer mainAttributes: Size, material

(VCP = vitreous clay pipe)

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Line feature: Sewer mainAttributes: Slope and slope direction

Sewer Infrastructure Features

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Line feature: Sewer tieAttribute: Service number

Sewer Infrastructure Features

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Sewer Labeling Quality Metrics

A. Number of Labels Placed• Total and % of ideal• Minimal leader length

Importance: Critical – Major – Minor

C. No Overlap• Label-label• Label-sewer tie

B. Labels in Preferred Position• Point (manhole)• Line (sewer mains & ties)• Area (streets)

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Sewer Labeling Quality Metrics

A. Number of Labels Placed• Total and % of ideal• Minimal leader length

Importance: Critical – Major – Minor

C. No Overlap• Label-label• Label-sewer tie

B. Labels in Preferred Position• Point (manhole)• Line (sewer mains & ties)• Area (streets)

32

Sewer Labeling Quality Metrics

A. Number of Labels Placed• Total and % of ideal• Minimal leader length

Importance: Critical – Major – Minor

C. No Overlap• Label-label• Label-sewer tie

B. Labels in Preferred Position• Point (manhole)• Line (sewer mains & ties)• Area (streets)

33

Approach and Timeline

1. Prepare for research (Dec – Feb)

2. Conduct research (Mar – May)

3. Develop conclusions (Jun – Jul)

4. Present findings (Aug – Oct)

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1. Prepare for Research

• Conduct literature review - COMPLETE

• Select case study maps - COMPLETE

• Design label classes, styles and hierarchy / weighting - COMPLETE

• Develop label placement quality metrics - COMPLETE

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Research Preparation

36

2. Conduct Research

A. Automated Labeling

• Apply automated ESRI labeling tools to case study maps• Standard labeling engine• Maplex

• Measure quality of automated results

• Iterate to improve quality using automated tools

• Select highest quality result (standard vs. Maplex) for remaining steps

37

2. Conduct Research

B. Manual labeling

• Complete manual adjustments

• Measure quality of manual results

• Compare quality of final automated vs manual

38

3. Develop Conclusions

• Strengths and limitations of current automated labeling tools

• Conditions under which manual placement becomes preferable

• Research limitations and potential for future study

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4. Present Findings

• Prepare for conference presentation

• Present at NACIS 2007 conference

40

Preliminary Findings

Standard Labeling Engine Maplex

Poor Acceptable Ideal

41

Questions?

42

ReferencesChristensen, Jon, Joe Marks, and Stuart Shieber. 1994. Placing text labels on maps and diagrams. Graphics Gems IV, Cambridge MA: Academic Press, 497-504.Christensen, Jon, Joe Marks, and Stuart Shieber. 1995. An empirical study of algorithms for point-feature label placement. ACM Transactions on Graphics (14)3: 203-232.Cook, Anthony C. and Christopher B. Jones. 1990. A Prolog interface to a cartographic database for name placement. In Proceedings of the International Symposium on Spatial Data Handling, International Geographical Union and International Cartographic Association, pp. 701-710.Doerschler, Jeffrey S. and Herbert Freeman. 1992. A rule-based system for dense-map name placement. Communications of the ACM (35)1: 68-79.Ebner, Dietmar, Gunner W. Klau and Rene Weiskirscher. 2003. Force-based label number maximization. Technical Report TR 186-1-03-02, Vienna: Vienna University of Technology.Edmondson, Shawn, Jon Christensen, Joe Marks, and Stuart M. Shieber. 1996. A general cartographic labeling algorithm. Cartographica (33)4: 13-23.Freeman, Herbert and John Ahn. 1984. AUTONAP – an expert system for automatic name placement. Proceedings of the International Symposium on Spatial Data Handling, International Geographical Union and International Cartographic Association, pp. 544-569.Freeman, Herbert and John Ahn. 1987. On the problem of placing names in a geographic map. International Journal of Pattern Recognition and Artificial Intelligence 1(1): 121-140. Hirsch, Steven A. 1982. An algorithm for automatic name placement around point data. The American Cartographer 9(1): 5-17.Imhof, Eduard. 1962. Die Anordnung der Namen in der Karte [Positioning names on maps]. Internationales Jahrbuch fur Kartographie, vol. 2, Verlagsgruppe Bertelsmann GmbH/Kartographisches Institut Bertelsman, pp. 93-129.Imhof, Eduard. 1975. Positioning names on maps. The American Cartographer 2(2): 128-144.Jones, Christopher B. 1989. Cartographic name placement with Prolog. IEEE Computer Graphics and Applications 9(5): 36-47.Kameda, Takayuki, and Keiko Imai. 2003. Map label placement for points and curves. IEICE Transaction Fundamentals E86-A(4): 835-840.Stadler, Georg, Tibor Steiner and Jurgen Beiglbock. 2006. A practical map labeling algorithm utilizing morphological image processing and force-directed methods. Cartography and Geographic Information Science 33(3): 207-215.Van Dijk, S., M. Van Krefeld, Tycho Strijk, and Alecander Wolff. 1999. Towards an evaluation of quality for label placement methods. Proceedings of the 19th International Cartographic Conference and 11th General Assembly, ed. by C. P. Keller, Ottawa, Ontario, pp. 57-64.Van Kreveld, M., Tycho Trijk and Alexander Wolff. 1999. Point labeling with sliding labels. Computational Geometry 13: pp. 21-47.Wood, Clifford H. 2000. Descriptive and illustrated guide for type placement in small scale maps. The Cartographic Journal 37(1): 5-18.Yoeli, P. 1972. The logic of automated map lettering. The Cartographic Journal 9(2): 99-108.Zoraster, Steven. 1997. Practical results using simulated annealing for point feature label placement. Cartography and Geographic Information Science 24(4): 228-238.