Layout

System and method for abstracting and visualizing a rout map

6952661

Abstract

A system and method for making computer-generated maps includes a different scale factor for each road in a route. The scale factors are used to optimize the route map against a target function that considers factors such as the number of false intersections in the route and the number of roads falling below a minimum length threshold. A refinement technique such as simulated annealing is used to find a solution to the target function. Each road in the scaled map is rendered to provide a finished product having the appearance of a hand-drawn map. The finished product includes context roads that intersect the main route but are not part of the main route. Furthermore, the hand-drawn map is optimized to the characteristics of the viewport used to visualize the map.


Claims

1. A method for optimizing a display of a route map, the method comprising:

fitting a collection of reference points in said route map with a probability distribution function, each said reference point corresponding to a position of an intersection in said route map;

deriving (i) a mean position of said collection of reference points, (ii) a first farthest position in which a member of said collection of reference points extends in a first direction away from said mean position, (iii) and a second farthest position to which a member of said collection of reference points extends in a direction that is orthogonal to a vector between said mean position and said first farthest position;

computing a bounding box, wherein a size and orientation of said bounding box is determined by said mean position, said first farthest position and said second farthest position; determining a direction of the long axis of said bounding box;

rotating said route map, by an amount that is sufficient to reorient said long axis so that said long axis lies in a predetermined orientation, to form a rotated route map;

and presenting a portion of said rotated route map, thereby optimizing said display of said route map.

2. The method of claim 1, wherein said probability function is selected from the group consisting of a binomial distribution, a Poisson distribution, and a Gaussian distribution.

3. The method of claim 1, wherein said predetermined orientation is chosen so that a starting point in said rotated route map is in a designated location.

4. A computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:

a map optimization module for optimizing a display of a route map, said map optimization module comprising: instructions for fitting a collection of reference points in said route map with a probability distribution function, each said reference point corresponding to a position of an intersection in said route map;

instructions for deriving (i) a mean position of said collection of reference points, (ii) a first farthest position in which a member of said collection of reference points extends in a first direction away from the mean position, (iii) and a second farthest position to which a member of said collection of reference points extends in a direction that is orthogonal to a vector between said mean position and said first farthest position; instructions for computing a bounding box, wherein a size and orientation of said bounding box is determined by said mean position, said first farthest position and said second farthest position; instructions for determining a direction of the long axis of said bounding box;

instructions for rotating said route map, by an amount that is sufficient to reorient said long axis so that said long axis lies in a predetermined orientation, to form a rotated route map; and

instructions for presenting a portion of said rotated route map, thereby optimizing said display of said route map.

5. The computer program product of claim 4, wherein said probability function is selected from the group consisting of a binomial distribution, a Poisson distribution, and a Gaussian distribution.

6. The computer program product of claim 4, wherein said predetermined orientation is chosen so that a starting point in said rotated route map is in a designated location.

7. A computer system for optimizing a display of a route map, the computer system comprising:

a central processing unit;

a memory, coupled to said central processing unit;

a viewport for displaying said route map;

a program module, executable by said central processing unit, said program module comprising:

instructions for fitting a collection of reference points in said route map with a probability distribution function, each said reference point corresponding to a position of an intersection in said route map;

instructions for deriving (i) a mean position of said collection of reference points, (ii) a first farthest position in which a member of the collection of reference points extends in a first direction away from the mean position, (iii) and a second farthest position to which a member of said collection of reference points extends in a direction that is orthogonal to a vector between said mean position and said first farthest position;

instructions for computing a bounding box, wherein a size and orientation of said bounding box is determined by said mean position, said first farthest position and said second farthest position; instructions for determining a direction of the long axis of said bounding box;

instructions for rotating said route map, by an amount that is sufficient to reorient said long axis so that said long axis lies in a predetermined orientation, to form a rotated route map; and

instructions for presenting a portion of said rotated route map on said viewport, thereby optimizing said display of said route map.

8. The computer system of claim 7, wherein said probability function is selected from the group consisting of a binomial distribution, a Poisson distribution, and a Gaussian distribution.

9. The computer system of claim 7, wherein said predetermined orientation is chosen so that a starting point in said rotated route map is displayed in a designated location in said viewport.

10. The computer system of claim 9, wherein said designated location is the top or bottom of said viewport.

11. The computer system of claim 7, wherein:

said viewport has a horizontal dimension x and a vertical dimension y;

said portion of said rotated route map displayed on said viewport representing a full vertical component of said rotated route map and a subset of a horizontal component of said rotated route map;

said program module further comprising:

instructions for associating a scroll bar with said horizontal dimension of said viewport, whereby, in response to directed input, the full horizontal component of said rotated route map is accessible.

12. The computer system of claim 7, wherein:

said viewport has a horizontal dimension x and a vertical dimension y;

said portion of said rotated route map displayed on said viewport representing a full horizontal component of said rotated route map and a subset of a vertical component of said rotated route map;

said program module further comprising:

instructions for associating a scroll bar with said vertical dimension of said viewport, whereby, in response to directed input, the full vertical component of said rotated route map is accessible.

13. The computer system of claim 12, wherein said predetermined orientation is vertical.

14. The computer system of claim 7, wherein said route map has a constant dimension and a variable dimension orthogonal to said constant dimension, the length of the variable dimension determined by a number of steps or a distance of a route within said route map.

15. The computer system of claim 7, wherein said computer system is a personal digital assistant.


Description

The present invention relates generally to a system and method for generating a route map. More particularly, this invention relates to a system and method for applying a unique scale factor to each road in a route map and for optimizing the positions of labels in the route map. Further, a method for rendering the appearance of roads in the route map is disclosed.

BACKGROUND

Route maps, when well designed, are an effective device for visualizing and communicating directions. Such maps have existed in various forms for centuries, and the recent availability of detailed geographic databases via the Internet has led to the widespread use of computer-generated route maps. Online mapping services typically provide directions as a set of maps complemented with text descriptions. Such on-line computer-generated maps are unsatisfactory, however, because the algorithms used to generate the maps disregard many of the techniques and principles used by human map-makers.

Effective use of a route map generally requires two distinct activities: (i) following a path until reaching a critical point and (ii) changing orientation at that point to follow another path. Thus, one of the most important types of information route maps can communicate are points of reorientation, that is, point along the route where someone must consciously turn from one path to another. However, existing computer-generated route maps fail to effectively communicate points of reorientation because they scale all the roads in the map by a constant scale factor. The scaling of all the roads in a route map by a constant scale factor is referred to herein as uniform scaling. As a result of uniform scaling, for routes of any reasonable length, uniform scaling frequently requires some roads to be very short. But it is often precisely these very short roads that connect critical turning points. Thus, uniform scaling can result in a loss of some of the most critical information found in a route map.

Another shortcoming in prior art computer-generated route maps is that they needlessly depict accurate length, angle, and curvature of each road in the route. Such accurate depictions are made at the expense of map readability. Psychological research indicates that most people distort distances, angles, and curvature when drawing route maps. See e.g., Tversky and Lee, "How space structures language," Spatial Cognition: An interdisciplinary approach to representation and processing of spatial knowledge, (eds.) Freska, Habel, and Wender, 1998, 157-175; Tversky and Lee, "Pictorial and Verbal Tools for Conveying Routes," COSIT 99, Conference Proceedings, Stade Germany, 1999, 51-64. Other psychological studies indicate that people maintain such distortions in their own mental representations of a route. See e.g., Tversky, "Distortions in Cognitive Maps," Geoforum 23, 1992, 131-138. Thus, adherence to accurate lengths and angles in prior art computer-generated maps runs counter to how humans conceptualize routes.

Computer-generated route maps can be classified into four major mapping styles: route highlight maps, TripTiks, overview/detail maps, and two dimensional nonlinear distortion maps. Route highlight maps simply highlight the route on a general road map of the region, as shown in FIG. 1. Since the purpose of general road maps is to provide an understanding of the entire road system in a region, such maps typically employ constant scale factors and display extraneous detail throughout the map. The constant scaling, as exhibited in FIG. 1, generally causes one of two problems. Either detailed turn information is lost because the scale factor is too large, or the scale factor is small enough to show the detail, but the map is very large. Since general road maps are not optimized to show any particular route, a route highlight map will often suffer from both a large scale factor and an inconvenient size. The clarity of the route in a route highlight map depends on the style of the highlighting since that is the only property differentiating the route from other roads. Usually the route is distinctively colored, but because general road maps provide context information over the entire map, the map is cluttered with extraneous information that makes it difficult to perceive the route and the individual reorientation points.

TripTiks are similar to route highlight maps, but they are specifically designed for communicating a particular route. As shown in FIG. 2, a TripTik map usually stretches over multiple rectangular pages, and each page is oriented so that the route runs roughly down the center of the page. Each TripTik page employs constant scaling, but the scale factor differs across pages. Changing the scale factor from page to page allows the TripTik to show more detailed turn information where needed. However, because the map stretches over many pages and the orientation and scale factor varies from page to page, forming a general understanding of the overall route is difficult.

Overview/detail maps combine multiple maps rendered at different scales to present a single route, as shown in FIG. 3. One of the maps (e.g., FIG. 3A) is scaled by a large factor so that it provides an overview of the entire route. Since the large scale factor of this map reduces the readability of local turn details, maps showing turn-by-turn information are provided (e.g., FIG. 3B). A constant scale factor is used for each map, but the scale factor differs across the maps. While an overview/detail map may seem like an effective combination, such maps are unsatisfactory in practice. The overview map rarely presents more than the overall direction and context of the route. Although turn-by-turn maps provide detailed information for every turn, the use of distinct maps for each turn, often with different orientation and scale, makes it difficult to understand how the maps correspond to one another. Therefore, the navigator has difficulty forming a cognitive model of the route.

To ensure clear communication of all of the reorientation points, some parts of a route's depiction may require a small scale factor while others require a large scale factor. Researchers have described attempts to use two dimensional nonlinear image distortion techniques on general road maps to provide focus-plus-context viewing. (See. e.g., Carpendale et al., "Three-Dimensional Pliable Surfaces: For the Effective Presentation of Visual Information," Proceedings of the ACM Symposium on User Interface Software and Technology, UIST 95, 1995, 217-226; Keahey, "The Generalized Detail-In-Context Problem," Proceedings of the IEEE Symposium on Information Visualization, IEEE Visualization 1998). These techniques allow users to choose regions of the map they want to focus on and then apply a nonlinear magnification, such as a spherical distortion, to enlarge these focus regions. Such two dimensional distortion allows detailed information to be displayed only where relevant and often produces general area maps that can be conveniently displayed on a single page. However, a major problem with nonlinear two-dimensional distortion is that the regions at the edges between the magnified and non-magnified portions of the map undergo extreme distortion.

In an effective route map, all essential components of the route, especially the roads, are easily identifiable. The route is clearly marked and readily apparent even at a quick glance. The map contains only as much information as is necessary and is easy to carry and manipulate. To further such design goals, map content, precision, and rendering style must be carefully optimized. Map content includes important parameters such as a route start and end, as well as points of reorientation. Although all maps are abstract representations of a route, there is a range of styles that can be used to render a map, with varying associations of accuracy and realism. An appropriate rendering style can greatly affect the readability and clarity of a map. Retinal properties such as color and line thickness are used to draw attention to important features of the map. Rendering style can also aid the user in interpreting how closely the map corresponds with the real world. Another important map design goal is the proper use of context information. The amount of context information included in the map greatly affects the utility of the map. Useful context information includes labels or names for a path on the route as well as context information along the route such as buildings, stop lights, or stop signs. When drawing a route map by hand, people most commonly use context information to indicate points of reorientation and, less frequently, to communicate progress along a road.

Environmental psychology studies have demonstrated that human generated route maps contain distortion. There are three primary types of distortion: (1) inaccurate path lengths, (2) incorrect turning angles at intersections, and (3) simplified road shape. For example, Tversky and Lee, COSIT 99 Conference Proceedings, 1999, 51-64, asked a group of students to sketch a route map between two locations near the Stanford University campus. Although they encouraged participants in their study to represent paths and intersections accurately, most did not. Most intersections were drawn at right angles regardless of their actual angle and seventy-one percent of the participants used simple generic curves and straight lines to represent roads. Even when participants intended to communicate the shape or length of the road accurately, they typically rendered these attributes incorrectly. Such distortion in the map is in fact beneficial because it increases the flexibility available to the map-maker in the design and layout of the map. Variably scaling the length of each road allows the map-maker to ensure all reorientation points are visible, while flexibility in choosing turning angles and road curvature allows the map to be simplified. Such distortions can simultaneously improve the readability and convenience of the route map with little adverse effect on its clarity and completeness.

Hand-drawn route maps often present a good combination of readability, clarity, completeness and convenience, as shown in FIG. 4. Instead of using a constant scale factor, hand-drawn maps only maintain the relative ordering of roads by length. While this ensures that longer roads appear longer than shorter roads in the map, each road is scaled by a different factor. Often the map designer does not know the exact length of the roads and only knows their lengths relative to one another. The flexibility of relative scaling allows hand-drawn route maps to fit within a manageable size and remain readable.

Hand-drawn route maps typically remove most contextual information that does not lie directly along the route. This strategy reduces overall clutter and improves clarity. The intersection angles in hand-drawn maps are generally incorrect, the precise shape of roads is often misrepresented, and the roads are typically depicted as generically straight or curved. These distortions make the map simpler and only remove unnecessary information. Hand-drawn route maps are rendered in a "sketchy" style typical of quick pen-and-ink doodling. Many navigators are familiar with such hand-drawn maps and the sketchy style is a subtle indicator of imprecision in the map.

In order to improve route map clarity, many algorithms have been developed for smoothing, interpolating, and simplifying roads in a route map. In the area of map rendering the most well-known simplification algorithms are Douglas & Peucker, "Algorithms for the reduction of the number of points required to represent a digitized line or its caricature," The Canadian Cartographer 10(2), 1973, 112-22; Ramer, "An iterative approach for polygonal approximation of planar closed curves," Computer Graphics and Image Processing. 1, 1972, 244-56; Visvalingam & Whyatt, "Line generalization by repeated elimination of points," Cartographic Journal. 30(1), 1993, 46-51; and Barkowsky, Latecki, and Richter, "Schematizing maps: Simplification of geographic shape by discrete curve evolution, " in Freksa, Brauer, Habel, and Wender (eds.): Spatial Cognition II, Springer-Verlag, Berlin, in press. Given a piecewise linear curve as a set of shape points, all of these methods remove some subset of the shape points to produce a simpler curve. Examples of shape points 3302 and turning points 3306 are provided in FIG. 33A. Each of these methods uses different criteria/metrics to decide which shape points to remove and which to retain. As roads become simpler both the perceptual benefits and processing speed increase. The most extreme form of simplification replaces the piecewise linear road with a single linear segment from the first shape point to the last shape point. Although this extreme approach produces a good approximation in most cases, it can cause the map to become misleading. Prior art algorithms for simplifying roads in a route map can generate three types of undesirable results:

(i) False Intersections. Roads that did not intersect before simplification falsely intersect after simplification. An example of a false intersection 3310 is found in FIG. 33A.

(ii) Missing Intersections. Roads that did intersect before simplification no longer intersect after simplification. An example of a missing intersection 3312 is found in FIG. 33B.

(iii) Inconsistent Turning Angles. The turning angle between roads can change substantially, even to the point where a left turn might appear as a right turn. An example of a wrong turn angle 3314 is found in FIG. 33C.

Based on the above background it is apparent that what is needed in the art is an improved system and method for making computer-generated maps. What is further needed in the art is a system and method for making computer generated maps that avoid the pitfalls found in existing map-making algorithms, such as the use of extraneous information and constant scaling.

SUMMARY OF THE INVENTION

The present invention provides an improved system and method for making computer-generated maps. In the present invention, each road in a route is individually scaled. The scale factor for each road is optimized using an objective function that considers a number of factors such as the number of false intersections and the number of roads that are shorter than a minimum threshold length. Thus, the scaled route fits in a predetermined viewport without loss of information about important turns. Refinement against the objective function is performed by one of many possible search algorithms such as greedy searches, simulated annealing schedules, or gradient descents. Greedy search algorithms are described in Cormen et al., Introduction to Algorithms, eds. Cormen, Leiserson, & Rivest, The MIT Press, Cambridge Mass., 1990, 329-355. Simulated annealing was first disclosed by Kirkpatrick et al. in the article "Optimization by Simulated Annealing," Science 220 1983, 671-680. Unlike prior art methods, some embodiments of the present invention provide simplification algorithms that ensure that problems such as false intersections, missing intersections, and inconsistent turning angles do not occur in the final scaled route map.

Map clutter in the scaled map is avoided by refining label positions against a novel target function that minimizes the number of roads the labels intersect, the number of labels that intersect each other, and the distance along the route between a label and the center of a road corresponding to the label. In one embodiment, simulated annealing is used to find a solution to the novel target function. The final scaled route map is rendered so that it has the appearance of a hand-drawn map. The rendered map clearly communicates every reorientation point in a readable and convenient form.

One embodiment of the present invention provides a method for rotating the route map to best fit the display aspect ratio. In this method, a collection of reference points in the route map are defined. Each reference point in the collection corresponds to a position of an intersection in the route map. The collection of reference points form a distribution in two dimensional space. Therefore, they can be fitted with a probability distribution function that defines the mean position of the collection of reference points in the two dimensional space as well as the farthest position in which a member of the collection of reference points extends in a first direction away from the mean position (i.e. a first extent) as well as the farthest position to which a member of the collection of reference points extends in a direction that is orthogonal to the vector between the mean position and the position of the first extent (i. e. a second extent). The mean, first extent, and second extent provide a description of the outer boundary of the reference points and a bounding box that denotes this outer boundary is computed. The bounding box is centered on the mean position and the sides of the bounding box are determined by the positions of the first extent and the second extent. The orientation of the bounding box is determined by the vector between the mean position and the position of the first extent. Based on this orientation, the route map is rotated by an amount that is sufficient to reorient the bounding box to a predetermined orientation, thus forming a rotated route map. A portion of the rotated route map is then presented, thereby optimizing the display of the route map.

Another embodiment of the present invention provides a method for placing an annotation or label in a route map. In the method, the route map is partitioned into an initial grid. The grid is composed of grid cells. Candidate grid cells, into which the annotation or label can be placed, are identified. Each of the candidate grid cells are free of objects associated with the route map. When the annotation or label will not fit in a single candidate grid cell, a search for grid cells having sufficient adjacent object free grid cells is conducted. This search is subject to the requirement that the candidate grid cell, and one or more of the adjacent object free grid cells, must be able to accommodate the annotation or label. When no candidate grid cells are found during the identifying or searching stages, a grid subdivision scheme is performed. The grid subdivision scheme subdivides a portion of the grid cells in the initial grid to form a new grid. Then, the identifying and searching steps are repeated using the new grid. When multiple candidate grid cells are found, each candidate grid cell is ranked based on a density of objects in grid cells that border each candidate grid cell. The candidate grid cell that borders grid cells having the lowest density of objects is selected as the candidate grid cell and all other candidate grid cells are discarded. The annotation or label is positioned in the candidate grid cell, thereby placing the annotation or label in the route map.

In another embodiment of the present invention, a plurality of labels are positioned in a route map. For each label in the plurality of labels, the following steps are performed:

(i) A plurality of constraint definitions are associated with the label. Each constraint definition in the plurality of constraint definitions uniquely defines a bounding box, label orientation, and layout style.

(ii) An initial constraint definition is selected from the plurality of constraint definitions.

(iii) A center of the label is positioned at a location within the bounding box defined by the initial constraint definition in accordance with the label orientation and layout style defined by the initial constraint definition.

The method further comprises choosing a label in the plurality of labels and determining a first score (S1) using a target function. The target function is determined by a position of the chosen label in the route map. Then, a constraint definition is selected from the plurality of constraint definitions associated with the selected label. The selected constraint definition is then applied. Application of the constraint definition includes the step of repositioning the center of the label inside the bounding box defined by the constraint definition, in accordance with the label orientation and layout style defined by the constraint definition. A second score (S2) is calculated using a target function that considers the repositioned label position. The new position for the label is accepted in accordance with a function that is determined by a comparison of S1 and S2. The choosing, determining, applying, calculating, and accepting steps are repeated until a first occurrence of an exit condition. Exemplary exit conditions include achievement of a suitably low score or the occurrence of a predetermined number of repetitions of the choosing, determining, applying, calculating, and accepting steps.

Still another embodiment of the present invention provides a method of preparing a route map that describes a path between a start and an end. In this method, the path from the start to the end is obtained. The path comprises an initial set of elements. Each element includes sufficient information to determine a direction. Further, each element intersects at least one other element in the initial set of elements. A first element in the initial set of elements includes a start and a second element in the set includes the end. A different scale factor is independently applied to each of at least two elements in the initial set of elements. Application of the different scale factor to each of the at least two elements produces a scaled set of elements. A total height and a total width of a rendering of each element in the scaled set of elements is estimated. Then, an image component is selected based on a function of the total height and the total width. Finally, an image of the scaled route map is formed by rendering each element in the scaled set of elements.

Another embodiment of the present invention includes a method of adding a cross street, and a cross street label associated with the cross street, to a route map that includes a main path. In the method, an intersection point at which the cross street intersects the main path is determined. The cross street is placed in the route map with the constraint that the cross street intersects the main path at a first test position that is randomly chosen from a segment of the main path that includes the intersection point. The cross street label is positioned at a second test position within a predetermined area. The predetermined area includes the intersection point. A length of the cross street is adjusted so that the cross street passes under the cross street label and intersects the main path. The first or second test position is perturbed by an random amount and a score of a function, i.e. scoring function, is obtained. The size of the random amount used to perturb the first or second test position is typically a small increment that is designed to see if a "tweak" in the first or second test position leads to an improved score. However, on occasion, the size of the random amount used to perturb the first or second test position is considerably larger, in order to prevent the scoring function from becoming trapped in a local minima. The scoring function is determined by a location of the cross street and the cross street label in the route map. The perturbing and obtaining steps are repeated until the score reaches a threshold value or the perturbing and obtaining steps have been executed a predetermined number of times. The cross street and the cross street label are added to the route map when the score reaches the threshold value. Furthermore, the cross street and the cross street label are not added to the route map when the perturbing, obtaining and determining steps have been executed the predetermined number of times before the score has reached the threshold value.

In still another embodiment of the present invention, a method of preparing a route map that describes a path between a start and an end is provided. In this method, the path from the start to the end is obtained. The path comprises an initial set of elements. Each element includes sufficient information to determine a direction and each element intersects at least one other element in the initial set of elements. A first element in the initial set of elements includes the start and a second element in the initial set of elements includes the end. A different scale factor is independently applied to each of at least two elements in the initial set of elements. Application of the different scale factor to each of the at least two elements produces a scaled set of elements. A rendering of each element in the scaled set of elements is created to form an intermediate map. A set of N breakpoints is identified in the intermediate map. Each breakpoint in the set of N breakpoints occurs in an element in the scaled set of elements, and a minimum value for N is determined by the expression:


where,

    • S is a number of elements in the scaled set of elements; and
    • M is a predetermined maximum number of elements.
      The intermediate map is then split into a set of N segment maps, each segment map including a different breakpoint. The set of N segment maps thereby comprises the route map.


    • Another embodiment of the present invention provides a method of simplifying a road in a route map. In the method, the road is approximated as a piecewise linear curve that includes a plurality of shape points. Each shape point in the plurality of shape points is connected by a linear segment to a respective shape point in the plurality of shape points. At least one point at which the road intersects another road in the route map is added to the plurality of shape points as an intersection point. Each shape point in the plurality of shape points that is (i) not a first shape point, (ii) a last shape point, or (iii) an intersection point, is marked. A check is made for false intersections between the road and another road in the route map and, when a false intersection is found, a first marked shape point and a last marked shape point in the plurality of shape points are unmarked. The checking step is repeated until no false intersection is found or there is no marked shape point in the plurality of shape points. When a shape point is marked, the piecewise linear curve is modified by replacing the marked shape point and each said linear segment connected to the marked shape point with a new linear segment that originates at a shape point or intersection point immediately proceeding the marked shape point and ends with a shape point or intersection point immediately succeeding the marked shape point. When a shape point is unmarked, the piecewise linear curve is modified by replacing the new linear segment associated with the shape point with (i) a first linear segment that is bounded by the shape point or intersection point immediately proceeding the marked shape point and the shape point and (ii) a second linear segment that is bounded by the shape point or intersection point succeeding the marked shape point and the shape point. In this way, the piecewise linear curve represents a smoothed road that corresponds to the road in said route map.

    BRIEF DESCRIPTION OF THE DRAWINGS

    FIG. 1 is a prior art route highlight map.

    FIG. 2 is a prior art TripTik map.

    FIG. 3 is a prior art Overview/Detail map.

    FIG. 4 is a prior art hand-drawn map.

    FIG. 5 is a map that is generated in accordance with one embodiment of the present invention.

    FIG. 6 illustrates a system for generating a route map in accordance with one embodiment of the present invention.

    FIG. 7 illustrates the processing steps used to optimize the length of individual roads in a route map using a greedy algorithm, in accordance with one embodiment of the present invention.

    FIG. 8 illustrates the processing steps used to optimize the length of individual roads in a route map using a simulated annealing schedule, in accordance with one embodiment of the present invention.

    FIG. 9 illustrates the processing steps used to optimize label positions in a route map using a simulated annealing schedule, in accordance with one embodiment of the present invention.

    FIG. 10 illustrates a map before and after road extensions are made so that labels are optimally associated with corresponding roads.

    FIGS. 11A, 11B, and 11C illustrate the conceptual steps used to identify the longest axis of a route and to rotate this axis in a predetermined direction, in accordance with one embodiment of the present invention.

    FIG. 12 illustrates a generalized problem of placing annotations on a route map.

    FIG. 13 illustrates the processing steps associated with one solution to the generalized problem of placing annotations in a route map in accordance with one embodiment of the present invention.

    FIG. 14 illustrates the spatial subdivision of a route map in order to identify regions of the route map that are suitable for the placement of annotations as well as labels.

    FIG. 15 illustrates a generalized problem, which arises in a spatial subdivision approach to placing a label or annotation in a constrained area, in which no empty grid cell can be found.

    FIG. 16 illustrates how nonuniform subdivision is used to solve the problem of using spatial subdivision to place a label or annotation in a constrained area.

    FIGS. 17A and 17B illustrate the use of bounding boxes and FIGS. 17C and 17D illustrate the use of orientation vectors that are present in some constraint definitions in accordance with one embodiment of the present invention.

    FIGS. 18A, 18B, 18C, 18D, 18E, and 18F illustrate various layout styles that are present in some constraint definitions in accordance with one embodiment of the present invention.

    FIG. 19 illustrates the processing steps used to optimize label positions in a route map using a simulated annealing schedule that includes usage of constraint definitions, in accordance with one embodiment of the present invention.

    FIG. 20 provides an overview of an embodiment of layout module 688 that makes use of expanded constraint definitions, in accordance with one embodiment of the present invention.

    FIG. 21 illustrates exemplary image components and text boxes used to compose forms, in accordance with one embodiment of the present invention.

    FIGS. 22A, 22B, and 22C illustrate various output forms in accordance with one embodiment of the present invention.

    FIG. 23 illustrates a scaled route map with cross streets in accordance with one embodiment of the present invention.

    FIG. 24 illustrates the general problem of determining an amount of visual clutter in a pixel based image of a route map.

    FIG. 25 illustrates a route map with several point features, such as exit numbers, restaurant locations, and city names included in accordance with one embodiment of the present invention.

    FIG. 26 illustrates a cluttered route map that would be difficult to use while driving.

    FIG. 27 illustrates the route map of FIG. 26 split into two segment maps which, taken together, comprise the route map of FIG. 26.

    FIGS. 28A, 28B, 28C and 28D illustrate various intermediate and segment maps in accordance with one embodiment of the present invention.

    FIG. 29 illustrates a scaled route map with a corresponding inset in accordance with one embodiment of the present invention.

    FIG. 30 illustrates how the use of an inset can be used to avoid the circularization of a predominantly North-South or East-West route map in accordance with one embodiment of the present invention.

    FIG. 31 illustrates how the use of an inset can be used to associate legible labels to roads that do not have legible labels in a corresponding main route map, in accordance with one embodiment of the present invention.

    FIG. 32A illustrates a route map before curve (road or element) simplification and FIG. 32B illustrates the route map of FIG. 32A after curve simplification, in accordance with one embodiment of the present invention.

    FIG. 33 illustrates how road simplification can introduce false intersections (33A), missing intersections (33B), and inconsistent turning angles (33C).

    FIG. 34 illustrates how a road is treated as a set of shape points (s) into which intersection points are introduced, in accordance with one embodiment of the present invention.

    FIG. 35 illustrates the intersection of roads r1 and r2 at a point 3502.

    FIGS. 36A and 36B respectively illustrate two different methods for identifying shape points to remove or retain from roads in a road map that are not part of a ramp, in accordance with one embodiment of the present invention.

    FIG. 37 illustrates aspects of shape points in a ramp that are measured in order to evaluate a relevance of a particular shape point in a ramp in a route map during a simplification process, in accordance with one embodiment of the present invention.

    FIG. 38 illustrates shape points in a ramp in a route map, in accordance with one embodiment of the present invention.

    FIG. 39 illustrates how a check for turn angle consistency is made when considering to drop a ramp from a route map, in accordance with one embodiment of the present invention.

    FIGS. 40A and 40C illustrate portions of an unscaled route map whereas FIGS. 40B and 40D show corresponding scaled route maps that respectively illustrate how scaling can lead to false intersections and missing intersections.

    FIG. 41A illustrates how a missing intersection is scored and FIG. 41B illustrates how a misplaced intersection is scored in accordance with one embodiment of the present invention.

    FIGS. 42A, 42B, and 42C illustrate several false intersection scenarios, showing for each false intersection point which direction the closest endpoint must travel to remove the knot formed by that false intersection point.

    FIG. 43 illustrates a knot that is produced by a false intersection upon scaling a route map.

    FIGS. 44A and 44B illustrate methods for resolving false intersections, in accordance with various embodiments of the present invention.

    FIGS. 45A and 45B illustrate two types of missing intersections that arise during route map scaling.

    FIGS. 46A and 46B illustrate methods for resolving missing intersections, in accordance with various embodiments of the present invention.

    FIGS. 47A and 47B illustrate the utility of using extended intersections, in accordance with one embodiment of the present invention.

    FIG. 48 illustrates how an extended intersection may work against the resolution of a false intersection during route map refinement.

    FIG. 49 illustrates a way to determine which extended intersections to add to a refinement score, in accordance with one embodiment of the present invention.

    Like reference numerals refer to corresponding parts throughout the several views of the drawings.

    DETAILED DESCRIPTION OF THE INVENTION

    The present invention provides a system and method for generating maps that have the benefits and characteristics of a hand-drawn map. Automatically generating route maps in this style is complex. Distorting aspects of the map can accentuate reorientation points, but it can also have detrimental effects such as introducing false intersections. Creating an effective route map generally requires searching a large space of possible map layouts for an optimal layout. An efficient multistage algorithm that couples a road layout refinement module with a label and annotation placement module is disclosed. The resulting map is rendered using subtle perceptual cues, such as a wavy hand-drawn style for drawing the paths, to communicate the distortion of scale and shape.

    The design goals of the present invention are:

    (i) Roads should be variably scaled so that all roads and reorientation points are clearly visible and easily labeled.

    (ii) If road A is longer than road B, then road A should be noticeably longer than road B in the map.

    (iii) The representation of a road only needs to convey general curvature and the significant changes in orientation.

    (iv) The precise angle of intersection of two roads is not important; instead it is sufficient to communicate clearly the action to be taken (turn left; turn right) and a generalized orientation.

    (v) The start and end of the route should be clearly marked.

    (vi) A "sketchy" style should be used to render a road in order to represent an imprecision of scale and orientation.

    (vii) The resulting map should fit in the desired viewport, such as a single sheet of paper, a computer display screen and/or a window in a graphical user interface.

    Generating a computer-based map in accordance with the above identified design goals is more difficult than generating a map in conventional computer-based styles. Variable road scaling provides some flexibility in choosing the length of each road to produce a clear and readable map. However, the relative ordering of roads by length must remain fixed and false intersections should not be introduced into the map. The space of all possible route-map layouts is extremely large, and therefore it is not feasible to blindly search for a layout that satisfies the design goals of the present invention. Rather, a multi-phase heuristic generate-and-test approach is used to obtain a map that satisfies the design principles of the present invention. FIG. 5 illustrates a map generated using the methods of the present invention.

    General Architecture

    Attention now turns to FIG. 6, which is a system in accordance with one embodiment of the present invention. FIG. 6 illustrates a network 620 that is operated in accordance with the present invention. Network 620 includes at least one user computer 622 and at least one server computer 624. User computer 622 and server computer 624 are connected by transmission channel 626, which may be any wired or wireless transmission channel.

    User computer 622 is any device that includes a Central Processing Unit (CPU) 630 connected to a random access memory 650, a network connection 634, and one or more user input/output ("i/o") devices 638 including output means 640. In some embodiments, system memory 650 includes read-only memory (ROM). Output means 640 is any device capable of communicating with a human and includes, for example, a monitor, voice user interfaces, and/or integrated graphic means such as mini-displays present in web-phones. Typically, user computer 622 includes a main non-volatile storage unit 636, preferably a hard disk drive, for storing software and data. Further, user computer 622 includes one or more internal buses 632 for interconnecting the aforementioned elements. In a typical embodiment, memory 650 includes an operating system 652 and an Internet browser 654.

    In some embodiments of the present invention, user computer 622 is a hand held device such as a Palm Pilot. Accordingly, in such embodiments, it is possible that user computer 622 does not have disk 636 and browser 654 is integrated seamlessly into operating system 652.

    Server computer 624 includes standard server components, including a network connection device 660, a CPU 662, a main non-volatile storage unit 664, and a random access memory 668. Further, server computer 624 includes one or more internal buses 666 for interconnecting the aforementioned elements. Memory 668 stores a set of computer programs, modules and data to implement the processing associated with the invention. In particular, a preferred embodiment of memory 668 includes an operating system 680 and a HTTP server 682. Memory 668 further includes direction parser 684, road layout module 686, label layout module 688, annotation module 690, and map renderer module 692. In some embodiments of the present invention, memory 668 also includes a direction database 694 and/or context database 696. As will be discussed in further detail below, server computer 624 further includes a shape simplification module 697 for smoothing roads in a route map, a map verticalization module 698 for optimizing the dimensions of a scaled route map to the dimensions of the viewport used to display the scaled route map, and a map division module 699 for breaking a complex scaled route map into a plurality of segment maps.

    Direction parser 684 reads directions from a source, such as a file, a database external to server 624, or a database resident in server 624. Direction parser 684 translates the directions into a graph. Nodes in the graph represent intersections, and edges represent the roads connecting the intersections. In one embodiment, system 620 does not contain a database of roads. Rather, all the information about the map is obtained from text directions stored offsite. In another embodiment, server 624 contains direction database 694, which is used to identify a suitable route between an origin and a destination.

    After directions have been parsed by direction parser 684, roads in the route map are scaled with road layout module 686. In one embodiment, road layout module 686 applies a constant scale factor to the entire map so that the map fits in a viewport having predetermined dimensions. As a result of this uniform scaling, the map often contains many roads that are too small to see or label. To remedy this, each road in the map, beginning with the smaller roads, is scaled by road layout module 686 until roads in the map are clearly visible. Since the length of roads is only increased in this step, the map ends up being larger than the size of the viewport. Thus, in subsequent steps, certain aspects of the map are reduced to yield a map that fits the dimensions of the desired viewport.

    In one embodiment of the present invention, the size of the map is reduced by repeatedly initiating a tracing procedure. In this embodiment, road layout module 686 executes the tracing procedure until the entire route is traced without identifying a road that exceeds the dimensions of the viewport. In the tracing procedure, each successive road in the route is examined, beginning at the route origin, until a road extending outside the viewport, i.e. an offending road, is identified. When an offending road is identified, each road that had been traced is examined to see if it is capable of being shortened. A road candidate is capable of being shortened if it is (i) longer than a specified minimum length, (ii) the relative ordering of the roads by length remains fixed even after the candidate has been shortened, and (iii) false intersections are avoided. In one aspect of this embodiment, road layout module 686 shortens road candidates using a greedy approach so that the candidate is shortened as much as possible, in order from longest to shortest, until the offending road is pulled back inside the viewport.

    Label layout module 688 is used to place labels on the scaled map produced by road layout module 686. To date, proper labeling of individual roads has been an intractable problem. Label layout module 688 solves this problem by refining a novel target function using a simulated annealing schedule. Simulated annealing has been used to refine label positions in prior art methods. Edmondson et al., Cartographica 33, 1997, 12-23. However, unlike Edmondson, which uses a limited set of discrete label positions, the present invention considers a continuous range of positions for label placement, and label placements are not limited to positions that are directly above or below the road. Furthermore, the present invention uses a more comprehensive target function that considers the number of roads each label intersects, the number of labels each label intersects, the distance the label is from the center of the road associated with the label, and whether the label is above or below the associated road. Finally, the present invention is advantageous because roads are extended when the label corresponding to the road is lengthy

    Annotation module 690 adds decorations, such as road extensions, to the route map of the present invention. Further, module 690 adds an icon for route start and end points. Road extensions accentuate reorientation points, and allow for a larger range of label positions to be considered. In this phase, all roads are extended by a small fixed amount. Then only those roads that need to be extended for the chosen labeling pattern are further lengthened. FIG. 10 illustrates the advantages of applying road extensions. In FIG. 10, 1002 represents a road map prior to road extension whereas 1004 represents the same road map after road extension. Labels now fit the corresponding roads and the map is easier to read. Geographic and/or commercial context information are added to the route map by annotation module 690 to help guide the user through the desired route. In one embodiment, such context information is obtained from context database 696.

    Map renderer module 692 renders the scaled route map. In this phase, a "sketchy" pen-and-ink style is applied to each road in the route map. That is, instead of drawing roads as straight lines, variation is introduced in the bend and width of each road to generate a hand-drawn look. In an approach similar to that of Markosian et al., SIGGRAPH 97 Conference Proceedings, 1997, 415-420, each road is broken into small segments and the position of each point is slightly shifted both normal and tangent to the segment direction. These points are then joined with a non-uniform rational b-spline (NURB) to create the final stroke. A NURB is a curve that interpolates data. Thus, given a set of points, a curve is generated passing through all the points. The thickness of the roads is then adjusted to emphasize the route and de-emphasize road extensions generated by annotation module 690.

    Now that an overview of one embodiment of the invention has been disclosed, a number of advantages of the present inventions are apparent. First, the present invention discloses a method for automatically generating a route map that has the clarity of a hand-drawn map. Such a map is produced by using a novel scaling function in which each road is scaled individually using the design criteria of the present invention. Further, a novel method for positioning labels on the map is disclosed. The refined label positions help provide a route map having improved clarity.

    Map Scaling

    Attention now turns to detailed embodiments of road layout module 686. The present invention contemplates several different implementations of road layout module 686. The different road layout module embodiments contemplated by the present invention include but are not limited to uniform scaling, fixed non-uniform scaling, as well as refinement of individual scale factors using a greedy search or simulated annealing schedule.

    In uniform scaling embodiments, a single scale factor that allows the graph created by direction parser 684 to fit in a desired viewport is computed. For viewports that are defined as an x by y pixel array, a single scale factor, pixelsPerMile, is computed by an assignment such as:

    in which the function ComputePixelsPerMile( ) determines the maximum number of pixels a mile of the route may have without causing the overall route to exceed the desired pixel-based viewport. One of skill in the art will appreciate that a single scale factor for viewports that are based on metrics other than pixels can be computed using functions analogous to ComputePixelsPerMile( ). Once a uniform scale factor has been identified by a function such as ComputePixelsPerMile( ), the uniform scale factor is applied to the length of each road, and intersection points between consecutive pairs of roads are updated to reflect the change in length of the roads. For pixel-based viewports, the application of the uniform scale factor to each road reduces to a conversion of miles to pixels. Thus, in such embodiments, the application of the constant scale factor to each road takes the form:

    (101) for each Road r {
    (102) r.lengthPxls = r.lengthMiles*pixelsPerMile;
    (103) }
    (104) SetRoadIntersectionPts( );


    In fixed non-uniform scaling embodiments, road layout module 686 includes a rescaleByBucket( ) function that breaks the range of road lengths (0, infinity) found in the route into N consecutive buckets [0, x1), [x1, x2), . . . [xN-1, xN), [xN, infinity). The function then scales the roads differently depending on which bucket they fall in. Small roads, those in the earlier buckets, are scaled to be longer, while longer roads are scaled to be shorter. In one embodiment, roads falling in the final bucket are capped at some maximum length. In another embodiment, roads falling in the first bucket are not allowed to fall below a minimum length. In yet another embodiment, the scale factor that is chosen for each bucket is subject to the constraint that the relative ordering of the roads by length remains fixed. In embodiments in which the route is to be scaled to a pixel-based viewport, each road is scaled by the uniform scale factor computed by the ComputePixelsPerMile( ) function described in the uniform scaling embodiment. Thus, one implementation in accordance with the non-uniform scaling embodiment, has the steps:

    (201) LayoutRoads( )
    (202) {
    (203) for each Road r {
    (204) r.lengthMiles = rescaleByBucket(r.lengthMiles);
    (205) r.lengthPxls = r.lengthMiles*pixelsPerMile;
    (206) }
    (207) SetRoadIntersectionPts( );
    (208) }


    Attention now turns to FIG. 7 which illustrates an embodiment of the present invention in which road layout module 686 refines the length of roads in the map using a greedy search algorithm. In processing step 702, road layout module 686 first computes a pixel to mile conversion factor and applies this factor to each road in the map so that the map fits into the desired viewport. Then, in processing step 704, the roads are sorted by length. The relative order of the roads, in terms of length, in the map as determined in processing step 704 is maintained throughout the remainder of the processing steps illustrated in FIG. 7. In some embodiments deviations in this relative ordering is allowed upon payment of a penalty. In processing step 706, all small roads are grown until each road is longer than a set minimum length. Because processing step 706 only lengthens roads, the route map is not likely to fit in the desired viewport after processing step 706 has been executed.

    To reduce the map so that it fits into the desired viewport, a search for roads that can be shortened is performed. In processing step 708, the route is traversed from the route origin. Each route in the road is examined (710-714) until a road that extends outside the viewport (offending road) is identified. When such a road is identified (710-Yes), a list of candidate roads in the portion of the route that had been traversed prior to identifying the offending road is collected (720). To qualify as a candidate road, a traversed road must be capable of being shortened without changing the relative ordering of the roads by length and without falling below a minimum road length. Further, a candidate road must be capable of being shortened without creating any false intersections between roads. Finally, the candidate road should be oriented within ±90 degrees of the offending road. Once a road candidate set has been generated, it is ordered by length, from longest to shortest (722).

    Once the candidate roads have been ordered, a shortening process is initiated. The shortening process takes advantage of the computational efficiency of a greedy algorithm to shorten the roads (724). The shortening process cycles through each candidate road in the ordered set of candidate roads and shortens the candidate as much as possible (726) before advancing to the next candidate in the ordered set (732). After the greedy algorithm is applied to a candidate road, a check is made to see if the offending road has been pulled back inside the viewport (728). If the offending road has been pulled back into the viewport (728-No), the shortening process ends and control returns to processing step 708.

    When the greedy algorithm has been applied to each candidate road in the ordered set without successfully pulling the offending road into the viewport (730-Yes), the shortening process repeats the process of applying the greedy algorithm to each road in the candidate list (724) until the offending road is pulled back into the viewport (728-No). The process in FIG. 7 continues until the complete route can be traversed without identifying a road that exceeds the dimensions of the viewport (714-Yes, 780). If such a traversal fails, the shortening process of steps 720-732 is executed and a new attempt to traverse the route is initiated 708.

    At times, an identified road that matches the candidate requirements indicated above will not be added to the road candidate set because there is some other road in the route that is the same length. Roads that have the same length as the identified road are termed blocking roads. If there is a blocking road, the identified road cannot be added to the road candidate set because, if it were shortened, the relative ordering of roads by length, as identified in processing step 704, would be destroyed. The occurrence of blocking roads is of interest because, in some circumstances, they prevent the processing steps of 724-732 from pulling the offending road into the viewport (728-No). In some embodiments, when a certain number of iterations of processing steps 724 through 732 fail to effect a solution (728-No) one or more of the blocking roads are shortened using the greedy algorithm discussed previously. Then, if the offending road still exceeds the dimensions of the viewport, a new road candidate set is generated (720) and processing steps 724 through 732 are executed until the offending road no longer exceeds the dimensions of the viewport (728-No).

    FIG. 8 illustrates another embodiment of road layout module 686 in which the length of roads in the map are refined with a simulated annealing schedule. In processing step 802, a single scale factor is applied to each road in the route map. In one embodiment, which is in accordance with this aspect of the invention, the scale factor is used to size the map produced by direction parser 684 so that it fits within the dimensions of the desired viewport. In another embodiment, the map is sized so that each road in the map is longer than a selected minimum length so that each road in the map is legible in the desired viewport.

    In the second phase of processing step 802, an initial parameter t is chosen. The use of a parameter t to obtain better heuristic solutions to a combinatorial optimization problem has it roots in the work of Kirkpatrick et al., Science 220, 4598, (1983). Kirkpatrick et al. noted the methods used to find the low-energy state of a material, in which a single crystal of the material is first melted by raising the temperature of the material. Then, the temperature of the material is slowly lowered in the vicinity of the freezing point of the material. In this way, the true low-energy state of the material, rather than some high energy-state such as a glass, is determined. Kirkpatrick et al. noted that the methods for finding the low-energy state of a material can be applied to other combinatorial optimization problems if a proper analogy to temperature as well as an appropriate probablistic function, which is driven by the this analogy to temperature, can be developed. The art has termed the analogy to temperature an effective temperature. Therefore, parameter t will henceforth be termed an effective temperature. It will be appreciated that any effective temperature t may be chosen in processing step 802. One of skill in the art will further appreciate that the refinement of an objective function using simulated annealing is most effective when high effective temperatures are chosen. There is no requirement that the effective temperature adhere to any physical dimension such as degrees Celcius, etc. Indeed, the dimensions of the effective temperature t used in the simulated annealing schedule adopts the same units as the objective function that is the subject of the optimization.

    In one embodiment, a starting effective temperature that is readily reduced by ten percent on a periodic basis is chosen, such as 1.0/log(3)*3. In another embodiment, the starting value of t is based on a function of one or more of the characteristics of the route to be scaled, such as the number of roads in the route, the number of intersections in the route, and/or the length of the route. In another embodiment, the starting value of t is selected based on the amount of resources available to compute the simulated annealing schedule. For example, the starting value of t is reduced below a pre-specified default value when the annealing schedule is to be run on a server that is currently refining several other routes or on a relatively slower client. In still another embodiment, the starting value of t is related to the form of the probability function used in processing step 814. It has been found, in fact, that the effective temperature does not have to be very large to produce a substantial probability of keeping a worse score. Therefore, in some embodiments, starting effective temperature t is not large.

    Once a single scale factor has been applied to each road in the route map and an initial starting effective temperature has been assigned, an iterative process begins. A counter is initialized in processing step 804 and, in processing step 806, the quality of the map (E1) is assessed using an objective function. It will be appreciated that the utility of the map produced by the simulated annealing schedule is dependent upon the development of an objective function that accurately balances the various features of the map that need to be optimized. In one embodiment, the objective function is dependent upon the number of false intersections each road in the route makes, the number of roads in the route that no longer have the same relative length that they had before the simulated annealing schedule was initiated, and the number of roads that fall below a minimum length. An objective function in accordance with this embodiment is: ##EQU1##
    where,
    • w1, w2 and w3 are independently selected weights;
    • false_intersection, is the number of false intersections road i makes;
    • N is the number of roads in the route;
    • num_w/o_rel_len is the number of roads that no longer have the same relative length that they had before simulated annealing schedule was initiated; and
    • num_short_roads is the number of roads that are shorter than a minimum length threshold.


    • After the quality (E1) of the map has been measured using the objective function, a scale factor is randomly generated and applied to a randomly selected road (808). In one embodiment, the scale factor is randomly chosen from a permissible range, such as zero to two. Thus, in such an embodiment, a random number generator is used to identify a number in the range zero to two, such as "0.6893." The random number is then applied to a randomly selected road in the route as a scale constant. For example, if the number is "0.6893" and the randomly selected road is the jth road in the route map, the jth road is shortened by 31.07 percent. In another embodiment, the permissible range for the random number is -0.1 to 0.1 and therefore, in such embodiments, application of the randomly chosen scale constant is capable of altering the length of the jth road by no more than ten percent.

    After the length of the jth road has been adjusted by the scale factor, the quality of the map (E2) is calculated using the same objective function used in processing step 806 (810). When the quality of the map has improved (E2<E1) (812-Yes), then the change made to the length of the jth road is accepted (830). When the quality of the map has not improved (E2>E1) (812-No) the change made to the length of the jth road is accepted with the probability:

    From the form of equation (1), it will be appreciated that the probability that the change is accepted, when (E2>E1), is lower at lower effective temperatures t. Equation (1) is implemented as processing steps 814 through 818 in FIG. 8. In processing step 814, exp-[(ΔE)/k*t)] is computed. In processing step 816, a number Pran in the interval 0 to 1 is generated. If Pran is less than exp-[(ΔE)/k*t)] (818-Yes), the change made to the jth road in processing step 808 is accepted (830). If Pran is more than exp-[(ΔE)/k*t)] (818-No), the change made to the jth road in processing step 808 is rejected (840). It will be appreciated that probability functions other than that disclosed in equation (1) are within the scope of the present invention.

    Acceptance of conditions (E2>E1) on a limited probabilistic basis is advantageous because it provides the refinement system with the capability of escaping local minima traps that do not represent a global solution to the objective function. One of skill in the art will appreciate, therefore, that probability functions other than that of equation (1) will advance the goals of the present invention. Representative probability functions include, for example, functions that are linearly or logarithmically dependent upon effective temperature, rather than exponentially dependent on effective temperature as described in equation (1).

    Processing steps 806 through 840 represent one iteration in the refinement process. In processing step 842 an iteration count is advanced. When the iteration count does not exceed the maximum iteration count, the process continues at step 806 (844-No). When the iteration count equals a maximum iteration flag (844-Yes), effective temperature t is reduced (846). One of skill in the art will appreciate that there are many different types of schedules that are used to reduce effective temperature t in various embodiments of processing step 846. All such schedules are within the scope of the present invention. In one embodiment, effective temperature t is reduced by ten percent. In another embodiment, effective temperature t is reduced by a constant value. For example, the starting effective temperature set in processing step 802 could be 20,000 and this effective temperature could be reduced by 300 each time processing step 846 is executed. In another embodiment the percentage decrease in effective temperature in processing step 846 is calculated as a function of the number of roads to be scaled.

    When the effective temperature has been reduced by an amount in processing step 846, a check is performed to determine whether the simulated annealing schedule should be terminated (848). In the embodiment illustrated in FIG. 8, the process is terminated (848-Yes, 850) when effective temperature t has fallen below a low effective temperature threshold or E2 falls below a predetermined low quality threshold. The low effective temperature threshold is any suitably chosen effective temperature that allows for a sufficient number of iterations of the refinement cycle at relatively low effective temperatures. When it is determined that the annealing schedule should not end (848-No), the process continues at step 804 with the reinitialization of iteration count i.

    In another embodiment of the present invention, a distinctly different exit condition than the one illustrated in FIG. 8 is used. In this alternative embodiment, a separate counter is maintained. This counter, which could be termed a stage counter, is incremented each time t is reduced in step 846. When the stage counter has exceeded a predetermined value, such as fifty, the simulating annealing process ends (850). In yet another embodiment, a counter tracks a consecutive number of times the arbitrary scale factor is rejected (840). When a set number of arbitrary changes in a row have been rejected, the route map is considered optimized and the process ends (850).

    Map Annotation

    In one embodiment, annotation module 690 is used to deterministically place context information on the map after the map has been scaled by road layout module 686. In one aspect of this embodiment, the context information represents points of geographical interest and helps to guide the user through the route to the destination. In another embodiment, the context information represents a form of advertisement that is paid for by subscribers. In one example in accordance with such embodiments, the subscriber is a fast food chain and the landmarks represent the location of each fast food franchise that is associated with the fast food chain. It will be appreciated that an important advantage of the present invention is that the route maps do not contain superfluous content. Thus, the route maps of the present invention are particularly well suited for use in conjunction with geographical landmarks that are paid for by subscribers. In one embodiment of the present invention, memory 668 of server 624 includes a context database 696 that is populated with context information that has been provided by and paid for by advertisers.

    Label Refinement

    Identification of an optimal position for each label in the route map improves the quality of the map because clutter and object overlap is reduced. The present invention optimizes label position by minimizing a novel target function that scores the position of a label using a unique set of label parameters. Importantly, rather than considering a small number of discrete positions for label placement, a continuous range of positions within a region around the center of the road being labeled are considered. This region includes positions that are not directly above or below the road being labeled. When a position that is not directly above or below the road is selected, the road is extended to the position of the label.

    In one embodiment, the target function is optimized using a simulated annealing schedule. FIG. 9 illustrates one embodiment in accordance with the present invention. In processing step 900, each label is placed at the center of the road corresponding to the label and an initial effective temperature t is selected. It will be appreciated that effective temperature it may be set to wide range of possible effective temperatures in processing step 900. In one embodiment, a starting effective temperature that is readily reduced by ten percent on a periodic basis, such as 1.0/log(3)*3, is chosen. In another embodiment, the starting effective temperature is based on a function of one or more of the characteristics of the route to be optimized, such as the number of labels in the route, the amount of context information along the route, and/or the length of the route. In another embodiment, the starting effective temperature is selected based on the amount of resources available to perform the simulated annealing calculations. For example, the initial effective temperature is set to a low value when the annealing schedule is to be run on a server that is currently refining several other routes or a client with a relatively slow central processing unit. In still another embodiment, the starting effective temperature t is determined by the nature of the probability function that is used to accept scores having S2>S1.

    In processing step 902 the stage counter is set to zero. The stage counter is incremented each time effective temperature t has been reduced. Once the initialization steps of processing step 900 have been performed, counter i is set to one (902) and a label j is randomly selected (904). The quality of the position of the jth label (S1) is measured using a target function, which is designed to measure label position quality, in processing step 906 and in processing step 908 the jth label is repositioned by a random amount. In step 908, the quality of the repositioned jth label (S2) is measured. An important advantage of the present invention is that the jth label is repositioned into any of a continuous range of values rather than a limited number of discrete positions. Further the target function used to compute S1 and S2 provides an improved method for assessing the quality of a label position. In one embodiment the target function includes the following components:

    (301) collect all objects that intersect the jth label
    (302) for each intersecting object {
    (303) case ROAD:
    (304) score += ROAD_PENALTY;
    (305) case LABEL:
    (306) score += LABEL_PENALTY;
    (307) case ANNOTATION:
    (308) score += ANNOTATION_PENALTY; }

    In line 301, all the objects that intersect the jth label are collected. Such objects include, for example, roads, other labels, and annotations such as context information. The target function loops through each of the collected objects (line 302). When the object is a road, a road penalty is added to the score (line 304), when the object is a label, a label penalty is added to the score (line 306) and when the object is an annotation, an annotation penalty is added to the score (line 308).

    In some embodiments, the target function includes one or more additional components. One such component is an off screen penalty. When the jth label is positioned such that a portion of the label exceeds the boundary of the viewport, an off screen penalty is added to the score. Another component is a "distance from the center of the corresponding road penalty." This penalty is determined by taking the product of a centering penalty and the normalized distance of the jth label from the road center. Additional components in the target function represent various constraints that are imposed on the label position. Constraints are used to bias label positions that are consistent with label position design criteria. For example, in one embodiment, it is preferable to position a label above the road rather than below the road. Thus, a below_the_road constraint penalty is added to the score of a label position that is below the road corresponding to the label. Another constraint penalty asks whether a road should be extended so that the road runs alongside the label. When it is determined that a road extension will provide better label to road correspondence, a road extension penalty is added to the target function score. Yet another constraint penalty is used when the label is positioned far away from the center of the corresponding road. In such cases, an arrow is positioned on the map to indicate the relationship between the label and the corresponding road and an arrow penalty is added to the target function.

    In one embodiment, the target function has the form:

    (401) float score = 0.0;
    (402) // Get all the objects that intersect the label
    (403) for each object {
    (404) case ROAD:
    (405) score += ROAD_PENALTY;
    (406) case LABEL:
    (407) score += LABEL_PENALTY;
    (408) case ANNOTATION:
    (409) score += ANNOTATION_PENALTY;
    (410) }
    (411) // Is label completely visible on viewport?
    (412) if not {
    (413) score += OFF_SCREEN_PENALTY;
    (414) }
    (415) score += normalized distance from road center *
    CENTERING_PENALTY;
    (416) score += constraint penalty;
    (417) return score;


    When the quality of the jth position has improved (S2<S1) (912-Yes), the new label position for the jth label is accepted (930). When the quality of the map has not improved (S2>S1) (912-No) there is a probability

    that the new label position for the jth label will be accepted. From the form of equation (2), it will be appreciated that, for cases in which (S2>S1), the probability that the change in label position will be accepted diminishes as effective temperature t is reduced. Equation (2) is implemented as processing steps 914 through 918 in FIG. 9. In processing step 914, exp-[(ΔS)/k*t)] is computed. In processing step 916, a number Pran, in the interval 0 to 1, is generated. If Pran is less than exp-[(ΔS)/k*t)] (918-Yes), the change made to the jth label position in processing step 908 is accepted (930). If Pran is more than exp-[(ΔS)/k*t)] (918-No), the change made to the jth label position in processing step 908 is rejected (940). It will be appreciated that probability functions other than the function shown in equation (2) and processing step 914 are within the scope of the present invention. Indeed, any probability function that is dependent upon effective temperature is suitable.

    Processing steps 904 through 940 represent one iteration in the annealing process. In processing step 942, an iteration count is advanced. When the iteration count does not exceed the maximum iteration count (944-No), the process continues at step 904. When the iteration count equals a maximum iteration flag (944-Yes), effective temperature t is reduced and the stage counter is advanced (946). One of skill in the art will appreciate that there are many possible different types of schedules that are used to reduce effective temperature t in various implementations of processing step 946. All such schedules are within the scope of the present invention. In one embodiment, effective temperature t is reduced by ten percent each time processing step 946 is executed. In another embodiment the percentage decrease in effective temperature t in processing step 946 is calculated as a function of the number of labels to be scaled. After processing step 946, a check is performed to determine whether the simulated annealing schedule should be terminated (948). When it is determined that the annealing schedule should not end (948-No), the process continues at step 902 with the reinitialization of iteration count i.

    In the embodiment illustrated in FIG. 9, the process is terminated (948-Yes, 950) when a maximum number of stages has been executed. In one embodiment, the maximum number of stages executed is fifty. In embodiments other than that illustrated in FIG. 9, criteria other than the stage count is used in processing step 948 to determine when the simulated annealing process should be terminated. Such criteria include terminating the process when effective temperature t has fallen below a low effective temperature threshold, when E2 or E1 falls below a predetermined low quality threshold, or when the consecutive number of times the new label position has been rejected exceeds a threshold value.

    Map Rendering

    The final phase of the process is the rendering of the route by map renderer module 692. In this phase, the route map is humanized. In some embodiments, techniques used to humanize the map include casting the roads in a "sketchy" pen-and-ink style, adding a breakage symbol to long roads that have been significantly scaled down by road layout module 686, providing an indication of road length for long roads in the route, adding an arrow to indicate which way is North, and/or adding insets that show enhanced route detail.

    Map renderer module 692 produces the "sketchy" style by breaking each road into small segments and slightly shifting the position of each segment both normal to the stroke direction and along the stroke directions. The rotated segments are then joined with a NURB to create the final stroke. Further, the thickness of the roads is adjusted to emphasize the route and de-emphasize route extensions. In a preferred embodiment, a hand-drawn font is used for the labels.

    Overview of Alternative Embodiments for Abstracting and Visualizing Route Maps

    Embodiments for producing scaled route maps have now been described in detail. In the following sections, details of alternative embodiments for scaling route maps are provided. Full appreciation of these alternative embodiments is best obtained by first providing an overview of the basic processing steps performed by these alternative embodiments.

    Obtain route directions. First, directions are obtained by direction parser 684 from a source such as direction database 694 (FIG. 6). Although direction database is depicted as being on the same server 624 as direction parser 684, it will be appreciated that there is no requirement that direction database 694 reside on the same server. Indeed, direction database 694 may take several different forms and reside at any address that is in communication with transmission channel 626.

    Road simplification. Once road directions are obtained, an initial route map is constructed. Then, as will be described in further detail below, a pass is made by road shape simplification module 697 at simplifying the initial route map. If successful, road shape simplification module 697 removes one or more shape points from some of the roads in the route map, thereby reducing the complexity of the route map without sacrificing map legibility and utility. Furthermore, the reduced complexity of a simplified route map gfacilitates computationally intensive map refinement and scaling that arises in subsequent processing stages.

    Map page design. In the map page design stage, the dimensions of the viewport that the map will be displayed in or printed onto are considered. A layout template is chosen by road layout module 686 based on the dimensions of the viewport. Furthermore, the route map is optionally rotated by map verticalization module 698 in order to optimize the dimensions of the route map to the dimensions of the viewport. When the route map includes several steps, map division module 699 is invoked in order to break the route map into a plurality of segment maps in a manner that is consistent with the selected layout template.

    Road layout. At this stage, road layout module 686 scales each road independently (i.e. nonuniformly). The nonuniform scaling is driven by an optimization algorithm such as simulated annealing in order to achieve a suitable scaled map. The target function used by the optimization algorithm utilizes a novel scoring strategy that is designed to quantify map scale quality.

    Label layout. Once the map has been scaled, the route map is populated with road labels by label layout module 688. Each label is associated with a constraint definition that defines the boundaries in which the label may be placed and the format of the label. Using these constraint definitions, label layout module 688 refines the label locations using an optimization algorithm having a target function that quantifies label position quality.

    Map Annotation. Cross streets, land marks and an optional North arrow are added to the map during the map annotation stage. Annotation module 690 identifies suitable landmarks that will assist the navigator while using the route map. Such landmarks may be derived from a source such as context database 696. It will be appreciated that annotation module 690 can be used in some embodiments for commercial benefit. For example, licensing schemes are envisioned in which a retailer pays to have the location of each franchise positioned on the map as landmarks.

    Map rendering. Other stages of the map scaling process considered the route map in an abstract sense. In the map rendering stage, the components of the route map, including the main route, cross streets, landmarks, and the North arrow are reduced from an abstract sense to an actual image. In one embodiment, this image is a pixel based image. The stage of the process is performed by map renderer module 692.

    Now that an overview of this series of alternative embodiments have been provided, novel aspects of the series of embodiments will be examined in detail.

    Alternative Scoring Functions Used in Road Layout Refinement

    As outlined in the overview, an important aspect of the map scaling process is performed by road layout module 686. Road layout module 686 scales each road in a route map in a nonuniform manner. In embodiments in which road layout module 686 includes a simulated annealing schedule the following steps are performed:
    • 1. Generate an initial road layout by growing all short roads to a desired minimum length.
    • 2. Obtain an initial score E for the initial road layout using an objective function and set an initial effective temperature.
    • 3. While E is greater than an acceptable score, the number of iterations is less than the maximum allowed iterations, and the effective temperature is above some lower threshold level, repeat steps four to eight.
    • 4. Choose a random road and grow or shrink it by a random amount; re-scale all roads so they fit inside the viewport.
    • 5. Obtain a new score E for the new road layout generated in step four.
    • 6. If new score E is less than initial score E, accept the new road layout generated in step four.
    • 7. If new score E is greater than initial score E, accept the new road layout in accordance with some decreasing probability, in order to escape local minima.
    • 8. Adjust effective temperature.


    • It will be appreciated that the simulated annealing protocol outlined above and described in detail in FIG. 8 is not limited to any specific scoring function. Indeed, various embodiments of road layout module 686 use a wide array of scoring functions to determine the initial score E1 (806FIG. 8) as well as new scores E2 (810 FIG. 8). Applicants have described an objective function in accordance with one embodiment of road layout module 686 that is determined by (i) the number of false intersections made be each road i in a route map, (ii) the number of roads that no longer have the same relative length that they had before simulated annealing schedule was initiated, and (iii) the number of roads that are shorter than a minimum length threshold.

    In another embodiment of road layout module 686, processing steps 806 and 810 in FIG. 8 use a scoring function represented by the following representative code.

    (501) Score( )
    (502) Score = 0.0;
    (503) Score += IntersectionScore( )
    (504) Score += ShuffleScore( )
    (505) Score += RoadLengthScore( )
    (506) Score += RatioScore( )
    (507) Score += EndPointDirectionScore( )
    (508) Score += EndPointDistanceScore( )


    Each subscore considers a specific aspect of the road layout, and are prioritized as follows:
    • Highest Priority
      • Intersections: maintaining existing intersections and not introducing false intersections.
      • Road length: scaling all roads to be readable.
      • Shuffles: maintaining relative lengths of the roads.
      • End Point Direction: maintaining overall orientation of route.
      • Ratios: maintaining ratios in lengths between roads.
    • Lowest Priority
      • End Point Distance: maintaining distance between start and destination points of the route.
        In this embodiment, the scoring function used by road layout module 686 assigns higher priority to the aspects of the road layout that are most important to resolve. For example, a map with missing and/or false intersections can be misleading. On the other hand, maintaining overall distance and orientation of the route is useful but not required for a navigator to follow the route. Thus, resolving intersections is given a higher priority than maintaining end point distance in this embodiment of road layout module 686.


    • Line 502 of the representative code initializes the variable "Score" to zero. The variable "Score" represents E1 (806FIG. 8) or E2 (810). Next, lines 503 through 508 each potentially add to the value of "Score." Higher values of score represent higher values for E1 and E2 and thus represent poor solutions. Each of the functions that contribute to the overall value of "Score" on lines 503 through 508 are discussed with more detail below.

    IntersectionScore( ). The first function to contribute to the variable "Score" in the representative code is function "IntersectionScore( )" on line 503. Maintaining proper intersections between roads is the highest priority in the disclosed scoring function. In the initialization of the annealing, all of the roads in the route map are grown to their desired minimum lengths. Growing the roads can lead to two problems: intersections may be introduced between roads that should not intersect (false intersections), or two roads that should intersect no longer intersect (missing intersections). FIG. 40 illustrates both of these scenarios. FIGS. 40A and 40C each represent an original map whereas FIGS. 40B and 40D represent perturbed maps. FIG. 40B represents a situation in which a false intersection 4002 arises. FIG. 40D represents a situation where a missing intersection 4004 arises. Both missing and false intersections can be extremely misleading and therefore are severely penalized in any proposed layout that has either of these problems.

    The role of the scoring function in road layout module 686 is to guide the layout algorithm to the desired layout. One approach to furthering this goal is to add a fixed constant penalty when either of these conditions exists. However, this scoring function does not provide adequate guidance because the same penalty is always added to the score no matter how severe the false or missing intersection. Suppose the route contains a missing intersection as shown by 4004 in FIG. 40D. If the layout is perturbed and the missing intersection points end up closer to one another but do not exactly match, the intersection score for this map will not change. The algorithm will not know that moving the missing intersection points closer together generates a better layout. In other words the annealing algorithm is less likely to converge. Thus, in this embodiment, a score is constructed that reflects the severity of the intersection problems in a manner that suggests how they might be resolved rather than using a constant penalty for each false or missing intersection. What follows is a description of how simple false and missing intersections are resolved independently by the disclosed scoring function. Next, a description is provided for how scoring must change when there are both false and missing intersections in a single map.

    Missing and Misplaced Intersections. If two roads should intersect but don't (missing intersection), a factor is added to the score that is related to the distance between the proper intersection point on each road. The proper intersection point is computed from the parametric value of the original intersection in the unscaled map. If the roads should intersect and do intersect but at the wrong point (misplaced intersection), a factor is also added that is related to the distance between the proper intersection point on each road. The scoring weight for a misplaced intersection is much less than for a missing intersection. This score is illustrated in FIG. 41. FIG. 41A represents how a missing intersection is scored whereas FIG. 41B represents how a misplaced intersection is scored. The general formulas for computing the intersections are:


    where d is the Euclidian distance between the two points that should intersect as represented in FIG. 41.

    Simple False Intersections. False intersections occur when the path incorrectly folds back on itself, forming a loop or knot. To remove false intersections, the knot must be unraveled. To remove any individual knot it is desirable to make the false intersection point move toward the closest endpoint (in pixels along the route) of the path (or similarly, make the closest endpoint move towards the false intersection point). FIG. 42 illustrates several false intersection scenarios, showing for each false intersection point which direction the closest endpoint must travel to remove the knot formed by that false intersection point. FIG. 42A represents the simplest case, one false intersection 4202. End point 4204 simply needs to move to the right to resolve the false intersection. FIGS. 42B and 42C show which direction endpoints should move to resolve each false intersection point independently. FIG. 42B represents a situation in which multiple false intersection points 4208 are near the same endpoint 4206. The two false intersection points 4208 are pulling endpoint 4206 in opposing directions. FIG. 42C represents the case of multiple false intersection points (4214, 4216) that are near different endpoints (4210, 4212). In this case, false intersection points 4214 and 4216 are entirely independent of each other.

    Computing the score for an individual false intersection point is relatively straightforward. It is desirable to move the false intersection point towards the closer endpoint of the route, or alternatively to move the closer endpoint towards the false intersection point. FIG. 43 illustrates a knot that is produced by false intersection 4302. One way to resolve false intersection 4302, is to push the endpoint that is closer to false intersection 4302 towards the false intersection. To determine which endpoint (4304 or 4306) is closer to false intersection 4302, the distance between each endpoint and the false intersection is computed and compared. Then, the endpoint that is closer to the false intersection is moved towards the false intersection.

    Viewing each false intersection independently, the score for each false intersection point is computed as the "distance in pixels along the route to the nearest end point" multiplied by a scoring weight. This is equivalent to conceptually building a scoring hill along the route that guides the false intersection point to the closer endpoint, where it can be removed. Therefore, the score for a single false intersection can be computed as:

    where d is the distance in pixels to the endpoint along the route, as opposed to straight line distance, as shown in FIG. 43. However, as illustrated by the scenario in FIG. 42B, if the score for each false intersection is computed this way, then when there are multiple false intersections the scores will push the endpoint in opposite directions. However, this problem is addressed by always counting only the score for the innermost false intersection (i.e. the one farthest from the endpoint). The difference between counting all false intersections and only the innermost false intersection is shown in FIG. 44. FIG. 44A illustrates the situation in which, if the scores for both false intersections 4404 are counted, endpoint 4402 is pulled equally in both directions, resulting in a plateau in the scoring function since a move of endpoint 4402 in either direction does not change the score. FIG. 44B illustrates the situation in which only the innermost false intersection is counted for each endpoint. In the situation described in FIG. 44B, once the innermost false intersection has been resolved, the remaining false intersection becomes the innermost false intersection and is subsequently resolved. In situations such as FIG. 42C, where there are two false intersections but they are both closer to different endpoints, both scores are counted against these respective endpoints.

    False Intersections and Missing Intersections In general, when both false and missing intersections occur in the same map they can be scored as previously described, and in most cases the scores will interact properly to resolve both problems. However, there is one exceptional situation. This situation occurs when a missing intersection occurs within the loop formed by a false intersection. Several variations of this situation are illustrated in FIG. 45. In FIG. 45A, one point 4502 of the missing intersection is within the loop formed by a false intersection 4504. In FIG. 45B, both points 4506 are within the loop formed by false intersection 4508. In both of the situations shown in FIG. 45, one score may push in one direction and the other score in the other direction, resulting in a stalemate in which neither problem can be resolved. FIG. 46 shows the same routes as FIG. 45, but with arrows 4610 added to indicate the direction that the two scores would move the endpoints 4602 and 4604.

    An important point to note about the situations arising in FIG. 45 is that resolving the missing intersection often resolves the false intersection. In FIG. 45, there is supposed to be an intersection, it is just occurring between the wrong roads. It is quite often the case when a missing intersection occurs within the loop of a false intersection that the false intersection is simply the missing intersection misplaced. This situation is resolved with one additional rule: if there is some point of a missing intersection inside the loop formed by a false intersection a constant penalty is added for the false intersection, not a hill-based score. Thus, both of the cases that are shown in FIG. 45 will use a constant penalty for the false intersection, as both contain at least one point of a missing intersection within the false intersection loop.

    With this introduction an algorithm for scoring missing and false intersections can now be stated with lines 601 through 633 of the illustrative code.

    (601) void score_false_intersection(Road* self, Road* other) {
    (602) if (missing_intersection_in_loop) {
    (603) // false intersection loop contains a missing intersection
    (604) if (closest_to_route_endpoint(self,other)) {
    (605) self->IncrementScore
    (FALSE_INTERSECTION_CNST);
    (606) } else {
    (607) // no missing intersection in loop
    (608) if (closest_to_route_endpoint(self,other)) {
    (609) self->IncrementScore(pixelsToClosestEndpoint *
    (610) FALSE_INTERSECTION_HILL);
    (611) // Compute the max possible extended intersection
    (612) // score. All false intersection scores must be increased
    (613) // by the max extended intersection score to ensure that
    (614) // there is no valley between solving all the false inter-
    (615) // sections and introducing the extended intersections.
    (616) self->IncrementScore(MaxExtendedIntersectionScore);
    (617) } } }
    (618) void ScoreMissingIntersection(Road* self, Road* other) {
    (619) double missingIntersectionScore = 0.0;
    (620) // We know where the two roads should have intersected
    (621) // in terms of T values along each road. Compute distance
    // between these two points.
    (622) for (each missing intersection between self and other) {
    (623) double dist = (ptSelf - ptOther).length( );
    (624) // Before the roads touch use a higher penalty. After they
    (625) // touch reduce the penalty constant to make sure that the
    (626) // anneal will maintain the touch.
    (627) if (no intersection between self and other) {
    (628) double missingScore = dist *
    MISSING_INTERSECTION;
    (629) self->IncrementScore(Road::INTERSECT,
    missingScore);
    (630) } else {
    (631) self->IncrementScore(Road::INTERSECT, dist *
    (632) MISPLACED_INTERSECTION);
    (633) } } }


    Examining lines 601 through 633 of the illustrative pseudo-code in detail, one will notice that an additional score, "MaxExtendedIntersectionScore" is added to the false intersection scores. This function is described below in conjunction with an explanation of the concept of extended intersections.

    Extended Intersections. In addition to avoiding actual intersections between roads, it is desirable to avoid having roads pass close enough to each other that they appear to touch. These situations are handled in one embodiment of road layout module 686 by using the concept of an extended intersection. Extended intersections between two roads are calculated by extending both endpoints of each road by a fixed number of pixels and then checking if the resulting roads intersect. This concept is illustrated in FIG. 47. In particular, in FIG. 47A, the roads do not actually intersect but are close to one another. In FIG. 47B, when the roads are extended by a fixed number of pixels, the roads do intersect. If an extended intersection does occur between two roads it is scored in the following manner for each of the two roads:

    (a) if the intersection occurs in the extended part of the road, as for road 4702 in FIG. 47A, then the number of pixels from the end of the extended road is computed and multiplied by a fixed constant.

    (b) if the intersection occurs within the unextended portion of the road, as for road 4704 in FIG. 47A, then a fixed constant, which is equal to the largest penalty that can be assigned for an intersection with the extended portion of the road, is added to the score.

    There is one complication with handling extended intersections. When trying to resolve a false intersection, extended intersections often cause many local minimums in the search space. This is illustrated in FIG. 48, where an extended intersection 4802 works against the resolution of false intersection 4804. To reduce the number of local minimums in the search space explored by the target function as much as possible, only extended intersections are counted towards the score when they are not likely to be counteracting the resolution of a false intersection. Implementation of this criteria requires two things:

    (a) knowing when to, and when not to, count an extended intersection towards the score, and

    (b) adding the largest possible extended intersection score to the base false intersection score. Otherwise, when a false intersection is resolved the target function starts counting a number of extended intersections, and their increased score may overwhelm the decrease in score from resolving the false intersection. This may cause a substantial local minimum in the search space that would prevent the resolution of most false intersections. However, in a preferred embodiment road layout module 686, the maximum extended intersection score is added to each false intersection score. This guarantees that the resolution of a false intersection will result in a decrease in score.

    A way to determine which extended intersections to add to the score is to divide the route into false intersection intervals. All roads between an endpoint of the map and a false intersection, or between a pair of false intersections are considered to be in the same false intersection interval. This concept is illustrated in FIG. 49. In FIG. 49, the same route shown in FIG. 48 is illustrated, but the route is segmented by false intersection intervals. In particular, there are three false intersection intervals in FIG. 49: (A) from start point 4802 up to, but not including, the first road with a false intersection, (BCDE) which is from the road with a false intersection up to the next road with a false intersection, and (FGH) which is from the last false intersection to the endpoint. Extended intersections are only counted between roads in the same false intersection interval. Thus, the extended intersection shown in FIG. 48 would not be counted. If only extended intersections that occur between roads in the same false intersection intervals are added, then the problem depicted in FIG. 48 will not occur.

    ShuffleScore( ). The second function to contribute to the variable "Score" in the representative code is function "ShuffleScore( )" on line 504. The purpose "ShuffleScore( )" is to maintain the relative lengths of the different roads in the scaled route map the same as they were in the unscaled route map. In function "ShuffleScore( )," for each pair of roads A and B in the route map, the ordering of the roads by length in the scaled map is compared with the ordering of the roads by length in the original unscaled map. If the ordering has changed, roads A and B are considered shuffled and a factor is added to the variable "Score" to reflect this. In one embodiment, however, roads are only considered shuffled when their difference in lengths is greater than some perceptual threshold. Typically, the perceptual threshold used is dependent upon the resolution and size of the viewport that is used to visualize the route map as well as factors such as whether the full scaled route map is being displayed in the viewport as opposed to a scaled up segment of the scaled route map. The purpose of the penalty applied by function "ShuffleScore( )" is to ensure that, whenever possible, the relative ordering of roads by length is maintained in the scaled route map.

    In one representative target function used by an embodiment of road layout module 686, "ShuffleScore( )" is represented by the following expression:
    • For each pair of roads (A,B)
      • Compare the ordering of the roads by length in the current map with the ordering of the roads by length in the original map. If the ordering has changed then add a constant penalty to the score to reflect this. Roads are only considered shuffled when their difference in lengths is greater than some perceptual threshold.


    • RoadLengthScore( ). The overall goal of the non-uniform scaling of maps that is implemented by road layout module 686 is to make all of the roads in the route large enough to be legible. This is tracked by the third function ("RoadLengthScore( )"), which contributes to the variable "Score", as found on line 505 of the representative code. In function "RoadLengthScore( )," the current length of each road in the route map is compared to a predetermined minimum desired length. If a road is less than the minimum desired length, then a factor is added to the variable "Score." The magnitude of this factor is a function of the power of the difference between the current length of the offending road and a predetermined minimum acceptable road length. The predetermined minimum acceptable road length is set to ensure that the road is long enough to be identifiable in the scaled route map. In some embodiments of the present invention, the predetermined minimum acceptable road length is designated by considering the dimensions of the viewport 640 (FIG. 6) used to display the scaled route map or the number of pixels in viewport 640. In one example, when viewport 640 is a 1024 by 768 pixel array, the predetermined minimum acceptable road length is 20 pixels. In another example, the predetermined minimum acceptable road length is set to four percent of the length of the shortest dimension of viewport 640. Thus, if viewport 640 has a display that is 5 by 6 centimeters, the predetermined minimum acceptable road length is set to 0.2 centimeters.

    In one representative target function used by an embodiment of road layout module 686, "RoadLengthScore( )" is represented by the following expression:
    • For each road (A)
      • Compare the current length to a predetermined minimum desired length. If less than the minimum desired length then add a factor to the score. The factor is related to a power of the difference between the current length and the desired minimum length. The minimum desired length is set to ensure the road is long enough to be perceived and labeled and that the relative lengths are preserved.


    • RatioScore( ). The fourth function to contribute to the variable "Score" is function "RatioScore( )," which is on line 506 of the representative code. One of the lowest priority contributors to "Score," function "RatioScore( )" is used to maintain the ratios between different road lengths. Function "RatioScore( )" examines each road A in the scaled route map whose length is greater than the predetermined minimum acceptable road length described in the discussion of function "RoadLengthScore( )" above. For each such road A in the scaled route map, the ratio of the length of the road is compared to the next shorter and next longer road in the route map. The ratios obtained from these comparisons is matched with the corresponding ratios obtained from the unscaled route map. When the ratio between road A and the next longer and next shorter road in the route map differs significantly in the scaled and unscaled route maps, a penalty is added to the variable "Score." The purpose of function "RatioScore( )" is to preserve road length ratios in the scaled route map from the unscaled route map that have sufficient space.

    In one representative target function used by an embodiment of road layout module 686, "RatioScore( )" is represented by the following expression:
    • For each road (A) whose length is greater than its minimum desired length:
      • Compare the ratio of this road's length to the next shorter and next longer road, capping the ratios at five, since in a non-uniform cap it is hard to maintain any larger ratio. Assign a penalty as:


    • EndPointDirectionScore( ). The fifth function to contribute to the variable "Score" in the representative code is function "EndPointDirectionScore( )" (line 507). This function adds a factor to the variable "Score" to reflect the difference in the orientation between the start and end addresses in the unscaled route map and in the scaled route map. The magnitude of the factor added to the variable "Score" by this function is dependent upon the extent of the difference in the orientation between the start and end addresses in the scaled and unscaled route maps. Large differences in the orientation yield a large magnitude while small differences yield a small magnitude.

    In one embodiment of road layout module 686, "EndPointDirectionScore( )" is represented by the following expression:


    EndPointDistancescore( ). The sixth function to contribute to the variable "Score" in the representative code is function "EndPointDistanceScore( )" on line 508 of the representative code. This function adds a factor to the variable "Score" that reflects the difference in distance between the start and end point addresses in the original unscaled route map and the current scaled route map. This function is particularly useful for route maps that have an overall U-shape. This function ensures that the start and finish of the route map will not get too close to one another.

    In one embodiment of road layout module 686, "EndPointDistanceScore( )" is represented by the following expression:


    It will be appreciated that the scoring function represented by lines 501 through 508 of the representative code merely illustrates one type of scoring function that is used in some embodiments of road layout module 686. In fact, many permutations of the scoring function represented by lines 501 through 508 of the representative code are possible. Such permutations include the use of only a subset of the functions outlined in the representative code to build the value of variable "Score." For instance, in some embodiments, only the functions "IntersectionScore( )" and "RoadLengthScore( )" are used. Other permutations of the scoring function illustrated by the representative code include the relative weighting of component functions so that some of the functions have a greater influence on the value of the variable "Score." Thus, for example, in some embodiments, the contribution of IntersectionScore( ) to the variable "Score" is up weighted relative to the contribution of "RoadLengthScore( )." Such weighting schemes may be dynamically imposed based on factors such as the complexity of the route, the size of the viewport used to display the route, the presence of anomalies such as a road in the route that is much longer than any other road in the route, as well as user specified preferences.

    Additional Label Refinement Embodiments

    Another important aspect of the overall process for producing a high quality map is performed by label layout module 688. Label layout module 688 places and optimizes labels that correspond to the various roads in the route map. One novel feature of label layout module 688 is that it will fix the position of the label for certain roads during refinement.

    FIG. 9 illustrates one embodiment of label layout module 688 (FIG. 6). Many different types of target functions may be used to refine the label position in the process illustrated in FIG. 9. Two such target functions are described by lines 301 through 308 and lines 401 through 417 of the illustrative code. In the previously described embodiments, a simulated annealing schedule was used to place labels within a continuous range of positions in a region around the center of the road corresponding to the label. Such a region is called a constraint. The type of constraint used in previously described embodiments is illustrated in FIG. 17A. In FIG. 17A, element 1802 illustrates the continuous range of positions that may be used to place the label that corresponds to road 1802. Element 1804 serves as a constraint because the center of the label is constrained to lie somewhere within element 1804. FIG. 17B illustrates the placement of label 1806 at one such acceptable location.

    The layout module 688 described in this section builds upon the constraint definition used in prior embodiments. The expanded constraint definition is used by the target function in the simulated annealing schedule of label layout module 688 to identify a suitable label position, orientation, and style. The constraint components in the expanded constraint definition include (i) a bounding box (e.g. element 1704 in FIG. 17A), (ii) an orientation (e.g. element 1710 in FIG. 17C), (iii) a layout style (e.g. FIG. 18A through 18F), and (iv) a scoring strategy.

    The bounding box defines where the center of the label layout can be positioned. Thus, in FIG. 17B, a label placed using the constraint defined by box 1704 can be placed in such a manner that the center of the label falls anywhere in box 1704. Orientation vectors define how a label should be rotated. Label 1706 in FIG. 17A is positioned along a vector that is parallel to the long axis of corresponding bounding box 1804. Using the expanded constraint definition, labels can adopt alternative orientations. For example, the label may be oriented so that it is orthogonal to the long axis of the corresponding bounding box. FIG. 17D illustrates the placement of a label in a rotated position.

    The layout style defines what text and images are created and how they are combined to make up the label when the given constraint is selected during annealing. FIG. 18 provides a number of exemplary layout styles. The layout style illustrated by FIG. 18A is a simple layout style in which the primary name for a street or highway is depicted. The layout style illustrated by FIG. 18B combines an arrow image with the primary name for a