EUROGRAPHICS 2014 / B. Lévy and J. Kautz(Guest Editors)

Volume 33 (2014), Number 2

Automatic Generation of Tourist Brochures

Michael Birsak 1 Przemyslaw Musialski 1 Peter Wonka 2,3 Michael Wimmer 1

1Vienna University of Technology, Austria 2KAUST, Saudi Arabia 3Arizona State University, USA

Multi-POILens

Single-POILens

OverviewMap M

web-search close-up viewtourist brochure

Figure 1: Automatic generation of tourist brochures on demand: the user selects a set of points of interest (POIs) obtained froma free web database in a particular region (e.g., City of Seattle). Our system generates a comprehensive but also detailed touristbrochure that provides walking or driving directions, detail lenses, and short descriptions of the selected POIs.

AbstractWe present a novel framework for the automatic generation of tourist brochures that include routing instructionsand additional information presented in the form of so-called detail lenses. The first contribution of this paperis the automatic creation of layouts for the brochures. Our approach is based on the minimization of an energyfunction that combines multiple goals: positioning of the lenses as close as possible to the corresponding regionshown in an overview map, keeping the number of lenses low, and an efficient numbering of the lenses. The secondcontribution is a route-aware simplification of the graph of streets used for traveling between the points of interest(POIs). This is done by reducing the graph consisting of all shortest paths through the minimization of an energyfunction. The output is a subset of street segments that enable traveling between all the POIs without considerabledetours, while at the same time guaranteeing a clutter-free visualization.

Categories and Subject Descriptors (according to ACM CCS): I.3.8 [Computer Graphics]: Applications—

1. Introduction

Travel brochures are special maps that allow a user to findout about points of interest (POI) in an area, and how to getto those POIs. Classical travel brochures are part of travelguides, available as handouts in hotels, or found as posters inpublic areas such as subway stations. The POIs are typicallyhighlighted (by using a different color or visual representa-tion), and have numbered cross-references to a separate listor description of the POIs. Classical travel brochures sufferfrom several problems: (1) it is tedious to find informationabout nearby POIs because they have to be cross-referencedinto an arbitrarily sorted list, (2) they are not suitable forlarger areas because the map level-of-detail will be too low

near important areas, e.g., near the POIs, and (3) they do notoffer guidance for efficient travel from one POI to another.

Problems (1) and (2) lead to a layout problem: how canthe POIs and detail information about them be laid out ona map so as to allow efficient cross-referencing between themap and the detail, even for larger areas? Problem (3) leadsto a routing problem: how is it possible to represent on asingle map efficient routes between multiple POIs? Previousresearch on route visualization has focused on single routemaps (from point A to point B) and on destination maps(from anywhere to a point A). Here, however, we have tosolve the problem how to get from any POI to any other POI,which is a new route visualization problem.

c© 2014 The Author(s)Computer Graphics Forum c© 2014 The Eurographics Association and JohnWiley & Sons Ltd. Published by John Wiley & Sons Ltd.

This is the authors preprint. The definitive version of the paper is available at http://diglib.eg.org and http://onlinelibrary.wiley.com

M. Birsak, P. Musialski, P. Wonka, M. Wimmer / Automatic Generation of Tourist Brochures

In this paper, we present a novel framework for generat-ing tourist brochures for a given set of POIs that contributessolutions to these problems:• Detail about POIs is provided by so-called detail lenses,

which contain a high-resolution map cut-out for one ormore POIs, together with auxiliary information like name,address, or a photo. Detail lenses address problem (2) andare discussed in Section 4. We also explain how to clusterlenses so as to avoid clutter and overlapping POI markersin the overview map.• Easy cross-referencing between the overview map and the

detail lenses is facilitated by arranging the detail lensesaround the overview map using an automatic layout al-gorithm that optimizes spatial proximity between a lensand its corresponding marker on the map. This algorithmaddresses problem (1) and is discussed in Section 5.• Finally, we calculate efficient paths between all POIs,

solving a multi-route problem and the attached route visu-alization problem. The challenge is to simplify the routegraph so as to provide legibility while maintaining routeefficiency. This addresses problem (3), and is discussed inSection 6.Figure 1 (center) shows an automatically generated tourist

brochure for Seattle, and a closeup of the particular elements(right). We evaluate the components of the framework withuser studies and quantitative measurements in Section 7.

2. Related WorkThere are three main steps involved in planning a trip: (1)the general decision-making step, in which the destinationof the trip is chosen, (2) the information-acquisition step,where data about the way and the destination is acquired, andfinally (3) the detailed decision making, where the preciseschedule of the trip is prepared [Zal96, FM98, SMSW90].The last two steps require quite an effort from the traveler,and often turn out as difficult tasks that are hard to solve op-timally. The introduction of the Internet for everyday usagehad a significant impact on trip planners, who discoveredthat medium as an important source of information. This in-fluence has been studied [LFC04, PF06] and shown that ithas a significant impact on the choice of the destinations androutes.

Furthermore, the availability of online geographic mapscombined with other information databases brought aboutvarious online tools with the aim to help the traveler to planthe trip. Shiraishi et al. [SSN∗05] presented a personal plan-ner for mobile devices that helps to prepare a trip sched-ule with multiple destinations under the constraints of timeand user preferences. Agrawala et al. [AS01] created routemaps that are similar to hand-drawn sketches. Grabler etal. [GASP08] proposed a system for the automatic gener-ation of tourist maps that simplify the overall visual com-plexity and reduce the appearance to a small number ofsignificant and easily recognizable landmarks placed on asimplified street network. Kopf et al. [KAB∗10] proposed

a system for selecting and laying out the important roadsbased on mental representations of road networks. Crandallet al. [CBHK09] proposed a system to analyze geotaggedInternet photo collections and to lay them out on schematicworld maps. Karnick et al. [KCJ∗10] introduced a systemto visualize routes using local detail lenses automaticallyplaced on the canvas. In this work we continue the idea ofdetail lenses that are used to amplify particular regions ofthe map. Most recently, Zheng et al. [ZYZ∗13] proposed asystem for trip planning along routes with sightseeing qual-ities. Their system produces routes from A to B that are en-riched by nearby POIs. In contrast, our approach produces aroute graph for multiple POIs without any specific direction.Another related work deals with the problems of effectiverouting. Bast et al. [BFSS07] provide a method to reducequickest path queries in road networks to a small numberof table lookups, and Sanders and Schulte [SS07] outlinealgorithms for transportation and routing problems beyondDijkstra’s shortest-path method.

Finally, our work is related to methods that deal with lay-outs, which in general refers to the process of determiningthe size and position of the visual objects in an informationpresentation. Lok et al. [LFN04] introduce a WeightMap tocontrol automated layouts. Jacobs et al. [JLS∗03] provide asystem for grid-based document layout generation, and DiIorio [DFV∗08] proposed to generate layouts by describ-ing their topological properties rather than geometric ones.Chen et al. [CBGS09] introduced a method for automaticgeneration of layouts in Mangas. Layouts received atten-tion not only in connection to documents and maps: Merrellet al. [MSL∗11] proposed a method for discrete layouts ofbuildings, and Yeh et al. [YYW∗12] introduced a system forthe synthesis of layouts based on probabilistic optimization.

3. Design Principles

To accomplish automatic design tasks, Agrawala et al.[ALB11] proposed to formulate design principles whichserve as guidelines for algorithmic solutions. For our pur-poses, we identified four design principles that should befulfilled as best as possible to produce a tourist brochure.In that sense, the principles can also be viewed as evalua-tion criteria by which the effectiveness of our design can beevaluated [ALB11], which we do in Section 7.

Spatial Grouping (P1): The first design principle is theidea of spatial grouping: in the case when the points of in-terest exhibit very dense clusters, or in other words, whenthe pins on the overview map become too close and can-not be rendered without overlap, they should be grouped.We do this using multi-POI lenses, which are representedby their own map pin.

Spatial Proximity (P2): The second principle relies on theobservation that a layout is easier to understand if coher-ent pieces of information are placed in proximity of oneanother and are not scattered across the page. In partic-

c© 2014 The Author(s)Computer Graphics Forum c© 2014 The Eurographics Association and John Wiley & Sons Ltd.

M. Birsak, P. Musialski, P. Wonka, M. Wimmer / Automatic Generation of Tourist Brochures

RegularGrid

Canvas S

OverviewMap MO

Collection ofDetail Lenses

Figure 2: The canvas of the tourist brochure is divided into cells of a regular grid. The content is composed of two basicelements: maps and detail lenses. All basic elements are placed in the cells of the regular grid.

ular, lenses and their corresponding map pins should beplaced as near as possible to each other.

Arrangement Similarity (P3): The third design principlestates that the spatial arrangement of the map pins shouldresemble as best as possible the arrangement of the re-spective lenses on the brochure page. We assume the userwill find the correspondences more intuitively if both ar-rangements are similar.

Route Simplicity (P4): The fourth design principle is theclarity of the routing graph. A major goal of our cus-tomized brochures is to provide the user routing directionsfrom any point to any other point in a manner that is easyto follow, so the graph should not be too dense and toocluttered.

4. Tourist Brochures with Maps and Detail LensesThe brochures are composed of top-view maps with routinginformation and a number of detail lenses, which emphasizeparticular points of interest.

4.1. Overview of Brochure ElementsAll elements are placed on a rectangular 2d domain withuser-specified format (e.g., DIN A3 landscape), which wecall canvas S. In order to align the elements on S, we employa grid G—a canonical page design pattern. In our case, thegrid is regular and subdivides the canvas into a set of equallysized cells with integer coordinates. The grid is filled withthree kinds of elements (Figure 2):

Map Cells: elements that form overview maps. We distin-guish between the main overview map MO and optionaloverview maps M1, . . . ,MnM , which are at a higher zoomlevel and cover subregions of MO. Each map is composedof a 4-connected set of tiles that does not have to be con-vex. In practice, it is usually a rectangular or L-shapedregion, although also U-, H-, or other shapes may occur.

Lens Cells: a lens is a unit of additional information thatdescribes a particular site. We distinguish between single-POI and multi-POI lenses. A single-POI lens containsinformation about exactly one point of interest, while amulti-POI lens contains multiple POIs combined into onelens. Our system groups POIs automatically depending ontheir spatial location in the main overview map.

Empty Cells: grid cells which have not been assigned any

information tiles remain empty. Our system fills themwith random photographs, but they could be used to dis-play advertisements or additional information.

4.2. Points of InterestThe high-level input to our system is a set of POIs, for ex-ample selected from a public online tourist database. In a setP = {P1,P2, . . . ,PnP} of nP POIs, each Pi encapsulates thefollowing attributes:• Pos(Pi) is the Mercator-projected position of Pi with each

component in meters,• Rect(Pi) is the geographic bounding box of Pi, which is

the minimum actual earth region that should be shown ina detail lens for Pi. The size of Pi is uniquely defined bythe size of a single cell in the layout grid G and a givenzoom level.

The geographical region covered by the POIs can be of var-ious size, e.g., a city, a region, or even an entire country.

4.3. MapsThe overview map MO is positioned on S in compliancewith the grid layout. Its geographic boundary is given by thebounding box of the boundaries of all POIs, so that

Rect(MO) = Rect

(nP⋃

i=1Rect(Pi)

).

The position and size of MO on S is determined interactivelyby the user. Optional map(s) of user-defined regions can alsobe positioned interactively on S in compliance with the gridlayout, such that M = {M0 := MO,M1,M2, . . . ,MnM} is theset of nM + 1 maps (overview map and maps of all user-defined regions) on S, where each Mi encapsulates the fol-lowing attributes:• Rect(Mi) is the geographic bounding box defining the ex-

act region on earth that is shown in Mi

• Scale(Mi) is the scale denominator of the map Mi.

Map Scales. For simplicity, our tourist brochures use atmost three different map scales. We have one map scale sM0

for the main overview map, one map scale sM1 for all op-tional overview maps and one map scale sD for all the detaillenses. The exact values for the map scales can differ be-tween different tourist brochures, but they always have tomeet the following requirement: sM0 < sM1 < sD .

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M. Birsak, P. Musialski, P. Wonka, M. Wimmer / Automatic Generation of Tourist Brochures

To make things even simpler, we do not use arbitrary mapscales, but fixed zoom levels as they are common in web-based mapping tools like Google Maps. A zoom level of0, for example, maps the whole equator of the earth ontoa small image with a side length of 256 pixels. Assuminga screen pixel density of 90.71 DPI, which results from apixel width of 0.28 mm, a zoom level of 0 corresponds toa scale denominator of 559,082,264.03. An incrementing ofthe zoom level by 1 then corresponds to a bisection of thescale denominator. In our tests, we often found the zoomlevels 12, 13 and 14 to perfectly fit our needs for the zoomlevels in the main overview map, optional overview mapsand detail lenses respectively. Zoom level 12, for example,corresponds to a scale denominator of 136,494.69.

Our system not only allows for standard screen pixel den-sity of 90.71 DPI but for an arbitrary value. For high-qualityprint, one could also choose a value of 300.0. If a zoom level(and therefore also the scale denominator sd) for a particu-lar map is chosen, the corresponding width in pixels wp caneasily be calculated by

wp = wm ·DPI

0.0254 · sd,

where wm is the width in the Mercator projection.

4.4. Detail LensesDetail lenses are elements that contain one or more POIsand a map that covers the union of their bounding boxes. Wedefine the set of detail lenses as L = {L1,L2, . . . ,Ll}, whereeach lens Li encapsulates a set of attributes:

• P(Li) is the set of all Pi contained in this lens (P⊆ P),

• Rect(Li) is the bounding box of the union of all Rect(Pj)of Pj ∈ P(Li),

• Pos(Li) is the center of Rect(Li),

• Scale(Li) is the scale denominator of the map of Li.

Detail lenses are created and placed automatically accordingto the input points of interest. To accomplish this, we intro-duce a lens creation process based on unsupervised cluster-ing of the spatial locations of the POIs. Following designprinciple (P1), we group the POIs which are located veryclose to each other to form multi-POI lenses. Such lensesare represented on the overview map by only one commonsymbol (cf. Fig. 1, right).

POI Grouping. For the clustering, we utilize the k-meansalgorithm on the input set P of POIs, where we use their spa-tial location on the map as a Euclidean 2d point. This step di-vides the POIs into a set of clusters C= {C1,C2, . . . ,Cl}withvarious numbers of Pj per Ci. Each cluster Ci corresponds toexactly one detail lens in the brochure. We use an iterativeapproach to determine the optimal number of clusters k.

We start with k = 1 for k-means and run the lens place-ment algorithm (see Section 5) to find a valid layout. Sincek = 1 results in only one cluster and thus one big multi-lenscontaining all POIs, it is usually impossible to find a region

in the grid to place it. Therefore, we iteratively increment kand run the placement algorithm until we find a valid layout.If no such an arrangement can be found even if the numberof clusters is equal to the number of POIs, i.e., only single-POI lenses are present, then there exists no solution for thecurrent set of overview maps and POIs. In this case, the userhas to choose a bigger grid, fewer POIs or smaller/feweroverview maps.

Single-POI. In the easiest case, a cluster Ci consists of justone POI and the corresponding lens is therefore a single-POIlens, which occupies per definition exactly one grid cell. Weuse one half of such a lens to display an information blockthat contains the name and some further data like address,type and phone number. To give the user an idea of how theactual location looks like, a photo is provided if available.Furthermore, a rating value based on user comments in theonline database is displayed inside the information block.The second half of the single-POI lens is covered by a de-tailed map showing the surrounding region centered aroundthe actual POI.

Multi-POI. If the number of POIs in the cluster Ci is big-ger than 1, we create a multi-POI lens that groups a numberof points of interest. We start by calculating the size of themap, that shows the region corresponding to the boundingbox of all the POIs of the lens. Then we identify the mini-mum number of grid cells in horizontal and vertical direc-tion needed to display the map according to the zoom-leveland canvas resolution. Usually, with this approach we endup with a layout of grid cells where we have not enough freehalf-cells that are untouched by the map to place the infor-mation blocks for all of the POIs of Ci. Therefore, we expandthe lens until there is enough space to place all informationblocks. We use a simple iterative method to find a suitablesize: In every iteration, we expand the lens into the directionthat corresponds to the axis with the smaller number of cells.

The layout optimization (cf. Section 5) as well as thecalculation of the routing graph (cf. Section 6) are accom-plished fully automatically, and in the following we describethe details of both components.

5. Automatic Layout OptimizationTo lay out refers to the process of positioning and arrangingcontentual entities on a canvas. From our point of view, themain challenge is the design of dynamic layouts, i.e., layoutswith a number of elements that varies depending on the con-tent, as in the case of custom brochures. Thus, we formulateit as an optimization task.

Cost Function. We want to place the lenses in compliancewith design principles (P2) and (P3) defined in Section 3.In order to enforce both of them, we formulate a global costfunction with two corresponding terms, eprox (for (P2)) andesim (for (P2)). The cost for placing lens Lk at position (i, j)in S is defined as:

fi, j,k = wk (λ1eprox +λ2esim) , (1)

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M. Birsak, P. Musialski, P. Wonka, M. Wimmer / Automatic Generation of Tourist Brochures

spanning treefull graph G* 2% cost increase 5% cost increase

Figure 3: Different steps of the graph simplification procedure described in Section 6. The first graph on the left is the unionof all shortest paths. Consecutive graphs have been created by removing the most redundant segments. The final graph is aspanning tree that cannot be simplified anymore.

with

i = 1,2, ...,m; j = 1,2, ...,n; k = 1,2, ..., l ,

and:

eprox =12‖pS

mc (Lk, i, j)− pSMmax(Lk)‖2 ,

esim = 1π

(cos−1(eang)

)+ 1

2 edist ,

eang =pMO(Lk) · pS

mc(Lk, i, j)

‖pMO(Lk)‖ · ‖pSmc(Lk, i, j)‖

,

edist =1√2|‖pMO(Lk)‖−‖pS

mc (Lk, i, j)‖| ,

where wk = |P(Lk)| is the number of POIs in the lens Lk,which scales the cost according to the importance of the lens.

In term eprox, S is the uniformly scaled canvas S suchthat its width wS and its height hS are both divided bymax(wS,hS). The variables pS

mc(Lk, i, j) and pSMmax

(Lk) re-fer to the position of the map center of the lens Lk whenpositioned at grid cell (i, j), and to the position of the corre-sponding lens marker in the overview map with the highestzoom factor respectively, both with respect to S. The termeprox is therefore half the squared distance between the mapcenter of the lens Lk and the corresponding marker. We mea-sure eprox with respect to S for normalization reasons, suchthat the squared distance can be at most 2. By dividing by 2,our term eprox is in the range of 0 and 1.

Term esim in turn consists of two terms. Both terms are ameasure of how similar the lens Lk is positioned in the gridcompared to the corresponding marker in the overview mapMO. The first term does this by comparing the angles, thesecond term by comparing the distances. All measurementsare done with respect to MO, which is the non-uniformlyscaled overview map MO such that wMO

= 1.0 and hMO=

1.0, and with respect to S, where wS = 1.0 and hS = 1.0. Thevariables pMO(Lk) and pS

mc(Lk, i, j) are then the position ofthe corresponding marker of Lk with respect to MO and theposition of the map center of the lens Lk positioned at gridcell (i, j) with respect to S, respectively.

Our cost function aims at the minimization of both terms:the sum of squared distances of all lenses (measured at theirrespective local map pin) to their corresponding pins in thenearest overview map, and the measure for the similarity ofthe position of the lenses in the grid compared to the positionof the markers in the overview map. In order to place thelenses in an optimal manner, we formulate the problem as abinary integer program (BIP) with the objective function:

E = minxi, j,k

m

∑i=1

n

∑j=1

l

∑k=1

fi, j,kxi, j,k , (2)

where m is the number of rows in the grid, n is the number ofcolumns in the grid, l is the number of lenses, and fi, j,k is thecost to place the lens Lk at grid cell (i, j). We introduce m ·n ·l binary variables of the form xi, j,k and define that xi, j,k = 1when the lens Lk is placed (always with respect to its upperleft cell) at grid cell (i, j), and xi, j,k = 0 otherwise.

Hard Constraints. The objective is solved subject to thefollowing constraints:

C1: Any lens Lk is positioned exactly once:m

∑i=1

n

∑j=1

xi, j,k = 1; k = 1,2, ..., l . (3)

The l constraints of type C1 ensure that each lens Lk ispositioned exactly once. We can achieve this goal by set-ting only one of the variables in the set of variables cor-responding to Lk to 1, and all the others to 0. The sumof all variables corresponding to one particular lens hastherefore to be 1.

C2: Lenses must be contained entirely inside the grid in Xand Y:

m

∑i=1

n

∑j=1

j · xi, j,k ≤ n−Wk +1; k = 1,2, ..., l , (4)

m

∑i=1

n

∑j=1

i · xi, j,k ≤ m−Hk +1; k = 1,2, ..., l , (5)

where Wk and Hk refer to the number of grid cells oc-cupied by the lens Lk on the discrete grid. The 2 · l con-straints of type C2 guarantee that no lens is (partially) out-

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M. Birsak, P. Musialski, P. Wonka, M. Wimmer / Automatic Generation of Tourist Brochures

side of the grid in the horizontal (Eq. 4) or vertical (Eq. 5)direction. Although we only have the variables xi, j,k forj = 1,2, ...,n in the X-direction, therefore only for existingcolumns, a lens can be partially outside when, for exam-ple, Wk1 = 2 for a particular lens Lk1 and xi,n,k1 = 1 for anyi ∈ {1,2, ...,m}, since the lenses are placed with respectto their upper left cell. The sum in the constraint gives usthe column in which the particular lens Lk is placed. Notethat the sum only gives us the correct column if the lensis positioned only once, which is already ensured by C1.When we know the column cL of the upper left cell, theremaining task is to guarantee that cL ≤ n−Wk + 1. Thesame applies in the vertical direction, where in row rL, wehave to guarantee that rL ≤ m−Hk +1.

C3: No more than one lens is placed at any grid cell:

l

∑k=1

i

∑a=max(1,i−Hk+1)

j

∑b=max(1, j−Wk+1)

xa,b,k ≤ 1

i = 1,2, ...,m; j = 1,2, ...,n .

(6)

The m ·n constraints of type C3 ensure that no more thanone lens occupies any grid cell. Unfortunately, it is notsufficient to only test every grid cell (i, j) if xi, j,k is 1 formore than one k ∈ {1,2, ..., l}, since lenses bigger than1×1 can occupy a grid cell also when they are positionedin a neighbor cell. The smallest row number where a lensLk positioned there would still range to the row i can eas-ily be computed by i−Hk + 1. Analogous, the smallestcolumn number can be computed by j−Wk + 1. In or-der to ensure that only one lens occupies a grid cell (i, j),we therefore have to sum over all lenses, and for everylens, over the whole region in which the lens would affectthe grid cell. The max functions in the constraint are justneeded to avoid access of a grid cell index smaller than 1.

C4: Map areas must not be occupied by lenses:

l

∑k=1

i

∑a=max(1,i−Hk+1)

j

∑b=max(1, j−Wk+1)

xa,b,k = 0

(i, j) ∈ I ,

(7)

where I is the set of all indices (i, j) occupied by anoverview map. The constraints of type C4 are very sim-ilar to the ones of C3. They ensure that grid cells whereoverview maps are placed are not occupied by lenses. Themaximum number of lenses in such grid cells has there-fore to be 0. There exists one constraint C4 for every in-dex.

We solve this optimization problem using the Gurobi-Library [Gur13], which leverages a linear-programmingbased branch-and-bound algorithm to find a good feasiblesolution. The resulting layout ensures that, given the spe-cific input, all lenses are placed in the best possible neigh-borhood of their corresponding map pins. Finally, to makethe brochure even easier to read, we perform a row-wise re-

enumeration of the lenses and their corresponding POIs in atop-to-bottom and left-to-right scheme.

6. Routing Graph

Our final design principle (P4) demands routing informationthat is easy to follow and in some sense “optimal”, i.e., theroute from any point to any other should be efficient (cf. Sec-tion 3). The most efficient solution would connect each pairof POIs with their shortest path. But this would result in aconsiderable number of overlapping paths. While a singleroute from A to B can be rendered in a very recognizableway, a whole network of multiple paths becomes clutteredquickly as the number of segments increases. Therefore, wepropose to simplify the network graph considerably, and toleave only a small subset of the original segments, whichstill provides fast (but not necessarily the fastest) connec-tions from any point to any other. Note that the requirementsare contradictory, and the task is a balancing task.

The formal goal is to find optimal routes between all POIs,such that it is possible to reach each POI from any other POI.This computation is carried out on a directed street-graph Gobtained from a geographical information system (GIS).

Graph G∗. We define the graph of all streets in theoverview map MO as a tuple G = (N,A), where N is theset of all nodes in G, with P⊂ N, and A is the set of all arcsconnecting the nodes. In that graph we compute the set of allshortest paths between all pairs (Pi,Pj) of P using Dijkstra’sshortest-path algorithm. We denote the shortest paths as R∗i jand their set as R∗. This set serves as a starting point foranother graph G∗ = (N∗,A∗), which is composed of only asubset of all nodes and arcs of the full graph G. To create G∗,we find all crossings, branches and intersections of the short-est paths in R∗, which we denote as the set K. Together withthe input POIs, they define the nodes of G∗, i.e., N∗ = K∪P.The set of edges A∗ of G∗ contains all sequences of arcs ofA that connect nodes N∗ and lie on the shortest paths R∗. Wedenote the elements of A∗ as sections si. In other words, G∗

is the graph defined by all shortest paths between all POIs,as depicted in Figure 3, left hand side.

Route-Aware Graph Simplification. The resulting graphbasically gives us routing directions how to reach from eachPOI all other POIs. Unfortunately, the graph is usually quitedense and ambiguous, which makes it difficult to grasp intu-itively. The ideal routing graph should be as sparse as pos-sible, or even just a spanning tree. In order to simplify G∗,we propose a greedy iterative procedure which pursues twogoals: keeping the driving/walking time as low as possiblewhile at the same time removing as many sections as possi-ble. Note that these goals are contradictory by their nature,since the removal of sections leads to an increase of driv-ing/walking time. During our approach, we successively re-duce G∗ and denote our residual graph G∗ = (N∗, A∗) andinitialize G∗ = G∗.

We continue by defining an energy function that reflects

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M. Birsak, P. Musialski, P. Wonka, M. Wimmer / Automatic Generation of Tourist Brochures

Mean

2.5

2.6

2.7

2.8

2.9

3.0

3.1

City 0 City 1 City 2 City 3

time

in se

cond

s

Random Ours

Median

2.5

2.6

2.7

2.8

2.9

3.0

3.1

City 0 City 1 City 2 City 3

time

in se

cond

s

Random Ours

Ratio and t-Test

0.0014

0.5756

0.0001 0.0004

1.1066

1.0095

1.10501.0932

0.96

0.98

1.00

1.02

1.04

1.06

1.08

1.10

1.12

City 0 City 1 City 2 City 30.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

sruO/

modnaR oitar

p-va

lue

Student T-Test

Ratio R/O

Figure 4: The results of the response of over 50 users in the layout readability test described in Section 7.1. The black bars inthe first chart indicate the standard error. The last chart shows the ratio R/O and the p-value of the Student’s t-test.

the quality of our residual graph as

E = λ ∑(Pi,Pj)

di j +(1−λ)‖A∗‖ (8)

where di j denotes the geographic distance along the shortestpath between Pi and Pj and ‖A∗‖ is the number of sectionsin the residual graph G∗.

Next we loop over all sections in A∗, and for each we re-move it temporarily from the graph G∗, which results in asimplified graph G∗′. In this step we omit all sections thatwould split the graph into disconnected components. Hav-ing a candidate G∗′ for the simplified graph, we again deter-mine all shortest paths between all pairs of POIs in G∗′ andagain compute the current energy using Equation 8. We addthe section back again and proceed with the next one untilwe have repeated the procedure for all sections. Finally, wechoose the simplified graph G∗′ that corresponds to the low-est energy Emin and set G∗ = G∗′. Please note that this pro-cedure usually increases, depending on the parameter λ, theoverall energy E over the graph compared to the initial fullG∗, since by removing a segment we interrupt at least one ofthe shortest paths. This is true in general, except when thereexist more than one shortest route with the same distance,which does not influence the method at all.

In order to further reduce the graph, we repeat the wholeprocedure on G∗ until a certain percentage of the originalenergy (e.g. 105%) is reached, or if none of the edges canbe removed anymore without splitting the graph into discon-nected pieces. In the latter case we have converged to a span-ning tree, which consists of a subset of nodes and edges ofG∗. Figure 3 shows the graph simplification results on dif-ferent stages.

7. EvaluationTo evaluate our method, we have carried out two user stud-ies, one to test the quality of the layout, and one to test thequality of the graph. Furthermore, we provide a quantitativeevaluation of the simplified routing graph.

7.1. Layout Readability TestTo provide evidence that our layout is friendly for humanperception as postulated by principles (P2) and (P3), we per-formed a user study. We asked 50 participants in an online

test to locate a pin on the map given a randomly selectedlens (indicated by a highlighting rectangle). We used fourtest sets from four cities with 12 lenses each. For each of thefour cities, we prepared two layouts, one with our method,referred to as O-set, and one with randomly placed lenses,referred to as R-set. Table 1 shows the factor of how muchlonger the sum of all corresponding distances on the R-setcompared to the O-set are (measured in pixels on the map).

For those 8 samples, we asked each user to find a corre-spondence by clicking on the pin on the map. The order inwhich the cites and the lenses were presented to the partici-pant was chosen randomly. We checked 11 lenses per sam-ple, which results in 88 altogether. We discarded the first 8hits from each user, and used the mean of the remaining 80for statistical evaluation (cf. Figure 4).

We confirmed the normal distribution of the data witha goodness-of-fit test (K-S test). To test the statistical sig-nificance of the difference between the O-set (test set) andthe R-set (control set), we performed the two-tailed pairedStudent’s t-test. For City0, City2, and City3 the differencewas highly significant (p≤ 0.0014, d f = 49). In the case ofCity1, our results are not significantly better (p≤ 0.57).

City 0 City 1 City 2 City 3

R/O 1.75 1.74 1.66 1.53

Table 1: Ratio of lens-pin distances on the R-sets comparedto O-sets, measured in pixels on the maps.

Figure 5: A distribution of pins and lenses that violates prin-ciple (P3).

Our interpretation of this result is that the lens layout inthe O-set for City1 in fact violates the design principles.Generally, principle (P1) is violated in all sets, since we testsingle lenses only. But also principle (P3) appears violated,

c© 2014 The Author(s)Computer Graphics Forum c© 2014 The Eurographics Association and John Wiley & Sons Ltd.

M. Birsak, P. Musialski, P. Wonka, M. Wimmer / Automatic Generation of Tourist Brochures

27

25

20

15

10

5

0

Berlin

3

1

2

45

Las Vegas

3

6

2

Paris Strasbourg

Figure 6: Evaluation of the user study 2. The color indi-cates how often a particular street-segment has been se-lected by the user. The dashed-path is the one determinedby our graph-simplification algorithm (cf. Section 7.2).

Berlin

0.40

0.60

0.80

1.00

1.20

1.40

0 1 2 3 4 5 6 7 8

Time RatioEdge Ratio

Las Vegas

0.40

0.60

0.80

1.00

1.20

1.40

0 1 2 3 4 5 6 7

Time RatioEdge Ratio

Paris

0.40

0.60

0.80

1.00

1.20

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Time RatioEdge Ratio

Strasbourg

0.40

0.60

0.80

1.00

1.20

1.40

0 1 2 3 4 5 6 7 8 9

Time RatioEdge Ratio

Figure 7: Charts showing the ratio between the travel time(blue) and the number of edges in the graph (red). Refer toSection 7.3.

since the lenses (2), (3), and (4) do not mirror the arrange-ment of the pints on the map, as shown in Figure 5 above (cf.additional material for full test images).

7.2. Graph Reliability TestWe performed another user study to evaluate the quality ofour algorithm for the graph simplification. For this we cre-ated four maps, each showing a region of a different city. Weinserted 5 to 6 POIs and the graph G∗ of shortest paths be-tween the POIs into each map. With these maps, we asked27 persons to fulfill the following task:

“Connect the points along the purple streets, such thatyou get the fastest connection from any point to any otherpoint. Use as small number of roads as possible. Notethat these requirements are contradictory, and the task isa balancing task. The order is arbitrary.”

We counted how often each street segment was selected bythe participants and used color coding to indicate streetswhich were rarely selected (green color) and streets whichwere selected often (red color). Additionally, we utilized ourapproach in order to calculate a reference solution. In Fig-ure 6, the results of overlaying our simplified graph withthe colored graph is shown. The majority of street segmentsthat were selected by the users are also part of our simplifiedgraph. This is an indication that our algorithm can automat-ically provide a street graph that consists of street segmentswhich would also be chosen intuitively by real persons. Notethat the examples were very small, and it would be far moredifficult for a real person to perform the same task for a big-ger city region.

7.3. Quantitative Graph EvaluationWe have also tested the performance of our simplified rout-ing graph quantitatively. First, we compute the ratio rG ofthe average travel time from A to B for all POIs on the fullgraph G∗ to the average travel time from A to B for all POIson the simplified graph G∗ (cf. Section 6). Next, we comparethe ratio rG to the ratio rS of total number of segments in therespective graphs. Table 2 and Figure 7 show those relations.As can be seen, the average travel time is only 15.59% longerthan on the optimal graph in the case of the city of Berlin, onthe other hand, the number of segments is reduced to 40.7%of the full graph in the best case. This considerably increasesthe simplicity and readability of the corresponding graph.

City Berlin Las Vegas Paris Strasbourg

rG 1.15585 1.26889 1.09981 1.18273rS 0.40704 0.44000 0.64286 0.42408

Table 2: Ratio rG of average travel times in G∗ to the av-erage travel time in our simplified graph G∗ and ratio rS oftotal number of segments in the respective graphs.

8. Results and DiscussionFigure 9 shows examples of our brochures. Further resultsare added as supplemental material. For all the results,the input is a set of points of interest that have been de-livered automatically by a travel-related web-service (i.e.,www.yelp.com) to a certain combination of keywords (e.g.,“museum”), and filtered by the user.

System Parameters. Table 3 lists all parameters of oursystem. Values denoted as ‘Default’ were fixed for all ourexperiments and examples.

Parameter Symbol Examples Set By

POIs P - UserFormat S A3, A4 User

Resolution G 9x5 UserMap M0, ...,Mn - UserDPI - 300 Default

Map Scales sM0 , sM1 , sD 14, 15 DefaultVariables λ,λ1,λ2 0.5, 0.1 Default

Table 3: Parameters in our system.

Comparison to Human Designers. We have reviewedseveral travel brochures created by human designers. Ourgeneral observations are:

• There are many tourist brochures where POIs are markedwith numbers and more details about the POIs are listedon the side of the map. The details can be a longer de-scription, photos, or just the name.

• Multiple tourist brochures use insets of maps of differentresolutions, but existing maps do not use as many as wedo. Detail maps for single POIs were not observed.

• Several tourist brochures are based on an artistic abstrac-tion of a map and some use traditional maps like we do.

c© 2014 The Author(s)Computer Graphics Forum c© 2014 The Eurographics Association and John Wiley & Sons Ltd.

M. Birsak, P. Musialski, P. Wonka, M. Wimmer / Automatic Generation of Tourist Brochures

City |Segin| |Segout | tsimpl [s]

Seattle 400 46 8.927Los Angeles 297 44 5.202

Table 4: Performance of the graph simplification (cf. Fig. 9).

• Several tourist brochures try to place details about thePOIs directly on the map.

• Some tourist brochures color map markers differently de-pending on their category (e.g. sightseeing, food, shop).

Figure 8 shows a tourist brochure of Brugan, Belgium,which was designed by a human. This brochure uses colorcoding for the categories of the POIs. The numbering of themarkers in the map, however, is not related to the positionof the corresponding detail information on the side of themap. This makes it quite hard to find a marker for a giveninformation block. In contrast, our system tries to place thedetail information in form of the lenses in the grid accordingto the position of the markers in the overview map. Addi-tionally, our lenses are enumerated in a top-to-bottom andleft-to-right fashion in order to make them easier to locate.

Another issue is the handling of spatial density of the con-tained information. Our system tries to encapsulate the de-tail of a region with a high number of POIs into one multi-POI lens. This has two benefits: it reduces visual clutter, andbundles local information spatially. In the example below,however, the city center is populated quite densely with nu-merous markers, but it does not provide a higher scale map,making it more difficult to read. Moreover, the correspond-ing detail blocks are scattered around. For this reason theuser needs to establish several cross-references to get the de-tail information of the POIs in one particular region.

Figure 8: A hand-made brochure from www.use-it.be.

Limitations. Indeed, our system can handle most compo-sitions of POIs. Nonetheless, in some cases it can happenthat the generated layouts do not obey the design principles,as is the case in Section 7.1, where the layout of City1 seemsnot to be perceived as better as the random layout. Our sys-tem can estimate layouts by calculating the correspondingenergy value. However, a low energy value might not al-ways guarantee that a map is perceived as more intuitive byhumans. Changing the value of λ in Eq. 1 might provide aremedy, but we do not expose the parameter to the user.

Another problem arises during routing by the occurrenceof one-way streets. If the graph consisting of all shortestpaths is minimized subject to the one-way streets, it is notalways possible to reach a spanning tree since it is likely thata POI can not be left using the same street that was used

City |P| #BIP Calls tpl [s]

Seattle 30 7 0.232Los Angeles 30 19 0.872

Table 5: Performance of the layout computation (cf. Fig. 9).The #BIP calls refers to the number of how often the k-means(cf. Sec. 4.4) algorithm, and thus also the BIP, have been run.

to reach it. For convenience, we did not further address thisproblem and regarded all streets as bi-directional. This is notbasically wrong, since one-way streets can usually be usedby pedestrians or even cyclists in both directions.

Lens Discrimination. We have experimented with differ-ent effects to provide best visual discrimination of the lenses.Our first draft solved the four-color problem such that neigh-boring lenses were always of different background color. Infact, this turned out to be perceived as too visually clutteredin preliminary user experiments. Thus, we decided to workwith a distance between lenses which is of the same widthas the colored border of each lens.

Interactive GUI. We have implemented a prototypic GUIwhere the user can enter the location and search terms in or-der to receive a set of POIs from a web-database. Moreover,she can resize and place the map, then the grouping and thelayout of the lenses is computed automatically.

Implementation and Performance. We have imple-mented a prototype of the brochure generator in C++ usingMS Visual Studio 2010. We also used OpenStreetMap as theGIS database and the Mapnik library for the vector-graphicsmap rendering purposes. For efficient graph computations,we used the Lemon Graph Library. For the GUI we usedC#/WPF. Table 5 shows the running time of the layout pro-cedure (Sec. 5), and Table 4 shows the running time of thegraph simplification algorithm (Sec. 6).

9. ConclusionsWe have presented a system for the automatic generation oftourist brochures with route information, which provide in-formation about a set of points of interest in a particular re-gion in a focus-and-context style by the use of detail lenses.

In addition, we contribute an algorithm for route-awaresimplification of routing graphs that provide near-optimalrouting directions on the one hand, and are simple andclutter-free on the other. We showed that such graphs matchwell with those selected by humans for traveling betweenarbitrarily distributed POIs. Moreover, we introduce a novellayout algorithm that optimizes a layout of a number of el-ements of different sizes in a discrete grid under specificneighborhood constraints. In the future we plan to extend theimplementation of our system to real-time interactive routeplanning on desktop and mobile devices.

AcknowledgmentsWe thank Matthias Bernhard and Michael Hecher for dis-cussions, all participants of the user studies for time and pa-

c© 2014 The Author(s)Computer Graphics Forum c© 2014 The Eurographics Association and John Wiley & Sons Ltd.

M. Birsak, P. Musialski, P. Wonka, M. Wimmer / Automatic Generation of Tourist Brochures

Brochure of Seattle Brochure of Los Angeles

Figure 9: Examples of our tourist brochure for Seattle and L.A. For more results please refer to additional material.

tience, and the anonymous reviewers for valuable comments.This work was funded by the FWF, project no. P23237-N23.

References[ALB11] AGRAWALA M., LI W., BERTHOUZOZ F.: Design prin-

ciples for visual communication. Communications of the ACM54, 4 (Apr. 2011), 60. 2

[AS01] AGRAWALA M., STOLTE C.: Rendering effective routemaps. In Proceedings of the 28th annual conference on Com-puter graphics and interactive techniques - SIGGRAPH ’01 (NewYork, New York, USA, Aug. 2001), ACM Press, pp. 241–249. 2

[BFSS07] BAST H., FUNKE S., SANDERS P., SCHULTES D.:Fast routing in road networks with transit nodes. Science (NewYork, N.Y.) 316, 5824 (Apr. 2007), 566. 2

[CBGS09] CHEN W.-C., BATTESTINI A., GELFAND N.,SETLUR V.: Visual summaries of popular landmarks from com-munity photo collections. In Proceedings of the seventeen ACMinternational conference on Multimedia - MM ’09 (New York,New York, USA, Oct. 2009), ACM Press, p. 789. 2

[CBHK09] CRANDALL D. J., BACKSTROM L., HUTTEN-LOCHER D., KLEINBERG J.: Mapping the world’s photos. InProceedings of the 18th international conference on World wideweb - WWW ’09 (New York, New York, USA, Apr. 2009), ACMPress, p. 761. 2

[DFV∗08] DI IORIO A., FURINI L., VITALI F., LUMLEY J., WI-LEY T.: Higher-level layout through topological abstraction. InProceeding of the eighth ACM symposium on Document engi-neering - DocEng ’08 (New York, New York, USA, Sept. 2008),ACM Press, p. 90. 2

[FM98] FODNESS D., MURRAY B.: A Typology of Tourist Infor-mation Search Strategies. Journal of Travel Research 37, 2 (Nov.1998), 108–119. 2

[GASP08] GRABLER F., AGRAWALA M., SUMNER R. W.,PAULY M.: Automatic generation of tourist maps. ACM Trans-actions on Graphics 27, 3 (Aug. 2008), 1. 2

[Gur13] GUROBI OPTIMIZATION, INC.: Gurobi optimizer refer-ence manual, 2013. 6

[JLS∗03] JACOBS C., LI W., SCHRIER E., BARGERON D.,SALESIN D.: Adaptive grid-based document layout. In ACMSIGGRAPH 2003 Papers on - SIGGRAPH ’03 (New York, NewYork, USA, July 2003), vol. 22, ACM Press, p. 838. 2

[KAB∗10] KOPF J., AGRAWALA M., BARGERON D., SALESIN

D., COHEN M.: Automatic generation of destination maps. ACMTransactions on Graphics 29, 6 (Dec. 2010), 1. 2

[KCJ∗10] KARNICK P., CLINE D., JESCHKE S., RAZDAN A.,WONKA P.: Route visualization using detail lenses. IEEE trans-actions on visualization and computer graphics 16, 2 (Jan. 2010),235–47. 2

[LFC04] LUO M., FENG R., CAI L. A.: Information Search Be-havior and Tourist Characteristics. Journal of Travel & TourismMarketing 17, 2-3 (Feb. 2004), 15–25. 2

[LFN04] LOK S., FEINER S., NGAI G.: Evaluation of visualbalance for automated layout. In Proceedings of the 9th inter-national conference on Intelligent user interface - IUI ’04 (NewYork, New York, USA, Jan. 2004), ACM Press, p. 101. 2

[MSL∗11] MERRELL P., SCHKUFZA E., LI Z., AGRAWALA M.,KOLTUN V.: Interactive furniture layout using interior designguidelines. ACM Transactions on Graphics 30, 4 (July 2011), 1.2

[PF06] PAN B., FESENMAIER D. R.: Online Information Search.Annals of Tourism Research 33, 3 (July 2006), 809–832. 2

[SMSW90] SNEPENGER D., MEGED K., SNELLING M., WOR-RALL K.: Information Search Strategies By Destination-NaiveTourists. Journal of Travel Research 29, 1 (July 1990), 13–16. 2

[SS07] SANDERS P., SCHULTES D.: Engineering fast route plan-ning algorithms. In WEA’07 Proceedings of the 6th internationalconference on Experimental algorithms (Rome, Italy, June 2007),Springer-Verlag, pp. 23–36. 2

[SSN∗05] SHIRAISHI T., SHIBATA M. N. N., NAGATA M., SHI-BATA N., MURATA Y., YASUMOTO K., ITO M.: A personal nav-igation system with a schedule planning facility based on multi-objective criteria. In Proc. of 2nd Int. Conference on MobileComputing and Ubiquitous Networking (2005). 2

[YYW∗12] YEH Y.-T., YANG L., WATSON M., GOODMANN. D., HANRAHAN P.: Synthesizing open worlds with con-straints using locally annealed reversible jump MCMC. ACMTransactions on Graphics 31, 4 (July 2012), 1–11. 2

[Zal96] ZALATAN A.: The determinants of planning time in va-cation travel. Tourism Management 17, 2 (Mar. 1996), 123–131.2

[ZYZ∗13] ZHENG Y.-T., YAN S., ZHA Z.-J., LI Y., ZHOU X.,CHUA T.-S., JAIN R.: GPSView: A scenic driving route planner.ACM Transactions on Multimedia Computing, Communications,and Applications 9, 1 (Feb. 2013), 1–18. 2

c© 2014 The Author(s)Computer Graphics Forum c© 2014 The Eurographics Association and John Wiley & Sons Ltd.