Continuous Dynamic Time Warping (CDTW) is a robust similarity measure for polygonal curves that has recently found a variety of applications. Despite its practical use, not much is known about the algorithmic complexity of computing it in 2D, especially when one requires either an exact solution or strong approximation guarantees. We fill this gap by introducing a 5-approximation algorithm with running time O(n⁵) under the 1-norm. This is the first constant-factor approximation for 2D CDTW with polynomial running time. We extend our algorithm to all polygonal norms on ℝ², which we subsequently use in order to achieve a (5+ε)-approximation with time complexity O(n⁵/ε^{1/2}) for CDTW in 2D under any fixed norm. The latter result in particular includes the usual Euclidean 2-norm.

Given two polygons of complexities \(n\) and \(m\) respectively, a fundamental problem in shape matching and geometric similarity is to compute their maximum area overlap under translation. For general simple polygons, the best-known algorithm runs in \(\mathcal{O}((nm)^2 \log(nm))\) time [Mount, Silverman, Wu ’96]. In a recent breakthrough that received the SoCG Best Paper Award 2025, Chan and Hair gave a linear-time algorithm for the special case when both polygons are convex. A key challenge in computational geometry is to design improved algorithms for other natural classes of polygons. We address this by presenting an \(\mathcal{O}((nm)^{3/2} \log(nm))\)-time algorithm for the case when both polygons are orthogonal, probably the most popular class of polygons besides convex and simple ones. This is the first algorithm for polygon overlap on orthogonal polygons that is faster than the almost 30 years old algorithm for general simple polygons.

We study well-known reconfiguration problems. Given a start and a target configuration of geometric objects in a polygon, we wonder whether we can move the objects from the start configuration to the target configuration while avoiding collisions between the objects and staying within the polygon. Problems of this type have been considered since the early 80s by roboticists and computational geometers. In this paper, we study some of the simplest possible variants where the objects are labeled or unlabeled unit squares or unit disks. In unlabeled reconfiguration, the objects are identical, so that any object is allowed to end at any of the targets positions. In the labeled variant, each object has a designated target position. The results for the labeled variants are direct consequences from our insights on the unlabeled versions. We show that it is PSPACE-hard to decide whether there exists a reconfiguration of (unlabeled/labeled) unit squares even in a simple polygon. Previously, it was only known to be PSPACE-hard in a polygon with holes for both the unlabeled and labeled version [Solovey and Halperin, Int. J. Robotics Res. 2016]. Our proof is based on a result of independent interest, namely that reconfiguration between two satisfying assignments of a formula of Monotone-Planar-3-Sat is also PSPACE-complete. The reduction from reconfiguration of Monotone-Planar-3-Sat to reconfiguration of unit squares extends techniques recently developed to show NP-hardness of packing unit squares in a simple polygon [Abrahamsen and Stade, FOCS 2024]. We also show PSPACE-hardness of reconfiguration of (unlabeled/labeled) unit disks in a polygon with holes. Previously, it was known that unlabeled reconfiguration of disks of two different sizes was PSPACE-hard [Brocken, van der Heijden, Kostitsyna, Lo-Wong and Surtel, FUN 2021].

We propose sublinear algorithms for probabilistic testing of the discrete and continuous Fréchet distance - a standard similarity measure for curves. We assume the algorithm is given access to the input curves via a query oracle: a query returns the set of vertices of the curve that lie within a radius δ of a specified vertex of the other curve. The goal is to use a small number of queries to determine with constant probability whether the two curves are similar (i.e., their discrete Fréchet distance is at most δ) or they are "ε-far" (for 0 < ε < 2) from being similar, i.e., more than an ε-fraction of the two curves must be ignored for them to become similar. We present two algorithms which are sublinear assuming that the curves are t-approximate shortest paths in the ambient metric space, for some t ≪ n. The first algorithm uses O(t/ε log t/ε) queries and is given the value of t in advance. The second algorithm does not have explicit knowledge of the value of t and therefore needs to gain implicit knowledge of the straightness of the input curves through its queries. We show that the discrete Fréchet distance can still be tested using roughly O({t³+t² log n}/ε) queries ignoring logarithmic factors in t. Our algorithms work in a matrix representation of the input and may be of independent interest to matrix testing. Our algorithms use a mild uniform sampling condition that constrains the edge lengths of the curves, similar to a polynomially bounded aspect ratio. Applied to testing the continuous Fréchet distance of t-straight curves, our algorithms can be used for (1+ε')-approximate testing using essentially the same bounds as stated above with an additional factor of poly(1/(ε')).

We study the problem of computing the Fréchet distance between two polygonal curves under transformations. First, we consider translations in the Euclidean plane. Given two curves $π$ and $σ$ of total complexity $n$ and a threshold $δ\geq 0$, we present an $\tilde{\mathcal{O}}(n^{7 + \frac{1}{3}})$ time algorithm to determine whether there exists a translation $t \in \mathbb{R}^2$ such that the Fréchet distance between $π$ and $σ+ t$ is at most $δ$. This improves on the previous best result, which is an $\mathcal{O}(n^8)$ time algorithm. We then generalize this result to any class of rationally parameterized transformations, which includes translation, rotation, scaling, and arbitrary affine transformations. For a class $\mathcal T$ of rationally parametrized transformations with $k$ degrees of freedom, we show that one can determine whether there is a transformation $τ\in \mathcal T$ such that the Fréchet distance between $π$ and $τ(σ)$ is at most $δ$ in $\tilde{\mathcal{O}}(n^{3k+\frac{4}{3}})$ time.

Map matching is a common task when analysing GPS tracks, such as vehicle trajectories. The goal is to match a recorded noisy polygonal curve to a path on the map, usually represented as a geometric graph. The Fréchet distance is a commonly used metric for curves, making it a natural fit. The map-matching problem is well-studied, yet until recently no-one tackled the data structure question: preprocess a given graph so that one can query the minimum Fréchet distance between all graph paths and a polygonal curve. Recently, Gudmundsson, Seybold, and Wong [Gudmundsson et al., 2023] studied this problem for arbitrary query polygonal curves and c-packed graphs. In this paper, we instead require the graphs to be λ-low-density t-spanners, which is significantly more representative of real-world networks. We also show how to report a path that minimises the distance efficiently rather than only returning the minimal distance, which was stated as an open problem in their paper.

Spanner constructions focus on the initial design of the network. However, networks tend to improve over time. In this paper, we focus on the improvement step. Given a graph and a budget k, which k edges do we add to the graph to minimise its dilation? Gudmundsson and Wong [TALG'22] provided the first positive result for this problem, but their approximation factor is linear in k. Our main result is a (2 √[r]{2} k^{1/r},2r)-bicriteria approximation that runs in O(n³ log n) time, for all r ≥ 1. In other words, if t^* is the minimum dilation after adding any k edges to a graph, then our algorithm adds O(k^{1+1/r}) edges to the graph to obtain a dilation of 2rt^*. Moreover, our analysis of the algorithm is tight under the Erdős girth conjecture.

The free space diagram is a popular tool to compute the well-known Fréchet distance. As the Fréchet distance is used in many different fields, many variants have been established to cover the specific needs of these applications. Often the question arises whether a certain pattern in the free space diagram is realizable, i.e., whether there exists a pair of polygonal chains whose free space diagram corresponds to it. The answer to this question may help in deciding the computational complexity of these distance measures, as well as allowing to design more efficient algorithms for restricted input classes that avoid certain free space patterns. Therefore we study the inverse problem: Given a potential free space diagram, do there exist curves that generate this diagram? Our problem of interest is closely tied to the classic Distance Geometry problem. We settle the complexity of Distance Geometry in ℝ^{>2}, showing ∃ℝ-hardness. We use this to show that for curves in ℝ^{≥2} the realizability problem is ∃ℝ-complete, both for continuous and for discrete Fréchet distance. We prove that the continuous case in ℝ¹ is only weakly NP-hard, and we provide a pseudo-polynomial time algorithm and show that it is fixed-parameter tractable. Interestingly, for the discrete case in ℝ¹ we show that the problem becomes solvable in polynomial time.

In 2015, Driemel, Krivošija, and Sohler introduced the k,ℓ -median clustering problem for polygonal curves under the Fréchet distance. Given a set of input curves, the problem asks to find k median curves of at most ℓ vertices each that minimize the sum of Fréchet distances over all input curves to their closest median curve. A major shortcoming of their algorithm is that the input curves are restricted to lie on the real line. In this article, we present a randomized bicriteria-approximation algorithm that works for polygonal curves in ℝ d and achieves approximation factor (1+ɛ) with respect to the clustering costs. The algorithm has worst-case running time linear in the number of curves, polynomial in the maximum number of vertices per curve (i.e., their complexity), and exponential in d , ℓ, 1/ɛ and 1/δ (i.e., the failure probability). We achieve this result through a shortcutting lemma, which guarantees the existence of a polygonal curve with similar cost as an optimal median curve of complexity ℓ, but of complexity at most 2ℓ -2, and whose vertices can be computed efficiently. We combine this lemma with the superset sampling technique by Kumar et al. to derive our clustering result. In doing so, we describe and analyze a generalization of the algorithm by Ackermann et al., which may be of independent interest.

We study the problem of constructing a data structure that can store a two-dimensional polygonal curve $P$, such that for any query segment $\overline{ab}$ one can efficiently compute the Fréchet distance between $P$ and $\overline{ab}$. First we present a data structure of size $O(n \log n)$ that can compute the Fréchet distance between $P$ and a horizontal query segment $\overline{ab}$ in $O(\log n)$ time, where $n$ is the number of vertices of $P$. In comparison to prior work, this significantly reduces the required space. We extend the type of queries allowed, as we allow a query to be a horizontal segment $\overline{ab}$ together with two points $s, t \in P$ (not necessarily vertices), and ask for the Fréchet distance between $\overline{ab}$ and the curve of $P$ in between $s$ and $t$. Using $O(n\log^2n)$ storage, such queries take $O(\log^3 n)$ time, simplifying and significantly improving previous results. We then generalize our results to query segments of arbitrary orientation. We present an $O(nk^{3+\varepsilon}+n^2)$ size data structure, where $k \in [1..n]$ is a parameter the user can choose, and $\varepsilon > 0$ is an arbitrarily small constant, such that given any segment $\overline{ab}$ and two points $s, t \in P$ we can compute the Fréchet distance between $\overline{ab}$ and the curve of $P$ in between $s$ and $t$ in $O((n/k)\log^2n+\log^4 n)$ time. This is the first result that allows efficient exact Fréchet distance queries for arbitrarily oriented segments. We also present two applications of our data structure: we show that we can compute a local $δ$-simplification (with respect to the Fréchet distance) of a polygonal curve in $O(n^{5/2+\varepsilon})$ time, and that we can efficiently find a translation of an arbitrary query segment $\overline{ab}$ that minimizes the Fréchet distance with respect to a subcurve of $P$.

In 2015, Driemel, Krivošija and Sohler introduced the (k, ℓ)-median problem for clustering polygonal curves under the Fréchet distance. Given a set of input curves, the problem asks to find k median curves of at most ℓ vertices each that minimize the sum of Fréchet distances over all input curves to their closest median curve. A major shortcoming of their algorithm is that the input curves are restricted to lie on the real line. In this paper, we present a randomized bicriteria-approximation algorithm that works for polygonal curves in ℝd and achieves approximation factor (1 + ∊) with respect to the clustering costs. The algorithm has worst-case running-time linear in the number of curves, polynomial in the maximum number of vertices per curve, i.e. their complexity, and exponential in d, ℓ, ∊ and δ, i.e., the failure probability. We achieve this result through a shortcutting lemma, which guarantees the existence of a polygonal curve with similar cost as an optimal median curve of complexity ℓ, but of complexity at most 2ℓ – 2, and whose vertices can be computed efficiently. We combine this lemma with the superset-sampling technique by Kumar et al. to derive our clustering result. In doing so, we describe and analyze a generalization of the algorithm by Ackermann et al., which may be of independent interest.

We introduce new distance measures for comparing straight-line embedded graphs based on the Fréchet distance and the weak Fréchet distance. These graph distances are defined using continuous mappings and thus take the combinatorial structure as well as the geometric embeddings of the graphs into account. We present a general algorithmic approach for computing these graph distances. Although we show that deciding the distances is NP-hard for general embedded graphs, we prove that our approach yields polynomial time algorithms if the graphs are trees, and for the distance based on the weak Fréchet distance if the graphs are planar embedded and if the embedding meets a certain geometric restriction. Moreover, we prove that deciding the distances based on the Fréchet distance remains NP-hard for planar embedded graphs and show how our general algorithmic approach yields an exponential time algorithm and a polynomial time approximation algorithm for this case.

Given the trajectories of one or several moving groups, we propose a new framework, the group diagram (GD) for representing these. Specifically, we seek a minimal GD as a concise representation of the groups maintaining the spatio-temporal structure of the groups’ movement. A GD is specified by three input values, namely a distance threshold, a similarity measure and a minimality criterion. For several variants of the GD, we give a comprehensive analysis of their computational complexity and present efficient approximation algorithms for their computation. Furthermore, we experimentally evaluate our algorithms on GPS data of migrating geese. Applying the proposed methods on these data sets reveals how the GD concisely represents the movement of the groups. This representation can be used for further analysis and for the formulation of new hypotheses for further ecological research, such as differences in movement patterns of groups on different surfaces or the shift of migration routes over several years. We use different similarity measures to summarize the migration routes of (i) a goose family for one migration period and to summarize (ii) the migration routes of one individual for several migration periods or (iii) the migration routes of several independent individuals for one migration period.

We introduce a new distance measure for comparing polygonal chains: the k-Fréchet distance. As the name implies, it is closely related to the well-studied Fréchet distance but detects similarities between curves that resemble each other only piecewise. The parameter k denotes the number of subcurves into which we divide the input curves (thus we allow up to k-1 "teleports" on each input curve). The k-Fréchet distance provides a nice transition between (weak) Fréchet distance and Hausdorff distance. However, we show that deciding this distance measure turns out to be NP-hard, which is interesting since both (weak) Fréchet and Hausdorff distance are computable in polynomial time. Nevertheless, we give several possibilities to deal with the hardness of the k-Fréchet distance: besides a short exponential-time algorithm for the general case, we give a polynomial-time algorithm for k=2, i.e., we ask that we subdivide our input curves into two subcurves each. We can also approximate the optimal k by factor 2. We then present a more intricate FPT algorithm using parameters k (the number of allowed subcurves) and z (the number of segments of one curve that intersect the epsilon-neighborhood of a point on the other curve).

We propose a new approach for constructing the underlying map from trajectory data. Our algorithm is based on the idea that road segments can be identified as stable subtrajectory clusters in the data. For this, we consider how subtrajectory clusters evolve for varying distance values, and choose stable values for these. In doing so we avoid a global proximity parameter. Within trajectory clusters, we choose representatives, which are combined to form the map. We experimentally evaluate our algorithm on vehicle and hiking tracking data. These experiments demonstrate that our approach can naturally separate roads that run close to each other and can deal with outliers in the data, two issues that are notoriously difficult in road network reconstruction.

Given two polygonal curves in the plane, there are many ways to define a notion of similarity between them. One popular measure is the Fréchet distance. Since it was proposed by Alt and Godau in 1992, many variants and extensions have been studied. Nonetheless, even more than 20 years later, the original $$O(n^2 \log n)$$ algorithm by Alt and Godau for computing the Fréchet distance remains the state of the art (here, n denotes the number of edges on each curve). This has led Helmut Alt to conjecture that the associated decision problem is 3SUM-hard. In recent work, Agarwal et al. show how to break the quadratic barrier for the discrete version of the Fréchet distance, where one considers sequences of points instead of polygonal curves. Building on their work, we give a randomized algorithm to compute the Fréchet distance between two polygonal curves in time $$O(n^2 \sqrt{\log n}(\log \log n)^{3/2})$$ on a pointer machine and in time $$O(n^2(\log \log n)^2)$$ on a word RAM. Furthermore, we show that there exists an algebraic decision tree for the decision problem of depth $$O(n^{2-\varepsilon })$$ , for some $$\varepsilon > 0$$ . We believe that this reveals an intriguing new aspect of this well-studied problem. Finally, we show how to obtain the first subquadratic algorithm for computing the weak Fréchet distance on a word RAM.

The analysis of interaction between movement trajectories is of interest for various domains when movement of multiple objects is concerned. Interaction often includes a delayed response, making it difficult to detect interaction with current methods that compare movement at specific time intervals. We propose analyses and visualizations, on a local and global scale, of delayed movement responses, where an action is followed by a reaction over time, on trajectories recorded simultaneously. We developed a novel approach to compute the global delay in subquadratic time using a fast Fourier transform (FFT). Central to our local analysis of delays is the computation of a matching between the trajectories in a so-called delay space. It encodes the similarities between all pairs of points of the trajectories. In the visualization, the edges of the matching are bundled into patches, such that shape and color of a patch help to encode changes in an interaction pattern. To evaluate our approach experimentally, we have implemented it as a prototype visual analytics tool and have applied the tool on three bidimensional data sets. For this we used various measures to compute the delay space, including the directional distance, a new similarity measure, which captures more complex interactions by combining directional and spatial characteristics. We compare matchings of various methods computing similarity between trajectories. We also compare various procedures to compute the matching in the delay space, specifically the Fréchet distance, dynamic time warping (DTW), and edit distance (ED). Finally, we demonstrate how to validate the consistency of pairwise matchings by computing matchings between more than two trajectories.

All known algorithms for the Fréchet distance between curves proceed in two steps: first, they construct an efficient oracle for the decision version; second, they use this oracle to find the optimum from a finite set of critical values. We present a novel approach that avoids the detour through the decision version. This gives the first quadratic time algorithm for the Fréchet distance between polygonal curves in $$\mathbb {R}^d$$ under polyhedral distance functions (e.g., $$L_1$$ and $$L_\infty $$ ). We also get a $$(1+\varepsilon )$$ -approximation of the Fréchet distance under the Euclidean metric, in quadratic time for any fixed $$\varepsilon > 0$$ . For the exact Euclidean case, our framework currently yields an algorithm with running time $$O(n^2 \log ^2 n)$$ . However, we conjecture that it may eventually lead to a faster exact algorithm.

The study of movement is progressing rapidly as a subdiscipline in Geographic Information Science (GIScience). At the fulcrum of this new research area in GIScience are movement observations. Movem...

Nowadays, an abundance of sensors are used to collect very large datasets of moving objects. The movement of these objects can be analysed by identifying common routes. For this, a cluster of trajectories must be defined and the pattern of each cluster discovered. In this article, we introduce a new pattern, called the Trajectory Box Plot (TBP), to summarize a set of trajectories following the same route. The TBP is an extension of the well-known descriptive statistics Box Plot concept. Each TBP is described by a median trajectory, a 3D box and a 3D fence. The median trajectory depicts the typical movement of mobile objects. The box and the fences (whiskers) describe the spatial and temporal spreading around the central tendency. TBPs are useful to summarize and analyse trajectory streams, understand their spatio-temporal density and detect outliers. In this article, visual analysis highlights how the TBP pattern effectively describes how the density of trajectory clusters change over time.

Interactions between trajectories need to be analyzed in various domains to gain insight into movement patterns. Such interactions often take place with some delayed response. We propose an approach to analyze and visualize delayed responses on two trajectories recorded simultaneously and with the same sampling rate. Central to our approach is the computation of a matching between the trajectories in a so-called delay space. We also introduce a new similarity measure between trajectories, which combines directional and spatial characteristics. To evaluate our approach experimentally, we have implemented it as a prototype visual analytics tool and have applied the tool on two datasets.

No abstract available.

We study the shortcut Fréchet distance, a natural variant of the Fréchet distance, that allows us to take shortcuts from and to any point along one of the curves. The classic Fréchet distance is a bottle-neck distance measure and hence quite sensitive to outliers. The shortcut Fréchet distance allows us to cut across outliers and hence produces more meaningful results when dealing with real world data. Driemel and Har-Peled recently described approximation algorithms for the restricted case where shortcuts have to start and end at input vertices. We show that, in the general case, the problem of computing the shortcut Fréchet distance is NP-hard. This is the first hardness result for a variant of the Fréchet distance between two polygonal curves in the plane. We also present two algorithms for the decision problem: a 3-approximation algorithm for the general case and an exact algorithm for the vertex-restricted case. Both algorithms run in O(n3 log n) time.

We present an algorithmic framework for criteria-based segmentation of trajectories that can efficiently process a large class of criteria. Criteria-based segmentation is the problem of subdividing a trajectory into a small number of parts such that each part satisfies a global criterion. Our framework can handle criteria that are stable, in the sense that these do not change their validity along the trajectory very often. This includes both increasing and decreasing monotone criteria. Our framework takes O(n log n) time for preprocessing and computation, where n is the number of data points. It surpasses the two previous algorithmic frameworks on criteria-based segmentation, which could only handle decreasing monotone criteria, or had a quadratic running time, respectively. Furthermore, we develop an efficient data structure for interactive parameter selection, and provide mechanisms to improve the exact position of break points in the segmentation. We demonstrate and evaluate our framework by performing case studies on real-world data sets.

Given two polygonal curves in the plane, there are many ways to define a notion of similarity between them. One measure that is extremely popular is the Fréchet distance. Since it has been proposed by Alt and Godau in 1992, many variants and extensions have been studied. Nonetheless, even more than 20 years later, the original O(n2 log n) algorithm by Alt and Godau for computing the Fréchet distance remains the state of the art (here n denotes the number of vertices on each curve). This has led Helmut Alt to conjecture that the associated decision problem is 3SUM-hard. In recent work, Agarwal et al. show how to break the quadratic barrier for the discrete version of the Fréchet distance, where one considers sequences of points instead of polygonal curves. Building on their work, we give a randomized algorithm to compute the Fréchet distance between two polygonal curves in time on a pointer machine and in time O(n2 (log log n)2) on a word RAM. Furthermore, we show that there exists an algebraic decision tree for the decision problem of depth O(n2∊), for some ∊ > 0. This provides evidence that the decision problem may not be 3SUM-hard after all and reveals an intriguing new aspect of this well-studied problem.

Increasing volumes of trajectory data require analysis methods which go beyond the visual. Methods for computing trajectory analysis typically assume linear interpolation between quasi-regular sampling points. This assumption, however, is often not realistic, and can lead to a meaningless analysis for sparsely and/or irregularly sampled data. We propose to use the space-time prism model instead, allowing to represent the influence of speed on possible trajectories within a volume. We give definitions for the similarity of trajectories in this model and describe algorithms for its computation using the Fréchet and the equal time distance.

A binary tanglegram is a drawing of a pair of rooted binary trees whose leaf sets are in one-to-one correspondence; matching leaves are connected by inter-tree edges. For applications, for example, in phylogenetics, it is essential that both trees are drawn without edge crossings and that the inter-tree edges have as few crossings as possible. It is known that finding a tanglegram with the minimum number of crossings is NP-hard and that the problem is fixed-parameter tractable with respect to that number. We prove that under the Unique Games Conjecture there is no constant-factor approximation for binary trees. We show that the problem is NP-hard even if both trees are complete binary trees. For this case we give an O(n 3)-time 2-approximation and a new, simple fixed-parameter algorithm. We show that the maximization version of the dual problem for binary trees can be reduced to a version of MaxCut for which the algorithm of Goemans and Williamson yields a 0.878-approximation.

Spatially aggregated data is frequently used in geographical applications. Often spatial data analysis on aggregated data is performed in the same way as on exact data, which ignores the fact that we do not know the actual locations of the data. We here propose models and methods to take aggregation into account. For this we focus on the problem of locating clusters in aggregated data. More specifically, we study the problem of locating clusters in spatially aggregated health data. The data is given as a subdivision into regions with two values per region, the number of cases and the size of the population at risk. We formulate the problem as finding a placement of a cluster window of a given shape such that a cluster function depending on the population at risk and the cases is maximized. We propose area-based models to calculate the cases (and the population at risk) within a cluster window. These models are based on the areas of intersection of the cluster window with the regions of the subdivision. We show how to compute a subdivision such that within each cell of the subdivision the areas of intersection are simple functions. We evaluate experimentally how taking aggregation into account influences the location of the clusters found.

We investigate the concept of a median among a set of trajectories. We establish criteria that a “median trajectory” should meet, and present two different methods to construct a median for a set of input trajectories. The first method is very simple, while the second method is more complicated and uses homotopy with respect to sufficiently large faces in the arrangement formed by the trajectories. We give algorithms for both methods, analyze the worst-case running time, and show that under certain assumptions both methods can be implemented efficiently. We empirically compare the output of both methods on randomly generated trajectories, and analyze whether the two methods yield medians that are according to our intuition. Our results suggest that the second method, using homotopy, performs considerably better.

In this paper we address the problem of segmenting a trajectory such that each segment is in some sense homogeneous. We formally define different spatio-temporal criteria under which a trajectory can be homogeneous, including location, heading, speed, velocity, curvature, sinuosity, and curviness. We present a framework that allows us to segment any trajectory into a minimum number of segments under any of these criteria, or any combination of these criteria. In this framework, the segmentation problem can generally be solved in O(n log n) time, where n is the number of edges of the trajectory to be segmented.

Time plays an important role in the analysis of moving object data. For many applications it is not sufficient to only compare objects at exactly the same times, or to consider only the geometry of their trajectories. We show how to leverage between these two approaches by extending a tool from curve analysis, namely the free space diagram. Our approach also allows us to take further attributes of the objects like speed or direction into account. We demonstrate the usefulness of the new tool by applying it to the problem of detecting single file movement. A single file is a set of moving entities, which are following each other, one behind the other. Our algorithm is the first one developed for detecting such movement patterns. For this application, we analyse demonstrate the performance of our tool both theoretically experimentally.

A natural time-dependent similarity measure for two trajectories is their average distance at corresponding times. We give algorithms for computing the most similar subtrajectories under this measure, assuming the two trajectories are given as two polygonal, possibly self-intersecting lines. When a minimum duration is specified for the subtrajectories, and they must start at exactly corresponding times in the input trajectories, we give a linear-time algorithm for computing the starting time and duration of the most similar subtrajectories. The algorithm is based on a result of independent interest: We present a linear-time algorithm to find, for a piece-wise monotone function, an interval of at least a given length that has minimum average value.

Curve matching is a fundamental problem that occurs in many applications. In this paper, we study the problem of measuring partial similarity between curves. Specifically, given two curves, we wish to maximize the total length of subcurves that are close to each other, where closeness is measured by the Fréchet distance, a common distance measure for curves. The resulting maximal length is called the partial Fréchet similarity between the two input curves. Given two polygonal curves P and Q in IRd of size m and n, respectively, we present the first exact algorithm that runs in polynomial time to compute ℱδ(P, Q), the partial Fréchet similarity between P and Q, under the L1 and L∞ norms. Specifically, we formulate the problem of computing ℱδ(P, Q) as a longest path problem, and solve it in O(mn(m + n) log(mn)) time, under the L1 or L∞ norm, using a “shortest-path map” type decomposition. To the best of our knowledge, this is the first paper to study this natural definition of partial curve similarity in the continuous setting (with all points in the curve considered), and present a polynomial-time exact algorithm for it.

We show that the vertices of any plane graph in which every face is incident to at least g vertices can be colored by ⌊(3g−5)/4⌋ colors so that every color appears in every face. This is nearly tight, as there are plane graphs where all faces are incident to at least g vertices and that admit no vertex coloring of this type with more than ⌊(3g+1)/4⌋ colors. We further show that the problem of determining whether a plane graph admits a vertex coloring by k colors in which all colors appear in every face is in ℘ for k=2 and is $\mathcal{NP}$ -complete for k=3,4. We refine this result for polychromatic 3-colorings restricted to 2-connected graphs which have face sizes from a prescribed (possibly infinite) set of integers. Thereby we find an almost complete characterization of these sets of integers (face sizes) for which the corresponding decision problem is in ℘, and for the others it is $\mathcal{NP}$ -complete.

We show that the vertices of any plane graph in which every face is of size at least g can be colored by (3g Àý 5)=4 colors so that every color appears in every face. This is nearly tight, as there are plane graphs that admit no vertex coloring of this type with more than (3g+1)=4 colors. We further show that the problem of determining whether a plane graph admits a vertex coloring by 3 colors in which all colors appear in every face is NP-complete even for graphs in which all faces are of size 3 or 4 only. If all faces are of size 3 this can be decided in polynomial time.

Road network data is often incomplete, making it hard to perform network analysis. This paper discusses the problem of extending partial road networks with reasonable links, using the concept of dilation (also known as crow flight conversion coefficient). To this end, we study how to connect a point (relevant location) inside a polygon (face of the known part of the road network) to the boundary so that the dilation from that point to any point on the boundary is not too large. We provide algorithms and heuristics, and give a computational and experimental analysis.

We study the problem of detecting a single file behavior in a set of trajectories. A group of entities is moving in single file if they are following each other, one behind the other. This movement pattern occurs often, among animals, humans, and vehicles. It is challenging to detect because it does not have a fixed layout.In this paper we first model the notion of following behind, on which we base our definition of single file. We present efficient algorithms for detecting following behind and single file behaviors. We test and evaluate these algorithms on real and generated test data.

We present the first polynomial-time algorithm for computing the Fréchet for a non-trivial class of surfaces: simple polygons. For this, we show that it suffices to consider homeomorphisms that map an arbitrary triangulation of one polygon to the other polygon such that diagonals of the triangulation are mapped to shortest paths in the other polygon.

City models have become central elements for visually communicating spatial information related to urban areas and have manifold applications. Our real-time nonphotorealistic rendering technique aims at abstract, comprehensible, and vivid drawings of assemblies of polygonal 3D urban objects. It takes into account related principles in cartography, cognition, and nonphotorealism. Technically, the geometry of a building is rendered using expressive line drawings to enhance the edges, two-tone or three-tone shading to draw the faces, and simulated shadows. The edge enhancement offers several degrees of freedom, such as interactively changing the style, width, tilt, color, transparency, and length of the strokes. Traditional drawings of cities and panoramas inspired the tone shading that achieves a pleasing visual color effect. The rendering technique can be applied not only to city models but to polygonal shapes in general.