3,051 research outputs found
Efficient MaxCount and threshold operators of moving objects
Calculating operators of continuously moving objects presents some unique challenges, especially when the operators involve aggregation or the concept of congestion, which happens when the number of moving objects in a changing or dynamic query space exceeds some threshold value. This paper presents the following six d-dimensional moving object operators: (1) MaxCount (or MinCount), which finds the Maximum (or Minimum) number of moving objects simultaneously present in the dynamic query space at any time during the query time interval. (2) CountRange, which finds a count of point objects whose trajectories intersect the dynamic query space during the query time interval. (3) ThresholdRange, which finds the set of time intervals during which the dynamic query space is congested. (4) ThresholdSum, which finds the total length of all the time intervals during which the dynamic query space is congested. (5) ThresholdCount, which finds the number of disjoint time intervals during which the dynamic query space is congested. And (6) ThresholdAverage, which finds the average length of time of all the time intervals when the dynamic query space is congested. For these operators separate algorithms are given to find only estimate or only precise values. Experimental results from more than 7,500 queries indicate that the estimation algorithms produce fast, efficient results with error under 5%
The Infati Data
The ability to perform meaningful empirical studies is of essence in research
in spatio-temporal query processing. Such studies are often necessary to gain
detailed insight into the functional and performance characteristics of
proposals for new query processing techniques.
We present a collection of spatio-temporal data, collected during an
intelligent speed adaptation project, termed INFATI, in which some two dozen
cars equipped with GPS receivers and logging equipment took part. We describe
how the data was collected and how it was "modified" to afford the drivers some
degree of anonymity.
We also present the road network in which the cars were moving during data
collection.
The GPS data is publicly available for non-commercial purposes. It is our
hope that this resource will help the spatio-temporal research community in its
efforts to develop new and better query processing techniques
Optimal Time-dependent Sequenced Route Queries in Road Networks
In this paper we present an algorithm for optimal processing of
time-dependent sequenced route queries in road networks, i.e., given a road
network where the travel time over an edge is time-dependent and a given
ordered list of categories of interest, we find the fastest route between an
origin and destination that passes through a sequence of points of interest
belonging to each of the specified categories of interest. For instance,
considering a city road network at a given departure time, one can find the
fastest route between one's work and his/her home, passing through a bank, a
supermarket and a restaurant, in this order. The main contribution of our work
is the consideration of the time dependency of the network, a realistic
characteristic of urban road networks, which has not been considered previously
when addressing the optimal sequenced route query. Our approach uses the A*
search paradigm that is equipped with an admissible heuristic function, thus
guaranteed to yield the optimal solution, along with a pruning scheme for
further reducing the search space. In order to compare our proposal we extended
a previously proposed solution aimed at non-time dependent sequenced route
queries, enabling it to deal with the time-dependency. Our experiments using
real and synthetic data sets have shown our proposed solution to be up to two
orders of magnitude faster than the temporally extended previous solution.Comment: 10 pages, 12 figures To be published as a short paper in the 23rd ACM
SIGSPATIA
Modelling potential movement in constrained travel environments using rough space-time prisms
The widespread adoption of location-aware technologies (LATs) has afforded analysts new opportunities for efficiently collecting trajectory data of moving individuals. These technologies enable measuring trajectories as a finite sample set of time-stamped locations. The uncertainty related to both finite sampling and measurement errors makes it often difficult to reconstruct and represent a trajectory followed by an individual in space-time. Time geography offers an interesting framework to deal with the potential path of an individual in between two sample locations. Although this potential path may be easily delineated for travels along networks, this will be less straightforward for more nonnetwork-constrained environments. Current models, however, have mostly concentrated on network environments on the one hand and do not account for the spatiotemporal uncertainties of input data on the other hand. This article simultaneously addresses both issues by developing a novel methodology to capture potential movement between uncertain space-time points in obstacle-constrained travel environments
Automated processing for map generalization using web services
In map generalization various operators are applied to the features of a map in order to maintain and improve the legibility of the map after the scale has been changed. These operators must be applied in the proper sequence and the quality of the results must be continuously evaluated. Cartographic constraints can be used to define the conditions that have to be met in order to make a map legible and compliant to the user needs. The combinatorial optimization approaches shown in this paper use cartographic constraints to control and restrict the selection and application of a variety of different independent generalization operators into an optimal sequence. Different optimization techniques including hill climbing, simulated annealing and genetic deep search are presented and evaluated experimentally by the example of the generalization of buildings in blocks. All algorithms used in this paper have been implemented in a web services framework. This allows the use of distributed and parallel processing in order to speed up the search for optimized generalization operator sequence
MiniMapathon: mappare il mondo a 10 anni
Il presente articolo illustra il primo e più grande esperimento al mondo di MiniMapathon, ovvero un mapathon (editing collaborativo di mappe) a scopo umanitario realizzato con 250 studenti di quarta e quinta della scuola primaria. Vengono illustrate le motivazioni che hanno portato al MiniMapathon, i risultati didattici, i benefici in termini di conoscenze e di atteggiamento verso la geoinformatica e le potenzialità di questa particolare attività , indagate attraverso questionari agli studenti e interviste ai docenti delle classi coinvolte
Constrained tGAP for generalisation between scales: the case of Dutch topographic data
This article presents the results of integrating large- and medium-scale data into a unified data structure. This structure can be used as a single non-redundant representation for the input data, which can be queried at any arbitrary scale between the source scales. The solution is based on the constrained topological Generalized Area Partition (tGAP), which stores the results of a generalization process applied to the large-scale dataset, and is controlled by the objects of the medium-scale dataset, which act as constraints on the large-scale objects. The result contains the accurate geometry of the large-scale objects enriched with the generalization knowledge of the medium-scale data, stored as references in the constraint tGAP structure. The advantage of this constrained approach over the original tGAP is the higher quality of the aggregated maps. The idea was implemented with real topographic datasets from The Netherlands for the large- (1:1000) and medium-scale (1:10,000) data. The approach is expected to be equally valid for any categorical map and for other scales as well
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