Map construction algorithms: a local evaluation through hiking data

Abstract

We study five existing map construction algorithms, designed and tested with urban vehicle data in mind, and apply them to hiking trajectories with different terrain characteristics. Our main goal is to better understand the existing strategies and their limitations, in order to shed new light into the current challenges for map construction algorithms.We carefully analyze the results obtained by each algorithm focusing on the local details of the generated maps. Our analysis includes the characterization of 10 types of common artifacts, which occur in the results of more than one algorithm, and 7 algorithmic-specific artifacts, which are consequences of different algorithmic strategies. This allows us to extract systematic conclusions about the main challenges to fully automatize the construction of maps from trajectory data, to detect the strengths and weaknesses of the potential different strategies, and to suggest possible ways to design higher-quality map construction methods.We consider that this analysis will be of help for designing new and better methods that perform well in wider and more realistic contexts, not only for road map or hiking reconstruction, but also for other types of trajectory data.

Improved Map Construction using Subtrajectory Clustering

“…We evaluate our method on two types of trajectory datasets, namely urban and hiking. These data sets were chosen due to their diversity, and the fact that they have been previously used for evaluating map construction methods [1,9]. The urban datasets of vehicle trajectories are Athens_s, Athens_l and Chicago, available at mapconstruction.org.…”

“…Additional information, such as popularity of routes, can also be extracted, however this builds on a geometrically correct underlying network. Although many algorithms for map construction have been proposed, most do well only on a global, but not a local scale [9]. We tackle this problem by improving an existing algorithm [4] to construct maps that are correct also on a local scale.…”

Intelligent geovisualizations for open government data (vision paper)

“…Rawgraphs [23] provides useful features for the automatic visualization of spreadsheet data, and could be modified for the case of geographic data. The SIGSPATIAL community has done some work on automatic generation of road networks from GPS traces (see e.g., [10,13]), which addresses one specific type of geovisualization (i.e., network), and might inform future approaches that automatically produce more complex geovisualizations (e.g., interactive maps and timelines). Zavala-Romero et al's work on generating web GIS without programming knowledge [44] is in line with the idea outlined here, but only implements a small subset of it (i.e., their tool automatically builds web GIS interfaces to visualize NetCDF data).…”

Vessel Trajectory Reconstruction Based on Functional Data Analysis Using Automatic Identification System Data

“…Point clustering regards trajectories as sets of points, and generates maps by clustering these points [8]. A K-means clustering algorithm is employed to cluster the input points [9].…”