iTour

Although tourist maps are useful resources for people to visit scenic areas, they are also commonly distorted and omit details according to the purposes and functions of a map. In this paper, we present iTour, a semi-automatic system that turns tourist maps into digital maps. By involving users in matching the road network of a tourist map and the paired standard map, our system computes road network correspondence between the two maps. By doing so, users can navigate on such GPS-enabled tourist maps using mobile devices. This transformation creates the possibility of augmenting a large number of tourist maps with digital map features. To evaluate the performance of matching road networks, we compared the presented semi-automatic interface to a manual interface. The results showed that the semi-automatic interface saved participants significant effort in generating correspondence and was perceived to require significantly less time by the participants. In addition, we conducted a field study of the iTour in comparison to using a tourist map and Google Maps together. Our results showed that iTour helped participants find their way during travel. The participants provided positive feedback on the combination of tourist maps and GPS location because of its highlights of important landmarks, showing users' locations relative to those landmarks, and saving the effort of switching tourist maps and Google Maps.

[1]  Süleyman Sirri Mara,et al.  Topological error correction of GIS vector data , 2010 .

[2]  Julie A. Kientz,et al.  Developing and Validating the User Burden Scale: A Tool for Assessing User Burden in Computing Systems , 2016, CHI.

[3]  Johannes Schöning,et al.  Informing online and mobile map design with the collective wisdom of cartographers , 2014, Conference on Designing Interactive Systems.

[4]  Maneesh Agrawala,et al.  Automatic generation of tourist maps , 2008, ACM Trans. Graph..

[5]  Attila Kuba,et al.  A Parallel 3D 12-Subiteration Thinning Algorithm , 1999, Graph. Model. Image Process..

[6]  Demin Xiong,et al.  Semiautomated matching for network database integration , 2004 .

[7]  B. Thomas,et al.  Usability Evaluation In Industry , 1996 .

[8]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Johannes Schöning,et al.  PhotoMap: using spontaneously taken images of public maps for pedestrian navigation tasks on mobile devices , 2009, Mobile HCI.

[10]  Q. Zhang,et al.  SPATIO-TEMPORAL MODELING IN ROAD NETWORK CHANGE DETECTION AND UPDATING , 2005 .

[11]  Thomas Devogele,et al.  Coastline Matching Process Based on the Discrete Fréchet Distance , 2006 .

[12]  David Salesin,et al.  Automatic generation of destination maps , 2010, ACM Trans. Graph..

[13]  S. Volz,et al.  LINKING DIFFERENT GEOSPATIAL DATABASES BY EXPLICIT RELATIONS , 2004 .

[14]  Min Deng,et al.  Extended Hausdorff distance for spatial objects in GIS , 2007, Int. J. Geogr. Inf. Sci..

[16]  Catriel Beeri,et al.  Object Fusion in Geographic Information Systems , 2004, VLDB.

[17]  Jan-Henrik Haunert,et al.  Drawing Road Networks with Focus Regions , 2011, IEEE Transactions on Visualization and Computer Graphics.

[18]  Takeo Igarashi,et al.  As-rigid-as-possible shape manipulation , 2005, SIGGRAPH '05.

[19]  Anthony E. Richardson,et al.  Development of a self-report measure of environmental spatial ability. , 2002 .

[20]  Bernhard Jenny Geometric distortion of schematic network maps , 2006 .

[21]  Meng Zhang,et al.  An iterative road-matching approach for the integration of postal data , 2007, Comput. Environ. Urban Syst..

[22]  Kimberly C. Kowal,et al.  Online Georeferencing for Libraries: The British Library Implementation of Georeferencer for Spatial Metadata Enhancement and Public Engagement , 2012 .

[23]  Udo W. Lipeck,et al.  Matching cartographic objects in spatial databases , 2004 .

[24]  Ulrik Brandes,et al.  Map Warping for the Annotation of Metro Maps , 2008, 2008 IEEE Pacific Visualization Symposium.

[25]  Soe-tsyr Yuan,et al.  Development of Conflation Components , 1999 .

[26]  Kori Inkpen Quinn,et al.  Map Morphing: Making Sense of Incongruent Maps , 2004, Graphics Interface.

[27]  Volker Walter,et al.  Matching spatial data sets: a statistical approach , 1999, Int. J. Geogr. Inf. Sci..

[28]  Thomas Devogele,et al.  Matching Networks with Different Levels of Detail , 2008, GeoInformatica.

[29]  L. Zhilin,et al.  Extended Hausdorff distance for spatial objects in GIS , 2007 .

[30]  Chris Stolte,et al.  Rendering effective route maps: improving usability through generalization , 2001, SIGGRAPH.

[31]  Anthony E. Lupien,et al.  A GENERAL APPROACH TO MAP CONFLATION , 2008 .

[32]  Francisco Javier Ariza-López,et al.  Digital map conflation: a review of the process and a proposal for classification , 2011, Int. J. Geogr. Inf. Sci..

[33]  William C. Halperin,et al.  EXPLORING SPATIAL FAMILIARITY , 1990 .

[34]  Alan Saalfeld,et al.  Conflation Automated map compilation , 1988, Int. J. Geogr. Inf. Sci..

[35]  Barbara Tversky,et al.  Some Ways that Maps and Diagrams Communicate , 2000, Spatial Cognition.

[36]  G. V. Goesseln,et al.  INTEGRATION OF GEOSCIENTIFIC DATA SETS AND THE GERMAN DIGITAL MAP USING A MATCHING APPROACH , 2004 .

[37]  Yang Li,et al.  Hierarchical route maps for efficient navigation , 2014, IUI.

[38]  Liqiu Meng,et al.  Georeferencing: a review of methods and applications , 2014, Ann. GIS.

[39]  Yehoshua Sagiv,et al.  Ad hoc matching of vectorial road networks , 2013, Int. J. Geogr. Inf. Sci..

[40]  Chao-Hung Lin,et al.  Drawing Road Networks with Mental Maps , 2014, IEEE Transactions on Visualization and Computer Graphics.

[41]  Ashweeni Kumar Beeharee,et al.  A natural wayfinding exploiting photos in pedestrian navigation systems , 2006, Mobile HCI.

[42]  J. F. Hangouet COMPUTATION OF THE HAUSDORFF DISTANCE BETWEEN PLANE VECTOR POLYLINES , 2008 .

[43]  David K. McGookin,et al.  Phases of Urban Tourists' Exploratory Navigation: A Field Study , 2016, Conference on Designing Interactive Systems.