Prijavno okno

Terenske vaje GTP

Terenske vaje - Sat, 04/15/2017 - 18:29
Kategorije: Novo na oglasni deski

Tajništvo (20. 4. 2017)

Obvestila - Pet., 04/14/2017 - 12:27
Kategorije: Novo na oglasni deski

GU v sredo, 12.4.2017

Govorilne ure - Wed, 04/12/2017 - 09:10
Kategorije: Novo na oglasni deski

Multi-Domain User-Generated Content Based Model to Enrich Road Network Data for Multi-Criteria Route Planning

Geographical analysis - Wed, 04/12/2017 - 08:05

By utilizing today's web-based technologies, people can act as sensors and share their perceptions, emotions and observations in a variety of data forms, such as images, videos, texts, Global Positioning System (GPS) trajectories and maps. These forms are collectively called user-generated content (UGC). These data are in different domains and have a multi-modality nature. Although recent efforts have probed the acquisition of local knowledge by using single-domain UGC data in specific applications, such efforts have not thus far presented a model considering multi-domain UGC specifically to enrich road network data. This article aims at presenting such a model wherein, with the help of each data domain of UGC, one aspect of people knowledge about the road segment is obtained. These different aspects of knowledge are integrated using a Skyline operator to support multi-criteria route finding. We name this model ERSBU (enriching road segments based on UGC). In ERSBU, road segments are basic spatial units, and their subjective properties have been extracted by using available UGC. The scenic score for each road segment was computed by using geo-tagged Panoramio photos. The accessibility level of each road segment to different facilities was calculated based on data captured from Wikimapia and OpenStreetMap. Moreover, for measuring the movement popularity of each road segment, Wikiloc and Everytrail GPS trajectories were utilized. For the implementation of the ERSBU model, Tehran region 6 was considered the case study area. The Evaluation of the results proved that road segments that achieved a high score based on knowledge extracted from UGC also mostly gained top scores by analyzing traditional maps. ERSBU allows users to accomplish more-qualitative path finding by considering the multi-view characteristics of road segments.

Zasedenost GIKL-a

Obvestila - Tue, 04/11/2017 - 12:14
Kategorije: Novo na oglasni deski

GTD1 - Geomorfologija

Terenske vaje - Pon., 04/10/2017 - 10:29
Kategorije: Novo na oglasni deski

Govorilne ure - Stepišnik

Govorilne ure - Pon., 04/10/2017 - 06:21
Kategorije: Novo na oglasni deski

Resnik Planinc - sprememba GU

Govorilne ure - Pon., 04/10/2017 - 01:37
Kategorije: Novo na oglasni deski

Nonlinear Multivariate Spatial Modeling Using NLPCA and Pair-Copulas

Geographical analysis - Pet., 04/07/2017 - 10:16

A novel geostatistical modeling approach is developed to model nonlinear multivariate spatial dependence using nonlinear principal component analysis (NLPCA) and pair-copulas. In spatial studies, multivariate measurements are frequently collected at each location. The dependence between such measurements can be complex. In this article, a multivariate geostatistical model is developed that can capture both nonlinear spatial dependence across locations and nonlinear dependence between measurements at a particular location. Nonlinear multivariate dependence between spatial variables is removed using NLPCA. Subsequently, a pair-copula based model is fitted to each transformed variable to model the univariate nonlinear spatial dependencies. NLPCA and pair-copulas, within the proposed model, are compared with stepwise conditional transformation (SCT) and conventional kriging. The results show that, for the two case studies presented, the proposed model that utilizes NLPCA and pair-copulas reproduces nonlinear multivariate structures and univariate distributions better than existing methods based on SCT and kriging.