There has been enormous progress in geospatial data acquisition in the last decade. Centralized data collection, mainly by land surveying offices and local government agencies, has changed dramatically to voluntary data provision by citizens. Among a broad list of initiatives dealing with user generated geospatial information, OpenStreetMap (OSM) is one of the most famous crowd-sourced products. It is believed that the quality of collected information remains a valid concern. Therefore, qualitative assessment of OSM data as the most significant instance of volunteered geospatial information (VGI) is a considerable issue in the geospatial information community. One aspect of VGI quality assessment pertains to its comparison with institutionally referenced geospatial databases. This paper proposes a new quality metric for assessment of VGI accuracy and as well as for quality analysis of OSM dataset by evaluating its consistency with that of a reference map produced by Municipality of Tehran, Iran. A gridded map is employed and heuristic metrics such as Minimum Bounding Geometry area and directional distribution (Standard Deviational Ellipse), evaluated for both VGI and referenced data, are separately compared in each grid. Finally, in order to have a specific output as an integrated quality metric for VGI, its consistency with ground-truth data is evaluated using fuzzy logic. The results of this research verify that the quality of OSM maps in the study area is fairly good, although the spatial distribution of uncertainty in VGI varies throughout the dataset.

Mobile positioning data are normally generated as scattered points in CDRs (Call Detail Records), which include the IDs of connected cell towers for call activities. To interpret mobile associations with towers, one needs to model the area where the mobile phone user is likely to be when connected. Voronoi cells, which define the area closest to each tower, are usually employed to this end. However, the accuracy of Voronoi tessellation is influenced by various environmental factors due to propagation of signals in different manners, such as diffraction, scattering and reflection. This non-specificity of cell shapes causes a major source of uncertainty in, say, trajectory reconstruction. This paper evaluates the effect of imprecision of cell shape on accuracy of CDR-based trajectory reconstruction. It further aims to reconstruct more accurate trajectories from the coarse CDR data through considering the wave propagation models in estimating the location of mobile phone users. Since many of the factors in radio wave propagation are geographical, GIS techniques will be incorporated into this procedure.