The United Nations Convention on the Rights of People with Disabilities recognizes the right of people with disabilities to attain full social participation without discrimination on the basis of disability. Furthermore, mobility is one of the most important life habits for achieving such participation. Providing people with disabilities with information regarding accessible paths and accessible urban places therefore plays a vital role in achieving these goals. The accessibility of urban places and pedestrian networks depends, however, on the interaction between human capabilities and environmental factors, and may be subdivided into physical or social factors. An optimal analysis of accessibility requires both kinds of factors, social as well as physical. Although there has been considerable work concerning the physical aspects of the environment, social aspects have been largely neglected. In this paper, we highlight the importance of the social dimension of environments and consider a more integrated approach for accessibility assessment. We highlight the ways by which social factors such as policies can be incorporated into accessibility assessment of pedestrian networks for people with motor disabilities. Furthermore, we propose a framework to assess the accessibility of pedestrian network segments that incorporates the confidence level of people with motor disabilities. This framework is then used as a tool to investigate the influence of different policies on accessibility conditions of pedestrian networks. The methodology is implemented in the Saint-Roch neighborhood in Quebec City and the effectiveness of three policy actions is examined by way of illustration.

Many disciplines are faced with the problem of handling time-series data. This study introduces an innovative visual representation for time series, namely the continuous triangular model. In the continuous triangular model, all subintervals of a time series can be represented in a two-dimensional continuous field, where every point represents a subinterval of the time series, and the value at the point is derived through a certain function (e.g. average or summation) of the time series within the subinterval. The continuous triangular model thus provides an explicit overview of time series at all different scales. In addition to time series, the continuous triangular model can be applied to a broader sense of linear data, such as traffic along a road. This study shows how the continuous triangular model can facilitate the visual analysis of different types of linear data. We also show how the coordinate interval space in the continuous triangular model can support the analysis of multiple time series through spatial analysis methods, including map algebra and cartographic modelling. Real-world datasets and scenarios are employed to demonstrate the usefulness of this approach.

Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers.

LiDAR technology can provide very detailed and highly accurate geospatial information on an urban scene for the creation of Virtual Geographic Environments (VGEs) for different applications. However, automatic 3D modeling and feature recognition from LiDAR point clouds are very complex tasks. This becomes even more complex when the data is incomplete (occlusion problem) or uncertain. In this paper, we propose to build a knowledge base comprising of ontology and semantic rules aiming at automatic feature recognition from point clouds in support of 3D modeling. First, several modules for ontology are defined from different perspectives to describe an urban scene. For instance, the spatial relations module allows the formalized representation of possible topological relations extracted from point clouds. Then, a knowledge base is proposed that contains different concepts, their properties and their relations, together with constraints and semantic rules. Then, instances and their specific relations form an urban scene and are added to the knowledge base as facts. Based on the knowledge and semantic rules, a reasoning process is carried out to extract semantic features of the objects and their components in the urban scene. Finally, several experiments are presented to show the validity of our approach to recognize different semantic features of buildings from LiDAR point clouds.

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