Understanding disability remains a challenge. Although the international community has largely embraced the idea that disability is the product of social and environmental practices, society still lacks good conceptual frameworks. In an era when the rights of persons with disabilities have been enshrined in international and national laws, such frameworks have become a necessity. Within the province of Quebec, Canada, the Disability Creation Process (DCP) model has served such a role. Furthermore, recent efforts to enrich the model enhance the applicability of this powerful tool to a broader range of contexts. As a result, the DCP model could be used more widely than it is today. In this paper we provide the foundations of the approach encapsulated by the model and explain how its usage guides policy development and service delivery within the province of Quebec. We also highlight its forward-looking capacities.

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.

Mobility is fundamental for social participation. Everyone benefits from pedestrian networks for their mobility and daily activities. People without disabilities may have little difficulty walking on narrow sidewalks, over potholes, and so on. However, people with motor disabilities (PWMD) may find it more difficult to deal with such conditions. For PWMD, even routine trips are often fraught with problems, with many different obstacles restricting their mobility and consequently rendering their participation in social and recreational activities difficult. The potential problems and risks associated with mobility for PWMD could be significantly reduced if navigation systems provided them with appropriate accessible routes. These routes should consider PWMD’s personal capabilities as well as sidewalk-network conditions. In this paper, we propose a novel approach for computing a user-specific route for PWMD. Such a route is personalized based on the user’s confidence to deal with obstacles such as slopes, uneven pavement, etc. We show how user reported confidence levels could be used to aggregate sidewalk conditions in a routing model to offer user-specific routes. The proposed methodology was developed using a fuzzy approach and is evaluated by manual wheelchair users in Quebec City.

Guest EditorialInternational audienceThe need to combine data from different sources has become increasingly important and is likely to be so in our increasingly networked world. There are many difficulties that must be overcome to ensure the efficient and, to the es=xtent possible, the automated integration of different data sources. Although different goals and constraints may apply to the problem, not infrequently data sources are combined to achieve data of better quality. The process is generall called 'data fusion', other terms may also be applied to the same problem, including 'data integration' and 'data conflation'. For example, the term fusion is used by the image community to embrace the problem of sensor fusion, or by the database community for parts of the interoperability ^roblem. The logic community uses it for knowledge fusion.This special issue, which takes up a set of papers presented at an international workshop in April 2001 in Quebec City, addresses the problem of data fusion from such a wide perspective, that is, including a range of tributary problems and a range of disciplines. The workshop was supported by both European 5th framework projects and several Canadian GEOIDE projects

Automatic semantic segmentation of point clouds observed in a 3D complex urban scene is a challenging issue. Semantic segmentation of urban scenes based on machine learning algorithm requires appropriate features to distinguish objects from mobile terrestrial and airborne LiDAR point clouds in point level. In this paper, we propose a pointwise semantic segmentation method based on our proposed features derived from Difference of Normal and the features “directional height above” that compare height difference between a given point and neighbors in eight directions in addition to the features based on normal estimation. Random forest classifier is chosen to classify points in mobile terrestrial and airborne LiDAR point clouds. The results obtained from our experiments show that the proposed features are effective for semantic segmentation of mobile terrestrial and airborne LiDAR point clouds, especially for vegetation, building and ground classes in an airborne LiDAR point clouds in urban areas.