Mapping as a knowledge translation tool for Ontario Early Years Centres: views from data analysts and managers

<p>Abstract</p> <p>Background</p> <p>Local Ontario Early Years Centres (OEYCs) collect timely and relevant local data, but knowledge translation is needed for the data to be useful. Maps represent an ideal tool to interpret local data. While geographic information system (GIS) technology is available, it is less clear what users require from this technology for evidence-informed program planning. We highlight initial challenges and opportunities encountered in implementing a mapping innovation (software and managerial decision-support) as a knowledge translation strategy.</p> <p>Methods</p> <p>Using focus groups, individual interviews and interactive software development events, we taped and transcribed verbatim our interactions with nine OEYCs in Ontario, Canada. Research participants were composed of data analysts and their managers. Deductive analysis of the data was based on the Ottawa Model of Research Use, focusing on the innovation (the mapping tool and maps), the potential adopters, and the environment.</p> <p>Results</p> <p>Challenges associated with the innovation included preconceived perceptions of a steep learning curve with GIS software. Challenges related to the potential adopters included conflicting ideas about tool integration into the organization and difficulty with map interpretation. Lack of funds, lack of availability of accurate data, and unrealistic reporting requirements represent environmental challenges.</p> <p>Conclusion</p> <p>Despite the clear need for mapping software and maps, there remain several challenges to their effective implementation. Some can be modified, while other challenges might require attention at the systemic level. Future research is needed to identify barriers and facilitators related to using mapping software and maps for decision-making by other users, and to subsequently develop mapping best practices guidelines to assist community-based agencies in circumventing some challenges, and support information equity across a region.</p

Fast morphological attribute operations using Tarjan's union-find algorithm

Morphological attribute openings and closings and related operators are generalizations of the area opening and closing, and allow filtering of images based on a wide variety of shape or size based criteria. A fast union-find algorithm for the computation of these operators is presented in this paper. The new algorithm has a worst case time complexity of O(N logN) where N is the image size, as opposed to O(N^2 logN) for the existing algorithm. Memory requirements are O(N) for both algorithms