By Fernando Bacao, Maribel Yasmina Santos, Marco Painho
This is a booklet is a suite of articles that might be submitted as complete papers to the AGILE annual foreign convention. those papers wade through a rigorous evaluation strategy and document unique and unpublished basic medical learn. these released hide major learn within the area of geographic info technological know-how platforms. This 12 months the focal point is on geographic details technology as an enabler of smarter towns and groups, hence we think contributions that aid visualize the position and contribution of GI technology of their development.
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Extra resources for AGILE 2015: Geographic Information Science as an Enabler of Smarter Cities and Communities
A Semantic Region Growing Algorithm: Extraction of Urban Settings 21 question of how to ﬁnd such settings is, to our knowledge, not well addressed and this paper presents preliminary results of an attempt to extract urban settings based on activities (or affordances). The underlying hypothesis is that people form regions by mentally grouping space into conceptually homogeneous areas in terms of the activities they potentially offer. Therefore, place types (represented by tags) are employed as a means of computing potential activities.
Fine-grained or signiﬁcant differences in place constellations can reveal how much the composition of a setting is suitable for someone’s preferences, or can be used to identify flaws in the naturally evolved or planned structure of a city. For instance, when people are required to drive with a car due to an inefﬁcient public transport system, or out of personal necessity, shopping areas with parking spots are certainly more attractive destinations. Cities without dedicated parking spots in the vicinity of shopping areas will ideally have an efﬁcient public transport.
4. Using the four largest cities in the United States as case studies, we discuss the influence of weights and semantic aspects of the approach. 44 C. , a city). We hence interpret every link as a pointer to a central place, where (a) the number of references in that speciﬁc link reflects the degree of dependency between the respective places, and (b) the total number of incoming references reflects the centrality—or relative importance—of a place. Following these assumptions, we can reveal the structure around a central place P as follows: • Retrieve all links pointing to P.