Preparing Simplified 3D Scenes of Multiple LODs of Buildings in Urban Areas Based on a Raster Approach and Information Theory

Cannabis use does not show homogeneous patterns in a country. In particular, urbanization appears to influence prevalence rates, with higher rates in urban areas. A hierarchical linear model (HLM) was employed to analyze these structural influences on individuals in Switzerland. Data for this analysis were taken from the Switzerland survey of Health Behavior in School-Aged Children (HBSC) Study, the most recent survey to assess drug use in a nationally representative sample of 3473 15-year-olds. A total of 1487 male and 1620 female students indicated their cannabis use and their attributions of drug use to friends. As second level variables we included address density in the 26 Swiss Cantons as an indicator of urbanization and officially recorded offences of cannabis use in the Cantons as an indicator of repressive policy. Attribution of drug use to friends is highly correlated with cannabis use. The correlation is even more pronounced in urban Cantons. However, no association between recorded offences and cannabis use was found. The results suggest that structural variables influence individuals. Living in an urban area effects the attribution of drug use to friends. On the other hand repressive policy does not affect individual use.

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Constructing and Deconstructing Concepts

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000337 ◽

2016 ◽

Vol 63 (5) ◽

pp. 249-262 ◽

Cited By ~ 2

Author(s):

Charles A. Doan ◽

Ronaldo Vigo

Keyword(s):

Information Theory ◽

Model Performance ◽

Unsupervised Classification ◽

Family Resemblance ◽

Category Membership ◽

Formal Models ◽

Human Beings ◽

One Dimensional ◽

Relational Patterns ◽

Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

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