IMPORTANCE Reduced availability of tobacco outlets is hypothesized to reduce smoking, but longitudinal evidence on this issue is scarce. OBJECTIVE To examine whether changes in distance from home to tobacco outlet are associated with changes in smoking behaviors. DESIGN, SETTING, AND PARTICIPANTS The data from 2 prospective cohort studies included geocoded residential addresses, addresses of tobacco outlets, and responses to smoking surveys in 2008 and 2012 (the Finnish Public Sector [FPS] study, n = 53 755) or 2003 and 2012 (the Health and Social Support [HeSSup] study, n = 11 924). All participants were smokers or ex-smokers at baseline. We used logistic regression in between-individual analyses and conditional logistic regression in case-crossover design analyses to examine change in walking distance from home to the nearest tobacco outlet as a predictor of quitting smoking in smokers and smoking relapse in ex-smokers. Study-specific estimates were pooled using fixed-effect meta-analysis. EXPOSURES Walking distance from home to the nearest tobacco outlet. MAIN OUTCOMES AND MEASURES Quitting smoking and smoking relapse as indicated by self-reported current and previous smoking at baseline and follow-up. RESULTS Overall, 20 729 men and women (age range 18-75 years) were recruited. Of the 6259 and 2090 baseline current smokers, 1744 (28%) and 818 (39%) quit, and of the 8959 and 3421 baseline ex-smokers, 617 (7%) and 205 (6%) relapsed in the FPS and HeSSup studies, respectively. Among the baseline smokers, a 500-m increase in distance from home to the nearest tobacco outlet was associated with a 16% increase in odds of quitting smoking in the between-individual analysis (pooled odds ratio, 1.16; 95% CI, 1.05-1.28) and 57% increase in within-individual analysis (pooled odds ratio, 1.57; 95% CI, 1.32-1.86), after adjusting for changes in self-reported marital and working status, substantial worsening of financial situation, illness in the family, and own health status. Increase in distance to the nearest tobacco outlet was not associated with smoking relapse among the ex-smokers. CONCLUSIONS AND RELEVANCE These data suggest that increase in distance from home to the nearest tobacco outlet may increase quitting among smokers. No effect of change in distance on relapse in ex-smokers was observed.

This research explores the value of personas for supporting professional advertisers to design adverts for social media. We test if a personified user group (PUG), when provided to online ad designers, results in better ad performance than when using a non-personified user group (NUG) that had no face picture or name. Our experiment has 30 participants that created Facebook ads using both PUG and NUG. We found that using PUG did increase advertising click performance of ads created by people who are more experienced with ads and personas. Moreover, an analysis of the ad texts showed that the use of PUG increased the empathy of the created ads, supporting the foundational empathy benefit cited in HCI literature. However, the use of PUG did not significantly increase purchase intent. The results imply that using PUG for online ad design evokes more empathy and improves click-through performance. More empathetic ads can have a positive impact on social media users, given that they appear to increase relevance.

Presence of ground surface stones is one indicator of economically important landmass deposits in the Arctic. The other indicator is a geomorphological category of the area. This work shows that ground stoniness can be automatically predicted with practical accuracy. Northern forests have less biomass and foliage, thus direct analysis of stoniness is possible from airborne laser scanning (ALS) data. A test set of 88 polygons covering 3.3 km 2 was human-classified and a method was developed to perform the stoniness classification over this set. The local curvature of the surface is approximated directly from the point cloud data without generating the Digital Terrain Model (DTM). The method performs well with area under curve AU C = 0.85 from Leave-Pair-Out cross-validation, and is rather insensitive to missing data, moderate forest cover and double-scanned areas.

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