Processing Spatial Keyword Query as a Top-k Aggregation Query

We examine the spatial keyword search problem to retrieve objects of interest that are ranked based on both their spatial proximity to the query location as well as the textual relevance of the object’s keywords. Existing solutions for the problem are based on either using a combination of textual and spatial indexes or using specialized hybrid indexes that integrate the indexing of both textual and spatial attribute values. In this paper, we propose a new approach that is based on modeling the problem as a top-k aggregation problem which enables the design of a scalable and efficient solution that is based on the ubiquitous inverted list index. Our performance study demonstrates that our approach outperforms the state-of-theart hybrid methods by a wide margin

Performance of Coffea arabica F1 hybrids in agroforestry and full-sun cropping systems in comparison with American pure line cultivars

Coffea arabica F1 hybrids derived from crosses between wild Sudan-Ethiopian and American cultivars and propagated by somatic embryogenesis have been obtained in Central America. These new hybrids considerably enhanced the genetic diversity of coffee in the region. We conducted 15 trials to assess whether using hybrids represents substantial genetic progress in terms of productivity in agroforestry and full-sun cropping systems. The new germplasm was grown in the same conditions as the best American cultivar (homozygous pure lines). The results showed that yields of hybrids were earlier and superior to those of American cultivars. The hybrids were also more stable than the American cultivars in all environments. In the agroforestry system, the mean yield of hybrids was 58% higher than that of the American cultivars, while the mean yield of hybrids in the full-sun system was 34% higher. Coffee-based agroforestry systems (AS) are considered effective in protecting the environment in the volcanic cordilleras of Central America. We found that introducing hybrids in coffee-based AS can considerably increase productivity. This finding could be a convincing argument to encourage coffee growers who have adopted the full-sun cropping system to return to agroforestry cropping systems. Finally, the conditions for large-scale dissemination of those new hybrids—which represent a major innovation for C. arabica cropping—was analysed

Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach

Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents’ stay and commuters’ travel exposure to outdoor PM 2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents’ stay activities in each analysis zone, and then focus on commuters and estimate their travel routes with a traffic assignment model. Based on air quality observations from monitoring stations and a spatial interpolation model, we estimate the outdoor PM 2.5 concentrations at a 500-m grid level and map them to road networks. We then estimate the travel exposure for each road segment by multiplying the PM 2.5 concentration and travel time spent on the road. By combining the estimated PM 2.5 exposure and housing price harnessed from online housing transaction platforms, we discover that in the winter, Beijing commuters with low wealth level are exposed to 13% more PM 2.5 per hour than those with high wealth level when staying at home, but exposed to less PM 2.5 by 5% when commuting the same distance (due to lighter traffic congestion in suburban areas). We also find that the residents from the southern suburbs of Beijing have both lower level of wealth and higher stay- and travel- exposure to PM 2.5 , especially in the winter. These findings inform more equitable environmental mitigation policies for future sustainable development in Beijing. Finally, or the first time in the literature, we compare the results of exposure estimated from passive data with subjective measures of perceived air quality (PAQ) from a survey. The PAQ data was collected via a mobile-app. The comparison confirms consistencies in results and the advantages of the big data for air pollution exposure assessments

Fusion helps diversification

A popular strategy for search result diversification is to first retrieve a set of documents utilizing a standard retrieval method and then rerank the results. We adopt a different perspective on the problem, based on data fusion. Starting from the hypothesis that data fusion can improve performance in terms of diversity metrics, we examine the impact of standard data fusion methods on result diversification. We take the output of a set of rankers, optimized for diversity or not, and find that data fusion can significantly improve state-of-the art diversification methods. We also introduce a new data fusion method, called diversified data fusion, which infers latent topics of a query using topic modeling, without leveraging outside information. Our experiments show that data fusion methods can enhance the performance of diversification and DDF significantly outperforms existing data fusion methods in terms of diversity metrics