In recent years,there has been widespread concern regarding the carbon footprint(CF)of food waste due to the key impact of CF on climate change,particularly as China’s food waste is rising with its economic developme...In recent years,there has been widespread concern regarding the carbon footprint(CF)of food waste due to the key impact of CF on climate change,particularly as China’s food waste is rising with its economic development.China has the largest scale of higher education in the world,and the amount of food waste in university canteens is considerable and cannot be ignored.This study attempts to assess the carbon footprint(CF)of food waste at Chinese universities for the first time based on a national survey.It is estimated that 1.55 million tons of food were wasted in Chinese university canteens in 2018,based on 9,192 samples covering 29 provinces in China.The associated CF was 2.51 Mt CO2eq.The top two food categories contributing to the total CF were meat and grains,accounting for 46.28%and 36.52%,respectively.Furthermore,the location of the university was significantly associated with the CF of plate waste.It also indicated that household income,meal satisfaction,sex,education,meal days,and food-saving campaigns were important factors influencing the CF of food waste.This study highlights areas that can help reduce the environmental impact of plate waste.It also provides targeted measures to reduce the associated CF of food waste in Chinese universities.展开更多
User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding ...User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.展开更多
基金The work was supported by the Chinese Academy of Sciences Key Technology Talent Program[2020M680659]National Natural Science Foundation of China[4217011372]Youth Innovation Promotion Association,Chinese Academy of Sciences[Ling-en Wang].
文摘In recent years,there has been widespread concern regarding the carbon footprint(CF)of food waste due to the key impact of CF on climate change,particularly as China’s food waste is rising with its economic development.China has the largest scale of higher education in the world,and the amount of food waste in university canteens is considerable and cannot be ignored.This study attempts to assess the carbon footprint(CF)of food waste at Chinese universities for the first time based on a national survey.It is estimated that 1.55 million tons of food were wasted in Chinese university canteens in 2018,based on 9,192 samples covering 29 provinces in China.The associated CF was 2.51 Mt CO2eq.The top two food categories contributing to the total CF were meat and grains,accounting for 46.28%and 36.52%,respectively.Furthermore,the location of the university was significantly associated with the CF of plate waste.It also indicated that household income,meal satisfaction,sex,education,meal days,and food-saving campaigns were important factors influencing the CF of food waste.This study highlights areas that can help reduce the environmental impact of plate waste.It also provides targeted measures to reduce the associated CF of food waste in Chinese universities.
文摘User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.
