<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"A novel trajectory similarity-based approach for location prediction."
Abstract:"Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people's future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user's social relations in real world and variances in trajectory and then utilize this association to model the user's mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers' trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity-based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity-based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [0];
Q3 = [0];
Q4 = [0];
Q5 = [0];
Q6 = [1].
A novel trajectory similarity-based approach for location prediction
Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people's future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user's social relations in real world and variances in trajectory and then utilize this association to model the user's mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers' trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity-based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity-based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches.