<|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:"Identification of spatial and functional interactions in Beijing based on trajectory data."
Abstract:"The spatial interactions in a city help us understand how the city spaces are structured. Human needs change constantly throughout the day, leading to changing functions of urban space and spatial interactions. Most existing studies on spatial interaction are confined to static interaction, which does not reflect the dynamic spatial interactions and urban space functions. Taxies are an important means of urban transportation, and their trajectories are time sensitive. The clusters of pick-up and drop-off points of taxies directly reflect the spatiotemporal human mobility pattern in a city, and their implicit semantic information can be used to infer the passengers' activities. Using Beijing as a case study, this paper employs taxi origin and destination trajectory data to explore the dynamic spatial and functional interaction patterns. First, we apply tensor decomposition to obtain the spatial and temporal patterns of human mobility. Second, geotagged Weibo texts are incorporated to extract the dynamic urban functional areas. Then, we obtain the spatial and functional interactions by trajectory clustering and visualization. The results reveal two daily patterns and four hourly patterns of human mobility. The spatial interactions and the functional interaction reveal the spatial patterns and semantics of human taxi travel behaviors in different time periods.."
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 = [];
Q3 = [0];
Q4 = [0];
Q5 = [];
Q6 = [1].
Identification of spatial and functional interactions in Beijing based on trajectory data
The spatial interactions in a city help us understand how the city spaces are structured. Human needs change constantly throughout the day, leading to changing functions of urban space and spatial interactions. Most existing studies on spatial interaction are confined to static interaction, which does not reflect the dynamic spatial interactions and urban space functions. Taxies are an important means of urban transportation, and their trajectories are time sensitive. The clusters of pick-up and drop-off points of taxies directly reflect the spatiotemporal human mobility pattern in a city, and their implicit semantic information can be used to infer the passengers' activities. Using Beijing as a case study, this paper employs taxi origin and destination trajectory data to explore the dynamic spatial and functional interaction patterns. First, we apply tensor decomposition to obtain the spatial and temporal patterns of human mobility. Second, geotagged Weibo texts are incorporated to extract the dynamic urban functional areas. Then, we obtain the spatial and functional interactions by trajectory clustering and visualization. The results reveal two daily patterns and four hourly patterns of human mobility. The spatial interactions and the functional interaction reveal the spatial patterns and semantics of human taxi travel behaviors in different time periods.