<|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:"Identifying Region-Wide Functions Using Urban Taxicab Trajectories."
Abstract:"With the urban development and enlargement, various regions such as residential zones and administrative districts now appear as parts of cities. People exhibit different mobility patterns in each region, which is closely relevant to region-wide functions. In this article, we propose a scheme to discover region-wide functions using large-scale Shanghai taxicab trajectories that capture enormous traces for more than 13,000 taxicabs over a period of about 3 years. We investigate these taxicab trajectories and conduct an extensive preliminary study. Then, we divide the city into disjointed regions using Voronoi decomposition. By incorporating people's pick-up and drop-off information, we refine the Voronoi partitioning results to identify region-wide functional areas. Finally, we study people's movement frequency on weekdays and weekends for every kind of urban functional regions. We also look into human mobility within or across the identified urban functional regions. Experimental results show that human movement is bounded with the function of urban regions, and more than 90% of people visit neighboring (less than 20km travel distance) functional regions with high probability.."
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];
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Q4 = [0];
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Q6 = [1].
Identifying Region-Wide Functions Using Urban Taxicab Trajectories
With the urban development and enlargement, various regions such as residential zones and administrative districts now appear as parts of cities. People exhibit different mobility patterns in each region, which is closely relevant to region-wide functions. In this article, we propose a scheme to discover region-wide functions using large-scale Shanghai taxicab trajectories that capture enormous traces for more than 13,000 taxicabs over a period of about 3 years. We investigate these taxicab trajectories and conduct an extensive preliminary study. Then, we divide the city into disjointed regions using Voronoi decomposition. By incorporating people's pick-up and drop-off information, we refine the Voronoi partitioning results to identify region-wide functional areas. Finally, we study people's movement frequency on weekdays and weekends for every kind of urban functional regions. We also look into human mobility within or across the identified urban functional regions. Experimental results show that human movement is bounded with the function of urban regions, and more than 90% of people visit neighboring (less than 20km travel distance) functional regions with high probability.