<|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:"Visual exploration of urban functions via spatio-temporal taxi OD data."
Abstract:"City is a complex system containing various kinds of functional areas. They are always defined by government planning and refining based on the actual requirements of citizens, which are of significant importance to urban developments, ranging from environmental governance and rail transportation to disease prevention and public security. Taxi is a major means of urban transportation, and the taxi trips record human behaviors and mobility patterns, which offer a valuable opportunity for users to get insights into urban functions. Therefore, we propose a visual analysis system in this paper, for an insightful exploration of urban functions based on spatio-temporal taxi OD trips. First, a matrix is defined to restructure spatio-temporal attributes of taxi OD data, and a Non-negative Matrix Factorization(NMF) is applied to classify and identify urban functional areas. Then, a set of visual encodings are designed to visualize mobility patterns of urban areas with different functions, such as the radial chart, the timeline view and the force-directed view. In addition, the spatio-temporal clustering model and the visual designs are all implemented in a visualization framework, with a set of convenient interactions integrated, enabling users to quickly identify areas of different urban functions and analyze the human mobility patterns across different urban areas. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system.."
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 = [0];
Q2 = [0];
Q3 = [];
Q4 = [0];
Q5 = [1];
Q6 = [1].
Visual exploration of urban functions via spatio-temporal taxi OD data
City is a complex system containing various kinds of functional areas. They are always defined by government planning and refining based on the actual requirements of citizens, which are of significant importance to urban developments, ranging from environmental governance and rail transportation to disease prevention and public security. Taxi is a major means of urban transportation, and the taxi trips record human behaviors and mobility patterns, which offer a valuable opportunity for users to get insights into urban functions. Therefore, we propose a visual analysis system in this paper, for an insightful exploration of urban functions based on spatio-temporal taxi OD trips. First, a matrix is defined to restructure spatio-temporal attributes of taxi OD data, and a Non-negative Matrix Factorization(NMF) is applied to classify and identify urban functional areas. Then, a set of visual encodings are designed to visualize mobility patterns of urban areas with different functions, such as the radial chart, the timeline view and the force-directed view. In addition, the spatio-temporal clustering model and the visual designs are all implemented in a visualization framework, with a set of convenient interactions integrated, enabling users to quickly identify areas of different urban functions and analyze the human mobility patterns across different urban areas. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system.