<|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:"Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model."
Abstract:"Pattern clustering is an effective method for exploring the regularities of human mobility scheduling and daily activities. There still remains the challenge of measuring the similarity between pairs of activity patterns that are in the form of categorical time series sequences. Existing studies measured similarity using binary vector or edit distance, but these methods were insufficient to characterize routine arrangement and time scheduling of daily activities. To address this issue, we cluster daily activities and identify regular patterns using a Markov-chain-based mixture model, which captures features of activity scheduling by Markov transition matrix as well as measures similarity with probability distribution. Logistic regression models are further built to test hypothetical relationships between activity patterns and socio-demographic characteristics. Results show there are three main human activity patterns in terms of daily routine arrangement and activity scheduling: working-educationoriented (WE-oriented), recreation-shopping-oriented (RS-oriented), and schooling-drop-off/pick-up-oriented (SDP-oriented). People in the WE-oriented pattern mainly engage with regular home-based commuting trips, while people in the RS-oriented pattern are involved in home-based shopping and entertainment events. With regard to the SDP-oriented pattern, people plan their trips under a restricted scheduling of schooling pickup/ drop-off. Each pattern clearly indicates long-term regularity of daily activity behaviors and corresponds to specific socio-demographics. Distinguishing three categories of residents with distinct life styles, this research would help accommodate travel demand from different groups of people in urban transportation planning.."
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 = [3,7];
Q3 = [];
Q4 = [0];
Q5 = [];
Q6 = [1].
Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model
Pattern clustering is an effective method for exploring the regularities of human mobility scheduling and daily activities. There still remains the challenge of measuring the similarity between pairs of activity patterns that are in the form of categorical time series sequences. Existing studies measured similarity using binary vector or edit distance, but these methods were insufficient to characterize routine arrangement and time scheduling of daily activities. To address this issue, we cluster daily activities and identify regular patterns using a Markov-chain-based mixture model, which captures features of activity scheduling by Markov transition matrix as well as measures similarity with probability distribution. Logistic regression models are further built to test hypothetical relationships between activity patterns and socio-demographic characteristics. Results show there are three main human activity patterns in terms of daily routine arrangement and activity scheduling: working-educationoriented (WE-oriented), recreation-shopping-oriented (RS-oriented), and schooling-drop-off/pick-up-oriented (SDP-oriented). People in the WE-oriented pattern mainly engage with regular home-based commuting trips, while people in the RS-oriented pattern are involved in home-based shopping and entertainment events. With regard to the SDP-oriented pattern, people plan their trips under a restricted scheduling of schooling pickup/ drop-off. Each pattern clearly indicates long-term regularity of daily activity behaviors and corresponds to specific socio-demographics. Distinguishing three categories of residents with distinct life styles, this research would help accommodate travel demand from different groups of people in urban transportation planning.