<|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:"Comparison of the spatiotemporal mobility patterns among typical subgroups of the actual population with mobile phone data: A case study of Beijing."
Abstract:"The study of urban dynamics from spatio-temporal information contained in massive human-tracking data has become a hotspot in research. As numerous human mobility patterns and fine-grained varying dynamics of the urban environment have been ascertained using various data and methods, the people shaping such diverse urban vitality remain poorly understood. This study aimed to profile different mobility patterns of typical subgroups in the actual population of the city and then quantify the differences among subgroups. Based on one-month data of mobile phone users with the recorded presence in Beijing, 8 subgroups with different purposes of visit and related spatio-temporal patterns were ascertained. By combing the short-term phenomenon with long-term observations, the varying urban dynamics structured by residents, usual travelers and visitors over time were explained. The mobility patterns of different subgroups were compared according to spatial and temporal indicators, including population distribution by time, average length of stay, distribution of visited places by ring roads, and Land-use Visitation Intensity. These findings have been successfully referenced in Beijing's planning practices. This study is of high implication to optimize the allocation of urban resources and enhance the capacity of urban facilities to satisfy the demands of the actual population in the city.."
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 = [2];
Q4 = [0];
Q5 = [1];
Q6 = [1].
Comparison of the spatiotemporal mobility patterns among typical subgroups of the actual population with mobile phone data: A case study of Beijing
The study of urban dynamics from spatio-temporal information contained in massive human-tracking data has become a hotspot in research. As numerous human mobility patterns and fine-grained varying dynamics of the urban environment have been ascertained using various data and methods, the people shaping such diverse urban vitality remain poorly understood. This study aimed to profile different mobility patterns of typical subgroups in the actual population of the city and then quantify the differences among subgroups. Based on one-month data of mobile phone users with the recorded presence in Beijing, 8 subgroups with different purposes of visit and related spatio-temporal patterns were ascertained. By combing the short-term phenomenon with long-term observations, the varying urban dynamics structured by residents, usual travelers and visitors over time were explained. The mobility patterns of different subgroups were compared according to spatial and temporal indicators, including population distribution by time, average length of stay, distribution of visited places by ring roads, and Land-use Visitation Intensity. These findings have been successfully referenced in Beijing's planning practices. This study is of high implication to optimize the allocation of urban resources and enhance the capacity of urban facilities to satisfy the demands of the actual population in the city.