<|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:"Bursty visitation of locations in human mobility."
Abstract:"Understanding individuals' travel patterns has great impact on practical issues such as traffic control and urban planning. Here, we analyze a 4G dataset of 1000 randomly selected individuals in Shijiazhuang city, China during half month which contains the position information of these cell phone users in each second and GPS logs of 182 volunteers in a period of over five years. We find that the dynamics of locations' visitations is characterized by bursts, namely the distributions of the relative variation of visitation traffic per day, week display a long tail. On that basis we propose the Exploration and Rank-shift Return model combined the classic Exploration and Preferential Return model with a rank-shift mechanism where every location may move up to a higher ranking position with probability. The model qualitatively recovers the statistical properties observed in the empirical data and reproduces the existence of bursty visitation of locations. (C) 2020 Elsevier B.V. All rights reserved.."
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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 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [0];
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Q6 = [1].
Bursty visitation of locations in human mobility
Understanding individuals' travel patterns has great impact on practical issues such as traffic control and urban planning. Here, we analyze a 4G dataset of 1000 randomly selected individuals in Shijiazhuang city, China during half month which contains the position information of these cell phone users in each second and GPS logs of 182 volunteers in a period of over five years. We find that the dynamics of locations' visitations is characterized by bursts, namely the distributions of the relative variation of visitation traffic per day, week display a long tail. On that basis we propose the Exploration and Rank-shift Return model combined the classic Exploration and Preferential Return model with a rank-shift mechanism where every location may move up to a higher ranking position with probability. The model qualitatively recovers the statistical properties observed in the empirical data and reproduces the existence of bursty visitation of locations. (C) 2020 Elsevier B.V. All rights reserved.