<|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:"Big Data-Driven Approach to Analyzing Spatio-Temporal Mobility Pattern."
Abstract:"It is imperative to understand human movement and behavior, from epidemic monitoring to complex communications. So far, most research and studies on investigating and interpreting human movements have traditionally depended on private and accumulated data such as mobile records. In this work, social network data is suggested as a proxy for human mobility, as it relies on a large amount of publicly accessible data. A mechanism for urban mobility mining and extraction scheme is proposed in this research to shed light on the importance and benefits of the publicly available social network data. Given the potential value of the Big Data obtained from social network platforms, we sought to demonstrate the process of analyzing and understanding human mobility patterns and activity behavior in urban areas through the social network data. Human mobility is far from spontaneous, follows well-defined statistical patterns. This research provides evidence of spatial and temporal regularity in human mobility patterns by examining daily individual trajectories of users covering an average time span of three years (2018 to 2020). Despite the diversity of individual movements history, we concluded that humans follow simple, reproducible patterns. Additionally, we studied and evaluated the effect of COVID-19 on human mobility and activity behavior in urban areas and established a strong association between human mobility and COVID-19 spread. Numerous years of mobility data analysis can reveal well-established trends, such as social or cultural activities, which serve as a baseline for detecting anomalies and changes in human mobility and activity behavior.."
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 = [];
Q5 = [1];
Q6 = [1].
Big Data-Driven Approach to Analyzing Spatio-Temporal Mobility Pattern
It is imperative to understand human movement and behavior, from epidemic monitoring to complex communications. So far, most research and studies on investigating and interpreting human movements have traditionally depended on private and accumulated data such as mobile records. In this work, social network data is suggested as a proxy for human mobility, as it relies on a large amount of publicly accessible data. A mechanism for urban mobility mining and extraction scheme is proposed in this research to shed light on the importance and benefits of the publicly available social network data. Given the potential value of the Big Data obtained from social network platforms, we sought to demonstrate the process of analyzing and understanding human mobility patterns and activity behavior in urban areas through the social network data. Human mobility is far from spontaneous, follows well-defined statistical patterns. This research provides evidence of spatial and temporal regularity in human mobility patterns by examining daily individual trajectories of users covering an average time span of three years (2018 to 2020). Despite the diversity of individual movements history, we concluded that humans follow simple, reproducible patterns. Additionally, we studied and evaluated the effect of COVID-19 on human mobility and activity behavior in urban areas and established a strong association between human mobility and COVID-19 spread. Numerous years of mobility data analysis can reveal well-established trends, such as social or cultural activities, which serve as a baseline for detecting anomalies and changes in human mobility and activity behavior.