<|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:"Exploring temporal heterogeneity in an intercity travel network: A comparative study between weekdays and holidays in China."
Abstract:"A largely unexplored application of Big Data in urban contexts is using human mobility data to study temporal heterogeneity in intercity travel networks. Hence, this paper explores China's intercity travel patterns and their dynamics, with a comparison between weekdays and holidays, to contribute to our understanding of these phenomena. Using passenger travel data inferred from Tencent Location Big Data during weekdays (April 11-15, 2016) and National Golden Week (October 1-7, 2016), we compare the spatial patterns of Chinese intercity travel on weekdays and during Golden Week. The results show that the average daily intercity travel during Golden Week is significantly higher than that during weekdays, but the travel distance and degree of network clustering are significantly lower. This indicates temporal heterogeneity in mapping the intercity travel network. On weekdays, the three major cities of Beijing, Shanghai, and Guangzhou take prominent core positions, while cities that are tourism destinations or transportation hubs are more attractive during Golden Week. The reasons behind these findings can be explained by geographical proximity, administrative division (proximity of cultural and policy systems), travel distance, and travel purposes.."
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 = [];
Q4 = [0];
Q5 = [];
Q6 = [1].
Exploring temporal heterogeneity in an intercity travel network: A comparative study between weekdays and holidays in China
A largely unexplored application of Big Data in urban contexts is using human mobility data to study temporal heterogeneity in intercity travel networks. Hence, this paper explores China's intercity travel patterns and their dynamics, with a comparison between weekdays and holidays, to contribute to our understanding of these phenomena. Using passenger travel data inferred from Tencent Location Big Data during weekdays (April 11-15, 2016) and National Golden Week (October 1-7, 2016), we compare the spatial patterns of Chinese intercity travel on weekdays and during Golden Week. The results show that the average daily intercity travel during Golden Week is significantly higher than that during weekdays, but the travel distance and degree of network clustering are significantly lower. This indicates temporal heterogeneity in mapping the intercity travel network. On weekdays, the three major cities of Beijing, Shanghai, and Guangzhou take prominent core positions, while cities that are tourism destinations or transportation hubs are more attractive during Golden Week. The reasons behind these findings can be explained by geographical proximity, administrative division (proximity of cultural and policy systems), travel distance, and travel purposes.