<|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:"Delineating urbanization source-sink regions in China: Evidence from mobile app data."
Abstract:"Traditional census data are ill-suited for uncovering the true population patterns and underlying social and economic dynamics in China as the census relies on information of population with registered household status. A large number of migrant workers are registered rural residents but spend most of a year working in cities that are hundreds or even thousands of miles away. It is termed annual spatial mismatch here for the separation of registered residence and workplace in China, in contrast to spatial mismatch that is known in the urban commuting literature in the west but on a daily basis. Big geo-data, such as the mobile app data, afford us a rare opportunity to examine this unique phenomenon. Specifically, this research uses a mobile app dataset of two epochs, i.e., prior to and during the Chinese Spring Festival, to capture the population patterns before and after the migrant workers return home, respectively. The difference between them reflects distinctive roles of an area plays in labor market, termed source-sink areas. A GIS-automated regionalization method is used to delineate China into hierarchical source-sink areas, characterizing various urbanization levels or distinctive roles in labor market. The study demonstrates the value of using human mobility data in urban and regional analysis on issues that were previously infeasible, especially in study areas without reliable data.."
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 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:

Delineating urbanization source-sink regions in China: Evidence from mobile app data
Traditional census data are ill-suited for uncovering the true population patterns and underlying social and economic dynamics in China as the census relies on information of population with registered household status. A large number of migrant workers are registered rural residents but spend most of a year working in cities that are hundreds or even thousands of miles away. It is termed annual spatial mismatch here for the separation of registered residence and workplace in China, in contrast to spatial mismatch that is known in the urban commuting literature in the west but on a daily basis. Big geo-data, such as the mobile app data, afford us a rare opportunity to examine this unique phenomenon. Specifically, this research uses a mobile app dataset of two epochs, i.e., prior to and during the Chinese Spring Festival, to capture the population patterns before and after the migrant workers return home, respectively. The difference between them reflects distinctive roles of an area plays in labor market, termed source-sink areas. A GIS-automated regionalization method is used to delineate China into hierarchical source-sink areas, characterizing various urbanization levels or distinctive roles in labor market. The study demonstrates the value of using human mobility data in urban and regional analysis on issues that were previously infeasible, especially in study areas without reliable data.