<|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:"An Analysis of Urban Sprawl and Prediction of Future Urban Town in Urban Area of Developing Nation: Case Study in India."
Abstract:"Cities and towns have been expanding everywhere around the globe. Rural areas are converting rapidly into towns and towns into cities, which increases urban expansion. Population pressure and industrialization are the key factors behind such a phenomenal change in spatial structure. Fringe or the suburbs areas are pressurized more by population as compared to the inner pocket of the metropolitan cities. The population increased rapidly in India from 68.33 million in 1981 to 121.01 million in 2011 (Census of India, 2011,,). It is predicted that India will hold the largest population of the world by the year 2028 which will force to people for moving from the rural area to the urban areas, for availing basic amenities (Sisodia et al., in: Berretti, Thampi Srivastava (eds) Intelligent systems technologies and applications, Springer, Cham, 2016). The huge migration will increase the demand for more space in cities which results in the situation of illegal, unplanned and unrestrained growth which is called urban sprawl. Therefore, planners should predict future urban sprawl in the city for sustainable development. Barasat Municipality from Kolkata Municipal Corporation has been taken for the case study. Satellite image of 1990, 2000, 2010 and 2017 has been used to detect the urban sprawl. Shannon entropy, urbanization index and built-up density have been used to evaluate the degree of transformation of land use pattern. In the study area, the study shows that the built-up area is rapidly increasing by 22.83% vacant land, but agricultural land decreased by almost - 12% from 1990 to 2017. Shannon entropy value represents the built-up pattern to show the urban sprawl in different directions of the study area. Many villages around the Barasat Municipality are converting into Census town and increasing built-up area in disperse pattern.."
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 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [3];
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Q6 = [1].
An Analysis of Urban Sprawl and Prediction of Future Urban Town in Urban Area of Developing Nation: Case Study in India
Cities and towns have been expanding everywhere around the globe. Rural areas are converting rapidly into towns and towns into cities, which increases urban expansion. Population pressure and industrialization are the key factors behind such a phenomenal change in spatial structure. Fringe or the suburbs areas are pressurized more by population as compared to the inner pocket of the metropolitan cities. The population increased rapidly in India from 68.33 million in 1981 to 121.01 million in 2011 (Census of India, 2011,,). It is predicted that India will hold the largest population of the world by the year 2028 which will force to people for moving from the rural area to the urban areas, for availing basic amenities (Sisodia et al., in: Berretti, Thampi Srivastava (eds) Intelligent systems technologies and applications, Springer, Cham, 2016). The huge migration will increase the demand for more space in cities which results in the situation of illegal, unplanned and unrestrained growth which is called urban sprawl. Therefore, planners should predict future urban sprawl in the city for sustainable development. Barasat Municipality from Kolkata Municipal Corporation has been taken for the case study. Satellite image of 1990, 2000, 2010 and 2017 has been used to detect the urban sprawl. Shannon entropy, urbanization index and built-up density have been used to evaluate the degree of transformation of land use pattern. In the study area, the study shows that the built-up area is rapidly increasing by 22.83% vacant land, but agricultural land decreased by almost - 12% from 1990 to 2017. Shannon entropy value represents the built-up pattern to show the urban sprawl in different directions of the study area. Many villages around the Barasat Municipality are converting into Census town and increasing built-up area in disperse pattern.