<|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:"Interplay between urban communities and human-crowd mobility: A study using contributed geospatial data sources."
Abstract:"An intense process of urbanization, witnessed particularly in the last decade, has stressed the need to comprehend human mobility behavior in urban settings. Although the emergence of contributed geospatial data (i.e., pervasive activity-based data) has contributed to substantial progress toward understanding human activity, the relationship between human-crowd mobility and the functional structure of a city is not yet well understood. In this context, the present research focuses on the intra-urban origin-destination matrix modeling founded on a combination of two major crowdsourced datasets as well as the inclusion of urban communities' structure. Specifically, the well-known radiation and PWO models were modified through first, identifying the communities embedded in the cyberspace network then employing the identified hierarchical structure of the spatial-interaction network for the formulation of the users' movement network and second, imposing proper input variables including the telecommunication activity volume and check-in frequency. The results obtained by various empirical analyses suggest that the modified community-constrained origin-destination flow estimation models exhibit better performance levels than those of alternative conventional mobility models.."
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 = [0];
Q3 = [2];
Q4 = [0];
Q5 = [];
Q6 = [1].
Interplay between urban communities and human-crowd mobility: A study using contributed geospatial data sources
An intense process of urbanization, witnessed particularly in the last decade, has stressed the need to comprehend human mobility behavior in urban settings. Although the emergence of contributed geospatial data (i.e., pervasive activity-based data) has contributed to substantial progress toward understanding human activity, the relationship between human-crowd mobility and the functional structure of a city is not yet well understood. In this context, the present research focuses on the intra-urban origin-destination matrix modeling founded on a combination of two major crowdsourced datasets as well as the inclusion of urban communities' structure. Specifically, the well-known radiation and PWO models were modified through first, identifying the communities embedded in the cyberspace network then employing the identified hierarchical structure of the spatial-interaction network for the formulation of the users' movement network and second, imposing proper input variables including the telecommunication activity volume and check-in frequency. The results obtained by various empirical analyses suggest that the modified community-constrained origin-destination flow estimation models exhibit better performance levels than those of alternative conventional mobility models.