<|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:"Visual exploration of urban functional zones based on augmented nonnegative tensor factorization."
Abstract:"A city contains a variety of different urban functions with referring to the purpose of land use to support the diverse needs of urban residents, such as residence, working and recreation. Exploring urban functional zones is a critical task, which provides valuable applications for business site selection, transportation management and urban planning. It is well known that location information and human mobility semantics both are significant factors for identifying urban functional zones, just considering one factor is not effective. However, most of existing techniques capture the transformation of functional zones and interpret the results based solely on location or mobility semantics, and lack the capacity to deal with the multifaceted features of urban data. To tackle these problems, in this paper, we propose an interactive visual analytics system for effectively exploring urban functional zones based on spatio-temporal OD data and Points of Interest data. We first adaptively partition the territory into region units based on adaptive blue noise sampling method, extract POI feature matrix (location information) as a prior knowledge, model multidimensional spatio-temporal OD data as a tensor for addressing multifaceted features and propose an augment tensor-based algorithm that enables users to simultaneously combine mobility semantics and inherent location information for identifying functional zones. In addition, we design a set of visual encodings to better understand and interpret the results in a visual and intuitive manner. This system has been demonstrated using two case studies with a real-world dataset of HangZhou city and domain-expert interviews.."
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 = [0];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [1].
Visual exploration of urban functional zones based on augmented nonnegative tensor factorization
A city contains a variety of different urban functions with referring to the purpose of land use to support the diverse needs of urban residents, such as residence, working and recreation. Exploring urban functional zones is a critical task, which provides valuable applications for business site selection, transportation management and urban planning. It is well known that location information and human mobility semantics both are significant factors for identifying urban functional zones, just considering one factor is not effective. However, most of existing techniques capture the transformation of functional zones and interpret the results based solely on location or mobility semantics, and lack the capacity to deal with the multifaceted features of urban data. To tackle these problems, in this paper, we propose an interactive visual analytics system for effectively exploring urban functional zones based on spatio-temporal OD data and Points of Interest data. We first adaptively partition the territory into region units based on adaptive blue noise sampling method, extract POI feature matrix (location information) as a prior knowledge, model multidimensional spatio-temporal OD data as a tensor for addressing multifaceted features and propose an augment tensor-based algorithm that enables users to simultaneously combine mobility semantics and inherent location information for identifying functional zones. In addition, we design a set of visual encodings to better understand and interpret the results in a visual and intuitive manner. This system has been demonstrated using two case studies with a real-world dataset of HangZhou city and domain-expert interviews.