<|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:"ViFi-MobiScanner: Observe Human Mobility via Vehicular Internet Service."
Abstract:"Exploring human mobility is essential for urban applications. To observe human mobility, various data-driven techniques based on different data sources, such as cell phone and transportation data, have been proposed. This paper investigates human mobility through the emerging vehicular Internet service on public bus system. The key idea is that if a passenger is using WiFi on the bus or his/her WiFi device is activated in the background, we know that the passenger is traveling on the bus. By fusing the network events generated by WiFi devices with the data from the automatic fare collection (AFC) system, and the bus GPS information, we exploit not only the origin but also the destination of a passenger. Based on this idea, we develop a novel system called ViFi-MobiScanner which consists of about 4, 800 mobile routers distributed in a city with 1, 992 KM2 urban area. We develop an ID matching algorithm that matches part of the users' network identities and their smartcard identities anonymously. As a result, we have built a set of labeled samples with the reference of observation from smartcard data and use them to train a classifier to infer users mobility from their network activities. We evaluate ViFi-MobiScanner with both field tests and collected datasets associated with 168 million network events, 3.6 million trips, and 1.4 million users. The evaluation results show that ViFi-MobiScanner increases the observability on the passengers and trips by about 53.9% and 48.1% over the smartcard observations. ViFi-MobiScanner also helps to estimate the passengers' destination that cannot be observed by current smartcard systems and the estimation can be accomplished in minutes. Thus it expands the observability of mobility in object, temporal and spatial dimensions and provides unique insights on human mobility at metropolitan scales.."
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].
ViFi-MobiScanner: Observe Human Mobility via Vehicular Internet Service
Exploring human mobility is essential for urban applications. To observe human mobility, various data-driven techniques based on different data sources, such as cell phone and transportation data, have been proposed. This paper investigates human mobility through the emerging vehicular Internet service on public bus system. The key idea is that if a passenger is using WiFi on the bus or his/her WiFi device is activated in the background, we know that the passenger is traveling on the bus. By fusing the network events generated by WiFi devices with the data from the automatic fare collection (AFC) system, and the bus GPS information, we exploit not only the origin but also the destination of a passenger. Based on this idea, we develop a novel system called ViFi-MobiScanner which consists of about 4, 800 mobile routers distributed in a city with 1, 992 KM2 urban area. We develop an ID matching algorithm that matches part of the users' network identities and their smartcard identities anonymously. As a result, we have built a set of labeled samples with the reference of observation from smartcard data and use them to train a classifier to infer users mobility from their network activities. We evaluate ViFi-MobiScanner with both field tests and collected datasets associated with 168 million network events, 3.6 million trips, and 1.4 million users. The evaluation results show that ViFi-MobiScanner increases the observability on the passengers and trips by about 53.9% and 48.1% over the smartcard observations. ViFi-MobiScanner also helps to estimate the passengers' destination that cannot be observed by current smartcard systems and the estimation can be accomplished in minutes. Thus it expands the observability of mobility in object, temporal and spatial dimensions and provides unique insights on human mobility at metropolitan scales.