<|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:"CrowdTelescope: Wi-Fi-positioning-based multi-grained spatiotemporal crowd flow prediction for smart campus."
Abstract:"Crowd flow prediction is one of the key problems in human mobility modeling, forecasting crowd flows of locations based on historical human mobility traces. Traditional human mobility traces (collected via telecommunication companies, online social media platforms, or field studies/experiments, etc.) suffer from severe data quality issues such as low precision, data sparsity, and insufficient coverage. In this paper, we investigate crowd flow prediction using Wi-Fi connection records on the campus of a university, which imply comprehensive, large-scale, high-coverage, and multi-grained (building/floor/room level) human mobility traces. However, we are facing not only non-trivial noises in the raw Wi-Fi connection data when extracting human mobility traces, but also the trade-off between location granularities and mobility patterns when modeling multi-grained crowd flow. Against this background, we propose CrowdTelescope, a Wi-Fi-positioning-based multi-grained spatiotemporal crowd flow prediction framework. We design a systematic approach for robust human mobility trace extraction from the noisy Wi-Fi connection records and adopt spatiotemporal Graph Neural Networks to model multi-grained crowd flow under a unified graph model for the three-level location hierarchy. We also develop a prototype system of CrowdTelescope, providing the interactive visualization of crowd flows on campus. We evaluate CrowdTelescope by collecting a Wi-Fi connection dataset on the campus of the University of Macau. Results show that CrowdTelescope can effectively extract informative human mobility traces from the noisy Wi-Fi connection records with an improvement of 3.3% over baselines, and also accurately predict on-campus crowd flow across different location granularities with 1.5%-24.1% improvements over baselines.."
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 = [];
Q4 = [0];
Q5 = [];
Q6 = [1].
CrowdTelescope: Wi-Fi-positioning-based multi-grained spatiotemporal crowd flow prediction for smart campus
Crowd flow prediction is one of the key problems in human mobility modeling, forecasting crowd flows of locations based on historical human mobility traces. Traditional human mobility traces (collected via telecommunication companies, online social media platforms, or field studies/experiments, etc.) suffer from severe data quality issues such as low precision, data sparsity, and insufficient coverage. In this paper, we investigate crowd flow prediction using Wi-Fi connection records on the campus of a university, which imply comprehensive, large-scale, high-coverage, and multi-grained (building/floor/room level) human mobility traces. However, we are facing not only non-trivial noises in the raw Wi-Fi connection data when extracting human mobility traces, but also the trade-off between location granularities and mobility patterns when modeling multi-grained crowd flow. Against this background, we propose CrowdTelescope, a Wi-Fi-positioning-based multi-grained spatiotemporal crowd flow prediction framework. We design a systematic approach for robust human mobility trace extraction from the noisy Wi-Fi connection records and adopt spatiotemporal Graph Neural Networks to model multi-grained crowd flow under a unified graph model for the three-level location hierarchy. We also develop a prototype system of CrowdTelescope, providing the interactive visualization of crowd flows on campus. We evaluate CrowdTelescope by collecting a Wi-Fi connection dataset on the campus of the University of Macau. Results show that CrowdTelescope can effectively extract informative human mobility traces from the noisy Wi-Fi connection records with an improvement of 3.3% over baselines, and also accurately predict on-campus crowd flow across different location granularities with 1.5%-24.1% improvements over baselines.