<|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:"The visual analytics of big, open public transport data - a framework and pipeline for monitoring system performance in Greater Sydney."
Abstract:"Many cities, countries and transport operators around the world are striving to design intelligent transport systems. These systems capture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility, interests, economic activity and lifestyles. They aim to provide services that can enable transportation customers and managers to be better informed and make safer and more efficient use of infrastructure. In developing principles, guidelines, methods and tools to enable synergistic work between humans and computer-generated information, the science of visual analytics continues to expand our understanding of data through effective and interactive visual interfaces. In this paper, we describe an application of visual analytics related to the study of movement and transportation systems. This application documents the use of rapid, 2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data. A novel approach to displaying such data through a generalisable framework visualisation system is demonstrated. This framework recalls over a year's worth of public transport performance data at a highly granular level in a fast, interactive browser-based environment. Greater Sydney, Australia forms a case study to highlight potential uses of the visualisation of such large, passively-collected data sets as an applied research scenario. In this paper, we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making. Furthermore, they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in services before and after policy decisions. The paper concludes by making recommendations on the value of this approach in embedding these or similar web-based systems in transport planning practice, performance management, optimisation and understanding of customer experience.."
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 = [0];
Q6 = [1].
The visual analytics of big, open public transport data - a framework and pipeline for monitoring system performance in Greater Sydney
Many cities, countries and transport operators around the world are striving to design intelligent transport systems. These systems capture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility, interests, economic activity and lifestyles. They aim to provide services that can enable transportation customers and managers to be better informed and make safer and more efficient use of infrastructure. In developing principles, guidelines, methods and tools to enable synergistic work between humans and computer-generated information, the science of visual analytics continues to expand our understanding of data through effective and interactive visual interfaces. In this paper, we describe an application of visual analytics related to the study of movement and transportation systems. This application documents the use of rapid, 2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data. A novel approach to displaying such data through a generalisable framework visualisation system is demonstrated. This framework recalls over a year's worth of public transport performance data at a highly granular level in a fast, interactive browser-based environment. Greater Sydney, Australia forms a case study to highlight potential uses of the visualisation of such large, passively-collected data sets as an applied research scenario. In this paper, we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making. Furthermore, they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in services before and after policy decisions. The paper concludes by making recommendations on the value of this approach in embedding these or similar web-based systems in transport planning practice, performance management, optimisation and understanding of customer experience.