Using User Generated Online Photos to Estimate and Monitor Air Pollution in Major Cities

With the rapid development of economy in China over the past decade, air pollution has become an increasingly serious problem in major cities and caused grave public health concerns in China. Recently, a number of studies have dealt with air quality and air pollution. Among them, some attempt to predict and monitor the air quality from different sources of information, ranging from deployed physical sensors to social media. These methods are either too expensive or unreliable, prompting us to search for a novel and effective way to sense the air quality. In this study, we propose to employ the state of the art in computer vision techniques to analyze photos that can be easily acquired from online social media. Next, we establish the correlation between the haze level computed directly from photos with the official PM 2.5 record of the taken city at the taken time. Our experiments based on both synthetic and real photos have shown the promise of this image-based approach to estimating and monitoring air pollution.Comment: ICIMCS '1

Sensing smog on social media : Rethinking tracing as the self-tracking of originary humanicity

MAI Issue 4: ISSN 2003-1674Peer reviewe

Social Media as a Sensor of Air Quality and Public Response in China

Abstract Background: Recent studies have demonstrated the utility of social media data sources for a wide range of public health goals, including disease surveillance, mental health trends, and health perceptions and sentiment. Most such research has focused on English--language social media for the task of disease surveillance. Objective: We investigate the utility of Chinese social media for monitoring air quality trends and related public perceptions and response. The goal is to determine if this data is suitable for learning actionable information about pollution levels and public response. Methods: We mine a collection of 93 million messages from Sina Weibo, China's largest microblogging service. We experiment with different filters to identify messages relevant to air quality, based on keyword matching and topic modeling. We evaluate the reliability of the data filters by comparing message volume per city to air particle pollution rates obtained from the Chinese government for 74 cities. Additionally, we perform a qualitative study of the content of pollution--related messages by coding a sample of 170 messages for relevance to air quality, and whether the message includes details such as a reactive behavior or a health concern. Results: The volume of pollution--related messages is highly correlated with particular pollution levels, with Pearson correlation values up to .718 (n=74, p < .001). Our qualitative results found that 67.1% of messages were relevant to air quality and of those, 79% were a first--hand report. Of first--hand reports, 28.4% indicated a reactive behavior and 18.9% expressed a health concern. Additionally, 3 messages of 170 requested that action be taken to improve quality. Conclusions: We have found quantitatively that message volume in Sina Weibo is indicative of true particle pollution levels, and we have found qualitatively that messages contain rich details including perceptions, behaviors, and self--reported health effects. Social media data can augment existing air pollution surveillance data, especially perception and health--related data that traditionally requires expensive surveys or interviews

The Importance of Internet of Things Security for Smart Cities

The purpose of this chapter is to provide an extensive overview of security-related problems in the context of smart cities. The impressive heterogeneity, ubiquity, miniaturization, autonomous and unpredictable behaviour of objects interconnected in Internet of Things, the real data deluges generated by them and, on the other side, the new hacking methods based on sensors and short-range communication technologies transform smart cities in complex environments in which the already-existing security analyses are not useful anymore. Specific security vulnerabilities, threats and solutions are approached from different areas of the smart cities’ infrastructure. As urban management should pay close attention to security and privacy protection, network protocols, identity management, standardization, trusted architecture, etc., this chapter will serve them as a start point for better decisions in security design and management

PM2.5 study : explore PM2.5 in Beijing using data mining methods and social media data

Air pollution is one of the worst outcomes from industrialization. Among other air pollutants, PM2.5 is believed to pose the greatest risks to human health as it can lodge deeply into people’s lungs. This study focuses on exploring predicting aerial PM2.5 values from traditional pollutants and wind information using data mining and statistical models, including K-means, Markov chain, SVR, OLS models. Additionally, trending topics on social media is also considered to analyze how PM2.5 influences people's daily life. Considering Sina Weibo is the most popular social media in China, OLS and SVR models were also implemented with Weibo dataset. Predictions based on this study are expected to help government and concerned organizations do better in environmental protection.Informatio

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c