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Traditionally most of the anomaly detection algorithms have been designed for 'static' datasets, in which all the observations are available at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need to devise adaptive models that take into account the dynamically changing characteristics of environments and detect anomalies in 'evolving' data. Over the last two decades, many algorithms have been proposed to detect anomalies in evolving data. Some of them consider scenarios where a sequence of objects (called data streams) with one or multiple features evolves over time. Whereas the others concentrate on more complex scenarios, where streaming objects with one or multiple features have causal/non-causal relationships with each other. The latter can be represented as evolving graphs. In this paper, we categorize existing strategies for detecting anomalies in both scenarios including the state-of-the-art techniques. Since label information is mostly unavailable in real-world applications when data evolves, we review the unsupervised approaches in this paper. We then present an interesting application example, i.e., forest re risk prediction, and conclude the paper with future research directions in this eld for researchers and industry.

Green spaces are believed to improve the well-being of users in urban areas. While there are urban research exploring the emotional benefits of green spaces, these works are based on user surveys and case studies, which are typically small in scale, intrusive, time-intensive and costly. In contrast to earlier works, we utilize a non-intrusive methodology to understand green space effects at large-scale and in greater detail, via digital traces left by Twitter users. Using this methodology, we perform an empirical study on the effects of green spaces on user sentiments and emotions in Melbourne, Australia and our main findings are: (i) tweets in green spaces evoke more positive and less negative emotions, compared to those in urban areas; (ii) each season affects various emotion types differently; (iii) there are interesting changes in sentiments based on the hour, day and month that a tweet was posted; and (iv) negative sentiments are typically associated with large transport infrastructures such as train interchanges, major road junctions and railway tracks. The novelty of our study is the combination of psychological theory, alongside data collection and analysis techniques on a large-scale Twitter dataset, which overcomes the limitations of traditional methods in urban research.

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