The grass is greener on the other side: understanding the effects of green spaces on Twitter user sentiments

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

Movement pattern analysis using Voronoi Diagrams

Analysis of clusters and movement patterns during emergency scenarios has the potential to provide vital information to key decision stakeholders. This paper presents a method using Voronoi Diagrams for defining both clusters and movement patterns in terms of voronoi cell properties: size, elongation, orientation and neighbourhood. Initial experimentation and testing against baseline methods using an evacuation trajectory dataset show promising results

Introducing a framework for automatically differentiating witness accounts of events from social media

Identifying Witnesses of events from social media is an opportunity to crowdsource real-time information to enhance numerous applications including emergency response in a crisis, filtering sources for journalism, and enhancing marketing services. Using a sporting event broadcast live to a proportionally much larger audience, this research demonstrates a significant increase in the number of Witnesses identified posting from the event venue, in comparison to the number identified from geotags alone. This is achieved by considering the text and image content of micro-blogs as additional evidence. This paper also reports progress towards the automatic categorisation of the additional text and image evidence, and modelling and testing this evidence for corroboration or conflict, using Dempster-Shafter Theory of Evidence

Clustering-based disambiguation of fine-grained place names from descriptions

Everyday place descriptions often contain place names of fine-grained features, such as buildings or businesses, that are more difficult to disambiguate than names referring to larger places, for example cities or natural geographic features. Fine-grained places are often significantly more frequent and more similar to each other, and disambiguation heuristics developed for larger places, such as those based on population or containment relationships, are often not applicable in these cases. In this research, we address the disambiguation of fine-grained place names from everyday place descriptions. For this purpose, we evaluate the performance of different existing clustering-based approaches, since clustering approaches require no more knowledge other than the locations of ambiguous place names. We consider not only approaches developed specifically for place name disambiguation, but also clustering algorithms developed for general data mining that could potentially be leveraged. We compare these methods with a novel algorithm, and show that the novel algorithm outperforms the other algorithms in terms of disambiguation precision and distance error over several tested datasets

From descriptions to depictions: A dynamic sketch map drawing strategy

People use verbal descriptions to communicate spatial information, externalising relevant parts of their mental spatial representations. In many situations, such as emergency response, the ability of a machine to interpret these verbal descriptions could assist in human-machine interaction. As a first step in such an endeavor, this paper presents an automatic approach that translates spatial objects and their spatial relations extracted from verbal descriptions via natural language processing into a plausible sketch map. The proposed methodology applies a hierarchical and dynamic sketch map drawing strategy that is inspired by heuristics people apply in their interpretation of place descriptions, in order to accommodate underspecifying, flexible and conflicting common language. The methodology is implemented and tested. This article ends with some insights for further research towards automatic interpretation of verbal place descriptions

Testing the event witnessing status of microbloggers from evidence in their micro-blogs

This paper demonstrates a framework of processes for identifying potential witnesses of events from evidence they post to social media. The research defines original evidence models for micro-blog content sources, the relative uncertainty of different evidence types, and models for testing evidence by combination. Methods to filter and extract evidence using automated and semi-automated means are demonstrated using a Twitter case study event. Further, an implementation to test extracted evidence using Dempster Shafer Theory of Evidence are presented. The results indicate that the inclusion of evidence from microblog text and linked image content can increase the number of micro-bloggers identified at events, in comparison to the number of micro-bloggers identified from geotags alone. Additionally, the number of micro-bloggers that can be tested for evidence corroboration or conflict, is increased by incorporating evidence identified in their posting history

Conventionalized gestures for the interaction of people in traffic with autonomous vehicles

The first autonomous vehicles are already tested in the public traffic. The rapid development in bringing this technology on roads attracts growing attention of research in the human interaction with autonomous vehicles. This paper focuses on the interaction of other road users with autonomous vehicles. These road users may be pedestrians who negotiate their right of way, other human drivers sharing the same road, or human traffic control officers. In order to learn about these road users in general, this paper aims to identify first the formalized hand signals applied by officers. The paper answers the question whether there is a general and universal language to interact with traffic. If so, then future work can identify elements of this universal language in the gestures of other road users, and facilitate an understanding between them and autonomous vehicles

Similarity matching for integrating spatial information extracted from place descriptions

Place descriptions are used in everyday communication as a common way to convey spatial information. Processing the information from place descriptions poses multiple significant challenges because these descriptions are written in natural language. In particular, corpora of place descriptions provide a plethora of human spatial knowledge beyond geographical information system, even if these descriptions refer to the same places in various ways. This article focuses on resolving ambiguous or synonymous place names from place descriptions by exploring the given relationships with other spatial features. It matches place names from multiple descriptions by developing a novel labelled graph matching process that relies solely on the comparison of string, linguistic and spatial similarities between identified places. This process uses unstructured place descriptions as an input, and produces a composite place graph with qualitative spatial relations from the descriptions. The performance of this novel process exceeds current toponym resolution by coping with non-gazetteered places

A graph database model for knowledge extracted from place descriptions

Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modelling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a number of limitations. The model is implemented using a graph database, and a management system has also been developed that allows operations including querying, mapping, and visualizing the stored knowledge in an extended place graph. Then three experimental tasks, namely georeferencing, reasoning, and querying, are selected to demonstrate the superiority of the extended model