Visualizing patterns in spatially ambiguous point data

As technologies permitting both the creation and retrieval of data containing spatial information continue to develop, so do the number of visualisations using such data. This spatial information will often comprise a place-name that may be ‘geocoded’ into coordinates, and displayed on a map, frequently using a ‘heatmap-style’ visualisation to reveal patterns in the data. Across a dataset, however, there is often ambiguity in the geographic scale to which a place-name refers (country, county, town, street etc.), and attempts to simultaneously map data at a multitude of different scales will result in the formation of ‘false hotspots’ within the map. These form at the centres of administrative areas (countries, counties, towns etc.) and introduce erroneous patterns into the dataset whilst obscuring real ones, resulting in misleading visualisations of the patterns in the dataset. This paper therefore proposes a new algorithm to intelligently redistribute data that would otherwise contribute to these ‘false hotspots’, removing them to locations that likely reflect real-world patterns at a homogenous scale, and so allow more representative visualisations to be created, without the negative effects of ‘false hotspots’ resulting from multi-scale data. This technique demonstrated on a sample dataset taken from Twitter, and validated against the ‘geotagged’ portion of the same dataset

After the Twitter X-pocalypse: Approaches to Characterising Human Behaviour in Agent-based Models and Beyond

Characterising human behaviour is challenging, and datasets about people often suffer from issues of misrepresentation. To account for misrepresentation, researchers have turned to data synthesis. Here, we implement a straightforward data synthesis approach that does not rely upon knowledge of dataset uncertainty and use it to parametrise predictors used in an agent-based model (ABM) to estimate visits by people to greenspaces in Glasgow. The predicted visits follow expected patterns, with more visits on weekends, during daylight, and to popular tourist destinations. The approach is easy to implement and allows researchers to combine datasets of varying veracity to predict human behaviour

Real-Time Event Analysis and Spatial Information Extraction From Text Using Social Media Data

Since the advent of websites that enable users to participate and interact with each other by sharing content in different forms, a plethora of possibly relevant information is at scientists\u27 fingertips. Consequently, this thesis elaborates on two distinct approaches to extract valuable information from social media data and sketches out the potential joint use case in the domain of natural disasters

The Impact of Community Cohesion on Crime

Community cohesion generally acts to increase the safety of communities by increasing informal guardianship, and enhancing the work of formal crime prevention organisations. Understanding the dynamics of local social interactions is essential for community building. However, community cohesion is difficult to empirically quantify, because there are no obvious and direct indicators of community cohesion collected at population levels within official datasets. A potentially more promising alternative for estimating community cohesion is through the use of data from social media. Social media offers an opportunity for exploring networks of social interactions in a local community. This research will use social media data to explore the impact of community cohesion on crime. Sentiment analysis of tweets can help to uncover patterns of community mood in different areas. Modelling of community engagement on Facebook is useful for understanding patterns of social interactions and the strength of social networks in local communities. The central contribution of this thesis is the use of new metrics that estimate popularity, commitment and virality known as the PCV indicators for quantifying community cohesion on social media. These metrics, combined with diversity statistics constructed from “traditional” Census data, provide a better correlate of community cohesion and crime. To demonstrate the viability of this novel method for estimating the impact of community cohesion, a model of community engagement and burglary rates is constructed using Leeds community areas as an example. By examining the diversity of different community areas and strength of their social networks, from traditional and new data sources; it was found that stability and strong social media engagement in a local area are associated with lower burglary rates. The proposed new method can provide a better alternative for estimating community cohesion and its impact on crime. It is recommended that policy planning for resource allocation and community building needs to consider social structure and social networks in different communities