Using topic modelling to analyse bus route data

The advent of the fourth industrial revolution and the need for connectedness have increased both data availability and quality. This data surge can also be seen in the transport and mobility industry. Anything from onboard global positioning system interfaces to vehicle trackers and wearable technology for passengers and drivers provide access to more data as an untapped source of valuable information and insights to many stakeholders. Topic modelling is traditionally used to structure and interpret text data from a large corpus of documents. In this paper, patterns in bus route data collected over several months by the onboard Global Positioning Systems (GPSs) of buses travelling in Gauteng and the Northwest province are analysed. Since topic modelling is traditionally used on text documents, the bus route coordinates had to be converted into a form readable by the algorithm. This is an ongoing project, but analyses thus far show that the most important terms per topic correspond to key nodes in city centres and points of interest where routes overlap. This information may be used in city planning to optimise the system of bus routes, terminals, and nodes. Organisations may also use this information for business development and job creation.Papers presented at the 40th International Southern African Transport Conference on 04 -08 July 202

Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time

We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales

Selbstbestimmung, Privatheit und Datenschutz

In diesem Open-Access-Sammelband werden die aktuelle Herausforderungen für Privatheit und Datenschutz aufgezeigt, die durch die zunehmende Digitalisierung entstehen. Die Beitragsautoren analysieren, wie diese durch Governancemechanismen adressiert werden können. Als Alternative zu einem rein profitorientierten Digitalkapitalismus bzw. Digitalautoritarismus wird für einen eigenständigen europäischen Weg beim Datenschutz argumentiert, der auf eine gemeinwohlorientierte Technikentwicklung abzielt. Insbesondere befassen sich die Beiträge mit den Möglichkeiten für die Stärkung der Selbstbestimmung in der Datenökonomie und mit algorithmischen Entscheidungssystemen