Historical collaborative geocoding

The latest developments in digital have provided large data sets that can increasingly easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with the temporal aspect and are based on a strict hierarchy (..., city, street, house number) that is hard or impossible to use with historical data. Indeed historical data are full of uncertainties (temporal aspect, semantic aspect, spatial precision, confidence in historical source, ...) that can not be resolved, as there is no way to go back in time to check. We propose an open source, open data, extensible solution for geocoding that is based on the building of gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address. The matching criteriae are customisable and include several dimensions (fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that they can be checked and collaboratively edited. The system is tested on Paris city for the 19-20th centuries, shows high returns rate and is fast enough to be used interactively.Comment: WORKING PAPE

Combining Biosensing Technology and Virtual Environments for Improved Urban Planning

The Urban Emotions initiative uses biosensing technology to determine how people feel in the city, which is of particular relevance for architecture and urban planning. While past experiments focused more on pedestrian or bicycle traffic, accessibility and wayfinding, this paper proposes the use of virtual models as a basis for human sensorial measurement. Virtual space offers the possibility of minimizing external (environmental) influences to focus on the evaluation of design impressions. Inspiration for the method was ‘Q-sorting’ according to Stephenson (1953) and, in the context of urban planning, Krause (1974). Virtual models of real situations are used to determine whether test participants respond positively or negatively to the architecture or their environment. 360° videos, virtual reality ambience and VR glasses are used as output devices. In this virtual environment, it is possible to create standardized, comparable laboratory situations allowing researchers to draw more reliable and focused conclusions about human responses to their physical environment. The challenge for the future will be to transfer this knowledge of citizens’ responses to the built environment into real design processes

Inviwo -- A Visualization System with Usage Abstraction Levels

The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities

Citizen Science: The Ring to Rule Them All?

There are many uncertainties about the future of e-Learning, but one thing is certain: e-Learning will be more data-driven in the future. The automation of data capturing, analysis and presentation, together with economic constraints that require evidence-based proof of impact, compels this data focus. On the other hand, the importance of community involvement in learning analytics and educational data mining is an accepted fact. Citizen science, at the nexus of community engagement, and data science can bridge the divide between data-driven and community-driven approaches to policy and content development. The rationale for this paper is the investigation of citizen science as an approach to collecting data for learning analytics in the field of e-Learning. Capturing data for policy and content development for learning analytics through citizen science projects is novel in the e-Learning field. Like any other new area, citizen science needs to be mapped in terms of the existing parent fields of data science and education so that differences and potential overlaps can be made explicit. This is important when considering conceptual or functional definitions, research tools and methodologies. A preliminary review of the literature has not provided any conceptual positioning of citizen science in relation to the research topics of learning analytics, data science, big data and visualisation in the e-Learning environment. The intent of this paper is firstly to present an overview of citizen science and the related research topics in the academic and practitioner literature based on a systematic literature review. Secondly, we propose a model that represents the relationship between citizen science and other salient concepts and shows how citizen science projects can be positioned in the e-Learning environment. Finally, we suggest research opportunities involving citizen science projects in the field of e-Learning.School of Computin

Experiences from the ImageCLEF Medical Retrieval and Annotation Tasks

The medical tasks in ImageCLEF have been run every year from 2004-2018 and many different tasks and data sets have been used over these years. The created resources are being used by many researchers well beyond the actual evaluation campaigns and are allowing to compare the performance of many techniques on the same grounds and in a reproducible way. Many of the larger data sets are from the medical literature, as such images are easier to obtain and to share than clinical data, which was used in a few smaller ImageCLEF challenges that are specifically marked with the disease type and anatomic region. This chapter describes the main results of the various tasks over the years, including data, participants, types of tasks evaluated and also the lessons learned in organizing such tasks for the scientific community