OpenStreetMap (OSM) is a collaborative project collecting geographical data of the entire world. The level of detail of OSM data and its data quality vary much across different regions and domains. In order to analyse such variations it is often necessary to research the history and evolution of the OSM data. The OpenStreetMap History Database (OSHDB) is a new data analysis tool for spatio-temporal geographical vector data. It is specifically optimized for working with OSM history data on a global scale and allows one to investigate the data evolution and user contributions in a flexible way. Benefits of the OSHDB are for example: to facilitate accessing OSM history data as a research subject and to assess the quality of OSM data by using intrinsic measures. This article describes the requirements of such a system and the resulting technical implementation of the OSHDB: the OSHDB data model and its application programming interface.

The comprehension of folksonomies is of high importance when making sense of Volunteered Geographic Information (VGI), in particular in the case of OpenStreetMap (OSM). So far, only little research has been conducted to understand the role and the evolution of folksonomies in VGI and OSM, which is despite the fact that without a comprehension of the folksonomies the thematic dimension of data can hardly be used. This article examines the history of the OSM folksonomy, with the aim to predict its future evolution. In particular, we explore how the documentation of the OSM folksonomy relates to its actual use in the data, and we investigate the historical and future scope and granularity of the folksonomy. Finally, a visualization technique is proposed to examine the folksonomy in more detail.

Epidemics and pandemics are geographical in nature and constitute spatial, temporal, and thematic phenomena across large ranges of scales: local infections with a global spread; short-term decisions by governments and institutions with long-term effects; and diverse effects of the disease on many aspects of our lives. Pandemics pose particular challenges to their visual representation by cartographic means. This article briefly summarizes some of these challenges and outlines ways to approach these. We discuss how to use the information usually available for telling the story of an epidemic, illustrated by the example of the 2019–2020 COVID-19 pandemic. The maps attached to this article demonstrate the discussed cartographic means.

Maps are good at representing geographic space, but texts have a stronger affordance of telling a story than maps. Telling stories is, however, important to make information more personal and to arrest the map user's attention. This paper contrasts the map and the text media in order to understand why texts are good at telling a story but conventional maps are not. We demonstrate that, by a modification of maps, appropriate structural features of the text media can be transferred to maps, which makes them more suitable for telling stories. This new concept for map design can lead to new interaction possibilities and provide insights into how maps can be used more effectively.

OpenStreetMap and other Volunteered Geographic Information datasets have been explored in the last years, with the aim of understanding how their meaning is rendered, of assessing their quality, and of understanding the community-driven process that creates and maintains the data. Research mostly focuses either on the data themselves while ignoring the social processes behind, or solely discusses the community-driven process without making sense of the data at a larger scale. A holistic understanding that takes these and other aspects into account is, however, seldom gained. This article describes a server infrastructure to collect and process data about different aspects of OpenStreetMap. The resulting data are offered publicly in a common container format, which fosters the simultaneous examination of different aspects with the aim of gaining a more holistic view and facilitates the results’ reproducibility. As an example of such uses, we discuss the project OSMvis. This project offers a number of visualizations, which use the datasets produced by the server infrastructure to explore and visually analyse different aspects of OpenStreetMap. While the server infrastructure can serve as a blueprint for similar endeavours, the created datasets are of interest themselves too.

The fact that an increasing number of people and local authorities are affected by natural hazards, especially floods, highlights the necessity of adequate mitigation and preparedness within disaster management. Many governments, though, have only insufficient monetary or technological capacities. One possible approach to tackle these issues is the acquisition of information by sketch maps complemented by questionnaires, which allows to digitally capture flood risk perception. We investigate which factors influence information collected by sketch maps and questionnaires in case studies in an area prone to pluvial flooding in Santiago de Chile. Our aim is to gain more information about the methods applied. Hereby, we focus on the spatial acquisition scale of sketch maps and personal characteristics of the participants, for example, whether they live at this very location of the survey (residents) or are pedestrians passing by. Our results show that the choice of the acquisition scale of the base map influences the amount and level of detail of information captured via sketch maps. Thus, detail base maps lead to more precise results when compared to reference data, especially in the case of residents. The results also reveal that the place of living of the respondents has an effect on the resulting information because on the neighborhood level the risk perception of residents is more detailed than the one of pedestrians. The study suggests that the integration of citizens via sketch maps can provide information about flood risk perception, and thus can influence the flood mitigation in the area.