With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure an important topic. In this paper, we introduce a new method for analyzing the demand for bicycle parking facilities in urban areas based on object detection of social media images. We use a subset of the YFCC100m dataset, a collection of posts from the social media platform Flickr, and utilize a state-of-the-art object detection algorithm to detect and classify moving and parked bicycles in the city of Dresden, Germany. We were able to retrieve the vast majority of bicycles while generating few false positives and classify them as either moving or stationary. We then conducted a case study in which we compare areas with a high density of parked bicycles with the number of currently available parking spots in the same areas and identify potential locations where new bicycle parking facilities can be introduced. With the results of the case study, we show that our approach is a useful additional data source for urban bicycle infrastructure planning because it provides information that is otherwise hard to obtain.

Volunteered geographic information is often generated as voluminous point data, leading to geometric and thematic clutter when presented on maps. To solve these clutter problems, cartography provides various point generalization operations such as aggregation, simplification or selection. While these operations reduce the total number of points and therefore improve the readability, information preservation could be harmed when specific spatial patterns disappear through the generalization process, possibly leading to false interpretations. However, sets of map generalization constraints that maintain spatial pattern characteristics of point data are still missing. To define constraints that support synoptic interpretation tasks, user behaviour while solving these tasks has to be analysed first. We conduct a study where participants have to perform such interpretation tasks, using a new method that combines think-aloud interviews and techniques from visual analytics. We reveal that the point density of a dataset has the biggest impact on the user behaviour and the respective task-solving strategy, independently from the actual task type executed. Furthermore, our results show that the graphical map complexity only has a minor impact on the user behaviour, and there is no evidence that point data cardinality influences task execution and the solution-finding strategies.

Point datasets that relate to highly populated places, such as ones retrieved from social media or volunteered geographic information in general, can often result in dense point clusters when presented on maps. Therefore, it can be useful to visualize the relevant point density information directly on the urban geometry to tackle the problem of point counting and density range identification in highly cluttered areas. One solution is to relate each point to the nearest geometry object. While this is a straightforward approach, its major drawback is that local point clusters could disappear by assigning them to larger objects, e.g., long roads. To address this issue, we introduce two new point density visualization approaches by which points are related to the underlying geometry objects. In this process, we use grid cells and heatmap contour lines to divide roads, squares, and pedestrian zones into subgeometry units. Comparison of our visualization approaches with conventional density visualization methods shows that our approaches provide a more comprehensive insight into the point distribution over space, i.e., over existing urban geometry.

Accessibility analyses are an essential step in the evaluation and planning of urban infrastructures such as transport or pipeline networks. However, these studies generally produce sharply defined lines (called isovarones) or areas (called isovarone areas) that represent the same or similar accessibility. Uncertainties in the input data are usually not taken into account. The aim of this contribution is, therefore, to set up a structured framework that describes the integration of uncertainty information for accessibility analyses. This framework takes uncertainties in the input data, in the processing step, in the target variables, and in the final visualization into account. Particular attention is paid, on the one hand, to the impact of the uncertainties in the target values, as these are key factors for reasoning and decision making. On the other hand, the visualization component is emphasized by applying a dichotomous classification of uncertainty visualization methods. This framework leads to a large set of possible combinations of uncertainty categories. Five selected examples that have been generated with a new software tool and that cover important combinations are presented and discussed.

With the beginning of the COVID-19 pandemic, the execution of eye-tracking user studies in indoor environments was no longer possible, and remote and contactless substitutes are needed. With this paper, we want to introduce an alternative method to eye tracking, completely feasible under COVID-19 restrictions. Our main technique are think aloud interviews, where participants constantly verbalize their thoughts as they move through a test. We record the screen and the mouse movements during the interviews, and analyse both the statements and the mouse positions afterwards. With this information, we can encode the approximate map position of the user’s attention for each second of the interview. This allows us to use the same visual methods as for eye-tracking studies, like attention maps or trajectory maps. We implement our method conducting a user study with 21 participants to identify user behaviour while solving high-level interpretation tasks, and with the results of this study, we can show that or new method provides a useful substitute for eye-tracking user studies.