<p><strong>Abstract.</strong> This study assesses the usefulness of cartograms when visualizing extended Floating Car Data (xFCD). Cartograms deform regions in a map proportionally to assigned values. We apply this method for visualizing highresolution extended Floating Car Data (xFCD). Elaborating on this, we perform a case study in Mönchengladbach, Germany using 1.8 Million record points containing information about carbon dioxide (CO2) emissions based on an xFCD dataset. Utilizing a diffusion-based approach, we compute cartograms. Findings indicate a good suitability for identifying areas with a higher (or lower) average emission of CO2. We provide a documented workflow to compute cartograms based on parameters from an extended floating car dataset. The quality and spatial distribution of the basic dataset turns out to be important. Choosing the correct spatial subdivision of the research area as a basis for deforming areas is significant as it strongly influences the visual output.</p>

The development and application of Intelligent Transportation Systems (ITSs) leads to a growing demand of traffic data. Floating Car Observers (FCOs) contribute by providing information about ambient traffic of vehicles while driving. We present an approach to implement an FCO that uses particulate matter sensors for obtaining road abrasion from cars driving ahead of a test vehicle. Using Random Forest (RF), we predict presence and absence of ambient traffic in the vicinity of test vehicle with particulate matter readings (𝑃𝑀 01 , 𝑃𝑀 2.5 , 𝑃𝑀 10 ) as predictor variables. Results show that RF reaches prediction accuracy ranging from 86 to 99 percent for different train/test split options when analysing individual trajectories as well as 88 to 91 percent accuracy when analysing all trajectories combined. We face limitations mainly when merging single trajectories, due to different initial ambient particulate matter values. We conclude that presence and absence of ambient traffic are predictable using Random Forest with road abrasion values as predictor variables. Further, rainfall events (that may cause wash-off effects on roads) do not significantly change the accuracy of our classification. Optimisation of the model and the need of testing more diverse weather and road conditions remain open tasks for future research. CCS CONCEPTS• Hardware → Sensors and actuators; Sensor applications and deployments; Sensor devices and platforms; • Computing methodologies → Machine learning.

This paper introduces a low-cost Simultaneous Localization And Mapping (SLAM) implementation for generating geodata for human-navigable maps. In contrast to prevalent thinking, we maintain that navigation by people who are not mobility-impaired does not need accurate maps down to millimetres or even centimetres. Basically, there is a need only to map the boundaries of spaces and to highlight walkable places and areas of potential decisions. The SLAM system presented here consists of an Arduino-based robot and controlling SLAMTerminal software. A case study conducted at the University of Augsburg, Germany shows that the proposed SLAM implementation is capable of producing a map suitable for helping pedestrians to navigate.

Calcareous alvar grasslands are one of the most species-rich habitats in Estonia. Land-use change and cessation of traditional agricultural practices have led to a decrease of the area of these valuable grasslands during the past century. Therefore, their conservation and restoration are becoming increasingly important. Efforts to restore these habitats have already been made in recent years. Land suitability analysis for potential restoration sites, using the machine learning technique Random Forest (RF), was performed for the first time in this study, which aimed to assess the use of RF for a suitability analysis of alvar grassland. RF predicted 610.91 km 2 of areas suitable for restoring alvar grasslands or for creating alvarlike habitats in Estonia. These areas include all existing alvar areas as well an additional 140.91 km 2 suitable for establishing new habitat similar to calcareous alvar grasslands. We discuss suitability analysis to help with restoration planning and find it to be a reasonable and efficient tool that has potential to provide relevant information. The quality of the prediction could be improved by including additional data relevant for alvar grasslands, such as soil depth, but such data was unfortunately unavailable.

We present an approach to use static traffic count data to find relatively representative areas within Floating Car Data (FCD) datasets. We perform a case study within the state of Nordrhein-Westfalen, Germany using enviroCar FCD and traffic count data obtained from Inductive Loop Detectors (ILD). Findings indicate that our approach combining FCD and traffic count data is capable of assessing suitable subsets within FCD datasets that contain a relatively high ratio of FCD records and ILD readings. We face challenges concerning the correct choice of traffic count data, counting individual FCD trajectories and defining a threshold by which an area can be considered as representative.