Using Cartograms for Visualizing extended Floating Car Data (xFCD)

Abstract

<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>

Räumliche Lärmanalyse anhand von erweiterten Floating-Car-Daten (xFCD)

“…Für umweltbezogene Analysen werden bevorzugt xFCD herangezogen, um neben den räumlichzeitlichen Informationen zusätzliche Daten von Sensoren und Fahrzeugen für beispielsweise die Beobachtung des Kraftstoffverbrauchs zu erhalten (Jakobsen et al 2013). Bröring et al (2015), Röger et al (2018), Häußler et al (2018) sowie Röger & Krisp (2019) benutzten ebenfalls xFCD für ihre umweltbezogenen Arbeiten.…”