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Pseudo-random dots for the initialization step (left); CCVT algorithm reduces the number of dots by a factor of two and creates a blue-noise distribution (right).

Pseudo-random dots for the initialization step (left); CCVT algorithm reduces the number of dots by a factor of two and creates a blue-noise distribution (right).

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Dot mapping is a traditional method for visualizing quantitative data, but current automated dot mapping techniques are limited. The most common automated method places dots pseudo-randomly within enumeration areas, which can result in overlapping dots and very dense dot clusters for areas with large values. These issues affect users’ ability to es...

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Context 1
... 3-5 illustrate the algorithm. In the initialization step, the set s 0 of pseudo- randomly distributed dots is produced (Figure 3 left). The CCVT blue-noise algorithm then reduces the number of dots in s 0 by half and stores them in s 1 ; the unit value is d 1 , and the distribution has blue-noise characteristics (Figure 3 right). ...
Context 2
... the initialization step, the set s 0 of pseudo- randomly distributed dots is produced (Figure 3 left). The CCVT blue-noise algorithm then reduces the number of dots in s 0 by half and stores them in s 1 ; the unit value is d 1 , and the distribution has blue-noise characteristics (Figure 3 right). The iterative proce- dure of identifying clusters and replacing clustered dots with fewer, larger dots begins with using the DBSCAN clustering algorithm to identify clusters from s n (Figure 4 left). ...

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... The specific verification attempt of one of a small set of entries then led us to create a novel visual representation we call "Motion Plausibility Profiles" (Sec. 6). These allowed us to analyze the data from specific individual contributors. ...
... More importantly, however, we provide evidence for many of the data biases and errors for habitat data derived from social media, i. e., data that was not collected in a citizen science context-we had extracted our own species habitat data from online image 6 In Table 1 we only list biases and errors we found, expected to find, or discussed above, but others exist in citizen science as noted, e. g., by Kandel et al. [37] and Waller [71]. There are also biases in general social media contribution that we did not identify in our data. ...
... Dataset contributions by the different services in our datasets: Entries from both datasets shown via graduated[6] pie charts, scaled by the logarithm (base 1.2) of the entry count in the respective grid cell. Legend as inFig. ...
Article
We present a case study on a journey about a personal data collection of carnivorous plant species habitats, and the resulting scientific exploration of location data biases, data errors, location hiding, and data plausibility. While initially driven by personal interest, our work led to the analysis and development of various means for visualizing threats to insight from geo-tagged social media data. In the course of this endeavor we analyzed local and global geographic distributions and their inaccuracies. We also contribute Motion Plausibility Profilesa new means for visualizing how believable a specific contributors location data is or if it was likely manipulated. We then compared our own repurposed social media dataset with data from a dedicated citizen science project. Compared to biases and errors in the literature on traditional citizen science data, with our visualizations we could also identify some new types or show new aspects for known ones. Moreover, we demonstrate several types of errors and biases for repurposed social media data.
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Dot maps are often used to display the distributions of populations over space. This paper details a method for extending dot maps in order to visualize changes in spatial patterns over time. Specifically, we outline a selective linear interpolation procedure to encode the time range in which dots are visible on a map, which then allows for temporal queries and animation. This methodology is exemplified first by animating population growth across the United States, and second, through an interactive application showing changing poverty distributions in Toronto, Canada.