IMAGE OF THE WEEK
Stillness Map 1: Ports of Los Angeles and Long Beach map by Harrison Cole. The circular shapes are the result of each ship swinging around its anchor point. There are a series of designated anchorages outside the ports (which results in the diagonal grid of circles) where ships can safely drop anchor without risking a collision with another ship. Each ship polygon has very low opacity, so the darker an area is, the longer a ship had occupied that space.
GOOD NEWS
Alumni Li San Hung, who earned his Ph.D. in 2016, and Rachel Passmore, who earned a B.S. in 2014, have recently published scholarly articles.
SWIG contributed $360 to the Centre Safe Basket Donation.
SWIG will hold a spring planning meeting on Tuesday, December, 9, at 5 p.m. EST
NEWS
In LivingMaps Network
Stillness Map 1: Ports of Los Angeles and Long Beach
By Harrison Cole
There is a backstory to this map. Between 27 November and 5 December of 2012, several hundred clerks at the Ports of Los Angeles and Long Beach went on strike. Port clerks are responsible for managing the transition of cargo between logistics networks and are essential to the operation of the ports. They also belonged to the International Longshore and Warehouse Union, so the rest of the 10,000 workers at the ports refused to cross the picket line in solidarity with the strikers, effectively shutting down the ports. During a typical day at either one of the ports (which are directly adjacent to each other), only one or two ships sit at anchor waiting to enter the harbour. During the strike, thirteen ships were at anchor at one time, together holding about $650m in cargo, with some waiting several days to unload. The clerks eventually emerged victorious, securing a better contract and assurance that the ports would not outsource their jobs. But it is the time during the work stoppage that my map concerns.
Penn State seeks nominations for Earthshot Prize for environmental solutions
Penn State was selected as an official nominator for the Earthshot Prize, a competition aimed at identifying the most promising solutions to environmental challenges. Faculty and staff are encouraged to either self-nominate or to nominate other researchers or projects that they see as strong examples of promising solutions. The internal deadline to submit expressions of interest is Thursday, Dec. 17.
“We are looking for the best and brightest projects and ideas that Penn State has to offer,” said Erica Smithwick, official nominator for the University. “The Earthshot Prize is an opportunity for Penn State to work collaboratively and nominate projects that could truly change our world for the better.”
RECENTLY PUBLISHED
Needs Assessment of Integrative Health Services at School-Based Health Centers
Passmore RC, Dunn M, Garbers S, Garth J, Gold MA.
Alternative Therapies in Health and Medicine
https://pubmed.ncbi.nlm.nih.gov/33245710/
Objective: The purpose of this needs assessment was to hear about adolescents’ experience with and interest in accessing integrative health services (IHS) at their school-based health centers (SBHCs) so that future education and service offerings could be better informed. Subjects: We surveyed 373 9th to 12th graders, of mostly low-income and minority status, who were enrolled as patients at 6 SBHCs in New York City, New York. Verbal consent was obtained prior to their completing a survey on provided mobile devices. Design: The 35-item anonymous survey asked about adolescents’ health goals, familiarity and experience with 14 different integrative health modalities and interest in learning about and accessing these modalities. Results: Among all patients, the most common health goal was improving sleep (65%). Before completing the needs assessment survey, almost all patients (98%) had heard of at least 1 integrative health modality and 69% had ever used any modality. On average, patients were interested in learning more about 7.6 of the modalities and were significantly more interested in learning about each modality from trained professionals than from trained peers or by themselves. Conclusions: Improving sleep was a central health goal for SBHC patients. The majority expressed interest in receiving information on massage, meditation and yoga from trained health professionals, and they wanted access to these modalities at their SBHCs. SBHCs are in a unique position of power in which they can bring desired, cost-effective integrative health modalities to marginalized students. Future efforts should expand provider training to support education on and delivery of these modalities and evaluation of their effectiveness at SBHCs.
Comparing the effects of climate change labelling on reactions of the Taiwanese public
Hung, L.-S.., Bayrak, M.M.
Nature Communications
https://doi.org/10.1038/s41467-020-19979-0
Scientists and the media are increasingly using the terms ‘climate emergency’ or ‘climate crisis’ to urge timely responses from the public and private sectors to combat the irreversible consequences of climate change. However, whether the latest trend in climate change labelling can result in stronger climate change risk perceptions in the public is unclear. Here we used survey data collected from 1,892 individuals across Taiwan in 2019 to compare the public’s reaction to a series of questions regarding climate change beliefs, communication, and behavioural intentions under two labels: ‘climate change’ and ‘climate crisis.’ The respondents had very similar responses to the questions using the two labels. However, we observed labelling effects for specific subgroups, with some questions using the climate crisis label actually leading to backlash effects compared with the response when using the climate change label. Our results suggest that even though the two labels provoke similar reactions from the general public, on a subgroup level, some backlash effects may become apparent. For this reason, the label ‘climate crisis’ should be strategically chosen.
User-centered Design and Evaluation of a Geovisualization Application Leveraging Aggregated QS Data
Jonathan K. Nelson, Alan M. MacEachren
Cartographic Perspectives
https://doi.org/10.14714/cp96.1631
Individual movement traces recorded by users of activity tracking applications such as Strava provide opportunities that extend beyond delivering personal value or insight to the individual who engages in these “quantified-self ” (QS) activities. The large volumes of data generated by these individuals, when aggregated and anonymized, can be used by city planners, Departments of Transportation, advocacy groups, and researchers to help make cities safer and more efficient. This opportunity, however, is constrained by the technical skills and resources available to those tasked with assessing bicycling behavior in urban centers. This paper aims to address the question of how to design cartographic interfaces to serve as mediated platforms for making large amounts of individual bicycling data more accessible, usable, and actionable. Principles of cartographic representation, geovisual analytics techniques, and best practices in user interface/experience design are employed to arrive at an effective visualization tool for a broad urban planning audience. We use scenario-based design methods to encapsulate knowledge of map use practice gleaned from the development process, and conduct a post-implementation two-part user study with seven domain experts to further assess the usability and utility of the interactive mapping tool.
Geographical feature classification from text using (active) convolutional neural networks
Yang, Liping & MacEachren, Alan & Mitra, Prasenjit
https://www.researchgate.net/publication/346446459_Geographical_feature_classification_from_text_using_active_convolutional_neural_networks
Deep learning can discover intricate patterns hidden in big data, and has much better scalability than traditional machine learning when the volume of data increases dramatically. Thus, deep learning has gained many successes in various domains and applications such as image classification, text classification, and machine translation. In this paper, we use deep learning to classify geographical features (e.g., mountains, rivers, landmarks, and cities) from text, using geolocated Wikipedia entries as the case study application. We employ one of the most commonly used deep learning architectures, convolutional neural networks (CNNs) and its integration with active learning (creating what we call active CNNs), to train the geographical feature classifiers on the Wikipedia text data set obtained from GeoNames (which provides the feature type for each geolocated entity). We evaluate the performance of CNNs and active CNNs with multiple metrics (i.e., accuracy, F1 score, and confusion matrix). Our experiment results demonstrated that CNNs and active CNNs can effectively classify geo-referenced text entities into predefined geographical features. In addition, our experiment results show that active CNNs outperform CNNs for hard to distinguish classes. In our experiment, we also compared results for hierarchical multi-class classification and flat multi-class classification, and the results show that hierarchical multi-class classification significantly outperforms flat multi-class classification for the data set we used.
