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1. Mapping and modeling the impact of climate change on recreational ecosystem services using machine learning and big data

   https://doi.org/10.1088/1748-9326/ac65a3  

   Manley, Kyle; Egoh, Benis N (April 2022, Environmental Research Letters)

   Abstract The use of recreational ecosystem services is highly dependent on the surrounding environmental and climate conditions. Due to this dependency, future recreational opportunities provided by nature are at risk from climate change. To understand how climate change will impact recreation we need to understand current recreational patterns, but traditional data is limited and low resolution. Fortunately, social media data presents an opportunity to overcome those data limitations and machine learning offers a tool to effectively use that big data. We use data from the social media site Flickr as a proxy for recreational visitation and random forest to model the relationships between social, environmental, and climate factors and recreation for the peak season (summer) in California. We then use the model to project how non-urban recreation will change as the climate changes. Our model shows that current patterns are exacerbated in the future under climate change, with currently popular summer recreation areas becoming more suitable and unpopular summer recreation areas becoming less suitable for recreation. Our model results have land management implications as recreation regions that see high visitation consequently experience impacts to surrounding ecosystems, ecosystem services, and infrastructure. This information can be used to include climate change impacts into land management plans to more effectively provide sustainable nature recreation opportunities for current and future generations. Furthermore, our study demonstrates that crowdsourced data and machine learning offer opportunities to better integrate socio-ecological systems into climate impacts research and more holistically understand climate change impacts to human well-being. 

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2. Development of food bank data visualizations using eye tracking

   https://doi.org/10.54941/ahfe1005087  

   Jiang, Steven; Ivuawuogu, Henry; Milad_Javadi, Amir; Hamilton, Mikaya; Davis, Lauren (January 2024, AHFE International, USA)

   Leitner, Christine; Nägele, Rainer; Bassano, Clara; Satterfield, Debra (Ed.)

   Food banks are key players in the fight against hunger. The complexity of the food bank operations data makes decision-making very challenging. Data visualization can allow food bank operations managers to quickly and easily understand the data and make evidence-based decisions. However, poorly designed visualizations could be confusing and/or misleading. This study uses eye-tracking technology to understand how users interact with various food bank data visualizations and use eye-tracking data to better design those visualizations. The findings of this study will have an impact on improving the effectiveness and efficiency of the food bank operations. 

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3. Multidisciplinary Education on Big Data + HPC + Atmospheric Sciences

   Wang, Jianwu; Gobbert, Matthias; Zhang, Zhibo; Gangopadhyay, Aryya; Page, Glenn (November 2017, Proceedings of the Workshop on Education for High-Performance Computing (EduHPC-17))

   We present a new initiative to create a training program or graduate-level course (cybertraining.umbc.edu) in big data applied to atmospheric sciences as application area and using high-performance computing as indispensable tool. The training consists of instruction in all three areas of "Big Data + HPC + Atmospheric Sciences" supported by teaching assistants and followed by faculty-guided project research in a multidisciplinary team of participants from each area. Participating graduate students, post-docs, and junior faculty from around the nation will be exposed to multidisciplinary research and have the opportunity for significant career impact. The paper discusses the challenges, proposed solutions, practical issues of the initiative, and how to integrate high-quality developmental program evaluation into the improvement of the initiative from the start to aid in ongoing development of the program. 

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4. The Personification of Big Data

   https://doi.org/10.1017/dsi.2019.409  

   Stevenson, Phillip Douglas; Mattson, Christopher Andrew (July 2019, Proceedings of the Design Society: International Conference on Engineering Design)

   Abstract Organizations all over the world, both national and international, gather demographic data so that the progress of nations and peoples can be tracked. This data is often made available to the public in the form of aggregated national level data or individual responses (microdata). Product designers likewise conduct surveys to better understand their customer and create personas. Personas are archetypes of the individuals who will use, maintain, sell or otherwise be affected by the products created by designers. Personas help designers better understand the person the product is designed for. Unfortunately, the process of collecting customer information and creating personas is often a slow and expensive process. In this paper, we introduce a new method of creating personas, leveraging publicly available databanks of both aggregated national level and information on individuals in the population. A computational persona generator is introduced that creates a population of personas that mirrors a real population in terms of size and statistics. Realistic individual personas are filtered from this population for use in product development. 

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5. Metropolitan Planning Organizations’ Uses of and Needs for Big Data

   https://doi.org/10.32866/001c.127143  

   Ugurel, Ekin; Wu, Xinhua; Wang, Ryan; Lee, Brian_H Y; Chen, Cynthia (December 2024, Findings)

   Big data products offer a new paradigm to understand and analyze human mobility patterns, a primary interest of long-range transportation planners. However, it remains unclear how widely these datasets are utilized by planners and to what extent they influence decision-making. We present the perspectives of more than 50 planners from MPOs across the United States. While we found a range of use cases, there was also a tendency to focus on a narrow set of applications.Transparency,regulation, andlegitimacyemerged as the primary factors influencing adoption decisions. 

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