Spatial Data Science: Closing the human-spatial computing-environment loop

Over the last decade, the term spatial computing has grown to have two different, though not entirely unrelated, definitions. The first definition of spatial computing stems from industry, where it refers primarily to new kinds of augmented, virtual, mixed-reality, and natural user interface technologies. A second definition coming out of academia takes a broader perspective that includes active research in geographic information science as well as the aforementioned novel UI technologies. Both senses reflect an ongoing shift toward increased interaction with computing interfaces and sensors embedded in the environment and how the use of these technologies influence how we behave and make sense of and even change the world we live in. Regardless of the definition, research in spatial computing is humming along nicely without the need to identify new research agendas or new labels for communities of researchers. However, as a field of research, it could be helpful to view spatial data science as the glue that coheres spatial computing with problem-solving and learning in the real world into a more holistic discipline.Comment: 2 pages, Spatial Data Science Symposiu

Accelerating legacy applications with spatial computing devices

Heterogeneous computing is the major driving factor in designing new energy-efficient high-performance computing systems. Despite the broad adoption of GPUs and other specialized architectures, the interest in spatial architectures like field-programmable gate arrays (FPGAs) has grown. While combining high performance, low power consumption and high adaptability constitute an advantage, these devices still suffer from a weak software ecosystem, which forces application developers to use tools requiring deep knowledge of the underlying system, often leaving legacy code (e.g., Fortran applications) unsupported. By realizing this, we describe a methodology for porting Fortran (legacy) code on modern FPGA architectures, with the target of preserving performance/power ratios. Aimed as an experience report, we considered an industrial computational fluid dynamics application to demonstrate that our methodology produces synthesizable OpenCL codes targeting Intel Arria10 and Stratix10 devices. Although performance gain is not far beyond that of the original CPU code (we obtained a relative speedup of x 0.59 and x 0.63, respectively, for a single optimized main kernel, while only on the Stratix10 we achieved x 2.56 by replicating the main optimized kernel 4 times), our results are quite encouraging to drawn the path for further investigations. This paper also reports some major criticalities in porting Fortran code on FPGA architectures

Applying Spatial Computing to Everyday Interactive Designs

In this position paper, we address the applicability of spatial computing in the field of interactive architecture. The process of designing large-scale interactive systems is cumbersome, due in fact to single design cycles (transforming ideas into prototypes) taking a period of time usually measured in months, most of it dedicated to writing the software controlling the system. As most interactive architecture projects pass through several design cycles interleaved with user studies, speeding up the generation of the needed software becomes of crucial importance. The global-to-local programming approach is in fact a perfect tool for this task. Describing complex behaviors with simple rules is rarely seen in the existing installations, the large majority employing a central computer for the control of the system. Building up on our previous experience in this area, we created HiveKit, a proof of concept allowing bridging between the two fields, giving non-specialists easy access to distributed algorithms. HiveKit is a software package which allows designers to specify the desired behavior and automatically generate and deploy the needed code on networks of embedded devices. We introduce several projects where HiveKit is employed and create an argument, based on user studies, favoring the need for large-scale adoption of such tools

Low-power spatial computing using dynamic threshold devices

Asynchronous spatial computing systems exhibit only localized communication, their overall data-flow being controlled by handshaking. It is therefore straightforward to determine when a particular part of such a system is active. We show that using thin-body double-gate fully depleted SOI transistors, the shift in threshold voltage that can be produced by modulating the back-gate bias is sufficient to reduce subthreshold leakage power by a factor of more than 104 in typical circuits. Using TBFDSOI devices in spatial computing architectures will allow overall power to be greatly reduced while maintaining high performance

Towards Carbon-Aware Spatial Computing: Challenges and Opportunities

Carbon-aware spatial computing (CASC) is focused on reducing the carbon footprint of spatial computing itself and leveraging spatial computing techniques to minimize carbon emissions in other domains. The significance of CASC lies in its potential to mitigate anthropogenic climate change by offering numerous societal applications, such as carbon-aware supply chain development and carbon-aware site selection. CASC is challenging because of the spatiotemporal variability and the high dimensionality of carbon emissions data, involving spatial coordinates and timestamps. Related work, known as carbon-aware computing, mostly focuses on job scheduling of cloud computing, and there is a lack of surveys and review papers detailing the potential of CASC on variant domains and applications. In this paper, we provide the vision of CASC by proposing a taxonomy of sub-domains within CASC and introducing ideas beyond job scheduling, such as carbon-smart site selection. We also briefly review the literature in selected sub-domains and highlight research challenges and opportunities. Given the societal importance of the topic, we encourage the scientific community to use this brief survey to expand the field of study into other related sub-domains and advance CASC more broadly

The Evolution of Spatial Computing and its Impact on UX Designers

With the impending global release of the Apple Vision Pro, spatial computing has become increasingly mainstreamed in today's world. User experience (UX) design will have to adapt to new technologies, but little practical research is available for guidance as UX moves towards the new era of spatial computing. Exploring the intersection between spatial computing and UX design requires extensive research which will be guided through the research question: 'How might the evolution of spatial computing impact UX design?' Spatial computing is on a trajectory towards a more seamless integration of both digital and physical worlds. To understand the future of spatial computing, a contextual analysis of the world was launched by gathering signals and identifying trends. The trends were cross referenced with spatial computing to understand how spatial computing could evolve in the next twenty years. The futures wheel identified eight themes that demonstrated potential future scenarios that UX designers must be mindful of. The study explored the impact of spatial computing on UX designers and developed recommendations to help them proactively prepare for the future. Spatial computing will need product designers to build ergonomic products to facilitate the easy transition between the digital and the real world. UX designers will need the skills to design for 3D and integrate spatial conceptualization when researching, prototyping, and designing. Designing to limit cognitive overload, distractions, and to visualize data safely will be the responsibility of UX designers. As Al is increasingly integrated into spatial computing, UX designers will have to understand how to utilize the personalization and data synthesis capabilities of generative Al, both responsibly and ethically. UX designers should be aware of the industries that are embracing this technology and explore opportunities in high demand sectors, such as the companies using digital twins. UX designers must learn the skill of designing collaborative spatial computing experiences to help remote work become more productive. UX designers must inform themselves of the harms and benefits of these technologies on the human brain, social life, privacy, and wellbeing, to design ethical experiences that enhance human life. Overall, UX designers have a large part to play when it comes to ensuring that this new era of spatial computing is beneficial to humanity

Web service based Grid workflow application in quantitative remote sensing retrieval

Along with the unprecedented data-collecting capability, the higher algorithm accuracy and real-time application requirements, redundant spatial computing model had been implemented. Traditionally these spatial computing models are stored in different application centers. To avoid waste of resource, Grid workflow provides a powerful tool for sharing both remote sensing data and processing middleware. In order to enhance the interoperability of the heterogeneous quantitative remote sensing retrieval model in the Grid workflow environment, we propose a web service based Grid workflow framework to improve this situation. According to the Open Geospatial Consortium (OGC) and web service standards, we implement a prototype of this framework. Through the experiment, we can find that web service can work well with Grid workflow and provide a management ability of remote sensing model. Also this approach can separate the application logic and process logic, providing the interoperability ability both in application and process layers