Computational Urban Science - Call for papers: Promoting Urban Computational Paradigms with Shareable Data, Models, Tools, and Frameworks

The following special issue in Computational Urban Science is open for submissions. The submission deadline is Dec 31, 2021. Manuscript can be submitted at any time before the deadline. Once it is accepted, it will be published online immediately with open access and social media promotion.

Call for papers:

Promoting Urban Computational Paradigms with Shareable Data, Models, Tools, and Frameworks

Guest Editors:

Dr. Xiao Huang, University of Arkansas, USA, xh010@uark.edu

Dr. Alexander Hohl, University of Utah, USA, alexander.hohl@geog.utah.edu (this opens in a new tab)

Dr. Zhenlong Li, University of South Carolina,  USA, zhenlong@mailbox.sc.edu

Aims and Scope:

Although the Big Data Era provides countless opportunities with the emerging of innovative data sources, it also poses new challenges, among which reproducibility and replicability (R & R) is facing a growing awareness (Kedron et al., 2021). The extensive usage of urban monitoring big data, such as satellite imagery, location-based services, street views, to list a few, uniquely emphasizes the importance of R & R in Urban Science from the intertwining perspectives of location privacy, geospatial data quality, computing scalablility, geoinformation shareability, and conclusion generalizability. To support reproducible computational studies, Choi et al. (2021) identified three thrusts: 1) open sharing of data and models online; 2) encapsulating computational models through containers and self-documented tutorials; and 3) developing Application Programming Interfaces (APIs) for programmatic control of complex computational models. In addition, other venues exist where R & R can be promoted, such as the development of visualization frameworks, data-sharing portals, and integrated cyberinfrastructures (e.g., Li et al., 2020).

In response to the R & R challenges in Urban Science and the growing open-sourcing trend in academia, this special issue of  Computational Urban Science encourages the submission of original papers that focus on tackling urban issues and problems by designing shareable data/products, developing analytical tools, launching online data visualization portals, constructing integrated cyberinfrastructures, and so on.

Submitted manuscripts could cover but not limited to the following themes:

• Shareable urban monitoring data and products that benefit urban science communities. 
• Online visualization, analytical, and data-sharing platforms that promote and facilitate data- and knowledge-sharing for both academia and the public.
• Development of reusable and interoperable analytical tools, packages, models, and data-accessing portals/APIs that advance urban sciences.
• Applied urban studies using designed data products, models, tools, and platforms. 
• Other research and visions related to reproducibility and replicability in computational urban science.

Please submit your article here:  https://www.editorialmanager.com/cusc/ (this opens in a new tab)

When submit your article, please select the designated Thematic Series in the "additional information Questionnaire" (the fourth step). 

Articles will undergo all of the journal's standard peer review and editorial processes outlined in its submission guidelines. (this opens in a new tab)

Reference:

Kedron, P., Li, W., Fotheringham, S., & Goodchild, M. (2021). Reproducibility and replicability: opportunities and challenges for geospatial research. International Journal of Geographical Information Science, 35(3), 427-445.

Choi, Y. D., Goodall, J. L., Sadler, J. M., Castronova, A. M., Bennett, A., Li, Z., ... & Tarboton, D. G. (2021). Toward open and reproducible environmental modeling by integrating online data repositories, computational environments, and model Application Programming Interfaces. Environmental Modelling & Software, 135, 104888.

Li, Z., Huang, Q., Jiang, Y., & Hu, F. (2020). SOVAS: a scalable online visual analytic system for big climate data analysis. International Journal of Geographical Information Science, 34(6), 1188-1209.