Internet Application for Interactive Visualization of Geophysical and Space Data: Approach, Architecture, Technologies

Document Type : Research Article


1 Corresponding Author, The Geophysical Center of the Russian Academy of Sciences, Moscow, Russia. E-mail:

2 The Geophysical Center of the Russian Academy of Sciences, Moscow, Russia. E-mail:

3 Sсhmidt Institute of Physics of the Earth of the Russian Academy of Sciences, Moscow, Russia. E-mail:

4 Department of Geophysical Research Methods (Department of Geophysics), Faculty of Mining and Oil, Ufa State Petroleum Technological University, Ufa, Russia. E-mail:


The proposed software solution is a tool developed for the analysis, forecast and visualization of geophysical data, which is collected and provided by a set of spatially distributed heterogeneous data repositories via standard web protocols (HTTP, HTTPs, FTP, etc.). They include ground magnetic observatories and stations, satellites, as well as various numerical models based on geophysical standards and specifications. The technological stack is limited with the tool’s web-based implementation and represented by integrated client- and server-side technologies with specialized frameworks and APIs. Client-side implementation is represented by several markup, styling and interaction software technologies, which are HTML5/CSS3/JavaScript with geospatial ESRI ArcGIS API for JavaScript available as the RESTful resources. Django web framework based on the “Model – View – Controller” architectural model represents server-side implementation, where Python is the main programming language used for the application’s business logic. The complete Web-based GIS represents a web portal with a set of services providing a rich instrumentation for the appropriate geophysical data analysis, processing, and visualization. Each tool upon execution provides an interactive geospatial image, which is generated according to the user request parameters or by default date-time settings. The proposed web services are freely available at and through the web browsers.


Main Subjects

Bajaj, D., Bharti, U., Goel, A., & Gupta S.C. (2009). PaaS providers and their offerings, International Journal of Scientific & Technology Research, 9(2), 4009–4015.
Bhatia, T., Singh, H., Litoria, P., & Pateriya, B. (2019). Web GIS Development using Portal for ArcGIS. ArcGIS Server and Web AppBuilder for ArcGIS, 10, 43-47.
Damyanov, I. (2019). Data Aggregation in Microservice Architecture. International Journal of Online and Biomedical Engineering (iJOE), 15. 81.
Dawson, E., Lowndes, J., & Reddy, P. (2013). The British Geological Survey's New Geomagnetic Data Web Service. Data Science Journal, 12, WDS75-WDS80.
Engebretson, M. J., Pilipenko, V. A., Ahmed, L. Y., Posch, J. L., Steinmetz, E. S., Moldwin, M. B., Connors, M. G., Weygand, J. M., Mann, I. R., Boteler, D. H., Russell, C. T., & Vorobev, A. V. (2019). Nighttime magnetic perturbation events observed in Arctic Canada: 1. Survey and statistical analysis, Journal of Geophysical Research: Space Physics, 124(9), 7442-7458.
Isaaks, E.H., & Mohan R. (1989). An Introduction to applied geostatistics – Oxford: Oxford University Press, 592 p.
Kim, J.R. (2020), A Study on the JSON Compatible Serialization Standard Method of LPG Data, The Journal of Next-Generation Convergence Technology Association, 4(6), 581–588.
Kolios, S., Vorobev, A., Vorobeva, G., & Stylios, Ch. (2017). GIS and environmental monitoring. Applications in the marine, atmospheric and geomagnetic fields. Cham, Switzerland: Springer International Publishing AG, 2017, 174 p.
Kudin, D. V., Soloviev, A. A., Sidorov, R. V., Starostenko, V. I., Sumaruk, Yu. P., & & Legostaeva O. V. (2021). Advanced production of quasi-definitive magnetic observatory data of the INTERMAGNET standard, Geomagnetism and Aeronomy, 61(1), 54–67.
Liu, Z., & Yan, T. (2021). Comparison of spatial interpolation methods based on ArcGIS. Journal of Physics: Conference Series, 012050.
Nebiker, S., Bleisch, S., & Gülch, E. (2010). State of the art and critical issues Virtual Globes. GIM International, 24(7), 17–21.
Newell, P. T., Liou, K., Zhang, Y., Sotirelis, T., Paxton, L. J., & Mitchell, E. J. (2014). OVATION Prime-2013: Extension of auroral precipitation model to higher disturbance levels. Space Weather, 12, 368–379.
Newell, P. T., Sotirelis T., & Wing S. (2009). Diffuse, monoenergetic, and broadband aurora: The global precipitation budget. J. Geophys. Res., 216, A09207.
Newell, P. T., Sotirelis, T., & Wing S. (2010). Seasonal variations in diffuse, monoenergetic, and broadband aurora. J. Geophys. Res., 115, A03216.
Potapov, V. I., Shafeeva, O. P., Gritsay, A. S., Makarov, V. V., Kuznetsova, O. P., & Kondratukova, L. K. (2019). Reliability in the model of an information system with client-server architecture. Journal of Physics: Conference Series, 1260, 022007.
Rhyne, T.-M., & MacEachren, A. (2004). Visualizing geospatial data, 31.
Soloviev, A., Khokhlov, A., Jalkovsky, E., Berezko, A., Lebedev, A., Kharin, E., Shestopalov, I., Mandea, M., Kuznetsov, V., Bondar, T., Mabie, J., Nisilevich, M., Nechitailenko, V., Rybkina, A., Pyatygina, O., & Shibaeva, A. (2013). The Atlas of the Earth's Magnetic Field (Eds.: A. Gvishiani, A. Frolov, V. Lapshin), Publ. GC RAS, Moscow, 361 pp.
Soloviev, A.A. (2018). Methods of geoinformatics and fuzzy mathematics in geophysical data analysis, Chebyshevskii Sbornik, 19(4), 194-214, (in Russian).
Vorobev, A.V., Pilipenko, V.A., Enikeev, T.A., & Vorobeva, G.R. (2020a). Geoinformation system for analyzing the dynamics of extreme geomagnetic disturbances from observations of ground stations. Computer Optics, 44(5), 782-790.
Vorobev, A. V., Pilipenko, V. A., Krasnoperov, R. I., Vorobeva, G. R., & Lorentzen, D. A. (2020b). Short-term forecast of the auroral oval position on the basis of the “virtual globe” technology. Russian Journal of Earth Sciences, 20(6), ES6001.
Vorobev, A.V., Pilipenko, V.A., Reshetnikov, A.G., Vorobeva, G.R., & Belov, M.D. (2020c). Web-oriented visualization of auroral oval geophysical parameters. Scientific Visualization, 12(3), 108–118.
Weimer, D. R. (2005). Improved ionospheric electrodynamic models and application to calculating Joule heating rates. Journal of geophysical research, 110, A05306.
Young, I., & Van Vliet, L. (1995). Recursive implementation of the Gaussian filter. Signal Processing, 44(2), 139-151.