GIScience research challenges for realizing discrete global grid systems as a Digital Earth

References

• Alderson, T., Mahdavi-Amiri, A., & Samavati, F. (2018). Offsetting spherical curves in vector and raster form. The Visual Computer, 34(6), 973–984.
   Web of Science ®Google Scholar
• Appel, M., & Pebesma, E. (2019). On-demand processing of data cubes from satellite image collections with the gdalcubes library. Data, 4(3), 92.
   Web of Science ®Google Scholar
• Barnsley, M., Møller-Jensen, L., & Barr, S. (2001). Inferring urban land use by spatial and structural pattern recognition. Remote Sensing and Urban Analysis. 115–144.
   Google Scholar
• Bhatia, S., Vira, V., Choksi, D., & Venkatachalam, P. (2013). An algorithm for generating geometric buffers for vector feature layers. Geo-spatial Information Science, 16(2), 130–138.
   Google Scholar
• Bouille, F. (1978). Structuring cartographic data and spatial processes with the hypergraph-based data structure - Structuring cartographic data and spatial processes with the hypergraph-based data structure - OICRF. Data Structures: Surficial and Multi-Dimensional, Harvard Papers on Geographic Information Systems, 5, 4–9.
   Google Scholar
• Bresenham, J. E. (1965). Algorithm for computer control of a digital plotter. IBM Systems Journal, 4(1), 25–30.
   Google Scholar
• Briggs, C., Burfurd, I., Duckham, M., Guntarik, O., Kerr, D., McMillan, M., & Saldias, D. S. M. (2020). Bridging the geospatial gap: Data about space and indigenous knowledge of place. Geography Compass, 14(11). doi:10.1111/gec3.12542
   Google Scholar
• Clementini, E., & Di Felice, P. (1996). An algebraic model for spatial objects with indeterminate boundaries. London: CRC Press.
   Google Scholar
• Craglia, M., de Bie, K., Jackson, D., Pesaresi, M., Remetey-Fülöpp, G., Wang, C., … Woodgate, P. (2012). Digital Earth 2020: Towards the vision for the next decade. International Journal of Digital Earth, 5(1), 4–21.
   Web of Science ®Google Scholar
• Curry, M. (2000). Digital Earth, convergence, and the discursive foundations of geographic information systems. Troy, NY: Lecture given at the conference on data, for Department of Architecture, Renssler Polytechnic Institute.
   Google Scholar
• Date, C. J. (1983). An introduction to database systems (Vol. 2). Reading, Mass: Addison-Wesley Pub.
   Google Scholar
• Dong, Q., Chen, J., & Liu, T. (2019). A topographically preserved road-network tile model and optimal routing method for virtual globes. Transactions in GIS, 23(2), 294–311.
   Web of Science ®Google Scholar
• Egenhofer, M., & Herring, J. (1990). Categorizing binary topological relations between regions, lines and points in geographic databases, the 9-intersection: Formalism and its use for natural language spatial predicates. Santa Barbara CA National Center for Geographic Information and Analysis Technical Report, 94(1), 1–28.
   Google Scholar
• Gibb, R., Cochrane, B., & Purss, M. (2021). OGC testbed-16: DGGS and DGGS API engineering report.
   Google Scholar
• Gumbula, J. N. (2005). Exploring the gupapuynga legacy: Strategies for developing the Galiwin’ku indigenous knowledge centre. Australian Academic & Research Libraries, 36(2), 23–26.
   Google Scholar
• Hall, J., Wecker, L., Ulmer, B., & Samavati, F. (2020). Disdyakis triacontahedron DGGS. ISPRS International Journal of Geo-Information, 9(5), 315.
   Web of Science ®Google Scholar
• Ham, Y., & Kim, J. (2020). Participatory sensing and digital Twin city: Updating virtual city models for enhanced risk-informed decision-making. Journal of Management in Engineering, 36 (3), 04020005. Publisher: American Society of Civil Engineers.
   Web of Science ®Google Scholar
• Hojati, M., Farmer, C., Feick, R., & Robertson, C. (2021). Decentralized geoprivacy: Leveraging social trust on the distributed web. International Journal of Geographical Information Science, 35(12), 2540–2566. doi: 10.1080/13658816.2021.1931236
   Web of Science ®Google Scholar
• Hojati, M., Robertson, C., & Feick, R. (2019). Uncertainty-based representation of object-based spatial phenomena using discrete global grid systems data model. Presented at Global Water Futures Annual Science Meeting - GWF-2019, Saskatoon, SK, CA.
   Google Scholar
• Hojati, M., & Robertson, C. (2020). Integrating cellular automata and discrete global grid systems: A case study into wildfire modelling. AGILE: GIScience Series, 1, 6. Citation Key: agile-giss-1-6-2020.
   Google Scholar
• Lewis, A, Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R., Sixsmith, J., Wu,W., Tan, P., Li, F., Killough, B., Minchin, S., Roberts, D.,Ayers, D., Bala, B., D., J., Dekker, A., Dhu, T., Hicks,A., Ip, A., Purss, M., Richards, C., Sagar, S., Trenham, C.,Wang, P., & Wang, L.Wei. (2017). The Australian Geoscience Data Cube — Foundations and lessons learned. Remote Sensing of Environment, 202, 276–292. doi:10.1016/j.rse.2017.03.015
   Web of Science ®Google Scholar
• Li, L., Hu, H., Zhu, H., Li, Y., & Zhang, H. (2017). Tiled vector data model for the geographical features of symbolized maps. PLOS ONE, 12(5), 1–26.
   Web of Science ®Google Scholar
• Li, M., McGrath, H., & Stefanakis, E. (2021). Integration of heterogeneous terrain data into discrete global grid systems. Cartography and Geographic Information Science, 48(6), 1–19.
   Web of Science ®Google Scholar
• Li, M., & Stefanakis, E. (2020a). Geo-feature modeling uncertainties in discrete global grids: A case study of downtown Calgary, Canada. Geomatica, 74(4), 175–195.
   Google Scholar
• Li, M., & Stefanakis, E. (2020b). Geospatial operations of discrete global grid systems—a comparison with traditional GIS. Journal of Geovisualization and Spatial Analysis, 4(2), 26.
   Google Scholar
• Lü, G., Chen, M., Yuan, L., Zhou, L., Wen, Y., Mingguan, W., … Sheng, Y. (2018). Geographic scenario: A possible foundation for further development of virtual geographic environments. International Journal of Digital Earth, 11(4), 356–368. Publisher: Taylor & Francis _eprint.
   Web of Science ®Google Scholar
• Luo, J., Zhang, W., Su, J., & Xiang, F. (2019). Hexagonal convolutional neural networks for hexagonal grids. IEEE Access, Sorrento, Italy, 7, 142738–142749.
   Google Scholar
• Mahdavi Amiri, A., Alderson, T., & Samavati, F. (2019). Geospatial data organization methods with emphasis on aperture-3 hexagonal discrete global grid systems. Cartographica: The International Journal for Geographic Information and Geovisualization, 54(1), 30–50.
   Google Scholar
• Mahdavi Amiri, Ali and Alderson, Troy, and Samavati, Faramarz. Geospatial Data Organization Methods with Emphasis on Aperture-3 Hexagonal Discrete Global Grid Systems. Cartographica: The International Journal for Geographic Information and Geovisualization, 54(1), 2019. doi:10.3138/cart.54.1.2018-0010
   Google Scholar
• Meentemeyer, V. (1989). Geographical perspectives of space, time, and scale. Landscape Ecology, 3(3), 163–173.
   Web of Science ®Google Scholar
• Murrieta-Flores, P., Favila-vtimes New Romanã¡zquez, M., & Flores-morã¡n, A. (2019). Spatial humanities 3.0: Qualitative spatial representation and semantic triples as new means of exploration of complex indigenous spatial representations in sixteenth century early colonial Mexican maps. International Journal of Humanities and Arts Computing, 13 (1), 53–68. Publisher: Edinburgh University Press.
   Google Scholar
• OGC. (1999). OpenGIS simple features specification for OLE/COM. Revision, 1(1), 11–20.
   Google Scholar
• OGC. (2020). OGC two dimensional tile matrix set and tile set metadata. Joan Masó and Jérôme St-Louis.
   Google Scholar
• Purss, M. B. J., Peterson, P. R., Strobl, P., Dow, C., Sabeur, Z. A., Gibb, R. G., & Ben, J. (2019). Datacubes: A discrete global grid systems perspective. Cartographica: The International Journal for Geographic Information and Geovisualization, 54(1), 63–71.
   Google Scholar
• Quesnot, T., & Roche, S. (2015). Platial or locational data? Toward the characterization of social location sharing. In 2015 48th Hawaii International Conference on System Sciences, Kauai, HI, USA (pp. 1973–1982).
   Google Scholar
• Ray, S., Simion, B., & Demke Brown, Angela. (2011). Jackpine: A benchmark to evaluate spatial database performance.In 2011 IEEE 27th International Conference on Data Engineering, NW Washington, DC, United States. (pp. 1139–1150).
   Google Scholar
• Roberts, S. A., Hall, G. B., & Calamai, P. H. (2011). Evolutionary multi-objective optimization for landscape system design. Journal of Geographical Systems, 13(3), 299–326.
   Web of Science ®Google Scholar
• Robertson, C., Chiranjib, C., Majid, H., & Roberts, S. A. (2020). An integrated environmental analytics system (IDEAS) based on a DGGS. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 214–228.
   Web of Science ®Google Scholar
• Rundstrom, R. A. (1995). GIS, indigenous peoples, and epistemological diversity. Cartography and Geographic Information Systems, 22(1), 45–57.
   Google Scholar
• Sahr, K., White, D., & Kimerling, A. J. (2003). Geodesic Discrete Global Grid Systems. Cartography and Geographic Information Science, 30(2), 121–134.
   Google Scholar
• Sahr, K. (2019). Central place indexing: Hierarchical linear indexing systems for mixed-aperture hexagonal discrete global grid systems. Cartographica: The International Journal for Geographic Information and Geovisualization, 54(1), 16–29.
   Google Scholar
• Schneider, M. (2014). Spatial Plateau Algebra for implementing fuzzy spatial objects in databases and GIS: Spatial plateau data types and operations. Applied Soft Computing, 16, 148–170.
   Web of Science ®Google Scholar
• Sherlock, M. J., Hasan, M., & Samavati, F. F. (2021). Interactive data styling and multifocal visualization for a multigrid web-based Digital Earth. International Journal of Digital Earth, 14(3), 288–310.
   Web of Science ®Google Scholar
• Sherlock, M. J., Tripathi, G., & Samavati, F. 2016 (December). Landsat 8 data modeled as DGGS data cubes.In AGU Fall Meeting Abstracts, San Francisco, CA (Vol. 2016, pp. IN53B–1886).
   Google Scholar
• Shuang, L., Cheng, C., Chen, B., & Meng, L. (2016). Integration and management of massive remote-sensing data based on GeoSOT subdivision model. Journal of Applied Remote Sensing, 10(3), 1–15.
   Google Scholar
• Sirdeshmukh, N., Verbree, E., Van Oosterom, P., Psomadaki, S., & Kodde, M. (2019). Utilizing a discrete global grid system for handling point clouds with varying locations, times, and levels of detail. Cartographica: The International Journal for Geographic Information and Geovisualization, 54(1), 4–15.
   Google Scholar
• Smith, B., & Mark, D. M. (2001). Geographical categories: An ontological investigation. International Journal of Geographical Information Science, 15(7), 591–612.
   Web of Science ®Google Scholar
• Thompson, M. M. (2016). Upside-down GIS: The future of citizen science and community participation. The Cartographic Journal, 53(4), 326–334. Publisher: Taylor & Francis _eprint.
   Web of Science ®Google Scholar
• Tim, D., Walker, S. B., & Mor, R. (2019). The state of open data: Histories and horizons. African Minds. Google-Books-ID: zKOfDwAAQBAJ.
   Google Scholar
• Tong, X. C., Ben, J., Liu, Y. Y., & Zhang, Y. S. (2013). Modeling and expression of vector data in the hexagonal discrete global grid system. In The international archives of the photogrammetry, remote sensing and spatial information sciences (Vol. XL-4-W2, pp. 15–25). Göttingen, Germany: Copernicus GmbH.
   Google Scholar
• Tripathi, G., Sherlock, M. J., Amiri, A. M., & Samavati, F. (2016). Wavelet transforms for managing landsat data sets on a geospatial client-server discrete global grid system. In AGU Fall Meeting Abstracts, San Francisco, CA (Vol. 2016, pp. IN53B–1882).
   Google Scholar
• Uber. (2020). Uber H3-js. Author. uber.github.io/h3
   Google Scholar
• Ulmer, B., Hall, J., & Samavati, F. (2020). General method for extending discrete global grid systems to three dimensions. ISPRS International Journal of Geo-Information, 9(4), 233.
   Web of Science ®Google Scholar
• Wang, R., Ben, J., Zhou, J., & Zheng, M. (2020). Indexing mixed aperture icosahedral hexagonal discrete global grid systems. ISPRS International Journal of Geo-Information, 9(3). doi:10.3390/ijgi9090532
   Google Scholar
• Wang, Z., Zhao, X., Sun, W., Luo, F., Yalu, L., & Duan, Y. (2021). Correlation analysis and reconstruction of the geometric evaluation indicator system of the discrete global grid. ISPRS International Journal of Geo-Information, 10(3), 115.
   Web of Science ®Google Scholar
• Wickham, H., Girlich, M., & Ruiz, E., RStudio. (2021). dbplyr: A ’dplyr’. Back End for Databases. https://github.com/tidyverse/dbplyr/
   Google Scholar
• Yang, M., & Biuk-Aghai, R. (2015). Enhanced hexagon-tiling algorithm for map-like information visualisation VINCI ’15: Proceedings of the 8th International Symposium on Visual Information Communication and Interaction , Pages 137–142.
   Google Scholar
• Yao, X., Guoqing, L., Xia, J., Ben, J., Cao, Q., Zhao, L., … Zhu, D. (2020). Enabling the big earth observation data via cloud computing and DGGS: Opportunities and challenges. Remote Sensing, 12 (1), 62. Number: 1 Publisher: Multidisciplinary Digital Publishing Institute.
   Web of Science ®Google Scholar
• Yuan, M. (2020). Modelling semantical 1, spatial and temporal information in a GIS. In Geographic information research (pp. 334–347). London: CRC Press.
   Google Scholar
• Zhang, J., & Gong, J. (2002). Hypergraph-based object-oriented model and hypergraph theory for GIS. Geo-spatial Information Science, 5(1), 37.
   Google Scholar
• Zhou, M., Chen, J., & Gong, J. (2016). A virtual globe-based vector data model: Quaternary quadrangle vector tile model. International Journal of Digital Earth, 9(3), 230–251.
   Web of Science ®Google Scholar