Remaining gaps in open source software for Big Spatial Data
The volume and coverage of spatial data has increased dramatically in recent years, with Earth observation programmes producing dozens of GB of data on a daily basis. The term Big Spatial Data is now applied to data sets that impose real challenges to researchers and practitioners alike. As rule, th...
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| Format: | Other/Unknown Material |
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PeerJ
2018
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| Online Access: | http://dx.doi.org/10.7287/peerj.preprints.27215v2 https://peerj.com/preprints/27215v2.pdf https://peerj.com/preprints/27215v2.xml https://peerj.com/preprints/27215v2.html |
| Summary: | The volume and coverage of spatial data has increased dramatically in recent years, with Earth observation programmes producing dozens of GB of data on a daily basis. The term Big Spatial Data is now applied to data sets that impose real challenges to researchers and practitioners alike. As rule, these data are provided in highly irregular geodesic grids, defined along equal intervals of latitude and longitude, a vastly inefficient and burdensome topology. Compounding the problem, users of such data end up taking geodesic coordinates in these grids as a Cartesian system, implicitly applying Marinus of Tyre's projection. A first approach towards the compactness of global geo-spatial data is to work in a Cartesian system produced by an equal-area projection. There are a good number to choose from, but those supported by common GIS software invariably relate to the sinusoidal or pseudo-cylindrical families, that impose important distortions of shape and distance. The land masses of Antarctica, Alaska, Canada, Greenland and Russia are particularly distorted with such projections. A more effective approach is to store and work with data in modern cartographic projections, in particular those defined with the Platonic and Archimedean solids. In spite of various attempts at open source software supporting these projections, in practice they remain today largely out of reach to GIS practitioners. This communication reviews persisting difficulties in working with global big spatial data, current strategies to address such difficulties, the compromises they impose and the remaining gaps in open source software. |
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