Discrete Global Grid Systems: The Journey from Academic Curiosity to Disruptive Technology

Transcription

1 Discrete Global Grid Systems: The Journey from Academic Curiosity to Disruptive Technology Dr. Matthew B.J. Purss 1 & Robert Gibb 2 1) Geoscience Australia 2) Manaaki Whenua Landcare Research NZ Copyright 2017 Open Geospatial Consortium

2 A Tale of two Journeys to DGGS Everything we do collectively places excess pressure on the Earth and it s systems Climate change River pollution Biodiversity loss Copyright 2014 Open Geospatial Consortium

3 A Tale of two Journeys to DGGS Some changes we are 100% responsible for, others are a complex mix of natural and human induced change Some obvious, some illusive Copyright 2014 Open Geospatial Consortium

4 A Tale of two Journeys to DGGS Individually and collectively we need to take action, to participate We need to see the see result locally and globally In extreme events we need a real-time feedback loop that tells us what proportion of every event is our responsibility Copyright 2014 Open Geospatial Consortium

5 A Tale of Two Journeys to DGGS Exploration Geophysics Global Seismology Unlocking the Landsat Archive The Australian Geoscience Datacube Copyright 2014 Open Geospatial Consortium

6 Conventional Approaches don t scale A transition from siloed data to integrated information is now an operational requirement

7 Discrete Global Grid Systems

8 Open Geospatial Consortium (OGC) standards OGC defines a DGGS as: a spatial reference system that uses a hierarchical tessellation of cells to partition and address the globe. DGGS are characterized by the properties of their cell structure, geoencoding, quantization strategy and associated mathematical functions Purss, et. al. 2017, OGC Discrete Global Grid Systems Abstract Specification Topic 21 [OGC r5]

9 DGGS & other OGC/ISO Abstract Spec Topics Topic 11: Metadata Topic 2: Spatial Referencing by Coordinates Topic 1: Feature Geometry DE-9IM Spatial Relationships Topic 20: Observation and Measurement

10 Copyright 2014 Open Geospatial Consortium

11 What are Discrete Global Grid Systems? Tetrahedron Cube Octahedron Dodecahedron Icosahedron Equal Area Tessellations of the Earth s Surface Model Fixed Cell Hierarchy Simple cell Types: Rectangular Triangular Hexagonal Massively Scalable nd Spatial Analyses 1D Array Processes Globally Unique Cell Indices Copyright 2014 Open Geospatial Consortium

12 Some flavours of DGGS

13 Some flavours of DGGS OGC

14 Some flavours of DGGS

15 Traditional GIS approaches are not the answer Vectorised Rasterised FeatureID: 61 FeatureType: Lake FeatureName: Lake Burley Griffin FeatureID: 11 FeatureType: Road FeatureName: Parks Way Red: Road Blue: Lake

16 Traditional GIS approaches are not the answer FeatureID: 61 FeatureType: Lake FeatureName: Lake Burley Griffin FeatureID: 11 FeatureType: Road FeatureName: Parks Way Red: Road Blue: Lake Vector world Raster world DGGS world FeatureID: 11 FeatureType: Road FeatureName: Parks Way FeatureID: 61 FeatureType: Lake FeatureName: Lake Burley Griffin

17 DGGS Opportunities to bridge the divide(s) DGGS Point (cell size precision) DGGS Vector DGGS Polygon Copyright 2014 Open Geospatial Consortium

18 DGGS Clouds DGGS Cell/Point Clouds DGGS Tag Clouds Copyright 2014 Open Geospatial Consortium

19 A lesson from globalisation What boosted global commerce more: free trade or containerisation? containerisation is associated with a 320% increase in bilateral trade over the first five years and 790% over 20 years. A bilateral free-trade agreement, by contrast, boosts trade by 45% over 20 years, and membership of GATT raises it by 285%. In other words, containers have boosted globalisation more than all trade agreements in the past 50 years put together. based on: Bernhofen et al., Estimating the effects of the container revolution on world trade, Journal of International Economics 98 (2016) Copyright 2014 Open Geospatial Consortium

20 Container Power Fit seemlessly together Allow (certain) different sizes Save time and avoid losses inherent to repackaging Addressable, traceable, exchangeable, arrangeable Imagine trying to ship a container that s slightly (e.g. 10%) different in size compared to the standard! Copyright 2014 Open Geospatial Consortium

21 What does that remind you off? Copyright 2014 Open Geospatial Consortium

22 The spatial analysis workflow Earth? Project to a planar surface Visualise Measure, compare, etc. in a (convenient) planar coordinate system Report results Copyright 2014 Open Geospatial Consortium

23 To Observe the Earth we no longer need paper maps If we wish to do some spatial analysis on the surface of the Earth, what we are really wanting to do is to compare features (and how they change) on the surface of the Earth This requires a global approach and the use of a fixed, but scalable grid based information management system. A Discrete Global Grid System!

24 To Observe the Earth we no longer need paper maps But Most traditional GIS software apply spatial analytics using Map Projections and Planar Geometries

25 To Observe the Earth we no longer need paper maps If we then un-project the results back onto the Earth s surface we can get some significant distortions of our data, particularly in the polar regions. This can lead to some significant issues for decision making and spatial data integration

26 Using Statistical Boundary only Bushfire Impact Zone Fast Array Processing Using DGGS Fire Impacting whole area R0 R1 R2 R3 R4 R5 R6 R7 R8 R0 R1 Fire Fire R2 Nil R3 Nil R4 Nil R5 R6 Fire Nil R7 R8 Fire Nil Fire Impacting ~44% of area Population (per dwelling) 65 People Impacted R00 0 R01 0 R02 3 R03 0 R04 0 R05 5 R06 0 R07 9 R People Impacted Access to Water 65 People at risk but water is available R070 R071 R072 R073 R074 R075 R076 R077 R078 water Nil water water Nil Nil water Nil Nil R740 R741 R742 R743 R744 R745 R746 R747 R748 Nil Nil Nil Nil Nil Nil Nil Nil Nil 22 People at risk 11 People in critical danger

27 Why integrate statistical and geospatial data? Unlocking new insights and data relationships that would not have been possible by analysing socio economic, environmental or geospatial data in isolation of each other; Promoting investment and capability building in geospatial and statistical information; Building institutional collaboration between geospatial and statistical communities.

28 Why integrate statistical and geospatial data? Local, sub national, national, regional, and global decision making processes (Samavati, F., & Runions, A., 2016, fig.3) Measuring and monitoring the targets and global indicator framework for the Sustainable Development Goals (SDGs) Supporting data sharing between institutions and enhancing the interoperability of geospatial and statistical information

29 The DGGS Opportunity

30 Thank you Dr. Matthew B.J. Purss 1 & Robert Gibb 2 1) Geoscience Australia 2) Manaaki Whenua Landcare Research NZ Copyright 2017 Open Geospatial Consortium