Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
4B - Data viz and Spatial Applications
Time:
Tuesday, 06/July/2021:
11:15am - 12:45pm

Session Chair: Natalia Soledad Morandeira
Zoom Host: Rachel Heyard
Replacement Zoom Host: Tuli Amutenya
Location: The Lounge #talk_dataviz_spatial
Session Topics:
Spatial analysis, Data visualisation


Presentations
11:15am - 11:35am
Talk-Live
ID: 216 / ses-04-B: 1
Regular Talk
Topics: Data visualisation
Keywords: high-dimensional data

Visual Diagnostics for Constrained Optimisation with Application to Guided Tours

H. Sherry Zhang1, Dianne Cook1, Ursula Laa2, Nicolas Langrené3, Patricia Menéndez1

1Monash University,; 2University of Natural Resources and Life Sciences; 3CSIRO Data61

Guided tour searches for interesting low-dimensional views of high-dimensional data via optimising a projection pursuit index function. The first paper of projection pursuit by Friedman and Tukey (1974) stated that “the technique used for maximising the projection index strongly influences both the statistical and the computational aspects of the procedure.” While much work has been done in proposing indices in the literature, less has been done on evaluating the performance of the optimisers. In this paper, we implement a data collection object in the optimisation of projection pursuit guided tour and introduce visual diagnostics based on the data object collected. These diagnostics and this workflow can be applied to a broad class of optimisers, to assess their performance. An R package, ferrn, has been created to implement the diagnostics.

Link to package or code repository.
https://github.com/huizezhang-sherry/ferrn


11:35am - 11:55am
Talk-Video
ID: 181 / ses-04-B: 2
Regular Talk
Topics: Spatial analysis
Keywords: open data, spatial data, data visualization, spatial analysis

geofi-package: Facilitating the access to key spatial datasets in Finland

Markus Kainu

National Social Insurance Institution of Finland (KELA), Finland

There is a groving demand for presenting statistical data on map. COVID-19 launched a race across internet in spatial data visualization where aesthetics, usability and real-timeliness are of high value. The demand for real-time data favours solutions that can be scripted, automated and refactored quickly and for that purpose we developed geofi R-package to facilitate access to key Finnish geospatial datasets. <https://ropengov.github.io/geofi/> geofi combines resources of two Statistics Finland APIs: the regional classification API and spatial data API. Time-series of regional classifications are shipped as on-board data and larger spatial data is fetched through a WFS-api consisting of administrative borders, zipcodes and both population and statistical grids at various resolutions. Package aims at being a onboarding technology into R ecosystem with clear and concrete vignettes covering the basics of spatial data manipulation, working with attribute data and step-by-step instruction for creating maps both for static and interactive applications. This talk describes the main functions and design principles of the packages and makes comparison with similar packages such as geobr for Brazil and geouy for Uruguay.

Link to package or code repository.
https://github.com/rOpenGov/geofi


11:55am - 12:15pm
Talk-Video
ID: 159 / ses-04-B: 3
Regular Talk
Topics: Data visualisation
Keywords: environmental sciences

Virtual Environments: Using R as a Frontend for 3D Rendering of Digital Landscapes

Michael J. Mahoney1, Colin M. Beier2, Aidan C. Ackerman3

1Graduate Program in Environmental Science, State University of New York College of Environmental Science and Forestry; 2Department of Sustainable Resources Management, State University of New York College of Environmental Science and Forestry; 3Department of Landscape Architecture, State University of New York College of Environmental Science and Forestry

This talk discusses a new approach to using R to create 3D landscape visualizations, which relies on external tooling designed specifically for detailed 3D rendering and interactive exploration. By using R as a frontend for high-performance rendering engines, users are able to quickly create data-defined renders which can then be interactively explored and manipulated. Two of the most promising engines for this approach are the (proprietary, source-available) Unity rendering engine, which excels at visualizing large swaths of land and the (free and open-source) Blender engine, which is well-adapted for visualizing smaller settings.

Our new {terrainr} package (available from CRAN) helps users quickly produce terrain surfaces from real-world data in Unity, visualizing environmental patterns and processes across large scales. Two new experimental packages, {mvdf} and {forthetrees}, focus on creating smaller-scale renders in Blender. Taken together, these packages suggest a way for users to create data-defined 3D renderings within R, using their preexisting coding abilities in the place of complex user interfaces to control powerful rendering engines. Our approach makes it possible for users to create renderings from data in these engines faster and easier than has been historically possible.