IM921 Visualisation

4^th March 2020

Coursework 2: Graphical Report

Fig 1. Australia Fires December 2019 ^[1]

This report will describe how to visually communicate recent Australian wildfire data made available by the NASA Fire Information Management System (FIRMS).

One of the goals of the project was to provide a more temporal, or chronological, presentation of the data. Since a dynamic presentation or animation was not permissible, another method to facilitate the presentation of chronological information needed to be developed. A spatial presentation seemed to offer many benefits as the data was comprised of spatial information. One of the issues, however, was that there were over 90,000 observations. What would be the best method to present that data? Small multiples (Park and Quealy) seemed the obvious solution. It would enable the chronological presentation of the hotspots data. Perhaps some trends would become apparent? An algorithm (see Appendix 1) was developed to plot the daily hotspot data for the month of December. Once the initial prototype had been completed a number of issues were apparent, there was still too much data being plotted to overwhelm the map, and the map was being cropped somehow (Tasmania was not being displayed).

It was hypothesised that the bitmap nature of the map was impacting the ability of the rendering engine to correctly display the map. A method (Logan) was found to covert the bitmap image a vector. Once this was accomplished, the map was displaying correctly. To further reduce the amount of data, it was decided to filter the data using a 95% confidence value as provided in the original demo file. This reduced the number of data points to about 30,000 – so, on average, there would be approximately 1000 data points per map. Even though the data had been reduced to 1000 points per map, there was still a lot of duplicates given the size of the maps. The ideal solution would have been to conduct a spatial point pattern analysis (Gimond). An example is shown in Fig 2. Various methods were explored using spatstat() (Baddeley 171), but the author was unsuccessful in integrating a map of Australia as the ‘observation window’ – to enable data to be plotted over the map. As a workaround, it was decided to investigate changing the shape of the plot point and its transparency. Various options were were explored and this comprise solution is shown in Fig 1.

Fig 2. Spatial Point Anaylsis, from: https://rspatial.org/raster/analysis/8-pointpat.html

It was decided to add a bar chart at the bottom to augment the ability to perceive changes in the number or density of hotspots. The bar chart x-axis was labelled with dates corresponding to those on the individual maps. A label over the maximum hotspot days was preferred over a y-axis scale due to space and alignment issues.

In conclusion, two methods of presenting the data were investigated. Both succeeded in separating the data into more manageable chunks which enabled better comprehension. The map view enabled viewers to see where the hot spots were occurring, and the bar chart enabled viewers to see when and how many. For example, a viewer could clearly see that there were two hotspot flare-ups towards the end of December which would not be visible in a single map view. It would have been interesting to investigate the Spatial Point Analysis further. The author believes that using such a technique would have resulted in a better overall presentation, given the size of the 31 maps.

Citations

Baddeley, Adrian. Analysing Spatial Point Patterns in R. 2008, p. 171.

FIRMS. https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/. Accessed 2 Mar. 2020.

Gimond, Manuel. Point Pattern Analysis in R | Intro to GIS and Spatial Analysis. mgimond.github.io, https://mgimond.github.io/Spatial/point-pattern-analysis-in-r.html. Accessed 3 Mar. 2020.

Logan, Murray. Tutorial 5.4 - Mapping and Spatial Analyses in R. https://www.flutterbys.com.au/stats/tut/tut5.4.html. Accessed 3 Mar. 2020.

Park, Haeyoun, and Kevin Quealy. Drought’s Footprint. https://archive.nytimes.com/www.nytimes.com/interactive/2012/07/20/us/drought-footprint.html?_r=0&smid=pl-share. Accessed 3 Mar. 2020.

1. The authour acknowledges the use of data and imagery from LANCE FIRMS operated by NASA's Earth Science Data and Information System (ESDIS) with funding provided by NASA Headquarters. ↑