Using and reading the map

The interactive map (Fig.1.) was built with kepler.gl python library in Jupyter NB. It is an open source geospatial visualisation platform launched by Uber’s visualisation team and enables users to perform various types of analysis such as heatmaps, choropleth, flows or grids, among others. Additionally, it allows to visualise data in either 2D or 3D and has built-in timeline capability.

Initially, fire points data (.csv format) was loaded and aggregated to 1x1 km grids. This made it easier to render the data and to show major patterns using color gradients. Therefore, the darker the grid, the bigger the number of fire occurrences within that particular grid.

So, how to actually make use of the tool?

To understand spatio-temporal characteristics of fire occurrences, the timeline tool is crucially important. It allows selecting desired timespan (days, weeks, months, years) and renders spatial data accordingly by dragging white boxes and using automated animation.

Fig.2. Timeline tool enables map animation.

By default, the FRP information to be displayed on the timeline is enabled. Hovering over the graph individual fire FRP for the specific date will be given. However, if desired, it can be deactivated from within the tool and will switch to show the frequency of fires for the respective period of time (Fig.2.).

Making use of the 3D support, I also visualised the average FRP of the grid with the height function — higher grids represent more powerful fires. This gives a contrasting picture between the number of fires and their collective radiative power within the grid in several (not all) cases.

Even though a heatmap approach could also do the trick, it would not have been possible to render it in 3D and therefore, would not have allowed for such contrasts.

Fig.3. The number of fires (dark red grids) do not always mean they are powerful in terms of FRP compared to much higher grids (orange) with fewer numbers of fires.

More specifically, as one can explore with the tool, the number of fires detected does not always correlate positively to the radiative power of that grid (Fig.3.).

It is worth noting that on individual level, there might have been detected much powerful fires within the grid with lower FRP but it might have been balanced by other, non-powerful fire occurrences within the same grid. Hence, the average.

A practical example, perhaps?

Fire occurrences were much higher in Kakheti region semi-desert areas during summer period in 2015, than in the rest of the country (Fig.4.). It could be explained by the droughts that are characteristic of such places. This will be true for other years as well.

Fig.4. The number of detected fires are especially higher in arid regions (Kakheti and Kvemo Kartli) during summertime.

Another example (Fig.5.) is the Borjomi gorge fire (information in Georgian) in August, 2017, as a result of which 100 ha of area was burned.

Fig.5. Borjomi gorge fires detected in August, 2017.

What is truly intriguing, is the fire prevalence in the Black Sea coast regions of the country (Fig.1.), as those are areas where the humidity is highest and therefore, with diminished probability of natural fires. Compared to the arid regions however, the strength of the fires (FRP) in coastal regions is much lower, whereas fires burn more vigorously in the former.

Dataset

NASA’s Earthdata portal includes openly accessible datasets at multiple scales on satellite imagery, natural hazards, and other valuable information on natural environment globally.

For this specific purpose, I used FIRMS (Fire Information for Resource Management System) dataset for Georgia from January 1, 2014 to December 31, 2018. Using remote sensing approaches (VIIRS and MODIS), the data provided by FIRMS platform keeps track of every possible fire occurrence on the planet by essentially recording the time (date and hour), location (latitude and longitude) and fire radiation power (FRP measured in Kelvin). You can read more about the data collection methodology and available data formats here.

Conclusion

To conclude, the interactive tool enables users to identify major spatial and temporal patterns of fire prevalence in the country and detect contrasts, similarities and even unexpected novelties. Kepler.gl library is indeed a helpful tool to understand these trends.