In this event, we have utilized data from various sources to analyze and predict bushfires and their potential impact on endangered species and flaged certain regions which lack fire prevention resources.
Arcgis integrated spatial Map : https://undatathon.maps.arcgis.com/apps/mapviewer/index.html?webmap=a406dce68acb4bae9fb6382d44551c5c
ShinyApp integrated spatial Map : https://ju-stats.shinyapps.io/FireApp/
In this part of the project, the goal was to create maps by overlaying various layers of data onto geographical areas. These data layers include information about fire stations, high-risk bush fire areas, biodiversity data, and endangered species areas map. One specific analysis carried out is calculating the distance between fire stations and the centroid (central point) of high-risk bush fire areas. This distance calculation is optimized to identify regions that are at high risk, where endangered species habitats are present, but there might be a lack of fire prevention resources. Essentially, this part of the project involves visually representing the data layers and using spatial analysis to determine areas of concern where the convergence of high risk and the presence of endangered species meet with a potential lack of fire prevention resources.
In the second part of the project, the focus is on prediction. Predictors are created using an Artificial Intelligence (AI) model. The model's training data is gathered from diverse sources as mentioned below. After obtaining the historial dependent variables about climates etc, we got the labeles data from historial records of bush fire. These were integrated using same date and geometry information.
Data Source: NPWS Fire History https://datasets.seed.nsw.gov.au/dataset/fire-history-wildfires-and-prescribed-burns-1e8b6
Description: This dataset provides valuable information about the history of wildfires and prescribed burns, offering insights into historical fire occurrences.
Data Source: CPC - Climate Prediction Center https://ftp.cpc.ncep.noaa.gov/cadb_v2/daily/
Description: daily data obtained from the Climate Prediction Center. This data helps us in forecasting and understanding the dynamics of bushfires.
Description: The IUCN Red List of Threatened Species™ contains global assessments for more than 150,300 species. More than 82% of these (>123,600 species) have spatial data.
Data Source: Fire and Rescue NSW (FRNSW) https://www.fire.nsw.gov.au/
Description: This data provide us with the number of fire station in NSW. More details of the extraction https://github.com/PJPDQ/dejas-repo/blob/main/fire_station_fetcher.ipynb
As part of our efforts, we have developed an open-source R-Shiny application that integrates geospatial data related to bushfires, fire stations, and endangered species. The primary objective of this application is to highlight regions at high risk of wildfires and are habitat of endangered species.
Link for ShinyApp: https://ju-stats.shinyapps.io/FireApp/
Example of critical area (Identified areas) Read more here https://storymaps.arcgis.com/stories/e038ffe87aa44c01ad2a43ec6eb843bc
Layer 3: Identifying the geographic boundaries of state forest regions and the habitats of endangered species in the state of New South Wales.
Future endeavors should prioritize the incorporation of an even broader range of data sources for training our AI model. This could encompass the integration of satellite-derived vegetation change data over time, soil moisture measurements, and more. Such diverse datasets can significantly improve the model's accuracy and applicability.
We see potential in expanding our work to a global level analysis.
Delving into localized quantification of endangered species can yield valuable insights. Focusing on smaller geographical areas allows for more precise conservation efforts and habitat protection. However, it's crucial to emphasize that such efforts depend heavily on comprehensive surveys and open access to relevant data sources.
In summary, these future directions encompass the integration of diverse data sources, global-scale analysis, and localized quantification of endangered species. The success of these endeavors will be contingent on collaborative survey efforts and the availability of open-access data.
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