background-color: #006DAE class: middle center hide-slide-number <div class="shade_black" style="width:60%;right:0;bottom:0;padding:10px;border: dashed 4px white;margin: auto;"> <i class="fas fa-exclamation-circle"></i> These slides are viewed best by Chrome and occasionally need to be refreshed if elements did not load properly. </div> <br> .white[Press the **right arrow** to progress to the next slide!] --- background-image: url(images/titleimage.png) background-size: cover class: hide-slide-number split-70 title-slide count: false .column.shade_black[.content[ <br> # .monash-blue.outline-text[The Role of R and Data Visualisation in Understanding Your World] <h2 class="monash-blue2 outline-text" style="font-size: 30pt!important;"></h2> <br> <h2 style="font-weight:900!important;"></h2> .bottom_abs.width100[ *Di Cook*
<i class="fas fa-link faa-float animated "></i>
[https://bit.ly/Cook-MalaysiaR](https://bit.ly/Cook-MalaysiaR) Malaysia R, Nov 7 2021 <br> ] ]] <div class="column transition monash-m-new delay-1s" style="clip-path:url(#swipe__clip-path);"> <div class="background-image" style="background-image:url('images/large.png');background-position: center;background-size:cover;margin-left:3px;"> <svg class="clip-svg absolute"> <defs> <clipPath id="swipe__clip-path" clipPathUnits="objectBoundingBox"> <polygon points="0.5745 0, 0.5 0.33, 0.42 0, 0 0, 0 1, 0.27 1, 0.27 0.59, 0.37 1, 0.634 1, 0.736 0.59, 0.736 1, 1 1, 1 0, 0.5745 0" /> </clipPath> </defs> </svg> </div> </div> --- # Outline <br><br> - Motivation - Examples - OECD PISA standardised scores - Bush fire causes - Open data, open source software, plotting data, statistical thinking - How do you get started or further develop your skills? --- background-image: url(images/australia_slipping.png) background-size: cover --- # Media coverage: Test scores <br><br>Every time the OECD PISA scores are released there are press articles lamenting the [decline in Australian scores]((https://theconversation.com/vital-signs-australias-slipping-student-scores-will-lead-to-greater-income-inequality-128301). And how badly Australian girls perform in math relative to boys. -- <br><br>The same can be observed in other countries, including [New Zealand]((https://www.stuff.co.nz/national/education/117890945/new-zealand-topend-in-oecds-latest-pisa-report-but-drop-in-achievements-worrying), and [Indonesia]((https://www.thejakartapost.com/news/2019/12/04/not-even-mediocre-indonesian-students-score-low-in-math-reading-science-pisa-report.html). --- # Media coverage of Australian bushfires (2019-2020) Who do you believe? Is it lightning đŠī¸ or đĨ arson? <a href="https://twitter.com/MRobertsQLD/status/1220588928706568193"> <img src="images/1602783588.png" width = "50%" style = "float: left"/> </a> <img src="images/bushfire-inforgraphic-not-normal-768x768.jpg" width = "50%" style = "float: right"/> --- class: transition middle # 1. Test scores <br> # 2. Bushfire ignition causes --- class: transition middle # OECD PISA Standardised test scores --- # Getting the data <br> <br> <br> - [OECD PISA](https://www.oecd.org/pisa/): Testing of 15 yr olds conducted every three years since 2000, from 43 to now 90 countries, and from 125k to now 600k students - `learningtower` package in R (Wang, Yacobellis, Siregar, Romanes, Fitter, Valentino Dalla Riva, Cook, Tierney, Dingorkar, 2021) --- # Change in scores (and countries participating) over time .pull-left[ <br> <img src="slides_files/figure-html/unnamed-chunk-2-1.gif" width="110%" /> ] .pull-right[ <br><br> Australia has performed consistently well over the years. Small drift down in recent years. <br><br> Malaysia makes an appearance in 2009, drops out in 2015 and returns 2018. <br> Qatar and Singapore enter late and grow quickly ] --- # Gender gap: selected countries 2018 scores <img src="slides_files/figure-html/PISA-1.png" width="100%" /> <center> Math gap .monash-blue2[is not] universal. Reading gap .monash-blue2[is] universal. </center> --- # Math gap: 2018 scores <img src="slides_files/figure-html/math_map-1.png" width="100%" /> --- # Reading gap: 2018 scores <img src="slides_files/figure-html/read_map-1.png" width="100%" /> --- # What we learn <br> <br><br> - Australia scores consistently highly, maybe small decline in recent years - Gender gap is in reading, not math --- class: transition middle # Bush fire ignition cause --- # đ Data Sources .monash-red2[**đĨ Historical fire origins**]: 2000-2019 .font_my_2[[Department of Environment, Land, Water and Planning](https://discover.data.vic.gov.au/dataset/fire-origins-current-and-historical)] .monash-red2[**đĄ Remote sensing data**]: .font_my_2[[Japan Aerospace Exploration Agency](https://www.eorc.jaxa.jp/ptree/userguide.html)] .font_my[ **Wind speed data**: 1-day, 7-day, ..., 2-year averages from .font_my_2[[Commonwealth Scientific and Industrial Research Organisation and Automated Surface Observing System](https://doi.org/10.25919/5c5106acbcb02)] **Temperature, Rainfall and Solar exposure**: 1-day, 7-day , 14-day, 28-day, ..., 720-day averages computed from .font_my_2[[Bureau of Meteorology](https://CRAN.R-project.org/package=bomrang)] **Fuel layer**: Forest type, forest height class, forest crown cover from .font_my_2[[Australian Bureau of Agricultural and Resource Economics](https://www.agriculture.gov.au/abares/forestsaustralia/forest-data-maps-and-tools/spatial-data/forest-cover)] **Road map**: Proximity to the nearest road using .font_my_2[[OpenStreetMap](%20https://www.openstreetmap.org%20)] **Fire stations**: Proximity to the nearest CFA station .font_my_2[[Department of Environment, Land, Water and Planning](https://discover.data.vic.gov.au/dataset/cfa-fire-station-vmfeat-geomark_point)] **Recreation sites**: Proximity to the nearest camping site .font_my_2[[Department of Environment, Land, Water and Planning](https://discover.data.vic.gov.au/dataset/recreation-sites)] ] --- # đĄ Remote sensing data Japan Aerospace Exploration Agency provides a hotspot product (reflected energy from the earth) taken from the **Himawari-8** satellite, access as described in [Williamson gist](https://gist.github.com/ozjimbob/80254988922140fec4c06e3a43d069a6) <img src="images/hotspots_before.png" style="width: 80%; float:center"/> --- # đģ Data fusion <img src="images/data_fusion.png" style="width: 100%; float:center"/> --- # Detect ignitions by clustering hotspot data <img src="images/clustering1.png" style="width: 90%; float:center"/> <img src="images/clustering2.png" style="width: 90%; float:center"/> Algorithm available in the `spotoroo` package (Li, Cook, Dodwell, 2021) and documented [here](https://github.com/TengMCing/Hotspots-Clustering-Algorithm/tree/master/paper-RJ). --- # đģ Estimated ignition spots 76,000 hotspots reduced to 1,000 ignition sites. <img src="images/hotspots_after.png" style="width: 100%; float:left"/> <!-- <img src="images/hotspots_before_summary.png" style="width: 50%; float:right"/> --> --- # đ Exploratory analysis of historical fire origins .font_my_2[ Text processing of 26 causes, reduced to four major causes. Lightning and accident were the two main sources of historical bushfire ignitions, which took up 41% and 34% respectively. There were 17% bushfires caused by arson. ] <img src="images/ignition_summary.png" style="width: 50%; float:left"/> <img src="images/ignition_year.png" style="width: 50%; float:right"/> --- # đ Spatial distribution of historical fire origins .font_my_2[ Roughly different spatial locations of ignition causes. Lightning bushfires were concentrated in the east of Victoria. Bushfires caused by arson were near Bendigo! ] <img src="images/density.png" style="width: 100%; float:left"/> --- # đ Proximity of historical fire origins .font_my_2[ Lightning-caused bushfires were further away from the CFA stations and roads. In contrast, bushfires caused by arson were closer to CFA stations and roads. ] <img src="images/density_cfa.png" style="width: 100%; float:left"/> --- # đ Model performance <br> <br> .monash-blue[The overall accuracy of our model was 74.95%.] - High accuracy with lightning and accident ignitions. - Less accurate predictions for arson and burning off. <br> <table class=" lightable-classic table" style="font-family: Cambria; width: auto !important; margin-left: auto; margin-right: auto; font-size: 20px; margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;"> </th> <th style="text-align:left;"> Lightning </th> <th style="text-align:right;"> Accident </th> <th style="text-align:right;"> Arson </th> <th style="text-align:right;"> Burning_off </th> <th style="text-align:right;"> Total </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Prediction:Lightning </td> <td style="text-align:left;"> 703 (0.9) </td> <td style="text-align:right;"> 77 (0.12) </td> <td style="text-align:right;"> 50 (0.15) </td> <td style="text-align:right;"> 44 (0.32) </td> <td style="text-align:right;"> 874 </td> </tr> <tr> <td style="text-align:left;"> Prediction:Accident </td> <td style="text-align:left;"> 51 (0.07) </td> <td style="text-align:right;"> 494 (0.78) </td> <td style="text-align:right;"> 89 (0.27) </td> <td style="text-align:right;"> 38 (0.28) </td> <td style="text-align:right;"> 672 </td> </tr> <tr> <td style="text-align:left;"> Prediction:Arson </td> <td style="text-align:left;"> 18 (0.02) </td> <td style="text-align:right;"> 55 (0.09) </td> <td style="text-align:right;"> 175 (0.54) </td> <td style="text-align:right;"> 22 (0.16) </td> <td style="text-align:right;"> 270 </td> </tr> <tr> <td style="text-align:left;"> Prediction:Burning_off </td> <td style="text-align:left;"> 5 (0.01) </td> <td style="text-align:right;"> 8 (0.01) </td> <td style="text-align:right;"> 11 (0.03) </td> <td style="text-align:right;"> 32 (0.24) </td> <td style="text-align:right;"> 56 </td> </tr> <tr> <td style="text-align:left;"> Total </td> <td style="text-align:left;"> 777 </td> <td style="text-align:right;"> 634 </td> <td style="text-align:right;"> 325 </td> <td style="text-align:right;"> 136 </td> <td style="text-align:right;"> 1872 </td> </tr> </tbody> </table> --- # đ **Prediction for 2019-2020 Australia bushfires**
--- # đ Summary of findings .monash-blue[- Majority of the bushfires in 2019-2020 season were caused by **lightning**.] - 138 bushfires caused by accidents which took up 14% of the total fires. Most of them were ignited in March. - 37 bushfires were caused by arsonists, and over half of them were in March. - Very few planned burns were predicted after October 2019 which suggests the correctness of our model. <br> <table class=" lightable-classic table" style="font-family: Cambria; width: auto !important; margin-left: auto; margin-right: auto; font-size: 20px; margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;"> Cause </th> <th style="text-align:right;"> Oct </th> <th style="text-align:right;"> Nov </th> <th style="text-align:right;"> Dec </th> <th style="text-align:right;"> Jan </th> <th style="text-align:right;"> Feb </th> <th style="text-align:right;"> Mar </th> <th style="text-align:right;"> Total </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Lightning </td> <td style="text-align:right;"> 19 </td> <td style="text-align:right;"> 57 </td> <td style="text-align:right;"> 315 </td> <td style="text-align:right;"> 266 </td> <td style="text-align:right;"> 32 </td> <td style="text-align:right;"> 149 </td> <td style="text-align:right;"> 838 (0.82) </td> </tr> <tr> <td style="text-align:left;"> Accident </td> <td style="text-align:right;"> 3 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:right;"> 34 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 80 </td> <td style="text-align:right;"> 138 (0.14) </td> </tr> <tr> <td style="text-align:left;"> Arson </td> <td style="text-align:right;"> 2 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 21 </td> <td style="text-align:right;"> 37 (0.04) </td> </tr> <tr> <td style="text-align:left;"> Burning_off </td> <td style="text-align:right;"> 7 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 9 (0.01) </td> </tr> </tbody> </table> --- # Shiny app: https://ebsmonash.shinyapps.io/VICfire/ <iframe src="https://ebsmonash.shinyapps.io/VICfire/?showcase=0" width="110%" height="550px" data-external="1"></iframe> --- # What we learn <br><br><br><br> The catastrophic bush fires of 2019-2020 were primarily ignited by lightning --- class: transition middle # Getting started and further developing your skills --- # Recommended resources <br> - [R for Data Science](https://r4ds.had.co.nz): An introduction to data analysis using R, with a tidyverse frame of mind. - [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/): Learn to make effective data plots. - [Statistical Thinking for the 21st Century](https://statsthinking21.github.io/statsthinking21-core-site/index.html): Harness computational tools to explore variation and uncertainty. - [Tidymodels ](https://www.tidymodels.org): Develop your statistical modeling and machine learning systematically and cohesively. - [Tidy Tuesday](https://github.com/rfordatascience/tidytuesday): Practice your skills on a wide range of contemporary data sets. --- # Summary <br><br> - I've shown you two examples where I have learned a lot about a topic in the news, with just .monash-blue2[open data] and my .monash-blue2[R skills], .monash-blue2[statistics] background and .monash-blue2[data visualisation skills]. - With open source software, that is .monash-blue2[R], and open data you can learn, a little statistical thinking, good data plotting skills, .monash-orange2[you too] can learn a lot about the world around you. - These tools are .monash-blue2[powerful social and economic equalisers] for today's world, and can equip you to .monash-blue2[combat misinformation], and work to develop a .monash-blue2[better environment] around you. --- background-image: url(images/titleimage.png) background-size: cover class: hide-slide-number split-70 count: false .column.shade_black[.content[ <br><br> ## Acknowledgements [https://bit.ly/Cook-MalaysiaR](https://bit.ly/Cook-MalaysiaR) Slides produced using [Rmarkdown](https://github.com/rstudio/rmarkdown) with [xaringan](https://github.com/yihui/xaringan) styling. Monash style by the kunoichi, Dr Emi Tanaka. `spotoroo` package is available on CRAN and [Patrick's GitHub repo](https://github.com/TengMCing/spotoroo). Current developments are available at [bushyr GitHub repo](https://github.com/numbats/bushyr). # Thanks for listening! <br> <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>. ]] <div class="column transition monash-m-new delay-1s" style="clip-path:url(#swipe__clip-path);"> <div class="background-image" style="background-image:url('images/large.png');background-position: center;background-size:cover;margin-left:3px;"> <svg class="clip-svg absolute"> <defs> <clipPath id="swipe__clip-path" clipPathUnits="objectBoundingBox"> <polygon points="0.5745 0, 0.5 0.33, 0.42 0, 0 0, 0 1, 0.27 1, 0.27 0.59, 0.37 1, 0.634 1, 0.736 0.59, 0.736 1, 1 1, 1 0, 0.5745 0" /> </clipPath> </defs> </svg> </div> </div>