class: monash-bg-blue center middle hide-slide-number <div class="bg-black white" style="width:45%;right:0;bottom:0;padding-left:5px;border: solid 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. See here for <a href=part2-session2.pdf>PDF <i class="fas fa-file-pdf"></i></a>. </div> <br> .white[Push the **right arrow key** to see the next slide.] --- count: false background-image: url(images/d2bg5.jpg) background-size: cover class: hide-slide-number title-slide <div class="grid-row" style="grid: 1fr / 2fr;"> .item.center[ # <span style="text-shadow: 2px 2px 30px white;">Data Visualization with R <br> Workshop Part 2</span> <!-- ## <span style="color:yellow;text-shadow: 2px 2px 30px black;">Making maps</span> --> ] .center.shade_black.animated.bounceInUp.slower[ <br><br> ## Making maps <br> Presented by Di Cook Department of Econometrics and Business Statistics <img src="images/monash-one-line-reversed.png" style="width:500px"><br> <span><i class="fas fa-envelope faa-float animated "></i></span> dicook@monash.edu <span><i class="fab fa-twitter faa-float animated faa-fast "></i></span> @statsgen .bottom_abs.width100.bg-black[ 6th Dec 2021 @ Statistical Society of Australia NSW Branch | Zoom ] ] </div> --- class: font_smaller # <img src="images/1920px-World_Health_Organization_Logo.svg.png" width="50px" style="vertical-align: middle;"> Tuberculosis incidence The TB data is from the [WHO]( https://www.who.int/tb/country/data/download/en/). ``` ## # A tibble: 40,800 × 5 ## country year age_group sex count ## <chr> <dbl> <fct> <chr> <dbl> ## 1 Afghanistan 1997 15-24 m 10 ## 2 Afghanistan 1997 25-34 m 6 ## 3 Afghanistan 1997 35-44 m 3 ## 4 Afghanistan 1997 45-54 m 5 ## 5 Afghanistan 1997 55-64 m 2 ## 6 Afghanistan 1997 65- m 0 ## 7 Afghanistan 1997 15-24 f 38 ## 8 Afghanistan 1997 25-34 f 36 ## 9 Afghanistan 1997 35-44 f 14 ## 10 Afghanistan 1997 45-54 f 8 ## # … with 40,790 more rows ``` --- class: question What is a choropleth map? -- <br> Why use a choropleth map? -- --- # How do we get a map? 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aes(x = long, y = lat)) + geom_point() + * coord_map() ``` ] ] .right-plot[ <br> <br> <img src="images/day2-session2/unnamed-chunk-4-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots .left-code[ <br> <br> Connect the dots <br> <br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, * group = group)) + geom_point() + * geom_line() + coord_map() ``` ] <br> <span class=" faa-tada animated " style=" color:#D93F00; display: -moz-inline-stack; display: inline-block; transform: rotate(0deg);">What happened?</span> ] .right-plot[ <br> <br> <img src="images/day2-session2/unnamed-chunk-5-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots .left-code[ <br> <br> Connect the dots <br> <br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, * group = group)) + #geom_point() + * geom_path() + coord_map() ``` ] ] .right-plot[ <br> <br> <img src="images/day2-session2/unnamed-chunk-6-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots <br> <br> .left-code[ This map doesn't have states and territory connections, and also sub-regions is not uniquely defining islands. <br><br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, * group = subregion)) + geom_path() + coord_map() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-7-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots <br> <br> .left-code[ We can also plot the map using `geom_polygon`, and fill with colour. <br><br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, group = group)) + * geom_polygon() + coord_map() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-8-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots <br> <br> .left-code[ Using a .monash-orange2[map theme] makes the result look more map-like <br><br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, group = group)) + geom_polygon() + coord_map() + * theme_map() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-10-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Tips for mapping .top-color-box[For data analysis, maps are a set of points, connected correctly to generate polygons.] -- .color-box[Note: It is important when converting spatial objects from a mapping software to a data analysis project is .monash-blue2["thinning" the map] to make it smaller and efficient to work with. See the `rmapshapr` package to help with this.] --- class: transition middle # Let's make a choropleth map of tuberculosis --- # Pre-process the data Aggregate counts across sex and age group for 2012 <br> .font_small[ ```r tb_2012 <- tb %>% filter(year == 2012) %>% rename(region = country) %>% group_by(region) %>% summarise(count = sum(count)) ggplot(tb_2012, aes(map_id = region)) + * geom_map(aes(fill = count), map = world_map, * color="grey70", size = 0.1, na.rm = TRUE) + expand_limits(x = world_map$long, y = world_map$lat) + scale_fill_viridis("Count") + theme_map() ``` ] --- <img src="images/day2-session2/unnamed-chunk-11-1.png" width="100%" style="display: block; margin: auto;" /> -- .corner-box[What happened to the USA? UK?] --- # Check the name matching .font_small[ ```r wm_names <- world_map %>% select(region) %>% distinct() tb_names <- tb %>% filter(year == 2012) %>% select(country) %>% distinct() *tb_miss_from_wm <- anti_join(tb_names, wm_names, * by=c("country" = "region")) *wm_miss_from_tb <- anti_join(wm_names, tb_names, * by=c("region" = "country")) ``` ] --- ```r DT::datatable(tb_miss_from_wm, width = 1150, height = 100) ``` <div id="htmlwidget-158ddb5aa30ebceb67fd" style="width:1150px;height:100px;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-158ddb5aa30ebceb67fd">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34"],["Antigua and Barbuda","Bolivia (Plurinational State of)","British Virgin Islands","Brunei Darussalam","Cabo Verde","China, Hong Kong SAR","China, Macao SAR","Congo","Côte d'Ivoire","Curaçao","Czechia","Democratic People's Republic of Korea","Eswatini","Iran (Islamic Republic of)","Lao People's Democratic Republic","Micronesia (Federated States of)","North Macedonia","Republic of Korea","Republic of Moldova","Russian Federation","Saint Kitts and Nevis","Saint Vincent and the Grenadines","Sint Maarten (Dutch part)","Syrian Arab Republic","Tokelau","Trinidad and Tobago","Tuvalu","United Kingdom of Great Britain and Northern Ireland","United Republic of Tanzania","United States of America","Venezuela (Bolivarian Republic of)","Viet Nam","Wallis and Futuna Islands","West Bank and Gaza Strip"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>country<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}]}},"evals":[],"jsHooks":[]}</script> --- ```r DT::datatable(wm_miss_from_tb, width = 1150, height = 100) ``` <div id="htmlwidget-f28709785b86e7a88866" style="width:1150px;height:100px;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-f28709785b86e7a88866">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50","51","52","53","54","55","56","57","58","59","60","61","62","63","64","65","66","67","68","69","70","71"],["Antarctica","French Southern and Antarctic Lands","Antigua","Barbuda","Saint Barthelemy","Bolivia","Brunei","Ivory Coast","Republic of Congo","Cape Verde","Curacao","Czech Republic","Canary Islands","Falkland Islands","Reunion","Mayotte","French Guiana","Martinique","Guadeloupe","Faroe Islands","Micronesia","UK","Guernsey","Heard Island","Isle of Man","Cocos Islands","Christmas Island","Chagos Archipelago","Iran","Jersey","Siachen Glacier","Nevis","Saint Kitts","South Korea","Kosovo","Laos","Liechtenstein","Saint Martin","Moldova","Macedonia","Norfolk Island","Bonaire","Sint Eustatius","Saba","Pitcairn Islands","North Korea","Madeira Islands","Azores","Palestine","Russia","Western Sahara","South Sandwich Islands","South Georgia","Saint Helena","Ascension Island","Saint Pierre and Miquelon","Swaziland","Sint Maarten","Syria","Trinidad","Tobago","Taiwan","Tanzania","USA","Vatican","Grenadines","Saint Vincent","Venezuela","Virgin Islands","Vietnam","Wallis and Futuna"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>region<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}]}},"evals":[],"jsHooks":[]}</script> --- .top-color-box[Countries often have different names across data sets. Ideally one could use isocodes (e.g. AUS, AU) but this is not common practice.] -- .code-box[.font_small[ ```r tb_fixed <- tb %>% mutate(region=recode(country, "United States of America" = "USA", "United Kingdom of Great Britain and Northern Ireland" = "UK", "Russian Federation" = "Russia", "Viet Nam" = "Vietnam", "Venezuela (Bolivarian Republic of)" = "Venezuela", "Bolivia (Plurinational State of)" = "Bolivia", "Czechia" = "Czech Republic", "Iran (Islamic Republic of)" = "Iran", "Iran (Islamic Republic of)" = "Laos", "Democratic People's Republic of Korea" = "North Korea", "Republic of Korea" = "South Korea", "United Republic of Tanzania" = "Tanzania", "Congo" = "Republic of Congo")) ``` ] ] --- <br><br> 😄 <span class=" faa-bounce animated " style=" display: -moz-inline-stack; display: inline-block; transform: rotate(0deg);">Try again!</span> .font_small[ ```r *tb_2012 <- tb_fixed %>% filter(year == 2012) %>% group_by(region) %>% summarise(count = sum(count)) ggplot(tb_2012, aes(map_id = region)) + * geom_map(aes(fill = count), map = world_map, * color = "grey70", size = 0.1, na.rm = TRUE) + expand_limits(x = world_map$long, y = world_map$lat) + scale_fill_viridis("Count") + theme_map() ``` ] --- <img src="images/day2-session2/unnamed-chunk-16-1.png" width="100%" style="display: block; margin: auto;" /> --- # Counts are typically skewed .left-code[ .font_small[ ```r ggplot(tb_2012, aes(x = count)) + geom_histogram() ``` ] <br> <br> Symmetrising count, helps visual perception of a choropleth map. <br> <br> <br> .font_small[ ```r ggplot(tb_2012, aes(x = count)) + geom_histogram() + * scale_x_log10() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-17-1.png" width="60%" style="display: block; margin: auto;" /> <img src="images/day2-session2/unnamed-chunk-18-1.png" width="60%" style="display: block; margin: auto;" /> ] --- # Choropleth on log scale <br><br> .font_small[ ```r *tb_2012_map <- world_map %>% left_join(tb_2012) ggplot(tb_2012_map, 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class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Date<\/th>\n <th>NAME<\/th>\n <th>cases<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":3},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script> ] .panel[.panel-name[R] .font_small[ ```r # Read the data # Replace null with 0, for three LGAs # Convert to long form to join with polygons # Make the date variables a proper date # Set NAs to 0, this is a reasonable assumption covid <- read_csv(here::here("data/melb_lga_covid.csv")) %>% mutate(Buloke = as.numeric(ifelse(Buloke == "null", "0", Buloke))) %>% mutate(Hindmarsh = as.numeric(ifelse(Hindmarsh == "null", "0", Hindmarsh))) %>% mutate(Towong = as.numeric(ifelse(Towong == "null", "0", Towong))) %>% pivot_longer(cols = Alpine:Yarriambiack, names_to="NAME", values_to="cases") %>% mutate(Date = ydm(paste0("2020/",Date))) %>% mutate(cases=replace_na(cases, 0)) DT::datatable(covid, width=1150, height=100) ``` ] ] ] --- ## Fix LGA names .panelset[ .panel[.panel-name[data] - Read the LGA data from `ozmaps` package. - This has LGAs for all of Australia. - Need to filter out Victoria LGAs, avoiding LGAs from other states with same name, and make the names match covid data names. This is done using a regex expression removing () state and LGA type text strings ] .panel[.panel-name[R] .font_small[ ```r data("abs_lga") vic_lga <- abs_lga %>% mutate(NAME = ifelse(NAME == "Latrobe (M) (Tas.)", "LatrobeM", NAME)) %>% mutate(NAME = ifelse(NAME == "Kingston (DC) (SA)", "KingstonSA", NAME)) %>% mutate(NAME = ifelse(NAME == "Bayside (A)", "BaysideA", NAME)) %>% mutate(NAME = str_replace(NAME, " \\(.+\\)", "")) %>% mutate(NAME = ifelse(NAME == "Colac-Otway", "Colac Otway", NAME)) vic_lga <- st_transform(vic_lga, 3395) # 3395 is EPSG CRS, equiv to WGS84 mercator, # see https://spatialreference.org/ref/epsg/?page=28 # cartogram() needs this to be set ``` ] ] ] --- ## Join and colour the map .panelset[ .panel[.panel-name[plot] <img src="images/day2-session2/covid-choropleth-1.png" width="50%" style="display: block; margin: auto;" /> ] .panel[.panel-name[R] .font_small[ ```r # Filter to final day, which is cumulative count covid_cum <- covid %>% filter(Date == max(Date)) # Join covid data to polygon data, remove LGAs with # missing values which should leave just Vic LGAs vic_lga_covid <- vic_lga %>% left_join(covid_cum, by="NAME") %>% filter(!is.na(cases)) # Make choropleth map, with appropriate colour palette p <- ggplot(vic_lga_covid) + geom_sf(aes(fill = cases, label=NAME), colour="white") + scale_fill_distiller("Cases", palette = "YlOrRd", direction=1) + theme_map() + theme(legend.position="bottom") p #ggplotly(p) ``` ] ] ] --- ## Get population data .panelset[ .panel[.panel-name[data] - Incorporate population data to make cartogram - Population from https://www.planning.vic.gov.au/land-use-and-population-research/victoria-in-future/tab-pages/victoria-in-future-data-tables - Polygons are transformed so that area matches, as best possible, to the population <div id="htmlwidget-f0954878792e2a01ad97" style="width:1150px;height:100px;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-f0954878792e2a01ad97">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50","51","52","53","54","55","56","57","58","59","60","61","62","63","64","65","66","67","68","69","70","71","72","73","74","75","76","77","78","79"],["Alpine","Ararat","Ballarat","Banyule","Bass Coast","Baw Baw","Bayside","Benalla","Boroondara","Brimbank","Buloke","Campaspe","Cardinia","Casey","Central Goldfields","Colac Otway","Corangamite","Darebin","East Gippsland","Frankston","Gannawarra","Glen Eira","Glenelg","Golden Plains","Greater Bendigo","Greater Dandenong","Greater Geelong","Greater Shepparton","Hepburn","Hindmarsh","Hobsons Bay","Horsham","Hume","Indigo","Kingston","Knox","Latrobe","Loddon","Macedon Ranges","Manningham","Mansfield","Maribyrnong","Maroondah","Melbourne","Melton","Mildura","Mitchell","Moira","Monash","Moonee Valley","Moorabool","Moreland","Mornington Peninsula","Mount Alexander","Moyne","Murrindindi","Nillumbik","Northern Grampians","Port Phillip","Pyrenees","Queenscliffe","South Gippsland","Southern Grampians","Stonnington","Strathbogie","Surf Coast","Swan Hill","Towong","Wangaratta","Warrnambool","Wellington","West Wimmera","Whitehorse","Whittlesea","Wodonga","Wyndham","Yarra","Yarra Ranges","Yarriambiack"],[12578,11746,103500,127447,33465,49296,102912,13981,177276,204190,6284,37596,97573,312789,13085,21363,16241,155126,45599,139502,10567,148583,19759,22016,112271,160222,239530,65071,15527,5787,93445,19885,207038,16166,158938,160353,74622,7559,47480,122571,8674,86942,114799,146097,141422,54658,41795,29486,192625,122871,32669,172289,161528,19094,16738,14053,64174,11570,108627,7315,2927,29121,16123,111003,10357,30465,20896,6046,28592,34243,43531,3933,169642,207058,40100,227008,92899,155227,6743]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>NAME<\/th>\n <th>pop<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":2},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script> ] .panel[.panel-name[R] .font_small[ ```r pop <- read_xlsx("../data/VIF2019_Population_Service_Ages_LGA_2036.xlsx", sheet=3, skip=13, col_names = FALSE) %>% select(`...4`, `...22`) %>% rename(NAME = `...4`, pop=`...22`) %>% filter(NAME != "Unincorporated Vic") %>% mutate(NAME = str_replace(NAME, " \\(.+\\)", "")) %>% mutate(NAME = ifelse(NAME == "Colac-Otway", "Colac Otway", NAME)) %>% mutate(pop = round(pop, 0)) DT::datatable(pop, width=1150, height=100) vic_lga_covid <- vic_lga_covid %>% left_join(pop, by="NAME") # Compute additional statistics vic_lga_covid <- vic_lga_covid %>% group_by(NAME) %>% mutate(cases_per10k = max(cases/pop*10000, 0), lnew_cases = log10(cases - min(cases) + 1)) %>% ungroup() ``` ] ] ] --- ## Make a cartogram .panelset[ .panel[.panel-name[plot] <img src="images/day2-session2/vic-cartogram-1.png" width="50%" style="display: block; margin: auto;" /> ] .panel[.panel-name[R] .font_small[ ```r # Make a contiguous cartogram vic_lga_covid_carto <- cartogram_cont(vic_lga_covid, "pop") # This st_cast() is necessary to get plotly to work vic_lga_covid_carto <- st_cast(vic_lga_covid_carto, "MULTIPOLYGON") p <- ggplot(vic_lga_covid_carto) + geom_sf(aes(fill = cases, label=NAME), colour="white") + scale_fill_distiller("Cases", palette = "YlOrRd", direction=1) + theme_map() + theme(legend.position="bottom") p ``` ] ] ] --- class: transition middle # Raster map with data overlaid --- .left-code[ .font_small[ ```r load(here::here("data/platypus.rda")) p <- ggplot(platypus) + geom_point(aes(x = Longitude, y = Latitude), alpha = 0.1) p ``` <img src="images/day2-session2/unnamed-chunk-27-1.png" width="100%" style="display: block; margin: auto;" /> ] ] .right-plot[ <br> <br> .font_small[ ```r p + coord_map() ``` <img src="images/day2-session2/unnamed-chunk-28-1.png" width="100%" style="display: block; margin: auto;" /> ] ] --- # Extract Open Street Map using `ggmap` <br><br> Download and save the map, so that you don't need to do multiple downloads. <br><br> .font_small[ ```r oz_bbox <- c(112.9, # min long -45, # min lat 159, # max long -10) # max lat *oz_map <- get_map(location = oz_bbox, source = "osm") save(oz_map, file=here::here("data/oz_map.rda")) ``` ] --- # Platypus occurrences across Australia <br><br> .left-code[ .font_small[ ```r load(here::here("data/oz_map.rda")) ggmap(oz_map) + geom_point(data = platypus, aes(x = Longitude, y = Latitude), alpha = 0.1, colour = "orange") + theme_map() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-31-1.png" width="100%" style="display: block; margin: auto;" /> ] --- class: exercise middle hide-slide-number <i class="fas fa-users"></i> # <i class="fas fa-code"></i> Open `part2-exercise-02.Rmd` <center> <div class="countdown clock" id="timer_61a7000a" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">15</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> </center> --- # Resources These are sites with lots of useful information about making maps in R: <br> - `ozmaps` package: https://github.com/mdsumner/ozmaps, https://mdsumner.github.io/ozmaps/ - `strayr` package: https://runapp-aus.github.io/strayr/ - https://www.littlemissdata.com/blog/maps - https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html - https://www.paulamoraga.com/book-geospatial/sec-spatialdataandCRS.html - https://rspatialdata.github.io --- class: font_smaller background-color: #e5e5e5 # Session Information .scroll-350[ ```r devtools::session_info() ``` ``` ## 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Github (r-lib/rlang@5aefc4a) ## Github (rstudio/rmarkdown@ebf0d09) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## Github (gadenbuie/xaringanExtra@5e2d80b) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## CRAN (R 4.1.0) ## ## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library ``` ] These slides are licensed under <br><center><a href="https://creativecommons.org/licenses/by-sa/3.0/au/"><img src="images/cc.svg" style="height:2em;"/><img src="images/by.svg" style="height:2em;"/><img src="images/sa.svg" style="height:2em;"/></a></center>