44:["$","title",null,{"children":"Global Cyclone Frequency and Mortality | Ben Oliver | Observable"}] 4c:T46c,# First Revision These kind of stink. It's hard to tell where the countries are and the graticules are not rendering. Even getting a TIFF file I could use was tricky, as I accidentally picked a dataset that was in ASCII format. I converted the .asc file into a .tif , which was an INSANE pain to set up in the first place. Apparently `pip install gdal` is horrendously broken and widely known to be broken, as you have to install your own wheel file and install it locally or use a package manager name Anaconda to get it (`conda install -c conda-forge gdal`), but I figured it out. For the first map, the Global Multihazard Mortality Frequency and Distribution helps identify and describe the rate of occurence of the hazards considered in the study that led to the creation of this dataset, which were cyclones (hurricanes), droughts, earthquakes, floods, landslides, and volcanoes. For the second map, the Global Multihazard Mortality Risks and Distribution helps identify and describe the risks of the same hazards considered in the study from the first graph. Also, **good lord** do these take a long time to render. ---4d:Ta25,# Revision 4 Holy cow, I thought I had somehow accidentally bricked my whole notebook. When I tried to add the toggle functionality, my notebook had all of the previous rasters maps wiped, and buffered for 3 hours straight. I added the following modifications to my code and I will never make the same mistake again. ```js viewof displayMode = Inputs.toggle(["Mortality Risk", "Hazard Frequency"], {label: "Select Dataset"}) ``` This created my toggle. Then I used it to change the title and raster: ```js title: displayMode === "Mortality Risk" ? "Global Cyclone Mortality Risks and Distribution" : "Global Cyclone Frequency and Distribution", ``` and ```js displayMode === "Mortality Risk" ? Plot.raster(hazardmort_raster[0], { width: hazardmort_raster.width, height: hazardmort_raster.height, x1: hazardmort_box[0], y1: hazardmort_box[3], x2: hazardmort_box[2], y2: hazardmort_box[1], interpolate: "barycentric", clip: "sphere" }) : Plot.raster(hazardfreq_raster[0], { width: hazardfreq_raster.width, height: hazardfreq_raster.height, x1: hazardfreq_box[0], y1: hazardfreq_box[3], x2: hazardfreq_box[2], y2: hazardfreq_box[1], interpolate: "barycentric", clip: "sphere" }), ``` I also tried pre-baking each map and than using some html to swap between them, and I could not get that to work ``` html

\${frequency}

\${mortality}

``` I even tried to shrink my TIFF files to a quarter of their original size using a rasterIO python script and that did not help. Either I am handling these files very wrongly or Observable does not have the capacity to deal with even low-resolution global rasters. I don't know if there is a good way to show the time I spent trying to get this to work, I even tried re-converting my original .asc file into a .tif though ArcGIS Pro instead of GDAL (it's pretty cool that we get a free subscription to this insane software just by being a student on our campus), and that did not do anything to fix my rasters. I did find a color scheme "Turbo" that I like more than "Cividis". - Ocean is black, which is way more visually appealing in my opinion - Stark changes in frequency and mortality are a lot more clear - Still preserves the data of the "Cividis" map --- 4e:T505,# Revision 5 Since I cant overlay them or make a dynamic toggle that works, I am just going to put them next to each other. I give up. You can see some interesting trends though. For all the talk in the US we have about hurricanes ravaging the southeastern US, It appears there are SO MANY on the eastern coasts of Asia and (even more surprisingly) Madagascar. The entirety of Japan is the same color as the worst parts of the US. However, when we add in the mortality element, it get smore interesting. Eastern India is blue-green on the frequency map, but is a deep, dark red on the mortality map. Cuba, Haiti, and the Dominican Republic share a similar pattern to eastern India. Japan has an opposite relationship to those four, where its frequency is that same deep, dark red, but the mortality is green-yellow with a few light to dark red spots. In 2000, according to the [UN's Human Development Index](https://hdr.undp.org/data-center/human-development-index#/indicies/HDI), India had a HDI of 0.490, Haiti had a HDI of 0.485, and the Dominican Republic had a 0.646. In 200, the world average HDI was 0.645, and Japan had a HDI of 0.883. I would speculate tat the difference in the observed trends can be attributed to socioeconomic development and infrastructure resilience.4f:[{"id":"7b6ff41e373e01d7b5b95773e297d40625bd9ccc1936a023a066a7edd8da5eaadec4ab7a565303539e41e001f2e6730f3ee1e259fae4f19dc59e8d6b2f2ec22b","href":"https://static.observableusercontent.com/files/7b6ff41e373e01d7b5b95773e297d40625bd9ccc1936a023a066a7edd8da5eaadec4ab7a565303539e41e001f2e6730f3ee1e259fae4f19dc59e8d6b2f2ec22b","name":"land-50m.json","type":"application/json","lastModified":1729232740353.201,"size":545534},{"id":"7c6167b65013c12f3978b4d8e85dd28a27e3b5eb580d1e76696ce5b0d399c196de2b02c45e734462931e1af823698e36bb072722253d5e03e7fb61222755bc0c","href":"https://static.observableusercontent.com/files/7c6167b65013c12f3978b4d8e85dd28a27e3b5eb580d1e76696ce5b0d399c196de2b02c45e734462931e1af823698e36bb072722253d5e03e7fb61222755bc0c","name":"countries-110m.json","type":"application/json","lastModified":1729235878379.36,"size":107761},{"id":"b18899d7b1372577c8fd0b2a608d29b5c997971ee0cf85030b1115f2ebc5f5668551f4423f224bca4e522b6f813a5bcc408ab13029b61a84d8eeeb38d6b9c9b6","href":"https://static.observableusercontent.com/files/b18899d7b1372577c8fd0b2a608d29b5c997971ee0cf85030b1115f2ebc5f5668551f4423f224bca4e522b6f813a5bcc408ab13029b61a84d8eeeb38d6b9c9b6","name":"gdcycmort.tif","type":"image/tiff","lastModified":1729262161479.028,"size":147725},{"id":"bd75159cc12306100bd2794957debd10266472ec7ab1d3aa5e72f0ffb4453a619241ac5b62495bddc490cbec8a5d90eb381fbbca4299183df390fc1735225dd1","href":"https://static.observableusercontent.com/files/bd75159cc12306100bd2794957debd10266472ec7ab1d3aa5e72f0ffb4453a619241ac5b62495bddc490cbec8a5d90eb381fbbca4299183df390fc1735225dd1","name":"gdcycfreq.tif","type":"image/tiff","lastModified":1729262161840.177,"size":135708}] 48:{"id":"ad8ab7311274f0fd","slug":"project-3-raster-data","title":"Global Cyclone Frequency and Mortality","thumbnail":null,"access_level":"unlisted","create_time":"$D2024-10-18T06:25:40.353Z","update_time":"$D2024-10-21T16:19:23.435Z","view_time":null,"first_public_time":null,"has_unpublished_edits":null,"fork":{"id":"19cbc978f7478a7b","slug":"plot-world-map","title":"Plot World Map","owner":{"id":"f35c755083683fe5","login":"observablehq","name":"Observable"}},"stars":0,"starred":false,"owner":{"type":"team","id":"146bb800b87ac42c","login":"ben-oliver","name":"Ben Oliver","avatar_url":"https://avatars-next.observableusercontent.com/a23317358e59ce3f36c17b9cc6fb372b110b82005792fb60e51c7fd985ec9460","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":368,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"42973841b2b730da","login":"ben-oliver","name":"Ben Oliver","avatar_url":"https://avatars-next.observableusercontent.com/b9ff4cb31ca9911e61c547136194e1473087214dd7ee051897935025ef6ab2d1","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"146bb800b87ac42c","login":"ben-oliver","name":"Ben Oliver","avatar_url":"https://avatars-next.observableusercontent.com/a23317358e59ce3f36c17b9cc6fb372b110b82005792fb60e51c7fd985ec9460","tier":"starter_2024","role":"editor"},{"type":"individual","id":"42973841b2b730da","login":"ben-oliver","name":"Ben Oliver","avatar_url":"https://avatars-next.observableusercontent.com/b9ff4cb31ca9911e61c547136194e1473087214dd7ee051897935025ef6ab2d1","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Global Cyclone Frequency and Mortality","cells":[{"id":0,"value":"# Global Cyclone Frequency and Mortality\n\nRegions with higher cyclone frequency ( found in `gdcycfreq.tif`) are likely to experience higher cyclone-related mortality rates (found in `gdcycmort.tif`), but the relationship may vary depending on factors such as socioeconomic development, early warning systems, and infrastructure resilience.\n\n\n---\n\n### Data from:\n\n[Global Cyclone Frequency and Distribution, v1 (2000)](https://sedac.ciesin.columbia.edu/data/set/ndh-cyclone-hazard-frequency-distribution)\n\n[Global Cyclone Mortality Risks and Distribution, v1 (2000)](https://sedac.ciesin.columbia.edu/data/set/ndh-cyclone-mortality-risks-distribution)\n\n---\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Frequency and Distribution\",\n color: {\n scheme: \"Viridis\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n Plot.graticule(),\n\n Plot.raster(hazardfreq_raster[0], { \n width: hazardfreq_raster.width,\n height: hazardfreq_raster.height,\n x1: hazardfreq_box[0], \n y1: hazardfreq_box[3],\n x2: hazardfreq_box[2],\n y2: hazardfreq_box[1],\n interpolate: \"nearest\",\n clip: \"sphere\"\n }),\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Mortality Risks and Distribution\",\n color: {\n scheme: \"Viridis\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n Plot.graticule(),\n\n Plot.raster(hazardmort_raster[0], { \n width: hazardmort_raster.width,\n height: hazardmort_raster.height,\n x1: hazardmort_box[0], \n y1: hazardmort_box[3],\n x2: hazardmort_box[2],\n y2: hazardmort_box[1],\n interpolate: \"nearest\",\n clip: \"sphere\"\n }),\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":139,"value":"$4c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":164,"value":"Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Frequency and Distribution\",\n color: {\n scheme: \"Viridis\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n \n\n Plot.raster(hazardfreq_raster[0], { \n width: hazardfreq_raster.width,\n height: hazardfreq_raster.height,\n x1: hazardfreq_box[0], \n y1: hazardfreq_box[3],\n x2: hazardfreq_box[2],\n y2: hazardfreq_box[1],\n interpolate: \"nearest\",\n clip: \"sphere\"\n }),\n\n Plot.geo(d3.geoGraticule().step([30, 15])(), {\n stroke: \"gray\",\n strokeOpacity: 0.5\n }),\n\n Plot.geo(countries, {\n fill : \"none\", \n stroke: \"#989898\",\n })\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":154,"value":"Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Mortality Risks and Distribution\",\n color: {\n scheme: \"Viridis\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n\n Plot.raster(hazardmort_raster[0], { \n width: hazardmort_raster.width,\n height: hazardmort_raster.height,\n x1: hazardmort_box[0], \n y1: hazardmort_box[3],\n x2: hazardmort_box[2],\n y2: hazardmort_box[1],\n interpolate: \"nearest\",\n clip: \"sphere\"\n }),\n\n Plot.geo(d3.geoGraticule().step([30, 15])(), {\n stroke: \"gray\",\n strokeOpacity: 0.5\n }),\n\n Plot.geo(countries, {\n fill : \"none\", \n stroke: \"#989898\",\n }),\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":189,"value":"# Revision 2\n\nNow that is way better! I still dont have a great way to overlap them but at least this looks a lot more appealing.\n\nGetting country borders in here was pretty tricky, I had to find a new .json to use, I could not figure out how to get country borders from the one that comes with the default Observable \"World Map\" when you fork it.\n\nIt seeems like despite not having as many disasters as other places in the word, sub-Saharan Africa is at really high rish of having very deadly disasters!\n\nI think I will find a more appealing color scheme though, this one seems a little stark and maybe not the most colorblind-friendly.\n\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":197,"value":"Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Frequency and Distribution\",\n color: {\n scheme: \"Cividis\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n \n\n Plot.raster(hazardfreq_raster[0], { \n width: hazardfreq_raster.width,\n height: hazardfreq_raster.height,\n x1: hazardfreq_box[0], \n y1: hazardfreq_box[3],\n x2: hazardfreq_box[2],\n y2: hazardfreq_box[1],\n interpolate: \"barycentric\",\n clip: \"sphere\"\n }),\n\n Plot.geo(d3.geoGraticule().step([30, 15])(), {\n stroke: \"gray\",\n strokeOpacity: 0.5\n }),\n\n Plot.geo(countries, {\n fill : \"none\", \n stroke: \"#989898\",\n })\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":195,"value":"Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Mortality Risks and Distribution\",\n color: {\n scheme: \"Cividis\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n\n Plot.raster(hazardmort_raster[0], { \n width: hazardmort_raster.width,\n height: hazardmort_raster.height,\n x1: hazardmort_box[0], \n y1: hazardmort_box[3],\n x2: hazardmort_box[2],\n y2: hazardmort_box[1],\n interpolate: \"barycentric\",\n clip: \"sphere\"\n }),\n\n Plot.geo(d3.geoGraticule().step([30, 15])(), {\n stroke: \"gray\",\n strokeOpacity: 0.5\n }),\n\n Plot.geo(countries, {\n fill : \"none\", \n stroke: \"#989898\",\n }),\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":206,"value":"# Third Revision\n\nSwitching to the \"Civdis\" color scheme has made this a decent amount better in my opinion\n - Way less saturated\n - Much more colorblind-friendly\n - Still effectively communicates the data\n\nI tried overlaying the two raster, and that didn't work because of layering. I wish there was way you could do opacity for these guys. Going to make an interactive toggle.\n\n---\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":278,"value":"Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Frequency and Distribution\",\n color: {\n scheme: \"Turbo\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n \n\n Plot.raster(hazardfreq_raster[0], { \n width: hazardfreq_raster.width,\n height: hazardfreq_raster.height,\n x1: hazardfreq_box[0], \n y1: hazardfreq_box[3],\n x2: hazardfreq_box[2],\n y2: hazardfreq_box[1],\n interpolate: \"barycentric\",\n clip: \"sphere\"\n }),\n\n Plot.geo(d3.geoGraticule().step([30, 15])(), {\n stroke: \"gray\",\n strokeOpacity: 0.5\n }),\n\n Plot.geo(countries, {\n fill : \"none\", \n stroke: \"#989898\",\n })\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":232,"value":"Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Mortality Risks and Distribution\",\n color: {\n scheme: \"Turbo\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n\n Plot.raster(hazardmort_raster[0], { \n width: hazardmort_raster.width,\n height: hazardmort_raster.height,\n x1: hazardmort_box[0], \n y1: hazardmort_box[3],\n x2: hazardmort_box[2],\n y2: hazardmort_box[1],\n interpolate: \"barycentric\",\n clip: \"sphere\"\n }),\n\n Plot.geo(d3.geoGraticule().step([30, 15])(), {\n stroke: \"gray\",\n strokeOpacity: 0.5\n }),\n\n Plot.geo(countries, {\n fill : \"none\", \n stroke: \"#989898\",\n }),\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":242,"value":"$4d","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":299,"value":"

${freq}

${mort}

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":294,"value":"$4e","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":133,"value":"---\n# Setup Stuff Below!\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":318,"value":"freq = Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Frequency and Distribution\",\n color: {\n scheme: \"Turbo\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n \n\n Plot.raster(hazardfreq_raster[0], { \n width: hazardfreq_raster.width,\n height: hazardfreq_raster.height,\n x1: hazardfreq_box[0], \n y1: hazardfreq_box[3],\n x2: hazardfreq_box[2],\n y2: hazardfreq_box[1],\n interpolate: \"barycentric\",\n clip: \"sphere\"\n }),\n\n Plot.geo(d3.geoGraticule().step([30, 15])(), {\n stroke: \"gray\",\n strokeOpacity: 0.5\n }),\n\n Plot.geo(countries, {\n fill : \"none\", \n stroke: \"#989898\",\n })\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":322,"value":"mort = Plot.plot({\n projection: {type: \"equal-earth\", rotate: [-10, 0]},\n width,\n title: \"Global Cyclone Mortality Risks and Distribution\",\n color: {\n scheme: \"Turbo\",\n legend: true,\n },\n \n marks: [\n Plot.geo(land, { fill: \"#eee\" }),\n Plot.sphere(),\n\n Plot.raster(hazardmort_raster[0], { \n width: hazardmort_raster.width,\n height: hazardmort_raster.height,\n x1: hazardmort_box[0], \n y1: hazardmort_box[3],\n x2: hazardmort_box[2],\n y2: hazardmort_box[1],\n interpolate: \"barycentric\",\n clip: \"sphere\"\n }),\n\n Plot.geo(d3.geoGraticule().step([30, 15])(), {\n stroke: \"gray\",\n strokeOpacity: 0.5\n }),\n\n Plot.geo(countries, {\n fill : \"none\", \n stroke: \"#989898\",\n }),\n \n ]\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"land = topojson.feature(land50m, land50m.objects.land)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"countries = topojson.feature(world, world.objects.countries)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"land50m = FileAttachment(\"land-50m.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":103,"value":"world = FileAttachment(\"countries-110m.json\").json();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"GeoTIFF = require(\"geotiff@2.0.7\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"hazardfreq = await FileAttachment(\"gdcycfreq.tif\")\n.arrayBuffer()\n.then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"hazardmort = await FileAttachment(\"gdcycmort.tif\")\n.arrayBuffer()\n.then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"hazardfreq_raster = await hazardfreq.readRasters()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"hazardmort_raster = await hazardmort.readRasters()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"hazardfreq_box = await hazardfreq.getImage().then((d) => d.getBoundingBox())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"hazardmort_box = await hazardmort.getImage().then((d) => d.getBoundingBox())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4f","stdlib":"1"}} 24:null