31:T41d,md` NASA Worldview has great point data:

From NASA: *The 375m I-band data complements the MODIS fire detections; they both show good agreement in hotspot detection but the improved spatial resolution of the 375m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.* There seem to be two main kinds of 375m VIIRS data, **NOAA-20** and **S-NPP**. The NOAA-20 data is only available as of January 2020, while the S-NPP goes further back, allowing us to obtain data from the October fires of 2019. For more information see [NASA Fire and Smoke Mission](https://www.nasa.gov/mission_pages/fires/main/missions/index.html). 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2f:{"title":"California Fire Maps Workbook","cells":[{"id":0,"value":"md`# California Fire Maps Workbook\n\nThis notebook collects information relevant to California wildfires, focused on data sources and maps. I've been able to pull some data directly into this notebook and would like to bring in more.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":242,"value":"toc(\"h2,h3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"md`## Fire Hotspots\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":82,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":103,"value":"md`### Active Fires (current data)\n[Nasa Active Fire Data](https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data) has 24h, 48h and 7d archives available as CSV (and shapefiles). \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":186,"value":"md`Most recent 24hours of **VIIRS 375m / NOAA-20** from [NASA Active Fire Data](https://firms2.modaps.eosdis.nasa.gov/active_fire/#firms-txt):`","pinned":true,"hidden":false,"mode":"ojs"},{"id":177,"value":"J1_24h = d3.csv(cors(\"https://firms2.modaps.eosdis.nasa.gov/data/active_fire/noaa-20-viirs-c2/csv/J1_VIIRS_C2_USA_contiguous_and_Hawaii_24h.csv\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":184,"value":"md`Most recent 24hours of **VIIRS 375m / S-NPP** from [NASA Active Fire Data](https://firms2.modaps.eosdis.nasa.gov/active_fire/#firms-txt):`","pinned":true,"hidden":false,"mode":"ojs"},{"id":97,"value":"V1_24h = d3.csv(cors(\"https://firms2.modaps.eosdis.nasa.gov/data/active_fire/suomi-npp-viirs-c2/csv/SUOMI_VIIRS_C2_USA_contiguous_and_Hawaii_24h.csv\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":119,"value":"md`### Historical data\nAttached to the notebook are a couple datasets obtained via an [Archive request](https://firms2.modaps.eosdis.nasa.gov/download/) from NASA VIIRS satellite data.\n\nI started visualizing this historical data in [this notebook](https://observablehq.com/@enjalot/historical-california-fire-map-workbook)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":256,"value":"videoembed","pinned":false,"hidden":false,"mode":"ojs"},{"id":253,"value":"import {videoembed} from \"@enjalot/historical-california-fire-map-workbook\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":144,"value":"md`The VIIRS 375m data from **NOAA-20**(JPSS-1) is only available for 2020, which is fine for the current situation:\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":206,"value":"md`\n File : fire_nrt_J1V-C2_6857.csv\n Data Source : VIIRS NOAA-20\n Start Date : 2020-08-01\n End Date : 2020-08-23\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":150,"value":"J1_08_2020 = d3.csvParse(await FileAttachment(\"fire_nrt_J1V-C2_6857.csv\").text(), d3.autoType)","pinned":true,"hidden":false,"mode":"ojs"},{"id":146,"value":"md`The VIIRS 375m data from **S-NPP** is available for 2020 as well as the October fires of 2019.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":157,"value":"md` \n File : fire_nrt_V1_6858.csv\n Data Source : VIIRS S-NPP\n Start Date : 2020-08-01\n End Date : 2020-08-23\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":155,"value":"V1_08_2020 = d3.csvParse(await FileAttachment(\"fire_nrt_V1_6858.csv\").text(), d3.autoType)","pinned":true,"hidden":false,"mode":"ojs"},{"id":158,"value":"md`\n File : fire_archive_V1_6856.csv\n Data Source : VIIRS S-NPP\n Start Date : 2019-10-01\n End Date : 2019-12-01\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":161,"value":"V1_10_2019 = d3.csvParse(await FileAttachment(\"fire_archive_V1_6856.csv\").text(), d3.autoType)","pinned":true,"hidden":false,"mode":"ojs"},{"id":79,"value":"md`need to use https://cors-anywhere.herokuapp.com/ to bring the data in from the earthdata server that powers the worldview map.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":69,"value":"function cors(url) {\n return \"https://cors-anywhere.herokuapp.com/\" + url\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"worldviewUrl = \"https://worldview.earthdata.nasa.gov/?v=-126.47280405316468,36.55739986096819,-118.59780405316468,41.02663814221819&t=2020-08-21-T00%3A27%3A24Z&l=MODIS_Terra_Thermal_Anomalies_All(hidden),MODIS_Aqua_Thermal_Anomalies_All(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Night,VIIRS_SNPP_Thermal_Anomalies_375m_Day,MODIS_Combined_Thermal_Anomalies_All,Reference_Labels(hidden),Reference_Features(hidden),Coastlines,MODIS_Aqua_SurfaceReflectance_Bands143,MODIS_Aqua_SurfaceReflectance_Bands721,MODIS_Terra_SurfaceReflectance_Bands143,MODIS_Terra_SurfaceReflectance_Bands721,VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":111,"value":"md` ## Fire Boundaries\n[LNU Complex StoryMap](https://arcg.is/qGizH) \n

\n\nTODO: Figure out a good way to bring in or calculate our own boundaries of the fires\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":128,"value":"md`## Evacuation\nIt would be good to be able to pull in the boundaries of evactuation orders all over the state. I'm also interested in seeing highways that are affected or potentially affected soon.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":91,"value":"md`\n[County of Sonoma Fire Incident Map](https://sonomacounty.maps.arcgis.com/apps/webappviewer/index.html?id=69a0e54e9e2b48c086d122027b21c961) is a clear presentation of the evacuation warnings in and around Sonoma County.\n

\n\nTODO: Figure out where the evacuation boundaries come from so we can pull those in.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":8,"value":"md`## Air Quality\n\n### Purple Air\nCan pull in sensor data from [Purple Air](https://www.purpleair.com/map?opt=1/mAQI/a10/cC0&select=18691#5.61/37.915/-121.014):\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":289,"value":"md`\nI've attempted to visualize historical PurpleNow data in a couple ways:\n\n[

\nMapbox hexbins](https://observablehq.com/@enjalot/purpleair-historical-data). This is an interactive map making it easier to zoom / pan to an area of interest.\n\n[

\nD3 hexbins](https://observablehq.com/@enjalot/purpleair-historical-data-d3). This map isn't interactive but should be easier to create more analytical / new visualization techniques from.\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":295,"value":"md`\nHere we take a look at the API requests you can make for PurpleAir data. I've noticed that this next call will often fail to fetch.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"d3.json(\"https://www.purpleair.com/data.json?opt=1/mAQI/a10/cC0&fetch=true&nwlat=43.167493434353474&selat=32.25997597364069&nwlng=-127.87343156694496&selng=-114.15416006614362&fields=pm_1\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":251,"value":"air = d3\n .json(\n \"https://www.purpleair.com/data.json?opt=1/mAQI/a10/cC0&fetch=true&nwlat=43.167493434353474&selat=32.25997597364069&nwlng=-127.87343156694496&selng=-114.15416006614362&fields=pm_1\"\n )\n .then(d =>\n Object.assign(\n d.data.map(b => Object.fromEntries(d.fields.map((f, i) => [f, b[i]]))),\n { columns: d.fields }\n )\n )","pinned":true,"hidden":false,"mode":"ojs"},{"id":18,"value":"d3.json(\"https://www.purpleair.com/json?show=13191\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":21,"value":"d3.json(\"https://api.thingspeak.com/channels/539574/fields/8.json?start=2020-08-15%2023:12:35&offset=0&round=2&average=10&api_key=L0YNWLQ86Q9NXEX9\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":137,"value":"md`### Airnow\nThis map leaves much to be desired, but can still be useful at a glance, we can [animates through the month of August](https://observablehq.com/@enjalot/bay-area-airnow-fall-2020).\n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":224,"value":"md`## Smoke\nIt would be great to get satellite data of smoke coverage\n[BlueSky Daily Runs](https://tools.airfire.org/websky/v2/run/standard/CANSAC-1.33km/2020081800/#viewer)\n\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":263,"value":"// this takes a while to respond\nbsky = d3.json(cors(\"https://tools.airfire.org/websky/v2/api/runs/standard/CANSAC-1.33km/\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":271,"value":"bskyBaseUrl = bsky.run_urls[0]","pinned":true,"hidden":false,"mode":"ojs"},{"id":267,"value":"html`

`","pinned":true,"hidden":false,"mode":"ojs"},{"id":281,"value":"md`It is relatively straightforward to get a set of images for a particular forecast, as well as a list of forecasts. The question that remains is to get the projection / rectification information for these images so they can be projected or reprojected into other mapping environments.\n\nSince it's a forecast what would be interesting is to be able to enter an address and get an estimate for how bad the smoke will be there for the next 24 hours or so.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":225,"value":"tweet('919008091017854976')","pinned":false,"hidden":false,"mode":"ojs"},{"id":284,"value":"md`### NOAA Smoke Map\n\n

\n\nhttps://hwp-viz.gsd.esrl.noaa.gov/smoke\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":131,"value":"md`## Fire Risk\nThe past two years have had some bad fires, and people have done helpful visualizations already. If we could bring some of the past in for context we may be able to determine safer routes. \n\n[Buildings in Sever Fire Hazard Zones](https://observablehq.com/@palewire/california-buildings-in-severe-fire-hazard-zones)\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":234,"value":"md`## Escape Routes\nThis [excellent news article](https://www.azcentral.com/in-depth/news/local/arizona-wildfires/2019/07/22/wildfire-risks-more-than-500-spots-have-greater-hazard-than-paradise/1434502001/) has several interesting views of fire risk, and includes a particularly salient observation around escape routes in an at-risk community.\n

\n\nTODO: What would it take to get clearly annotated escape routes in areas where fires are occurring or likely to occur?\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":235,"value":"// import {toc} from \"@mbostock/toc\"\nimport {toc} from \"@nebrius/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":228,"value":"import {tweet} from \"@mbostock/tweet\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"d3 = require(\"d3\")","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}