25:["$","title",null,{"children":"Adaptation Atlas Hackathon Datasets | Adaptation Atlas | Observable"}] 49:T52e,## Importable Datasets Some datasets from this notebook can be directly imported into other notebooks. These will be shown with an **"Import With:"** command that you can copy and use within other notebooks to import them. These datasets are stored as FileAttachments and can be loaded into other notebooks through multiple ways: 1. By importing the database it is a part of: > ```js import {exposure_db} from "b2a8f20822affaca" > ``` 2. Downloading the File Attachment from the "paperclip icon (📎)" and attaching it to a new notebook or performing analysis The first method is the easiest, but my have a slight performance penalty if not all of the datasets used in a database are used. ## Atlas S3 Datasets For additional datasets from the Adaptation Atlas, access them through our AWS S3 bucket. These datasets include more of our "raw" data and raster format data. These datasets will require further analysis and processing to be used, but they can provide additional insights not possible with the tabular data provided directly within this notebook. Each dataset link is provided in two formats: * **s3://** – for programmatic access (e.g., in code or data pipelines) * **https://** – for direct downloads or tools that don’t support `s3://` Use the toggle below to switch between link formats.4a:T400,map = { let boundary_geojson = admin_boundaries[adminLevel.value] || watershed_boundaries[adminLevel.value]; return Plot.plot({ height: 550, projection: { type: "azimuthal-equal-area", domain: boundary_geojson }, marks: [ Plot.geo(boundary_geojson), Plot.geo(boundary_geojson, Plot.pointer( Plot.centroid({ stroke: "#2E7636", strokeWidth: 1.5, }))), Plot.tip( boundary_geojson.features, Plot.pointer( Plot.centroid({ channels: { name: { label: "Admin Name:", value: (d) => { console.log(d) return d.properties[adminLevel.adminName0] } }, name1: { label: "Higher Admin:", value: (d) => d.properties[adminLevel.adminName1] }, }, // format: { // name: true, // data: (d) => selector.formatFunc(d) // } }) ) ) ] }) }4b:T513,dropdown_section( "Crop Value Of Production (VoP)", false, md` This dataset provides subnational estimates of the Value of Production (VoP) for major crops across Sub-Saharan Africa. It is based on a version of MapSpam adapted for the African Agricultural Adaptation Atlas using 2020 production numbers. VoP was calculated by combining national FAOSTAT production, price, and value data with SPAM gridded crop production rasters, disaggregated by farming system (subsistence, low, high, irrigated, and all). National VoP is spatially allocated to raster surfaces to show where economic value is concentrated, producing outputs in nominal USD per pixel. This data was then extracted and aggregated to get sum of VoP per admin unit. `, md` **Unit:** Nominal USD 2020 **S3 Path**: > ${s3_root.value} **Use in Observable:** > \`\`\`js import {exposure_db} from "b2a8f20822affaca" exposure_db.query(\` SELECT admin1_name, crop, "value" FROM crop_vop WHERE Isfinite("value") AND iso3 = 'NGA' AND admin1_name IS NOT NULL ORDER BY "value" DESC LIMIT 6 \`) \`\`\` > `, md` **Example Format:** > ${rowToMDTable(await exposure_db.query(`SELECT * FROM crop_vop WHERE Isfinite("value") AND admin1_name IS NULL ORDER BY "value" DESC LIMIT 5`))} ` )4c:T472,dropdown_section( "Livestock", false, md` This dataset provides subnational estimates of the Value of Production (VoP) of key livestock species (cattle, goats, sheep, pigs, and poultry) across Sub-Saharan Africa. VoP was calculated by combining gridded livestock population data (GLW4) with national-level FAOSTAT production, price, and value data, adjusted using World Bank exchange rates and purchasing power parity (PPP) statistics. The national VoP is spatially distributed to raster surfaces according to livestock densities and further split between highland and tropical zones. `, md` **Unit:** Nominal USD 2020 **Use in Observable:** > \`\`\`js import {exposure_db} from "b2a8f20822affaca" exposure_db.query(\` SELECT admin1_name, crop, "value" FROM livestock_vop WHERE Isfinite("value") AND crop = 'cattle_highland' AND admin1_name IS NULL ORDER BY "value" DESC LIMIT 6 \`) \`\`\` > `, md` **Example Format:** > ${rowToMDTable(await exposure_db.query(`SELECT * FROM livestock_vop WHERE Isfinite("value") AND crop = 'cattle_highland' ORDER BY "value" DESC LIMIT 5`))} ` )4d:T6d9,dropdown_section( "Hazard Definitions", true, md` | Code | Name / Description | | ----------------- | --------------------------------------------------------------------------------------- | | **HSH-max-max** | Human heat stress index – maximum value | | **HSH-mean-mean** | Human heat stress index – mean value | | **NDD-mean** | Number of dry days (precipitation < 1 mm) per season/year | | **NDWL0-mean** | Number of waterlogging days (soil moisture at 0% between field capacity and saturation) | | **NDWS-mean** | Number of water stress days (Ea/Ep < 0.5; <50% water demand met) | | **NTx20–NTx50** | Number of heat stress days above threshold (20°C up to 50°C, step = 1°C) | | **PTOT-sum** | Total precipitation per season | | **TAI-mean** | Thornthwaite Aridity Index (precip vs. PET balance) | | **TAVG-mean** | Mean daily temperature | | **THI-max-max** | Cattle thermal humidity index – maximum | | **THI-mean-mean** | Cattle thermal humidity index – mean | | **TMAX-max** | Maximum of maximum daily temperatures | | **TMIN-min** | Minimum of minimum daily temperatures | `, )4e:T6e4,dropdown_section( "Dataset Definitions", true, md` | Variable | Description | Unit | | ----------- | ----------------------------------------------------------------------- | ------- | | DER.sos | Start-of-season: date of maximum positive derivative (fastest green-up) | Date | | TRS2.sos | Start-of-season: date when curve crosses 20% of seasonal amplitude | Date | | TRS5.sos | Start-of-season: date when curve crosses 50% of seasonal amplitude | Date | | DER.eos | End-of-season: date of maximum negative derivative (fastest senescence) | Date | | TRS2.eos | End-of-season: date when curve falls below 20% of seasonal amplitude | Date | | TRS5.eos | End-of-season: date when curve falls below 50% of seasonal amplitude | Date | | meth | Curve-fitting method selected as 'best' | Text | | R2 | Coefficient of determination for selected model | Numeric | | NSE | Nash–Sutcliffe Efficiency | Numeric | | RMSE | Root-mean-square error of fit | Numeric | | R | Pearson correlation between fitted and observed NDVI | Numeric | | pvalue | p-value of the correlation (R) | Numeric | | pixel | Raster cell index in original NDVI stack | Integer | | admin1_name | Name of admin1 region | Text | | flag | Flag to indicate year and season number (format: year_season#) | Text | ` )4f:T572,dropdown_section( "Start and End of Season", false, md` This dataset provides pixel based estimates of Start-of-season (SOS) and end-of-season (EOS) based on multiple metrics derived from NDVI analysis. These are derived using multiple methods such as the DER method (maximum positive/negative derivative) and threshold-based approaches (TRS2: 20% seasonal amplitude, TRS5: 50% seasonal amplitude). Users are advised to rely on DER values for seasonal analysis and timing, but TRS2 and TRS5 may also be useful. The other variables are included, but it is expected that they will be less useful than the aformentions sos and eos metrics. Quality statistics are also provided for these estimates to assist in removing noise and creating a more robust analysis. See the description section above to gain more insight into the different columns. `, md` **Use in Observable:** > \`\`\`js import {phenology_db} from "b2a8f20822affaca" phenology_db.query(\` SELECT admin1_name, "DER.sos", "DER.eos", "DER.eos" - "DER.sos" AS season_length_days, r2, season_yr, season_num, FROM kenya_phenology WHERE season_num = 1 LIMIT 6 \`) \`\`\` > `, md` **Example Format:** > ${rowToMDTable(await phenology_db.query(`SELECT admin1_name, "DER.sos", "DER.eos", "TRS5.sos", "TRS5.eos", r2, r, season_yr, season_num, FROM kenya_phenology LIMIT 2`))} ` )50:T40a,dropdown_section( "Seasonal Rainfall", false, md` This dataset provides total rainfall (mm) across the different SOS and EOS dates provided in the dataset. This is provided for 4 different start and end of season variables: DER, TRS2, TRS5, and greenup/scenescence. As above, it is recomended to use DER or the TRS variables rather than greenup. `, md` **Unit:** Total rainfall across season dates in millimeters **Use in Observable:** > \`\`\`js import {phenology_db} from "b2a8f20822affaca" phenology_db.query(\` SELECT admin1_name, "DER.sos" as season_start, "DER.eos" as season_end, "DER.eos" - "DER.sos" AS season_length_days, "rain_der" as season_rainfall_mm r2, season_yr, season_num, FROM kenya_phenology WHERE season_num = 1 LIMIT 6 \`) \`\`\` > `, md` **Example Format:** > ${rowToMDTable(await phenology_db.query(`SELECT admin1_name, rain_der, rain_trs5, rain_trs2, rain_greenup_scenescence, season_yr, season_num, FROM kenya_phenology LIMIT 2`))} ` )51:T455,dropdown_section( "Pest and Disease Performance", false, md` These datasets show projected changes in pest and pathogen performance (%), based on thermal performance curves (TPCs) and climate characteristics. - 1 = same as today - \\>1 = increased pest performance - <1 = decreased pest performance Data are provided as rasters aggregated to administrative boundaries (mean values). Projections are available across multiple GCMs, SSPs, and time periods (2040, 2060). `, md` **Description:** Change in pest Thermal Performance Curve **Unit:** Percent change **S3 Path**: > ${s3_root.value + s3_pest_paths.thermal_performance} **Local Use:** > \`\`\`js import {pest_db} from "b2a8f20822affaca" pest_db.query(\` SELECT * FROM pest_performance WHERE iso3 = 'AGO' -- Get all admin 1 data for Angola and admin1_name IS NOT NULL and Admin2_name IS NULL and model != 'ENSEMBLEmean' LIMIT 6 \`) \`\`\` > `, md` **Example Format:** > ${rowToMDTable(await pest_db.query(`SELECT * FROM pest_performance WHERE model = 'ENSEMBLEmean' AND iso3 = 'KEN' LIMIT 5`))} ` 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\n $\"Logo$ \n $\"Alliance$ \n $\"CGIAR$ \n

\n \n $\"Main$ \n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":56,"value":"tocOneliner({\n selector: \"h1,h2\", // selectors to include (can comma-separate, like \"h1,h2,h3\")\n heading :\"In this notebook\", // heading for ToC\n delim : ' | ', // delimiter between links\n skip: [nbTitle, \"Imports\"], // text content for headers to exclude\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":115,"value":"# Overview","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":287,"value":"$49","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":472,"value":"Inputs.form(\n [ \n Inputs.bind(\n Inputs.radio(s3_roots, {label: \"S3 link Format\", format: x => x.label, value: s3_roots[0]}),\n viewof s3_root\n )\n ]\n\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":623,"value":"Datasets stored in the AWS S3 are in cloud optimized formats such as Parquet, GeoParquet, and Cloud-Optimized GeoTIFF. This allows for partial reading and direct access without needing to download the full dataset, saving bandwidth and processing time.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":113,"value":"# Boundaries\n\nThe Atlas uses boundaries derived from FAO’s GAUL (admin 0–2) and WMO Basins and Sub-Basins (WMOBB) datasets. These analysis-ready boundaries are designed for aggregation and analytical tasks, with disputed regions handled consistently across countries.\n\n**Import Visualization Boundaries:**\n```{js}\nimport {admin_boundaries} from \"b2a8f20822affaca\"\nimport {watershed_boundaries} from \"b2a8f20822affaca\"\n```","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1002,"value":"available_formats({GeoParquet:true, GeoJson: true, TopoJson: true, GeoPackage: true, Shapefile: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":452,"value":"dropdown_section(\n \"Analysis boundaries\",\n false,\n md`These are high resolution datasets of the boundaries used in the Adaptation Atlas. They are larger file sizes and are great for analysis, but perform poorly for visualizations. These can be found and accessed on the Atlas S3 bucket.\n\n ${\n Object.entries(s3_boundary_paths)\n .map(([key, value]) => `**${key}**: \\n${s3_root.value + value} `)\n .join(\"\\n\")\n }\n`\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":461,"value":"dropdown_section(\n \"Simplified boundaries for visualization\",\n true,\n md`\nThe boundaries provided in this notebook have been ** simplified** to make them lighter and more suitable for visualization. They are derived from the analysis-ready boundaries but were simplified using *mapshaper*.\n\n**Note:** These simplified boundaries are **not recommended for spatial analysis or raster–vector extraction**. For analytical purposes, please use the full **analysis-ready boundaries** described above.\n\n**Import With:**\n\\`\\`\\`{js}\nimport {admin_boundaries} from \"b2a8f20822affaca\"\nimport {watershed_boundaries} from \"b2a8f20822affaca\"\n\\`\\`\\`\n`\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":199,"value":"viewof adminLevel = {\n let opts = [\n {value: \"adm0\", label:\"Country\", adminName0: \"admin0_name\", adminName1: null },\n {value: \"adm1\", label:\"Admin 1\", adminName0: \"admin1_name\", adminName1: \"admin0_name\"},\n {value: \"adm2\", label:\"Admin 2\", adminName0: \"admin2_name\", adminName1: \"admin0_name\"},\n {value: \"watershed\", label:\"Watershed\", adminName0: \"WMOBB_DESC\", adminName1: \"WMOBB_NAME\"}\n ]\n \n return Inputs.radio(opts, {label: \"Boundary Visualization Type\", format: x => x.label, value: opts[0]})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":247,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":145,"value":"# Exposure Datasets","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":147,"value":"In the context of climate risk, exposure refers to the presence of people, crops and livestock, among other systems that could be affected by climate hazards. It captures the distribution and extent of assets or populations that may experience climate-related events, such as extreme rainfall, drought, or heatwaves, and is a key component in assessing potential climate vulnerability and risk.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":835,"value":"**Import Exposure Tables:**\n```js\nimport {exposure_db} from \"b2a8f20822affaca\"\n\nexposure_db.describeTables()\n// Example Use:\nexposure_db.query(`\nSELECT *\nFROM exposure\nWHERE Isfinite(\"value\")\nORDER BY \"value\" DESC\nLIMIT 6\n`)\n```","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":962,"value":"available_formats({Parquet:true, GeoTIFF: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":576,"value":"dropdown_section(\n \"Population\",\n false,\n md`This dataset provides subnational estimates of total, urban, and rural population across SSA. It is based on WorldPop 2020, with population divided into rural, urban, and total categories. Urban and rural areas are masked using Africapolis urban area data. The values are in number of people. Any decimals are an artifact of pixel based extraction and aggregation and can be rounded.`\n,\n md` \n**Unit:** Number of people \n\n**Use in Observable:** \n> \n\\`\\`\\`js\nimport {exposure_db} from \"b2a8f20822affaca\"\nexposure_db.query(\\`\nSELECT *\nFROM population\nWHERE type = 'total'\n\\`)\n\\`\\`\\`\n> \n`,\n md`\n**Example Format:** \n\n> ${rowToMDTable(await exposure_db.query(`SELECT * FROM population WHERE type = 'rural' AND admin1_name IS NULL LIMIT 5`))}\n`, \n md`\n### Raster Datasets\n ${\n Object.entries(s3_pop_rasters)\n .map(([key, value]) => `**${key}**: \\n${s3_root.value + value} `)\n .join(\"\\n\")\n }\n`\n )","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":582,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":585,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":889,"value":"dropdown_section(\n \"All Crop and Livestock Exposure\",\n false,\n md`\nThis dataset provides subnational estimates for a wide range of exposure variables, including livestock numbers, crop and livestock value of production (VoP, including the datasets mentioned above), crop production, and harvested area. It also includes disaggregated crop data by production system (rainfed vs. irrigated).\n`,\n md` \n**Unit:** Varies by exposure type - see exposure and unit columns\n\n**Use in Observable:** \n> \n\\`\\`\\`js\nimport {exposure_db} from \"b2a8f20822affaca\"\nexposure_db.query(\\`\nSELECT *\nFROM exposure\nWHERE tech = 'irr'\n\\`)\n\\`\\`\\`\n> \n`,\n md`\n**Example Format:** \n\n> ${\nrowToMDTable(await exposure_db.query(`SELECT * FROM exposure WHERE tech = 'rf-all' LIMIT 5`))\n}\n`\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":152,"value":"# Hazard Datasets","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":167,"value":"This dataset provides a summary of climate hazards calculated and aggregated across first-level administrative regions (admin1). The hazards are provided for four future timeframes, four SSP scenarios (ssp126, ssp245, etc.), plus historical reference periods. The definitions of these hazards should be seen below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1189,"value":"available_formats({parquet:true, GeoTIFF: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1152,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1146,"value":"dropdown_section(\n \"Hazard Timeperiod Means\",\n false,\n md`This dataset provides summarized climate hazard indices extracted from gridded raster data. Hazards (e.g., heat stress, precipitation extremes) are calculated per pixel across a given timeframe (season or year), then aggregated within administrative boundaries using mean. The result is a table of average hazard values per administrative unit (national, sub-national, local) under different climate scenarios for each timeframe. Provided is the multi-model ensemble data across 18 GCMs from NexGDDP.`,\n md` \n**Local Use:** \n> \n\\`\\`\\`js\nimport {hazard_db} from \"b2a8f20822affaca\"\nhazard_db.query(\\`\n SELECT *\n FROM hazard_mean\n WHERE admin1_name IS NULL\n AND timeframe = '2041-2060'\n AND hazard = 'NDD-mean'\n LIMIT 10\n\\`)\n\\`\\`\\`\n> \n`,md`\n**Example Format:** \n\n> ${rowToMDTable(await hazard_db.query(`SELECT * FROM hazard_mean LIMIT 4`))}\n`\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":610,"value":"dropdown_section(\n \"Monthly & Yearly Hazard Values and Anomalies\",\n false,\n md`This dataset contains seasonal climate hazard summaries for subnational administrative units across Africa. Monthly hazard data have been aggregated into 3-month “seasons” (e.g., DJF, MAM) or annual periods, with columns reporting the mean, minimum, maximum, and standard deviation for each hazard and corresponding anomalies relative to historical baselines. It provides a ready-to-use resource for analyzing climate variability and extremes at administrative scales.`,\n md`This dataset is provided for the historic timeframe, 2021-2040, 2041-2060, and 2061-2080\n`,\n md` \n**Local Use:** \n> \n\\`\\`\\`js\nimport {hazard_db} from \"b2a8f20822affaca\"\nhazard_db.query(\\`\n SELECT *\n FROM hazard_monthly_historic\n WHERE admin1_name IS NULL\n AND season = 'annual'\n LIMIT 10\n\\`)\n\\`\\`\\`\n> \n`,md`\n**Example Format:** \n\n> ${rowToMDTable(await hazard_db.query(`SELECT * FROM hazard_monthly_historic LIMIT 4`))}\n`\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":590,"value":"# Phenology Dataset\nThis dataset provide sub-national estimates of key seasonal metrics for crop and vegetation systems, derived from remote sensing (GLASS NDVI) and climate data (Chirps v3). This dataset is just provided for Kenya.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1079,"value":"**Import Phenology Dataset:**\n```js\nimport {phenology_db} from \"b2a8f20822affaca\"\n\nphenology_db.describeTables()\n// Example Use:\nphenology_db.query(`\nSELECT *\nFROM kenya_phenology\nWHERE season_num = 1\nLIMIT 6\n`)\n```","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1128,"value":"available_formats({parquet:true, GeoTIFF: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1093,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":594,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1062,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":597,"value":"# Pests and Diseases\nThis dataset contains projections of future pest and disease impacts on crops, as well as thermal performance, across Africa. The data is provided in tabular format and can be linked to administrative boundaries using admin names. Projections are available for two SSPs (Shared Socioeconomic Pathways) and two timeframes: 2021–2040 and 2041–2060. The data includes 5 GCMs + the Ensemble mean of the 5 GCMs. In general the multi-model ensemble will be the most useful, but the other models are provided to allow for further analysis and insight if needed.\n\n**Important:** This dataset is owned and produced by Nicholas Galle and Prof. Jason Rohr from Rohr Labs at the University of Notre Dame and should be cited accordingly. It is provided solely for use in this hackathon and must not be published, redistributed, or used for other research or applications without the explicit permission of the data owners. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":690,"value":"**Import Pest Data:**\n```js\nimport {pest_db} from \"b2a8f20822affaca\"\n\npest_db.describeTables()\n// Example Use:\npest_db.query(`SELECT * FROM pest_performance WHERE model = 'ENSEMBLEmean' LIMIT 6`)\nsomeData = await pest_db.query(`SELECT * FROM pest_yieldImpact WHERE model = 'ENSEMBLEmean'`)\n```","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":959,"value":"available_formats({parquet:true, GeoTIFF: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":599,"value":"dropdown_section(\n \"Pest and Disease Crop Yield Impacts\",\n false,\n md`\nThese datasets show the projected indirect effects of climate change on crop yields, mediated by pests and pathogens. Values are expressed as the change in yield loss (%) relative to today. They are the mean value of change per admin area.\n\n - 0 = no change in yield loss\n\n - Positive values = greater yield loss (negative impact on yield)\n\n - Negative values = less yield loss\n`,\n md` \n**Unit:** Percent change \n**S3 Path**: \n> ${s3_root.value + s3_pest_paths.yield_impact} \n\n**Local Use:** \n> \n\\`\\`\\`js\nimport {pest_db} from \"b2a8f20822affaca\"\npest_db.query(\\`\n SELECT *\n FROM pest_yieldImpact\n WHERE iso3 = 'AGO' -- Get all admin 1 data for Angola\n and admin1_name IS NOT NULL\n and Admin2_name IS NULL\n and model != 'ENSEMBLEmean'\n LIMIT 6\n\\`)\n\\`\\`\\`\n> \n`,\n md`\n**Example Format:** \n\n> ${rowToMDTable(await pest_db.query(`SELECT * FROM pest_yieldImpact WHERE model = 'ENSEMBLEmean' AND iso3 = 'KEN' LIMIT 5`))}\n`\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":607,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":189,"value":"# Conclusion","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":193,"value":"**Additional Resources:**\n- [**Notebook Style and Developer Guide**](https://observablehq.com/d/8a953dc1cde63a86)\n- [**Existing Adaptation Atlas Notebooks**](https://observablehq.com/collection/@adaptationatlas/data-explorations)\n- [**Example:** Use Python and Google Earth Engine to process soil data](https://colab.research.google.com/drive/1Cz8G1uUn6pdvadVjq9ikppyFeU36uE-3?usp=sharing)\n- [Adaptation Atlas Data Guide](https://adaptationatlas.github.io/data-management/structure.html)\n- [Atlas Data FAQ](https://adaptationatlas.github.io/data-management/faq.html)\n- [Observable JS Documentation](https://observablehq.com/documentation/cells/observable-javascript)\n- [Observable JS vs vanilla Javascript](https://observablehq.com/@observablehq/observable-javascript)\n- [Data wrangling in JS vs R, Python, and SQL](https://observablehq.com/@observablehq/data-wrangling-translations)\n- [Adaptation Atlas ObservableHQ](https://observablehq.com/@adaptationatlas)\n- [Zindi Challenge Link]()","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":44,"value":"# Appendix\nThis section is all the underlying code and variables to power the notebook","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":65,"value":"nbTitle = \"Adaptation Atlas Hackathon Datasets\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"## Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":52,"value":"import {tocOneliner} from \"8a953dc1cde63a86\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"lipsum = require('https://bundle.run/lorem-ipsum@1.0.6')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"### Default Adaptation Atlas Styling\nThis can be found [here](https://observablehq.com/d/8a953dc1cde63a86) to be modified and adjusted","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":10,"value":"import {atlas_stylesheet} from \"8a953dc1cde63a86\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"atlas_stylesheet","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"### Paths to S3 data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":370,"value":"s3_roots = { let bucket = \"digital-atlas\"\n return [\n {value: `https://${bucket}.s3.amazonaws.com/`, label:\"HTTP URL\"},\n {value: `s3://${bucket}/`, label:\"S3 URI\" },\n \n ]\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":349,"value":"viewof s3_root = {\n return Inputs.radio(s3_roots, {label: \"S3 link Format\", format: x => x.label, value: s3_roots[0]})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":390,"value":"s3_boundary_paths = new Object({\n admin0: \"domain=boundaries/type=admin/source=gaul2024/region=africa/processing=analysis-ready/level=adm0/atlas_gaul24_a0_africa.parquet\",\n admin1: \"domain=boundaries/type=admin/source=gaul2024/region=africa/processing=analysis-ready/level=adm1/atlas_gaul24_a1_africa.parquet\",\n admin2:\"domain=boundaries/type=admin/source=gaul2024/region=africa/processing=analysis-ready/level=adm2/atlas_gaul24_a2_africa.parquet\",\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":635,"value":"s3_pest_paths = new Object({\n thermal_performance:\"domain=impacts/type=pest-disease/source=rohr_notre-dame/region=africa/processing=aggregated/pest-tcp-perf_pct.parquet\",\n yield_impact: \"domain=impacts/type=pest-disease/source=rohr_notre-dame/region=africa/processing=aggregated/pest-yield-impact_pct.parquet\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":539,"value":"### Importable datasets","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":673,"value":"#### Boundaries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":81,"value":"admin_boundaries = {\n let a0_topoj = await FileAttachment(\"atlas_gaul24_a0_africa_simple-vlowres@1.topojson\").json();\n let a1_topoj = await FileAttachment(\"atlas_gaul24_a1_africa_simple-vlowres.topojson\").json();\n let a2_topoj = await FileAttachment(\"atlas_gaul24_a2_africa_simple-lowres.topojson\").json();\n\n let a0_geojson = topojson.feature(\n a0_topoj,\n a0_topoj.objects[\"atlas_gaul24_a0_africa\"]\n );\n let a1_geojson = topojson.feature(\n a1_topoj,\n a1_topoj.objects[\"atlas_gaul24_a1_africa\"]\n );\n let a2_geojson = topojson.feature(\n a2_topoj,\n a2_topoj.objects[\"atlas_gaul24_a2_africa\"]\n );\n\n return {\n adm0: a0_geojson,\n adm1: a1_geojson,\n adm2: a2_geojson,\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":232,"value":"watershed_boundaries = {\n let ws_topoj = await FileAttachment(\"africa_wmobb_basins.topojson\").json()\n let ws_geoj = topojson.feature(\n ws_topoj,\n ws_topoj.objects[\"africa_wmobb_basins\"]\n );\n return {\n watershed: ws_geoj\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":676,"value":"#### Exposure","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":654,"value":"exposure_datasets = new Object({\n // population: null,\n crop_vop: FileAttachment(\"spam_vop_nominal-usd-2020_all_adm.parquet\"),\n livestock_vop: FileAttachment(\"glw4-2020_vop_nominal-usd-2020_adm.parquet\"),\n population: FileAttachment(\"hpop_adm_sum.parquet\"),\n // This is a large dataset & hurts page load time. It should be removed if not used\n exposure: FileAttachment(\"exposure_adm_sum_spam-2020_glw4-2020.parquet\") \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":659,"value":"exposure_db = DuckDBClient.of(exposure_datasets)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1045,"value":"s3_pop_rasters = new Object({\n total_pop: \"domain=exposure/type=population/source=worldpop2020/region=ssa/processing=analysis-ready/variable=total_pop.tif\",\n rural_pop: \"domain=exposure/type=population/source=worldpop2020/region=ssa/processing=analysis-ready/variable=rural_pop.tif\",\n urban_pop: \"domain=exposure/type=population/source=worldpop2020/region=ssa/processing=analysis-ready/variable=urban_pop.tif\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1053,"value":"s3_vop_rasters = new Object({\n crop_vop: \"domain=exposure/type=crop/source=spam2020v1r2_ssa/region=ssa/processing=atlas-harmonized/variable=*/\"\n\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1069,"value":"#### Phenology","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1073,"value":"phenology_datasets = new Object({\n kenya_phenology : FileAttachment(\"ken_seasonal-phenology-rain.parquet\")\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1071,"value":"phenology_db = DuckDBClient.of(phenology_datasets)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":678,"value":"#### Pests","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":681,"value":"pest_datasets = new Object({\n // population: null,\n pest_performance: FileAttachment(\"pest-tcp-perf_pct.parquet\"),\n pest_yieldImpact: FileAttachment(\"pest-yield-impact_pct.parquet\")\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":684,"value":"pest_db = DuckDBClient.of(pest_datasets)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1160,"value":"#### Hazards","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1165,"value":"hazard_datasets = new Object({\n hazard_mean: FileAttachment(\"haz-means_adm_ENSEMBLEmean.parquet\"),\n hazard_monthly_historic : FileAttachment(\"haz_3months_adm_mean_historic_anomaly-historic_ensemble_seasons_fix.parquet\")\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1168,"value":"hazard_db = DuckDBClient.of(hazard_datasets)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":568,"value":"### Notebook Helpers","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":477,"value":"dropdown_section = (title, initiallyOpen, ...contents) => {\n return html`\n

\n ${title}\n

\n ${contents.map(c => html`\n

\n ${c} \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":716,"value":"function rowToMDTable(rows) {\n if (!rows.length) return \"\";\n \n const headers = Object.keys(rows[0]);\n const separator = `| ${headers.map(() => \"---\").join(\" | \")} |`;\n\n const formatVal = val => {\n if (typeof val !== \"number\") return val ?? \"\";\n // show up to 3 decimals, but trim trailing zeros\n return parseFloat(val.toFixed(3)).toString();\n };\n\n return [\n `| ${headers.join(\" | \")} |`,\n separator,\n ...rows.map(row =>\n `| ${headers.map(h => formatVal(row[h])).join(\" | \")} |`\n )\n ].join(\"\\n\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":982,"value":"available_formats = (formats) => {\n const icon_style = \"font-weight:bold;font-size:1em\"\n const icon = (ok) => ok \n ? html`✓`\n : html`✗`;\n return html`\n Available Formats:\n

\n ${Object.entries(formats).map(([name, ok], i, arr) => html`\n ${icon(ok)} ${name}${i < arr.length - 1 ? ' |' : ''}\n `)}\n

\n `;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W52","stdlib":"1"}} 24:null