[{"_1":2,"_183":-5,"_184":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_160":161},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_91":92,"_113":114,"_151":-5,"_152":11,"_153":17,"_154":11,"_155":-5,"_156":11,"_157":-5,"_158":159},"canonicalUrl","https://hub.docker.com/r/scl3/task_hii_power/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","scl3","reponame","task_hii_power","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_46":47,"_49":50,"_51":52,"_53":11,"_54":55,"_56":57,"_58":59,"_39":31,"_28":29,"_61":62,"_63":48,"_64":65,"_66":67,"_68":69,"_70":-5,"_71":65,"_72":73,"_74":11,"_75":29,"_76":77,"_82":11,"_83":84,"_88":29,"_89":90},"categories",[38,43],{"_39":40,"_41":42},"name","Machine learning & AI","slug","machine-learning-and-ai",{"_39":44,"_41":45},"Data science","data-science","content_types",[48],"image","date_registered","2020-06-04T16:41:40.910044Z","description","Earth Engine task for calculating power driver","is_private","last_updated","2022-05-27T14:20:59.952271Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[60],"application/vnd.docker.container.image.v1+json","pull_count",425,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","HII POWER DRIVER\n---------------\n\n## What does this task do?\n\nThis task calculates the impact of power on the terrestrial surface as one of the key drivers for a combined \n[Human Impact Index](https://github.com/SpeciesConservationLandscapes/task_hii_weightedsum). \n\"Impact\" is a pressure score based on the intensity of electricity usage as measured by \"Night Time Lights\" datasets. \nAn inter-calibrated nighttime lights dataset with values ranging from 0 - 63 is transformed to a pressure score of \n0 - 10 using equal interval quantiles, calculated from the earliest available image (1992) using the `quantile_calc` \nfunction in `input_preprocess.py`. These are:\n\n ```\n quantiles = {\n \"0\": {\"value\": 0, \"min\": 0, \"max\": 0},\n \"1\": {\"value\": 1, \"min\": 1, \"max\": 4},\n \"2\": {\"value\": 2, \"min\": 5, \"max\": 5},\n \"3\": {\"value\": 3, \"min\": 6, \"max\": 6},\n \"4\": {\"value\": 4, \"min\": 7, \"max\": 7},\n \"5\": {\"value\": 5, \"min\": 8, \"max\": 9},\n \"6\": {\"value\": 6, \"min\": 10, \"max\": 11},\n \"7\": {\"value\": 7, \"min\": 12, \"max\": 16},\n \"8\": {\"value\": 8, \"min\": 17, \"max\": 30},\n \"9\": {\"value\": 9, \"min\": 31, \"max\": 62},\n \"10\": {\"value\": 10, \"min\": 63, \"max\": 63},\n ```\nThe output HII driver calculated by this task is, like all other HII drivers, unitless; it refers to an absolute 0-10\nscale but is not normalized to it, so the actual range of values may be smaller than 0-10.\n\n## Input Dataset Calibration\nThe HII power driver (0-10) is calculated from the previous calendar year's calibrated nightlights dataset, which is \nproduced on demand by the task by using the `HIIPowerPreprocessTask` task. Two distinct source nightlight datasets are \nused to calculate this calibrated version following the methods below; these are the \nDefense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) and the \nVisible Infrared Imaging Radiometry Suite (VIIRS) on the Suomi National Polar-orbiting Partnership Satellite. \nDMSP provides data from 1992 - 2013 and VIIRS provides data from 2012 through the present.\n\nInconsistencies within the DMSP time series require implementing intra-calibration within the DMSP dataset. \nHere the intra-calibrated dataset produced by [Li et al. 2020](https://www.nature.com/articles/s41597-020-0510-y) \nis used. Key differences between the DMSP and VIIRS datasets also require inter-calibration of the two datasets. \nIn addition to the calibration process to match VIIRS to DMSP, VIIRS requires substantial noise reduction. \nThese key differences are:\n\n| Description | DMSP | VIIRS | Calibrated |\n| :--- | :--- | :--- | :--- |\n| Date Range | 1992 - 2013 | 2012 - present | 1992 - present |\n| Temporal Resolution | Annual | Monthly | Annual |\n| Spatial Resolution | 30 arc-second | 15 arc-seconds | 30 arc-second |\n| Pixel Value | Digital Number | Radiance | Digital Number |\n| Data Range | 0 - 63 | -1.5 - 193564.92 | 0 - 63 |\n\n### Noise removal\n\n1. Annual composites are created using the median value of all pixels for a given year with raw values greater than 0.\n2. General noise (which generally increases with distance from the equator) is removed by creating an image mask using \n a pixel latitude image. An exponential function is used to stretch the threshold values from a minimum to \n maximum threshold of 0.1 and 0.75 respectively from 0° to ±60°. The formula for this is:\n\n t = (|lat|⁴ / 60⁴) x (t_max - t_min) + (t_min)\n\n Where:
\n t is the final threshold image
\n lat is a latitude image
\n t_min is the minimum threshold
\n t_max is the maximum threshold\n\n### Calibration steps\n\n1. Regression coefficients are calculated to convert VIIRS values (beginning with 2014) to DMSP values by comparing \n pixel values of areas with stable lights through time. These are determined statically by selecting pixels from \n the DMSP time series from 2000 - 2013 that have a standard deviation less than 2. A stratified sample of 300 pixel \n values for each available DMSP value as of the latest available image (2013) is calculated and used to derive \n constant slope and intercept coefficients. See the `stable_light_points` function in `input_preprocess.py`.\n2. The resulting equation to transform VIIRS values to match DMSP is determined to be: \n calibrated_viirs = log(viirs) * 11.62 + 19.4 [R²: 67.16]\n3. Values in the calibrated image greater than 63 are set 63, matching the range of values from DMSP.\n\nThe result and goal is an annual night lights dataset derived from VIIRS that is calibrated to the pre-2013 \nDMSP values, which is then transformed into the HII power driver by the main task.\n\n## Variables and Defaults\n\n### Environment variables\n```\nSERVICE_ACCOUNT_KEY=\n```\n\n### Class constants\n\n```\nscale=300\n```\n\n## Usage\n\n*All parameters may be specified in the environment as well as the command line.*\n\n```\n/app # python task.py --help\nusage: task.py [-h] [-d TASKDATE] [--overwrite]\n\noptional arguments:\n -h, --help show this help message and exit\n -d TASKDATE, --taskdate TASKDATE\n --overwrite overwrite existing outputs instead of incrementing\n```\n\nIn addition, the `HIIPowerPreprocessTask` task can be called to run one of three preprocessing steps used in the \nmain `task.py`, with the following constants available as arguments:\n```\nBUCKET = \"hii-scratch\"\nCALC_PREVIOUS_ANNUAL_VIIRS = \"viirs\"\nCALC_CALIBRATION_COEFFICIENTS = \"coefficients\"\nCALC_WEIGHTING_QUANTILES = \"quantiles\"\n```\nThe first of these, `viirs`, is used on demand by the main power `task.py`.\n```\n/app# python input_preprocess.py --help\nusage: input_preprocess.py [-h] [-d TASKDATE] [-j [{viirs,coefficients,quantiles}]] [--overwrite]\n\noptional arguments:\n -h, --help show this help message and exit\n -d TASKDATE, --taskdate TASKDATE\n -j [{viirs,coefficients,quantiles}], --job [{viirs,coefficients,quantiles}]\n Calculation to run with HIIPowerPreprocessTask.\n --overwrite overwrite existing outputs instead of incrementing\n```\n\n### License\nCopyright (C) 2022 Wildlife Conservation Society\nThe files in this repository are part of the task framework for calculating \nHuman Impact Index and Species Conservation Landscapes (https://github.com/SpeciesConservationLandscapes) \nand are released under the GPL license:\nhttps://www.gnu.org/licenses/#GPL\nSee [LICENSE](./LICENSE) for details.\n","has_starred","hub_user","immutable_tags_settings",{"_78":11,"_79":80},"enabled","rules",[81],".*","is_automated","permissions",{"_85":17,"_86":11,"_87":11},"read","write","admin","user","storage_size",1361695617,"orgData",{"_93":94,"_95":96,"_97":98,"_99":29,"_100":101,"_102":103,"_104":105,"_106":103,"_107":108,"_109":103,"_110":103,"_111":103,"_112":17},"id","630abdaa9e6245f79ff9a62a1cb8eabe","uuid","630abdaa-9e62-45f7-9ff9-a62a1cb8eabe","type","organization","orgname","full_name","Species Conservation Landscapes","location","","company","Wildlife Conservation Society","profile_url","date_joined","2019-10-10T18:36:02.529009Z","gravatar_url","gravatar_email","badge","is_active","recentTags",[115],{"_116":48,"_117":118,"_119":120,"_93":121,"_122":123,"_54":140,"_141":118,"_142":29,"_143":60,"_39":144,"_145":146,"_147":131,"_148":140,"_149":69,"_150":17},"content_type","creator",7912090,"full_size",306701758,104609266,"images",[124],{"_125":126,"_127":128,"_129":103,"_130":131,"_132":133,"_134":135,"_136":103,"_137":-5,"_138":120,"_66":69,"_139":-5},"architecture","amd64","digest","sha256:756ffcde0dc3e77b74304959bc492dc07ab405a86ff6736db513f710a17ea516","features","last_pulled","2026-09-23T02:34:15.696413253Z","last_pushed","2022-05-27T14:21:00Z","os","linux","os_features","os_version","size","variant","2022-05-27T14:20:59.814526Z","last_updater","last_updater_username","media_type","latest","repository",9189065,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_34":162,"_171":172,"_158":181,"_182":11},{"_36":163,"_46":166,"_49":50,"_51":52,"_53":11,"_54":55,"_56":57,"_58":167,"_39":31,"_28":29,"_61":62,"_63":48,"_64":65,"_66":67,"_68":69,"_70":-5,"_71":65,"_72":73,"_74":11,"_75":29,"_76":168,"_82":11,"_83":170,"_88":29,"_89":90},[164,165],{"_39":40,"_41":42},{"_39":44,"_41":45},[48],[60],{"_78":11,"_79":169},[81],{"_85":17,"_86":11,"_87":11},"tags",{"_173":67,"_174":-5,"_175":-5,"_176":177},"count","next","previous","results",[178],{"_116":48,"_117":118,"_119":120,"_93":121,"_122":179,"_54":140,"_141":118,"_142":29,"_143":60,"_39":144,"_145":146,"_147":131,"_148":140,"_149":69,"_150":17},[180],{"_125":126,"_127":128,"_129":103,"_130":131,"_132":133,"_134":135,"_136":103,"_137":-5,"_138":120,"_66":69,"_139":-5},[],"canDeleteTags","actionData","errors"]

scl3/task_hii_power

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