Dataset

This dataset was created by Oggi Jack's Brother

Released under Apache 2.0

Contents

This set of data files is one of the four test data sets acquired by the USDOT Data Capture and Management program. It contains the following data for the six months from May 1 2011 to October 31 2011: -Raw and cleaned data for traffic detectors deployed by Washington Department of Transportation (WSDOT) along I-5 in Seattle. Data includes 20-second raw reports. -Incident response records from the WSDOT's Washington Incident Tracking System (WITS). -A record of all messages and travel times posted on WSDOT's Active Traffic -Management signs and conventional variable message signs on I-5. -Loop detector volume and occupancy data from arterials parallel to I-5, estimated travel times on arterials derived from Automatic License Plate Reader (ALPR) data, and arterial signal timing plans. -Scheduled and actual bus arrival times from King County Metro buses and Sound Transit buses. -Incidents on I-5 during the six month period -Seattle weather data for the six month period -A dataset of GPS breadcrumb data from commercial trucks described in the documentation is not available to the public because of data ownership and privacy issues. This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov. Note: All extras are attached in Seattle Freeway Travel Times https://data.transportation.gov/Automobiles/Seattle-Freeway-Travel-Times/9v5g-t8u8

Data provided consists of Basic Safety Messages (BSM) generated by the Trajectory Converter Analysis (TCA) tool with input from VISSIM calibrated simulations of the I-405 corridor in Seattle, Washington. The Seattle I-405 data environment includes data for a variety of network operational conditions, market penetrations of connected vehicles and communication strategies along the I-405 travel corridor.

This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

This data is derived from sensor stations placed on bridges and surface streets within city limits. Each station has a temperature sensor that measures the temperature of the street surface and a sensor that measures the ambient air temperature at the station each second. Those values are averaged into temperature readings that are recorded by the station every minute. The dataset is updated hourly with new data. Only the most recent 48 hours of data is stored in the dataset.

The purpose of the data set is to provide multi-modal data and contextual information (weather and incidents) that can be used to research and develop applications for the USDOT Dynamic Mobility Applications (DMA) program.

This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

Additional related data can be found here: https://data.transportation.gov/Automobiles/Seattle-20-Second-Freeway/ixg2-6cni

description: This digital elevation model (DEM) is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer. The DEMs created for this project were developed using the NOAA National Weather Service's Weather Forecast Office (WFO) boundaries. Because the WFO boundaries can cover large areas, the WFO DEM was divided into smaller DEMs to ensure more manageable file sizes. The Seattle (WA) WFO DEM was split into three smaller DEMs. They are divided along county lines and are: 1. Seattle (WA) WFO - Grays Harbor County 2. Seattle (WA) WFO - Clallam, Jefferson, Kitsap, Mason, Pierce, and Thurston Counties 3. Seattle (WA) WFO - Whatcom, San Juan, Skagit, Island, Snohomish, and King Counties This metadata record describes the DEM for Seattle (WA) WFO - Grays Harbor County. The DEM includes the best available lidar data known to exist at the time of DEM creation for the coastal areas of Grays Harbor County, that met project specifications. The DEM is derived from LiDAR datasets collected for the Puget Sound LiDAR Consortium (PSLC), United States Geological Survey (USGS) and the Oregon Department of Geology and Mineral Industries (DOGAMI), available from the NOAA Digital Coast. Hydrographic breaklines used in the creation of the DEM were delineated using LiDAR intensity imagery generated from each constituent dataset. The DEMs are hydro flattened such that water elevations are less than or equal to 0 meters. The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 5 meters.; abstract: This digital elevation model (DEM) is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer. The DEMs created for this project were developed using the NOAA National Weather Service's Weather Forecast Office (WFO) boundaries. Because the WFO boundaries can cover large areas, the WFO DEM was divided into smaller DEMs to ensure more manageable file sizes. The Seattle (WA) WFO DEM was split into three smaller DEMs. They are divided along county lines and are: 1. Seattle (WA) WFO - Grays Harbor County 2. Seattle (WA) WFO - Clallam, Jefferson, Kitsap, Mason, Pierce, and Thurston Counties 3. Seattle (WA) WFO - Whatcom, San Juan, Skagit, Island, Snohomish, and King Counties This metadata record describes the DEM for Seattle (WA) WFO - Grays Harbor County. The DEM includes the best available lidar data known to exist at the time of DEM creation for the coastal areas of Grays Harbor County, that met project specifications. The DEM is derived from LiDAR datasets collected for the Puget Sound LiDAR Consortium (PSLC), United States Geological Survey (USGS) and the Oregon Department of Geology and Mineral Industries (DOGAMI), available from the NOAA Digital Coast. Hydrographic breaklines used in the creation of the DEM were delineated using LiDAR intensity imagery generated from each constituent dataset. The DEMs are hydro flattened such that water elevations are less than or equal to 0 meters. The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 5 meters.

Dataset

This dataset was created by CeliaHNK

Contents

The National Weather Service (NWS) provides weather, water and climate data, forecasts, warnings, and impact-based decision support services for the protection of life and property and enhancement of the national economy. This StoryMap is an overview of the volunteer program at NWS Seattle.

Dataset

This dataset was created by tobi joshua

Contents

This layer shows the aggregated emissions resulting from energy consumption in buildings across different neighborhoods and sectors (i.e., residential, commercial and industrial). The data is mapped to census tracts. This layer has been populated with utility energy consumption data procured directly from Seattle City Light (electricity), aggregated and anonymized by sector, quarter, and census tract. Some tracts have their data combined and averaged with neighboring tracts for privacy purposes. If data is aggregated in a tract, the "grouped flag" field will read "true".For more information please visit the One Seattle Climate Portal item description page.

The One Seattle Climate Portal is a publicly available map-based website that houses more frequent and granular data indicators of emissions in Seattle’s neighborhoods to allow for better policy and programmatic decision making.

Seattle has typically relied on our biennial communitywide GHG emissions inventory reports to track progress towards our climate reduction goals. However, the data in these reports are annual and city-wide, meaning that they are not a good base from which to make equitable policy and program decisions. To address this, the Green New Deal Executive Order directed OSE and IT to develop more frequent and granular indicators of our climate progress.

Over the past 18 months, the Office of Sustainability & Environment (OSE) worked with both internal and external stakeholders to identify data improvements, gaps, and community needs. The Portal as launched today is a culmination of those efforts, and will improve on the data in our GHG inventories in the following ways:Transportation: trips by mode, VMTs, and emissions estimates are now available by census tract and paired with the City’s Race and Social Equity Index as a base layer.Buildings: emissions from building energy use (gas and electricity) in the residential, commercial, and industrial sectors are now available on a quarterly basis and by census tract. This data is also paired with the City’s Race and Social Equity Index as a base layer.

OSE is collaborating on new updates to the portal which aim to incorporate community-led data efforts, as well as ways to spatially track city-led investments like those through the JumpStart funded Green New Deal Opportunity Fund, share climate stories, and track more indicators of a healthy and sustainable city.

Rainwatch map service URI descriptor

This project investigates the impacts on residential power demand during warm summers when air quality is compromised by smoke from wildfires. We hypothesize that the energy use increases when the air is smoky because of additional purchase and use of air conditioners and air purifiers when temperatures are warm and the air is smoky from wildfires because windows must be kept closed, eliminating the evening cooling ability practiced by homeowners. We'll focus our analysis in the Seattle area using Seattle City Light energy use data and SeaTac weather station data. U.S. Air Quality Index (AQI), EPA’s index for reporting air quality ranging from 0 to 500, will be used for air quality data. The timeframe will initially focus on June through August during 2015 through 2018.

SDOT Snow and Ice Level of Service Treatment and Routes, based on the Winter Weather Storm Response Plan. Level of Service is a hierarchy of snow and ice response in achieving bare and wet pavement road condition. Annual snow and ice routes maintained by Seattle Department of Transportation. The snow and ice routes are displayed in the Winter Weather Storm Response Map (external and internal). Refresh Cycle: Manually refreshed annually prior to November 1st and on as needed basis.

Timeseries data from 'SEATTLE-TACOMA INTERNATIONAL AIRPORT , WA (KSEA)' (gov_noaa_awc_ksea) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=wind_speed_qc_agg,wind_speed_of_gust_qc_agg,wind_speed_of_gust,air_pressure_at_mean_sea_level,visibility_in_air,wind_from_direction,air_temperature_qc_agg,wind_from_direction_qc_agg,air_temperature,air_pressure_at_mean_sea_level_qc_agg,dew_point_temperature_qc_agg,z,wind_speed,time,visibility_in_air_qc_agg,dew_point_temperature&time>=max(time)-3days Easternmost_Easting=-122.317 featureType=TimeSeries geospatial_lat_max=47.45 geospatial_lat_min=47.45 geospatial_lat_units=degrees_north geospatial_lon_max=-122.317 geospatial_lon_min=-122.317 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from NOAA National Weather Service (NWS) at https://aviationweather.gov/data/metar/?id=KSEA id=119164 infoUrl=https://sensors.ioos.us/#metadata/119164/station institution=NOAA National Weather Service (NWS) naming_authority=com.axiomdatascience Northernmost_Northing=47.45 platform=fixed platform_name=SEATTLE-TACOMA INTERNATIONAL AIRPORT , WA (KSEA) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://aviationweather.gov/data/metar/?id=KSEA,https://aviationweather.gov/data/metar/?id=KSEA, sourceUrl=https://aviationweather.gov/data/metar/?id=KSEA Southernmost_Northing=47.45 standard_name_vocabulary=CF Standard Name Table v72 station_id=119164 time_coverage_end=2025-07-10T17:53:00Z time_coverage_start=2022-07-11T20:53:00Z Westernmost_Easting=-122.317

Timeseries data from 'SEATTLE BOEING FIELD, WA (KBFI)' (gov_noaa_awc_kbfi) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=wind_speed_qc_agg,wind_speed_of_gust_qc_agg,wind_speed_of_gust,air_pressure_at_mean_sea_level,visibility_in_air,wind_from_direction,air_temperature_qc_agg,wind_from_direction_qc_agg,air_temperature,air_pressure_at_mean_sea_level_qc_agg,dew_point_temperature_qc_agg,z,wind_speed,time,visibility_in_air_qc_agg,dew_point_temperature&time>=max(time)-3days Easternmost_Easting=-122.317 featureType=TimeSeries geospatial_lat_max=47.55 geospatial_lat_min=47.55 geospatial_lat_units=degrees_north geospatial_lon_max=-122.317 geospatial_lon_min=-122.317 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from NOAA National Weather Service (NWS) at https://aviationweather.gov/data/metar/?id=KBFI id=119163 infoUrl=https://sensors.ioos.us/#metadata/119163/station institution=NOAA National Weather Service (NWS) naming_authority=com.axiomdatascience Northernmost_Northing=47.55 platform=fixed platform_name=SEATTLE BOEING FIELD, WA (KBFI) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://aviationweather.gov/data/metar/?id=KBFI,https://aviationweather.gov/data/metar/?id=KBFI, sourceUrl=https://aviationweather.gov/data/metar/?id=KBFI Southernmost_Northing=47.55 standard_name_vocabulary=CF Standard Name Table v72 station_id=119163 time_coverage_end=2025-07-12T12:53:00Z time_coverage_start=2022-07-11T20:53:00Z Westernmost_Easting=-122.317

This layer shows the aggregated emissions resulting from energy consumption in buildings across different neighborhoods and sectors (i.e., residential, commercial and industrial). The data is mapped to census tracts. This layer has been populated with utility energy consumption data procured directly from Puget Sound Energy (gas), aggregated and anonymized by sector, quarter, and census tract. Some tracts have their data combined and averaged with neighboring tracts for privacy purposes. If data is aggregated in a tract, the "grouped flag" field will read "true". For more information please visit the One Seattle Climate Portal item description page.

Part 2 of 3 folders of building satellite images. This data item consists of top-down satellite building views extracted from Mapbox Satellite Imagery. Mapbox offers a comprehensive global raster tileset, which includes high-resolution satellite and aerial imagery.Images are sourced from various providers, including NASA, USGS, Maxar, and Nearmaps, as described in their documentation: https://docs.mapbox.com/help/glossary/mapbox-satellite/. The original tiles are obtained with Zoom level 19. The code to extract building specific top-down views are provided in the accompanying repository.

This statistic shows cities in the United States with the highest number of heavy cloud cover days per year. In Seattle, Washington in 2011 there were 226 days with heavy cloud. In Portland, Oregon 222 of heavy cloud were recorded in 2011.

AbstractRats are abundant and ubiquitous in urban environments. There has been increasing attention to the need for evidence-based, integrated rat management and surveillance approaches because rats can compromise public health and impose economic costs. Yet there are few studies that characterize rat distributions in sewers and there are no studies that incorporate the complexity of sewer networks that encompass multiple sewer lines, all comprised of their own unique characteristics. To address this knowledge gap, this study identifies sewer characteristics that are associated with rat presence in the city of Seattle’s urban sewer system. We obtained sewer baiting data from 1752 geotagged manholes to monitor rat presence and constructed generalized additive models to account for spatial autocorrelation. Sewer rats were unevenly distributed across sampled manholes with clusters of higher rat presence at upper elevations, within sanitary pipes, narrower pipes, pipes at a shallower depth, and older pipes. These findings are important because identifying features of urban sewers that promote rat presence may allow municipalities to target areas for rat control activities and sewer maintenance. These findings suggest the need to evaluate additional characteristics of the surface environment and identify the factors driving rat movement within sewers, across the surface, and between the surface and the sewers. , MethodsData was collected in the port city of Seattle, Washington USA (47.6°N, 122.3°W) between February 2016 and September 2019 as a part of Seattle’s ongoing rat sewer baiting program. In the baiting program, manholes across the city were geotagged in map grids, where all grids in one zone were baited before moving to the next zone. The method for monitoring a manhole includes an initial assessment with four non-toxic Talon Weather BlocTM bait blocks. Blocks were suspended from the manhole so that they hung just above the sewer surface. Bait consumption was measured 10 days after the initial visit to monitor rat presence. Rats were considered present in manholes if some bait was consumed and/or signs of rodents (e.g., rodent gnaw marks, rat droppings) were observed. Data regarding the consumption of non-toxic bait (rats were considered either present or absent in each manhole) were joined with three publicly accessible municipal datasets. These datasets included 10 manhole characteristics (point features), 21 sewer line characteristics (line features), and 2 surface characteristics. Two weather characteristics, temperature (average monthly temperature) and precipitation (cumulative monthly precipitation), were obtained from the Western Regional Climate Center and Seattle Weather Blog., Usage notesWe used RSudio (version 1.3) for statistical analyses and ArcGIS Pro (version 2.6.0) for spatial analyses.