,This layer shows total trips by mode and their corresponding emissions across different neighborhoods in Seattle. The data is mapped to census tracts.,,,

,This layer shows total trips by mode and their corresponding emissions across different neighborhoods in Seattle. The data is mapped to census tracts.,,,

,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".,

,This layer shows total trips by mode and their corresponding emissions across different neighborhoods in Seattle. The data is mapped to census tracts.,,,

,The Central Valley Flood Protection Plan (CVFPP) recommends that the California Department of Water Resources (DWR) develop a system for tracking performance of the flood system, including the following actions:,• Track the outcomes from flood investments to demonstrate value.,• Monitor and track outcomes of multi-benefit projects over time.,• Create a tracking system of operations and maintenance investments and outcomes to demonstrate the value that Local Maintaining Agencies attain for their investments.,• Track and report changes in the hydrologic and sea level rise conditions and subsidence over time through updates to the Flood System Status Report (FSSR),These recommendations stem from progressive work during the development of the 2012 CVFPP and subsequent 2017 CVFPP update. The DWR Flood Performance Tracking System tracks the CVFPP outcomes related to: (1) improving flood risk management and (2) enhancing ecosystem vitality. This tracking system has the ability to track the status, trends, and changes over time of the ecosystem (including the Conservation Strategy’s Measurable Objectives [CSMOs] as of 2016) outlined in the Conservation Strategy document here: https://cawaterlibrary.net/wp-content/uploads/2017/10/ConservStrat-Nov2016.pdf along with the Flood System metrics outlined in the Flood System Status Report here: https://water.ca.gov/Programs/Flood-Management/Flood-Planning-and-Studies/Central-Valley-Flood-Protection-Plan.,The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.1, dated September 11, 2019.,This data set was not produced by DWR. Data were originally developed and supplied by ESA, under contract to California Department of Water Resources. DWR makes no warranties or guarantees — either expressed or implied — as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data.,Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov.,

,Data source is DWW.polygon_plgn_pv using the following definition query, PLY_LIFECYCLE_CODE IN ( 'C' ,'UNK', 'T', 'TBC', 'U', 'PC') AND PLY_FEATYPE_CODE = 'PND'. This layer does not display when zoomed out beyond 1:24,000. Labels do not display when zoomed out beyond 1:3,000 and are based on the attribute DESCRIPTION.,Refreshed weekly.,

,This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.,University of Vermont Spatial Analysis Laboratory in collaboration with City of Seattle.,This dataset consists of City of Seattle SDOT Urban Forestry Management Units which cover the following tree canopy categories:,,,For more information, please see the 2021 Tree Canopy Assessment.,

,Easements on Seattle Parks and Recreation owned properties or Easements relating to Seattle Parks and Recreation. Easement Types includes: Conservation, Access, Utility, Drainage and Wastewater, Driveway, Retaining Wall, Seawall.,The SPR Easements Layer information is not complete or current. Use with caution.,