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Description: City-Wide Evaluation of Areas Unsuitable for Infiltration Areas unsuitable for infiltration (AUI) were identified in support of the green stormwater infrastructure (GSI) assessment for combined sewer overflow (CS0) mitigation. GSI is a decentralized approach for reducing runoff from development using infiltration, evapotranspiration, or stormwater reuse. GSI can be a tool to complement traditional means for managing CSOs.AUI include steep slopes, potential groundwater contamination sites, and high groundwater table or bedrock near the ground surface. A GIS analysis was conducted to identify AUIs in Seattle Public Utility (SPU) Long Term Control Plan (LTCP) CSO basins. A general description of the analysis is provided below for each type of AUI. Steep SlopesSteep slopes could potentially be made unstable by infiltrating water into the ground, and are therefore unsuitable for infiltration. Identification of AUI due to steep slopes included the following areas, listed in order of priority:Areas designated as too steep for infiltration in the SPU Geotechnical Report for five basins (North Union Bay, Fremont/Wallingford, Interbay, Magnolia, Montlake) Note: for basins with data from the Geotechnical Report, the slope analyses discussed below were not conducted. Only the Geotechnical Report data was used in the AUI identification for steep slopes.Areas designated as potential slides by SPU (“potslide” shapefile) with a 500-ft uphill buffer.Areas designated as steep slope by SPU (“steepslp” shapefile) with a 100-ft uphill buffer. These areas are assumed to be minor steep slopes; therefore, a minimum buffer of 100-ft was used.Areas identified as steep slope by a SPU consultant (“Seattle_Upslope_Buffers_spc” shapefile). According to SPU, this data includes a buffer.Areas with slopes greater than 40% and an elevation difference of more than 10 feet, as identified using LiDAR data. Note: no buffer was applied to this steep slope data.Potential Groundwater ContaminationInfiltration where potential of contamination exists can cause the spread of contaminates to groundwater; therefore these areas are not suitable for infiltration. Areas with potential contaminates were identified using a 100-foot buffer around the following sites:Confirmed and Suspected Contaminated Sites (CSCS) (“carto_haz_cscs_pt” shapefile)Leaking Underground Storage Tank (LUST) Sites (“carto_haz_lust_pt” shapefile)Landfills (“landfill” shapefile)Bedrock/Groundwater Table Near Ground SurfacePermeability assessment data from SPU (“Perm_Assessment” shapefile) was used to show areas where bedrock or groundwater table is near the ground surface, as these areas are unsuitable for effective infiltration. Detailed Description of GIS AnalysisThe GIS analyses to determine steep slopes using LiDAR and to apply buffers to SPU steep slope data required multiple steps to complete. The steps taken for these analyses are included below, in detail, for reference. See “LiDAR Slope Analysis” and “Uphill Buffer to Steep Slope Features” sections.Specific steps of the GIS analysis to create an AUI feature class, which incorporates all AUI types into a single set of data, are also provided in detail below. See the section named “Create Areas Unsuitable for Infiltration (AUI) GIS Feature Class.”Once AUI were identified, parcels with greater than 5% AUI were selected to show which parcels would not be suitable for infiltration projects. This process is described below in the section named “Identify Parcels with Area >5% AUI.”LiDAR Slope AnalysisThe following steps were followed to identify slopes 40% or greater with an elevation difference of more than 10 feet using LiDAR data. Begin with LiDAR data of with extent of CSO modeling basins.Use Spatial Analyst à Surface Analysis à Slope to calculate slope. Choose “Percent Rise” option to calculate percent slope (opposed to degree of slope)Use Raster Calculator to extract cells >= 40 percent riseOutRas = Select([tstslp], "Value >= 40")Results are placed in temporary raster file (being created in memory)Use Raster Calculator to convert floating point raster to integer raster.Int(<Slope Raster Name>)Results in temporary raster file being created (in memory)Convert raster to polygons. Spatial Analyst Toolbar à Raster to featuresDissolve this shapefile – now you have one polygonField calculate “ID” field = “1”. This provides an attribute upon which to base the dissolve.Data Management Tools à Features à Multipart to Singlepart Now you have lots of polygons againField calculate “ORIG_FID” field = [FID] + 1. This creates a unique ID fieldSpatial Analyst Tools à Zonal à Zonal Statistics as Table Input raster or feature zone data - use singlepart polygon shapefile Zone field – “ORIG_FID”Input value raster – lidar rasterNote: if there are a large number of records in the singlepart attribute table, then the “ORIG_FID” field may need to be of type “text” to avoid errorsJoin this zonal statistics table to the singlepart polygon shapefile Join based on “ORIG_FID” fieldSelect by attribute, using Range field greater than 10. Export these to shapefile, which will now contain slopes 40% or greater with a difference between min/max elevation greater than 10. Uphill Buffer to Steep Slope FeaturesThe following steps were followed to create uphill buffers of 100 feet (for “steepslp” shapefile) and 500 feet (for “potslide” shapefile) for steep slope data provided by Seattle Public Utilities.This analysis used an AML script downloaded from ESRI (“buffer2.aml) which was modified to produce uphill buffers.Insure DEM (LiDAR) and steep slope data are in the same projection – script will give erroneous results if the projections of the two data sets differ.Retrieve DEM (LiDAR) or use LiDAR from analysis described above for steep slopes.Convert DEM (LiDAR) to NAD 83 HARN projectionUse ArcToolbox – Projections and Transformations\Raster\Project RasterConvert DEM (LiDAR) to integerUse ArcToolbox – Spatial Analyst Tools/Math/IintCreate “Clip” feature class to define boundary of analysisInsure “Clip” is >= 500-ft outside of CSO basins feature class (so “potslide” buffer is fully captured)Clip DEM (LiDAR) to “Clip” feature classSelect slope data to include in analysis Select slope data (e.g. “steepslp” or “potslide” shapefile)Convert steep slope polygons to line featuresRequires ArcINFO licenseArcToolbox – Data Management Tools/Features/Feature to LineDefine projection of slope feature class as NAD 83 HARN projectionConvert steep slope line features to coverageCopy LIDAR and steep slope coverage to ARCINFO Workstation workspace (typically this is c:\workspace)Convert line coverage to double precisionWas receiving the following error when running the script: “FATAL ERROR; Workspace is full (NEXTBV). Bailing out of BUFFER" According to ESRI help, this error is due to the difference between the fuzzy tolerance and buffer distance not being sufficient to allow the polygon topology for the output coverage to be resolved. I don’t really understand this, but the fact the line coverage is likely single precision contributes to this problem.A fix of using the following 2 arc commands appears to address this error:“precision double double”“copy <in_line_coverage> <out_line_coverage>I believe these commands convert the line coverage to double precisionSplit steep slope line coverage into shorter line segmentsUse Arc “DENSIFYARC” command in ARCINFO WorkstationDENSIFYARC <in_coverage> <out_coverage> <distance> arcUsed distance of 50Use Arc “CLEAN” command to adjust fuzzy tolerance of steep slope coverage in ARCINFO WorkstationCLEAN <in_coverage> <out_coverage> <dangle> <fuzzy tolerance>CLEAN <in_coverage> # # 1 (or a similar low number; i.e. lower the number, the better)Run “Buffer2.aml” in ARCINFO WorkstationSyntax: &run buffer2.aml <slope line coverage> <dem> <distance value>For SPU steep slope data (“steepslp”), used distance value of 100For SPU potential slide data (“potslide”), used distance value of 500Create Areas Unsuitable for Infiltration (AUI) GIS Feature ClassThe following steps were followed to incorporate all AUIs into one feature class, and tag each with type of for unsuitability.Created 100 foot buffer around contaminated sites or landfills carto_haz_cscs_pt.shp called carto_haz_cscc_pt_Buffercarto_haz_lust_pt.shp called carto_haz_lust_pt_Bufferlandfill.shp (Note – landfill.shp includes a 1000ft buffer around landfills already, so selected only the landfill features, not the buffer features, and created a buffer around that) called landfills_Buffer.shpCreated GSI_geotech.shp by merging GSI_Geotech_Feas shapefiles for Fre/Wall, Interbay, Montlake, NUB.GSI_Geotech_Feas shapefiles contain results of SPU Geotechnical Report for 5 basins in GIS format.Merge steepslp uphill buffer with stpslp shapefile and potslide uphill buffer with potslide shapefileUnioned all of these shapefiles (plus slope shapefiles) together to create AUI shapefilecarto_haz_cscc_pt_Buffercarto_haz_lust_pt_Bufferlandfills_Bufferperm_assessmentGSI_geotech Seattle_Upslope_Buffers_spcLiDAR steep slope analysis: SteepSlope_LidarSPU Steep slope data w/ buffer: buffer_SteepSlpSPU potential slide data w/ buffer: buffer_ptsldNote: SPU had previously provided AUI data for Ballard, which was used in place of the AUI estimated by the above procedure.Identify Parcels with Area >5% AUIThe following steps were followed to identify parcels with >5% AUI and create a parcel feature class showing parcels unsuitable for infiltration.Select by location, parcels intersecting AUI, and create a feature class of the selected parcels named “AUI_X_Parcel_select.”Union “AUI_X_Parcel_select” with AUI (“AUI_X_Parcel_ union”) Select AUI slices from this unioned layer (“FID_AUI_X” >=0)Create layer from selected featuresDissolve AUI slices based on PIN (Parcel ID) and return the maximum area in the dissolved attribute table, “MAX_AREA.” (“AUI_X_PIN_dissolve”)Add Field “AUI_area.” Calculate geometry to find area of dissolved AUI features.Add Field “AUI_prcnt.” Field calculate percentage of parcel = “AUI_area”/”MAX_AREA” * 100Add Field “_5_plus.” Field calculate IF [AUI_prcnt]>5 THENB="Yes"ENDIFJoin “AUI_PIN_dissolve” with “AUI_X_Parcel_select” based on PINSelect parcels where “_5_plus” = Yes, create feature class from selected features.“AUI_X_PARCEL”Add right of way AUI to “AUI_X_PARCEL”Assume parcel data with NO PIN value is right of waySelect features from “AUI_X_dissolve” with PIN=””In edit mode, copy the selected feature and paste to “AUI_PARCEL”
Description: Adopted by Ordinance Number: 122738 relating to environmentally critical areas (ECA) to designate and regulate Peat Settlement-prone Geologic Hazard Areas. Data set represents City of Seattle identified peat bogs as delineated by UW GeoMapNW per Kathy Troost et al Geologic Map of Seattle, circa 2005 along with intersecting parcels.A subset of the King County KCGIS parcel layer (July 2008 parcels) were made through a spatial intersection using layer, "GISP.DPD.BOGS_TROOST_2007", which represents bog delineations. There are two peat settlement prone category types(Category I and II) in the layer. The parcel boudaries were dissolved and digitally joined to create continuity between blocks.
Description: <DIV STYLE="text-align:Left;"><DIV><P><SPAN><SPAN>This data began as a traditional geologic map of the Seattle area published by the U.S. Geological Survey (USGS) in 1962. The transformation from print to virtual reality went through several metamorphoses. H.H. Waldron and his USGS co-workers issued the geologic map in 1962. D.B. Booth and J. Sackett digitized the USGS map. SPU updated the digitized version and extracted the area within the City of Seattle.</SPAN></SPAN></P></DIV></DIV>
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Display the 20 Large City Clerk neighborhood boundaries, along with their smaller neighborhood boundaries.</SPAN></P></DIV></DIV></DIV>
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