A custom Python Tool Box exclusively for ESRI ArcMap Application. This toolbox contains two tools: 1. Duplicate Value Calculator : - to search duplicate values in a specified Attribute Field of Table /FeautureClass and populate user defined text for such records in another specified Attribute Field of same Table/FeatureClass. If no Attribute Field is selected to populate text, a default Attribute Field will be added with Name as "DUPLICATE_{Name of Field for Search Duplicate values}".

Further, User can imply SQL Expression to limit the records to be searched as per requirement.

Caution : This Tool modifies the SCHEMA of selected Table/FeatureClass if no Attribute Field is selected to populate text for duplicate values. So preconsider to choose both Attribute Fields - One for Duplicate Search and other for Text against duplicate value if You are concerned about to add new field to Your Table/FeatureClass.

1. Delete Rows : - to delete Rows from input Table/FeatureClass. Put an SQL Expression for records filter, otherwise all rows will be deleted.

The hypsometric integral (HI) is one of the most commonly used measures that geomorphologists use to describe the shape of the Earth’s surface. A hypsometric integral is usually calculated by plotting the cumulative height and the cumulative area under that height for individual watersheds and then taking the area under that curve to get the hypsometric integral. In a GIS hypsometric integral is calculated by slicing watersheds into elevation bands and plotting the cumulative area for each band. Due to the iterative nature that is required for calculating hypsometric integral it tends to be one of the harder to calculate watershed variables, and thus the need for an automated tool. Although there are instructions online for how to calculate HI in ArcGIS this tool automates the processes and doesn’t require users to do their own plotting or export results to spreadsheets.

This toolbox contains two models. Hypsometric Integral (for shapefiles only) is the main model that most users will want to run. Hypsometric Integral (submodel) is a model that is nested within the Hypsometric Integral (for shapefiles only) model and doesn’t need to be run by itself. The tool computes the hypsometric integral for a given watershed. A new shapefile will be created representing the same watershed the user inputs, but includes a new field, "HI," representing hypsometric integral percentages.

In some instances the Hypsometric Integral (for shapefiles) will show up with a red X and won’t be useable. The workaround for this is to open the Hypsometric Integral (for shapefiles) tool in edit mode (ModelBuilder) delete the Hypsometric Integral (submodel) and drag in your version of the Hypsometric Integral (submodel). Re-connect the following parameters: input DEM, Input Watershed, TempWorkspace, and then connect the output (HI Values for all Watersheds) to the Append tool. Click save.

Bathymetry depth contours for the Wachusett Reservoir were developed by Department of Conservation and Recreation (DCR), Division of Water Supply Protection (DWSP) Environmental Quality (EQ) staff in 2011. These 10-foot contours were developed using the following processing steps within ArcMap, and more recently in ArcGIS Pro. This layer was derived using the following steps in ArcGIS Pro. The Focal Statistics tool (Neighborhood = circle, with 5 cell radius, Mean statistics type) was used to slightly smooth the bathymetric DEM. The Contour tool was used to generate 10-foot contour polygons, with a base contour of 0. The two lowest contour intervals were merged using the Edit ribbon; this was done to merge the BCB 380 to 390 and BCB 390.5 contours together into the 0 to 10 foot depth contour. New attribute fields were added to convert the Boston City Base (BCB) elevations (used by DCR and MWRA) into bathymetric depth in feet. This was then used to calculate a "Depth Range" attribute field (text). The Erase tool was used to remove any bathymetry contour area that overlapped with the Wachusett Islands layer. This small area of overlap resulted from the Focal Statistics tool and smoothing process. The layer was projected into Massachusetts State Plane coordinates. Finally, to improve drawing performance, the Simplify Shared Edges tool was used with the Douglas-Peucker simplification algorithm, a 2 meter tolerance and a 10 square meter minimum area. A custom symbology was applied using the "Depth Range" attribute field.

Unincorporated Urban Growth Areas (UGA) as defined by the Growth Management Act (GMA). The annual update is conducted by collecting UGA polygons directly from each of Washington's 39 counties. As of 2025, there are 27 counties with UGAs.All UGA polygons are normalized against the Department of Revenue's (DOR) "City Boundaries" layer (shared to the Washington Geoportal a.k.a. the GIS Open Data site: geo.wa.gov). The City Boundaries layer was processed into this UGA layer such that any overlapping area of UGA polygons (from authoritative individual counties) was erased. Since DOR polygons and county-sourced UGA polygons do not have perfect topology, many slivers resulted after the erase operation. These are attempted to be irradicated by these processing steps. "Multipart To Singlepart" Esri tool; exploded all polygons to be individualSlivers were mathematically identified using a 4 acre area threshold and a 0.3 "thinness ratio" threshold as described by Esri's "Polygon Sliver" tool. These slivers are merged into the neighboring features using Esri's "Eliminate" tool.Polygons that are less than 5,000 sq. ft. and not part of a DOR city (CITY_NM = Null) were also merged via the "Eliminate" tool. (many very small slivers were manually found yet mathematically did not meet the thinness ratio threshold)The final 8 polygons less than 25 sq. ft. were manually deleted (also slivers but were not lined up against another feature and missed by the "Eliminate" tool runs)Dissolved all features back to multipart using all fieldsAll UGAs polygons remaining are unincorporated areas beyond the city limits. Any polygon with CITY_NM populated originated from the DOR "City Boundaries" layer. The DOR's City Boundaries are updated quarterly by DOR. For the purposes of this UGA layer, the city boundaries was downloaded one time (4/24/2025) and will not be updated quarterly. Therefore, if precise city limits are required by any user of UGA boundaries, please refer to the city boundaries layer and conduct any geoprocessing needed. The DOR's "City Boundaries" layer is available here:https://www.arcgis.com/home/item.html?id=69fcb668dc8d49ea8010b6e33e42a13aData is updated in conjunction with the annual statewide parcel layer update. Latest update completed April 2025.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal Buffers (this dataset)Without Coastal BuffersPlace AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

This layer generally describes Geologically Hazardous Areas as defined in TMC 13.11.700, including erosion and landslide hazard areas. It is used to review changes to these areas including development proposals, proposals for vegetation modification, and potential violations for compliance with critical area and building codes.This layer was derived from 2018 bare earth lidar. The initial analysis steps include: slope tool to create a % rise surface then using the int tool and reclassify using the 0-15, 15-25, 25-40 and >40 percent slope. Those classifications were converted to polygons. Further refinement was done to reduce the number of polygons. All areas in the >15% classification were deleted, all polygons <200ft in length and all polygons < 100 sq. ft. in area were deleted. Additional simplifying was done to create smoother boundaries of areas and a series of positive and negative buffers was used to remove holes in areas. Additional refinement to this was done including: (Deleted polygons <= 200 sq. ft. for Slope Category 15 - 25%. Deleted polygons <= 100 sq. ft. for Slope Category 25 - 40% & Over 40%)Data Steward contact: Craig Kuntz, ckuntz@cityoftacoma.org or Lisa Spadoni, Natural Resources Program Manager, lspadoni@cityoftacoma.org.

Dataset description-br /- This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following: * a) Barcelona city boundaries * b) Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB) * c) Barcelona greater city (Urban Atlas) * d) Barcelona functional urban area (Urban Atlas) * e) Milan city boundaries * f) Milan metropolitan area, Piano Intercomunale Milanese (PIM) * g) Milan greater city (Urban Atlas) * h) Milan functional urban area (Urban Atlas)-br /- In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2). -br /- -br /- -br /- Dataset composition-br /- The dataset is provided in .csv format and is composed of: -br /- _IMD15_BCN_MI_Sources.csv_: Information on data sources -br /- _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona: * a) Barcelona city boundaries (label: bcn_city) * b) Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb) * c) Barcelona greater city (Urban Atlas) (label: bcn_grc) * d) Barcelona functional urban area (Urban Atlas) (label: bcn_fua)-br /- _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan: * e) Milan city boundaries (label: mi_city) * f) Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim) * g) Milan greater city (Urban Atlas) (label: mi_grc) * h) Milan functional urban area (Urban Atlas) (label: mi_fua)-br /- _IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h). -br /- Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM. -br /- In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included: * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan); * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area); * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness; * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.-br /- -br /- -br /- Further information on the Dataset-br /- This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.-br /- 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster. 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA. 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.-br /- Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h). -br /- Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following: * Clipping: Arctoolbox - Data management tools - Raster - Raster Processing - Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping. * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox - Data management tools - Raster - Raster properties - Delete Raster Attribute Table and then through Arctoolbox - Data management tools - Raster - Raster properties - Build Raster Attribute Table; the "overwrite" option has been selected. -br /- -br /- Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal - Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.-br /- The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

This dataset is a modified version of the FWS developed data depicting “Highly Important Landscapes”, as outlined in Memorandum FWS/AES/058711 and provided to the Wildlife Habitat Spatial analysis Lab on October 29th 2014. Other names and acronyms used to refer to this dataset have included: Areas of Significance (AoSs - name of GIS data set provided by FWS), Strongholds (FWS), and Sagebrush Focal Areas (SFAs - BLM). The BLM will refer to these data as Sagebrush Focal Areas (SFAs). Data were provided as a series of ArcGIS map packages which, when extracted, contained several datasets each. Based on the recommendation of the FWS Geographer/Ecologist (email communication, see data originator for contact information) the dataset called “Outiline_AreasofSignificance” was utilized as the source for subsequent analysis and refinement. Metadata was not provided by the FWS for this dataset. For detailed information regarding the dataset’s creation refer to Memorandum FWS/AES/058711 or contact the FWS directly. Several operations and modifications were made to this source data, as outlined in the “Description” and “Process Step” sections of this metadata file. Generally: The source data was named by the Wildlife Habitat Spatial Analysis Lab to identify polygons as described (but not identified in the GIS) in the FWS memorandum. The Nevada/California EIS modified portions within their decision space in concert with local FWS personnel and provided the modified data back to the Wildlife Habitat Spatial Analysis Lab. Gaps around Nevada State borders, introduced by the NVCA edits, were then closed as was a large gap between the southern Idaho & southeast Oregon present in the original dataset. Features with an area below 40 acres were then identified and, based on FWS guidance, either removed or retained. Finally, guidance from BLM WO resulted in the removal of additional areas, primarily non-habitat with BLM surface or subsurface management authority. Data were then provided to each EIS for use in FEIS development. Based on guidance from WO, SFAs were to be limited to BLM decision space (surface/sub-surface management areas) within PHMA. Each EIS was asked to provide the limited SFA dataset back to the National Operations Center to ensure consistent representation and analysis. Returned SFA data, modified by each individual EIS, was then consolidated at the BLM’s National Operations Center retaining the three standardized fields contained in this dataset.Several Modifications from the original FWS dataset have been made. Below is a summary of each modification.1. The data as received from FWS: 16,514,163 acres & 1 record.2. Edited to name SFAs by Wildlife Habitat Spatial Analysis Lab:Upon receipt of the “Outiline_AreasofSignificance” dataset from the FWS, a copy was made and the one existing & unnamed record was exploded in an edit session within ArcMap. A text field, “AoS_Name”, was added. Using the maps provided with Memorandum FWS/AES/058711, polygons were manually selected and the “AoS_Name” field was calculated to match the names as illustrated. Once all polygons in the exploded dataset were appropriately named, the dataset was dissolved, resulting in one record representing each of the seven SFAs identified in the memorandum.3. The NVCA EIS made modifications in concert with local FWS staff. Metadata and detailed change descriptions were not returned with the modified data. Contact Leisa Wesch, GIS Specialist, BLM Nevada State Office, 775-861-6421, lwesch@blm.gov, for details.4. Once the data was returned to the Wildlife Habitat Spatial Analysis Lab from the NVCA EIS, gaps surrounding the State of NV were closed. These gaps were introduced by the NVCA edits, exacerbated by them, or existed in the data as provided by the FWS. The gap closing was performed in an edit session by either extending each polygon towards each other or by creating a new polygon, which covered the gap, and merging it with the existing features. In addition to the gaps around state boundaries, a large area between the S. Idaho and S.E. Oregon SFAs was filled in. To accomplish this, ADPP habitat (current as of January 2015) and BLM GSSP SMA data were used to create a new polygon representing PHMA and BLM management that connected the two existing SFAs.5. In an effort to simplify the FWS dataset, features whose areas were less than 40 acres were identified and FWS was consulted for guidance on possible removal. To do so, features from #4 above were exploded once again in an ArcMap edit session. Features whose areas were less than forty acres were selected and exported (770 total features). This dataset was provided to the FWS and then returned with specific guidance on inclusion/exclusion via email by Lara Juliusson (lara_juliusson@fws.gov). The specific guidance was:a. Remove all features whose area is less than 10 acresb. Remove features identified as slivers (the thinness ratio was calculated and slivers identified by Lara Juliusson according to https://tereshenkov.wordpress.com/2014/04/08/fighting-sliver-polygons-in-arcgis-thinness-ratio/) and whose area was less than 20 acres.c. Remove features with areas less than 20 acres NOT identified as slivers and NOT adjacent to other features.d. Keep the remainder of features identified as less than 40 acres.To accomplish “a” and “b”, above, a simple selection was applied to the dataset representing features less than 40 acres. The select by location tool was used, set to select identical, to select these features from the dataset created in step 4 above. The records count was confirmed as matching between the two data sets and then these features were deleted. To accomplish “c” above, a field (“AdjacentSH”, added by FWS but not calculated) was calculated to identify features touching or intersecting other features. A series of selections was used: first to select records 6. Based on direction from the BLM Washington Office, the portion of the Upper Missouri River Breaks National Monument (UMRBNM) that was included in the FWS SFA dataset was removed. The BLM NOC GSSP NLCS dataset was used to erase these areas from #5 above. Resulting sliver polygons were also removed and geometry was repaired.7. In addition to removing UMRBNM, the BLM Washington Office also directed the removal of Non-ADPP habitat within the SFAs, on BLM managed lands, falling outside of Designated Wilderness’ & Wilderness Study Areas. An exception was the retention of the Donkey Hills ACEC and adjacent BLM lands. The BLM NOC GSSP NLCS datasets were used in conjunction with a dataset containing all ADPP habitat, BLM SMA and BLM sub-surface management unioned into one file to identify and delete these areas.8. The resulting dataset, after steps 2 – 8 above were completed, was dissolved to the SFA name field yielding this feature class with one record per SFA area.9. Data were provided to each EIS for use in FEIS allocation decision data development.10. Data were subset to BLM decision space (surface/sub-surface) within PHMA by each EIS and returned to the NOC.11. Due to variations in field names and values, three standardized fields were created and calculated by the NOC:a. SFA Name – The name of the SFA.b. Subsurface – Binary “Yes” or “No” to indicated federal subsurface estate.c. SMA – Represents BLM, USFS, other federal and non-federal surface management 12. The consolidated data (with standardized field names and values) were dissolved on the three fields illustrated above and geometry was repaired, resulting in this dataset.

Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion. Purpose These data layers were created in ArcGIS as part of the analysis to …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion. Purpose These data layers were created in ArcGIS as part of the analysis to investigate surface water - groundwater connectivity in the Namoi subregion. The data layers provide several of the figures presented in the Namoi 2.1.5 Surface water - groundwater interactions report. Dataset History Extracted points inside Namoi subregion boundary. Converted bore and pipe values to Hydrocode format, changed heading of 'Value' column to 'Waterlevel' and removed unnecessary columns then joined to Updated_NSW_GroundWaterLevel_data_analysis_v01\NGIS_NSW_Bore_Join_Hydmeas_unique_bores.shp clipped to only include those bores within the Namoi subregion. Selected only those bores with sample dates between >=26/4/2012 and <31/7/2012. Then removed 4 gauges due to anomalous ref_pt_height values or WaterElev values higher than Land_Elev values. Then added new columns of calculations: WaterElev = TsRefElev - Water_Leve DepthWater = WaterElev - Ref_pt_height Ref_pt_height = TsRefElev - LandElev Alternatively - Selected only those bores with sample dates between >=1/5/2006 and <1/7/2006 2012_Wat_Elev - This raster was created by interpolating Water_Elev field points from HydmeasJune2012_only.shp, using Spatial Analyst - Topo to Raster tool. And using the alluvium boundary (NAM_113_Aquifer1_NamoiAlluviums.shp) as a boundary input source. 12_dw_olp_enf - Select out only those bores that are in both source files. Then using depthwater in Topo to Raster, with alluvium as the boundary, ENFORCE field chosen, and using only those bores present in 2012 and 2006 dataset. 2012dw1km_alu - Clipped the 'watercourselines' layer to the Namoi Subregion, then selected 'Major' water courses only. Then used the Geoprocessing 'Buffer' tool to create a polygon delineating an area 1km around all the major streams in the Namoi subregion. selected points from HydmeasJune2012_only.shp that were within 1km of features the WatercourseLines then used the selected points and the 1km buffer around the major water courses and the Topo to Raster tool in Spatial analyst to create the raster. Then used the alluvium boundary to truncate the raster, to limit to the area of interest. 12_minus_06 - Select out bores from the 2006 dataset that are also in the 2012 dataset. Then create a raster using depth_water in topo to raster, with ENFORCE field chosen to remove sinks, and alluvium as boundary. Then, using Map Algebra - Raster Calculator, subtract the raster just created from 12_dw_olp_enf Dataset Citation Bioregional Assessment Programme (2017) Namoi bore analysis rasters. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/7604087e-859c-4a92-8548-0aa274e8a226. Dataset Ancestors Derived From Bioregional Assessment areas v02 Derived From Gippsland Project boundary Derived From Bioregional Assessment areas v04 Derived From Upper Namoi groundwater management zones Derived From Natural Resource Management (NRM) Regions 2010 Derived From Bioregional Assessment areas v03 Derived From Victoria - Seamless Geology 2014 Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013 Derived From Bioregional Assessment areas v01 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

This feature layer displays the locations (and rough direction) of aerial photographs taken near the Pretty Ridge and Rocky Run coal mining operations, and USFS Road 249, also known as "Haul Road #2 on 3/9/2023. Purpose:To show mining impacts and activity within the South Fork of Cherry Watershed in their environmental context.Source and Date:Photos were taken on 3/9/2023.Processing:ABRA converted the geo-tagged photos to a shapefile in ArcMap. Next a custom tool was emplyed to add, remove and calculate field values (Lat/long coordinates, URL links to photos, etc.). The zipped shapefile was uplaoded to ArcGIS Online and published there as a feature service.Symbolization:SFCR_Flight_20230309: purple arrow pointing in general direction of photo frame

To create this app:Make a map of the AfDB projects CSV file in the Training Materials group.Download the CSV file, click Map (at the top of the page), and drag and drop the file onto your mapFrom the layer menu on your Projects layer choose Change Symbols and show the projects using Unique Symbols and the Status of field.Make a second map of the AfDB projects shown using Unique Symbols and the Sector field.HINT: Create a copy of your first map using Save As... and modify the copy.Assemble your story map on the Esri Story Maps websiteGo to storymaps.arcgis.comAt the top of the site, click AppsFind the Story Map Tabbed app and click Build a Tabbed Story MapFollow the instructions in the app builder. Add the maps you made in previous steps and copy the text from this sample app to your app. Explore and experiment with the app configuration settings.=============OPTIONAL - Make a third map of the AFDB projects summarized by country and add it to your story map.Add the World Countries layer to your map (Add > Search for Layers)From the layer menu on your Projects layer choose Perform Analysis > Summarize Data > Aggregate Points and run the tool to summarize the projects in each country.HINT: UNCHECK "Keep areas with no points"Experiment with changing the symbols and settings on your new layer and remove other unnecessary layers.Save AS... a new map.At the top of the site, click My Content.Find your story map application item, open its Details page, and click Configure App.Use the builder to add your third map and a description to the app and save it.

The data shows Ordnance Survey pond locations, where they match with the surveyed location of a priority habitat pond. The location and attributes of these priority habitat ponds does not currently exists for end users, ecologists, community groups and other stakeholders. The layer will be used to identify, conserve and enhance these features.OS Ponds (taken from the MasterMap Topography layer hydrology>static water) that have a matching pond survey (Clean Water for Wildlife of Priority Ponds) (see data within this folder for these layers) within their geometry, or within 30m of their edge. proximity was created (using NEAR tool) for points and then simplified (to remove 1 to many relationship) and joined to OS polygons using FID unique value. Unnecessary OS fields have been deleted.This data was created using data from Clean Water for Wildlife of Priority Ponds Clean Water for Wildlife - Freshwater Habitats Trust under a CC-BY licence with OS MasterMap data under the PSGA licence.It is published by Natural England under the Non-Commercial Government Licence due to the quantity of OS data contained within it.Full metadata can be viewed on data.gov.uk.

This point feature layer links to aerial photographs of lands, waters and mining activities in the South Fork of Cherry River watershed.Purpose:To show mining impacts and activity within the South Fork of Cherry Watershed in their environmental context.Source and Date:Photos were taken on 11/13/2023.Processing:ABRA converted the geo-tagged photos to a shapefile in ArcMap. Next a custom tool was emplyed to add, remove and calculate field values (Lat/long coordinates, URL links to photos, etc.). The zipped shapefile was uplaoded to ArcGIS Online and published there as a feature service.Symbolization:SFCR_Flight_20231113: purple arrow pointing in general direction of photo frame

The Scottish Government (SG) Urban Rural Classification provides a consistent way of defining urban and rural areas across Scotland. The classification aids policy development and the understanding of issues facing urban, rural and remote communities. It is based upon two main criteria: (i) population as defined by National Records of Scotland (NRS), and (ii) accessibility based on drive time analysis to differentiate between accessible and remote areas in Scotland. The classification can be analysed in a two, three, six or eight fold form. The two-fold classification simply distinguishes between urban and rural areas through two categories, urban and rural, while the three-fold classification splits the rural category between accessible and remote. Most commonly used is the 6-fold classification which distinguishes between urban, rural, and remote areas through six categories. The 8-fold classification further distinguishes between remote and very remote regions. The Classification is normally updated on a biennial basis, with the current dataset reflective of the year 2020. Data for previous versions are available for download in ESRI Shapefile format.The following processes were performed by Esri: The simplify polygon tool was run to reduce the number of vertices, fields were deleted and changed in the attribute table.

Bathymetry data from the western Greenland continental margin were collected on R/V Neil Armstrong in 2023 (AR7601; Hatfield et al., 2023) to study the Last Glacial Maximum extent and initial deglacial retreat of the Greenland Ice Sheet. The multibeam data were collected with a Kongsberg EM 122 (12 kHz, 1° x 2° beam width). XBTs and CTD casts conducted throughout the cruise were applied to the multibeam data for corrections to sound speed in the water column during acquisition. Data were processed for erroneous points and to remove data artifacts as much as possible using QPS Qimera (https://qps.nl/qimera/). Conditions were icy, which caused lots of turns, reversing, and variable speeds during data collection. Data were gridded at different resolutions based on depth of features and regional size of resulting grid area. These range from 35 m cell size to 15 m cell size. The resultant grids are in ESRI ASCII grid format. The data sets were primarily collected on the slope from the northern side of four trough mouth fans (TMF). Each file name refers to the area in which it was collected (Melville, Upernavik, Uummannaq, Disko) and the grid cell size in meters. Additional grids with just trough mouth fan (TMF) data, data through the trough itself, and/or a transit between systems have that information in the file name. The Disko_20.asc grid has a spatial reference of WGS84/UTM Zone 21N (EPSG:32621). All other grids have a spatial reference of WGS84/UTM Zone 20N (EPSG:32620). These data sets were collected for the project The Baffin Bay Deglacial Experiment (BADEX). Funding was provided by the U.S. National Science Foundation under awards OCE21-12498, OCE21-12529, OCE21-12536, OCE21-12547, and OCE23-00114.

This dataset represents completed chemical land treatments on BLM managed lands in the states of Oregon and Washington. Chemical treatments are applications of herbicide or pesticide, to control or kill pests and invasive plants, or fertilizer to enhance plant growth sourced from the BLM HUB..EDITS: The following edits were applied to the dataset in order to reduce polygon clutter, remove duplicate records, and generally make the dataset more useful for its intended purpose of overlaying treatment perimeters with other layers for landscape scale, cross-jurisdictional planning:Delete Identical tool using shape as the input parameter Giant polygons incommensurate with treated acres were manually removedEntries with treated acres <1 acre were removedEntries with GIS acres <10 acres were removedA Polsby-Popper test was used to remove polygons that appeared as perfect circles - the multitude of circle polygons representing vague treatment locations often cluttered the map and could be used for meaningful analysisA ratio of the area of polygons resulting from the Minimum Bounding Geometry tool compared to actual GIS acres of each polygon was used to remove perfect squares (values closer to 1 were perfect squares or rectangles) for a similar reason as the perfect circles

Sec. 368 Corridor Label: Depicts names of designated Section 368 Energy CorridorsSec. 368 Corridor Milepost: This layer depicts milepost point locations along the designated (per the requirements of Section 368 of the Energy Policy Act of 2005) as West-wide energy corridor centerlines in Bureau of Land Management and U.S. Forest Service Records of Decision in connection with the final Programmatic Environmental Impact Statement, Designation of Energy Corridors on Federal Land in the 11 Western States, November 2008. It is intended only as a means to describe locations along the designated corridors. Gaps in the corridor centerlines exist where federal land is not present and there are no designated corridors in these locations, however the gap distances are accounted for in the mileposting, and some mileposts exist in the gaps for continuity in the referencing system.Sec. 368 Designated Corridor - Current: This layer depicts areas which have been designated (per the requirements of Section 368 of the Energy Policy Act of 2005) as West-wide energy corridors in Bureau of Land Management and U.S. Forest Service management plans in connection with the final Programmatic Environmental Impact Statement, Designation of Energy Corridors on Federal Land in the 11Western States, November 2008 and the subsequent Records of Decision.Sec. 368 Designated Corridor - Historic: This layer depicts areas which have been Prohibited from Designation or Revised (per the requirements of Section 368 of the Energy Policy Act of 2005) as West-wide energy corridors in Bureau of Land Management and U.S. Forest Service management plans in connection with the final Programmatic Environmental Impact Statement, Designation of Energy Corridors on Federal Land in the 11Western States, November 2008 and the subsequent Records of Decision.Sec. 368 Designated Corridor Centerline: This layer depicts lines which have been designated (per the requirements of Section 368 of the Energy Policy Act of 2005) as West-wide energy corridor centerlines in Bureau of Land Management and U.S. Forest Service management plans in connection with the final Programmatic Environmental Impact Statement, Designation of Energy Corridors on Federal Land in the 11Western States, November 2008, and the subsequent Records of Decision. Each segment is also attributed with starting and ending mileposts.Regional Review Boundary: Regional review boundaries for Section 368 Energy Corridor reviews.Transmission Line (Wyoming BLM): This feature class contains existing above-ground transmission line geometry across the state of Wyoming. It was digitized from the 2015 NAIP aerial imagery dataset, and was checked for content against the Wyoming Infrastructure Authority data (via NREX) and Platts database data supplied by the BLM National Operations Center. This feature class will continue to be updated on an annual basis in correlation with the BLM's aviation hazards map products revision schedule.Legacy Locally Designated Corridor Area: The dataset consists of locally designated corridors. The dataset was created by combining corridors from multiple BLM sources. Datasets:Existing utility corridors on Kingman Field Office lands (received 9/3/14) Utah corridors (received 9/11/14)Designated BLM utility corridors in Montana (received 9/3/14)Utility corridors as identified by the Resource Management Plan on land managed by the USDOI Bureau of Land Management in the San Luis Valley in SouthCentral Colorado (received 5/14/09)Utility Corridors for the BLM California Desert District (received 7/10/09)Utility corridors in Nevada identified in various land use plans (received 9/3/14) Corridors in Nevada (received 11/3/08)Corridors in the Southern Nevada District Office (received 10/26/16) ROW Corridor designated in Gunnison RMP (received 10/20/2017)Text and map-based descriptions of corridors to remove in Arizona (received 11/8/2017)Legacy Locally Designated Corridor Centerline: This map is designed to display the utility corridors identified in various land use plans. It is a line coverage where lines are assigned labels of existing (some utility in the corridor) corridor, a designated (no utility using the corridor yet) corridor.BLM Solar Energy Zone: This dataset represents Solar Energy Zones available for utility-grade solar energy development under the Bureau of Land Management's Solar Energy Program Western Solar Plan. For details and definitions, see the website at http://blmsolar.anl.gov/sez/.BLM Solar Energy Zone Labels: This feature class was developed to represent Solar Energy Zones as part of the Bureau of Land Management's Solar Energy Program Western Solar Plan.BLM AZ Renewable Energy Dev. Areas: BLM RDEP ROD data. Restoration Design Energy Project Record of Decision, January 2013. This represents the REDA data based upon known resources listed in the ROD Table 2-1, Areas with Known Sensitive Resources (Eliminated from REDA Consideration), known at the time of January 2013. The REDAs may be changed in the future based upon changes in sensitive resources or further analysis and site specific analysis and new baseline data. RDEP decisions are only BLM-administered lands.Bureau of Land Management, Arizona State Office, in conjunction with Environmental Management and Planning Solutions, Incorporated (EMPSi).BLM DRECP Development Focus Area (DFA): This feature class represents Development Focus Areas (DFAs) in the Desert Renewable Energy Conservation Plan (DRECP) Region.BLM DRECP Variance Land: This feature class represents Variance Process Lands in the DRECP.WGA Western Renewable Energy Zone: Depicts renewable energy zone points centered in "geographic areas with at least 1,500 MW of high quality renewable energy within a 100 mile radius", as developed by the Western Governors'Association and U.S. Department of Energy in June 2009. Methodology used to create the dataare described in the WGA report: "Western Renewable Energy Zones - Phase 1Report: Mapping concentrated, high quality resources to meet demand in the WesternInterconnection's distant markets." June 2009.

Click ☆ Add to Favorites under the thumbnail for easy access to this service in Pro.*Incident Service*Not for use in mobile data collectionFor Field Maps training, use the Web Map Templates in the Incident Resources GroupThis service is for GISS use in ArcGIS Pro. Delete is enabled on this service. For more information on using the GISS Edit service, please see the GISS Workflow site.NIFS Quick Links:Mobile Edit ServiceStructure Triage Mobile Edit ServiceRepair Status ServiceStrategic Operations ServiceMobile View ServiceInternal View ServiceStructure Triage GISS Edit Service (GISS Only)IRIN Edit Service (IRIN Only)Archive ServiceTRAINING GISS Edit Service (GISS Only)Ideas for improvement? Use the GeoOps Suggestion BoxThis feature service is based on the National Wildfire Coordinating Group (NWCG) data standard for Wildland Fire Event. The Wildland Fire Event data standard defines the minimum attributes necessary for collection, storage and dissemination of incident based data on wildland fires (wildfires and prescribed fires). The standard is not intended for long term data storage, rather a standard to assist in the creation of incident-based data management tools, minimum standards for data exchange, and to assist users in meeting NWCG Standards for Geospatial Operations (GeoOps).The complete GeoOps Symbology can be found here.

Pond/Vault Component TypesDetention (Flow Through): Detention ponds and vaults temporarily store stormwater and typically releases flow over a few hours or a few days. Flow through means flow enters the pond through an inlet and exits through an outlet.Detention (Backup): Detention ponds and vaults temporarily store stormwater and typically releases flow over a few hours or a few days. Backup means that flow enters the pond through an inlet and exits through an outlet.Stormwater Treatment Wetland: Stormwater treatment wetlands use gravity settling, physical, chemical and biological processes of plant uptake and bacterial degradation to remove pollutants. They are similar to wetponds in size, but have a deep cell as well as a shallow cell that promotes plant growth.Wet (Water Quality): Drainage facilities for water quality treatment that contain a permanent pool of water. They are designed to optimize water quality by providing long retention times (on the order of a week or more) to settle out particles of fine sediment to which pollutants such as heavy metals adsorb, and to allow biologic activity to occur that metabolizes nutrients and organic pollutants. For wetvaults, the permanent pool of water is covered by a lid which blocks sunlight from entering the facility, limiting light-dependent biologic activity. Depth is >3’.Combined (Wet and Detention): Have the appearance of a detention facility but contain a permanent pool of water as well.Oil Water Separator: A vault, usually underground designed to provide a quiescent environment to separate oil from water. Floatables (e.g., styrofoam) are also removed.Infiltration: A pond or vault that is designed to use the hydrologic process of water soaking into the ground (commonly referred to as percolation) to dispose of surface and storm water runoff. Will have a sand or rock bottom. Has no control, but will have an emergency overflow.Settling Pond: Settling ponds are those ponds designed to let sediments settle out before the stormwater enters the adjacent infiltration pond.