Feature layer generated from running the Merge Layers solution.

Feature layer generated from running the Merge Layers solution.

This ArcGIS model inserts a file name into a feature class attribute table. The tool allows an user to identify features by a field that reference the name of the original file. It is useful when an user have to merge multiple feature classes and needs to identify which layer the features come from.

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. Currently, there are multiple, freely available fire datasets that identify wildfire and prescribed fire burned areas across the United States. However, these datasets are all limited in some way. Their time periods could cover only a couple of decades or they may have stopped collecting data many years ago. Their spatial footprints may be limited to a specific geographic area or agency. Their attribute data may be limited to nothing more than a polygon and a year. None of the existing datasets provides a comprehensive picture of fires that have burned throughout the last few centuries. Our dataset uses these existing layers and utilizes a series of both manual processes and ArcGIS Python (arcpy) scripts to merge these existing datasets into a single dataset that encompasses the known wildfires and prescribed fires within the United States and certain territories. Forty different fire layers were utilized in this dataset. First, these datasets were ranked by order of observed quality (Tiers). The datasets were given a common set of attribute fields and as many of these fields were populated as possible within each dataset. All fire layers were then merged together (the merged dataset) by their common attributes to created a merged dataset containing all fire polygons. Polygons were then processed in order of Tier (1-8) so that overlapping polygons in the same year and Tier were dissolved together. Overlapping polygons in subsequent Tiers were removed from the dataset. Attributes from the original datasets of all intersecting polygons in the same year across all Tiers were also merged so that all attributes from all Tiers were included, but only the polygons from the highest ranking Tier were dissolved to form the fire polygon. The resulting product (the combined dataset) has only one fire per year in a given area with one set of attributes. While it combines wildfire data from 40 wildfire layers and therefore has more complete information on wildfires than the datasets that went into it, this dataset has also has its own set of limitations. Please see the Data Quality attributes within the metadata record for additional information on this dataset's limitations. Overall, we believe this dataset is designed be to a comprehensive collection of fire boundaries within the United States and provides a more thorough and complete picture of fires across the United States when compared to the datasets that went into it.

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset.

Toolbox Use
License
Creative Commons-PDDC
Recommended Citation
Welty JL, Jeffries MI, Arkle RS, Pilliod DS, Kemp SK. 2021. GIS Clipping and Summarization Toolbox: U.S. Geological Survey Software Release. https://doi.org/10.5066/P99X8558

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.

Purpose

County 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 Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities 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 Buffers
4. Place Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places (Coming Soon)
   
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system
• Place Name: CDTFA incorporated (city) or county name
• County: 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 System
• GNIS_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 System
• Place Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area names
• CNTY Abbr: CalTrans Division of Local Assistance abbreviations of county names
• Area_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.

Accuracy

CDTFA"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

Feature layer generated from running the Merge Layers solution.

This provides a polygon coastline and islands layer which is based on the Topo50 products. It is a combination of the following layers:

• NZ Coastlines (Topo 1:50k)
• NZ Island Polygons (Topo 1:50k)
• NZ Auckland Is Island Polygons (Topo, 1:50k)
• NZ Campbell Is / Motu Ihupuku Island Polygons (Topo, 1:50k)
• NZ Antipodes Is Island Polygons (Topo, 1:25k)
• NZ Kermadec Is Island Polygons (Topo, 1:25k)
• NZ Bounty Is Island Polygons (Topo, 1:25k)
• NZ Snares Is / Tini Heke Island Polygons (Topo, 1:25k)
• NZ Chatham Is island polygons (Topo, 1:50k)

This topographic coastline is the line forming the boundary between the land and sea, defined by mean high water.

Islands from the NZ Island Polygons layer that lie within the NZ Coastline and Chatham Islands areas (i.e. islands in lakes, rivers and estuaries) have been removed.

The GIS workflow to create the layer is:

1. NZ Coastlines were converted from a polyline to a polygon using a polyline to polygon tool.
2. The resulting coastal polygon was then used as an input into an erase tool and run against the NZ Island Polygon layer to remove all islands lying within the NZ Mainland and Stewart Island.
3. This was then merged with the NZ Chatham Is island polygons (Topo, 1:50k) that have had the islands within the main island polygon removed, NZ Auckland Is Island Polygons (Topo, 1:50k), NZ Campbell Is / Motu Ihupuku Island, NZ Antipodes Is Island Polygons (Topo, 1:25k), NZ Kermadec Is Island Polygons (Topo, 1:25k), NZ Bounty Is Island Polygons (Topo, 1:25k) and NZ Snares Is / Tini Heke Island Polygons (Topo, 1:25k) layers using a merge tool.

For more detailed description of each layer refer to the layer urls above.

APIs and web services This dataset is available via ArcGIS Online and ArcGIS REST services, as well as our standard APIs. LDS APIs and OGC web services ArcGIS Online map services ArcGIS REST API

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - No changes v1.0 - Initial public release Description Borders of all Water Management Areas (WMAs) across the 11 western-most states of the coterminous United States are available filtered through a single source. The legal name for this set of boundaries varies state-by-state. The data is provided as two compressed shapefiles. One, stateWMAs, contains data for all 11 states. For 10 of those states, Arizona being the exception, the polygons represent the legal management boundaries used by those states to manage their surface and groundwater resources respectively. WMAs refer to the set of boundaries a particular state uses to manage its water resources. Each set of boundaries was collected from the states individually, and then merged into one spatial layer. The merging process included renaming some columns to enable merging with all other source layers, as well as removing columns deemed not required for followup analysis. The retained columns for each boundary are: basinNum - the state provided unique numerical ID; basinName - the state provided English name of the area, where applicable; state - the state name; and uniID - a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID. Arizona is unique within this collection of states in that surface and groundwater resources are managed using two separate sets of boundaries. During our followup analysis (Grogan et al., in review) we decided to focus on one set of boundaries, those for surface water. This is due to the recommendation of our hydrologists that the surface water boundary set is a more realist representation of how water moves across the landscape, as a few of the groundwater boundaries are based on political and/or economic considerations. Therefore, the Arizona surface WMAs are included within stateWMAs. The Arizona groundwater WMAs are provided as a separate file, azGroundWMAs, as a companion to the first file for completeness and general reference. WMA spatial boundary data sources by state: Arizona: Arizona Surface Water Watersheds; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/surface-watershed/explore?location=34.158174%2C-111.970823%2C7.50 Arizona: Arizona Ground Water Basins; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/groundwater-basin-2/explore?location=34.158174%2C-111.970823%2C7.50 California: California CalWater 2.2.1; Collected February, 2020; https://www.mlml.calstate.edu/mpsl-mlml/data-center/data-entry-tools/data-tools/gis-shapefile-layers/ Colorado: Colorado Water District Boundaries; Collected February, 2020; https://www.colorado.gov/pacific/cdss/gis-data-category Idaho: Idaho Department of Water Resources (IDWR) Administrative Basins; Collected November, 2015; https://data-idwr.opendata.arcgis.com/datasets/fb0df7d688a04074bad92ca8ef74cc26_4/explore?location=45.018686%2C-113.862284%2C6.93 Montana: Collected June, 2019; Directly contacted Montana Department of Natural Resources and Conservation (DNRC) Office of Information Technology (OIT) Nevada: Nevada State Engineer Admin Basin Boundaries; Collected April, 2020 https://ndwr.maps.arcgis.com/apps/mapviewer/index.html?layers=1364d0c3a0284fa1bcd90f952b2b9f1c New Mexico: New Mexico Office of the State Engineer (OSE) Declared Groundwater Basins; Collected April, 2020 https://geospatialdata-ose.opendata.arcgis.com/datasets/ose-declared-groundwater-basins/explore?location=34.179783%2C-105.996542%2C7.51 Oregon: Oregon Water Resources Department (OWRD) Administrative Basins; Collected February, 2020; https://www.oregon.gov/OWRD/access_Data/Pages/Data.aspx Utah: Utah Adjudication Books; Collected April, 2020; https://opendata.gis.utah.gov/datasets/utahDNR::utah-adjudication-books/explore?location=39.497165%2C-111.587782%2C-1.00 Washington: Washington Water Resource Inventory Areas (WRIA); Collected June, 2017; https://ecology.wa.gov/Research-Data/Data-resources/Geographic-Information-Systems-GIS/Data Wyoming: Wyoming State Engineer's Office Board of Control Water Districts; Collected June, 2019; Directly contacted Wyoming State Engineer's Office

Feature layer generated from running the Merge Layers solution.

Feature layer generated from running the Merge Layers solution.

This eelgrass layer includes the maximum extent of eelgrass beds that have been surveyed in the San Francisco Bay shown in green. It was created by merging the Bay-wide eelgrass surveys conducted by Merkel & Associates, Inc. (Merkel) in 2003, 2009, 2014, and a Richardson Bay survey conducted by Merkel in 2019. Merkel has granted permission for public use of these data. These eelgrass surveys represent the best available data on comprehensive eelgrass extent throughout San Francisco Bay in 2021 and are developed using a combination of acoustic and aerial surveys and site-specific ground truthing. This layer may be used as a reference to determine potential direct and indirect impacts to eelgrass habitat from dredging projects. These data do not replace the need for site-specific eelgrass surveys.Data from the 2003, 2009, and 2014 eelgrass surveys and associated Merkel reports which include information on mapping methodology are available for download on the San Francisco Estuary Institute’s (SFEI) website. Methods for creating this layer are as follows:Downloaded the Merkel Baywide Eelgrass Surveys for 2003, 2009, and 2014 from SFEI and combined into a single layer. Obtained original Richardson Bay 2019 eelgrass survey data from Merkel. Loaded all layers into ArcGIS Pro © ESRI and re-projected all data to the NAD 1983 UTM Zone 10N coordinate system. Ran union of both the SFEI and Richardson Bay 2019 layers. Merged features to create one single attribute table for eelgrass cover from all survey years. Removed extraneous columns in the attribute table, recalculated area fields based on new extent, and applied symbology.

Input Data

NOAA Continuously Updated Shoreline Product (CUSP), accessed 1-11-2023; read a 1-page factsheet about CUSP; view and download CUSP data in the NOAA Shoreline Data Explorer (to download, select “Download CUSP by Region” and select Southeast Caribbean)
Southeast Blueprint 2023 subregions: Caribbean

Mapping Steps

Make a copy of the Southeast Caribbean CUSP feature line dataset and reproject it to ESPG 5070.
For the big island of Puerto Rico, special steps were required to deal with CUSP shorelines that did not connect across large rivers.
  Add and calculate a field to use to dissolve the lines.
    Dissolve the lines using the dissolve function, which reveals where there are gaps in the shoreline.
    Use the integrate tool to snap together nearby nodes, using a tolerance of 8 m. This connects the disconnected lines on the big island of Puerto Rico.
    Convert these modified shorelines to a polygon.
    Add and calculate a dissolve field, then dissolve using the dissolve tool. This is necessary because interior waterbodies on the big island of Puerto Rico also have shorelines in the CUSP data. This step produces a layer where inland waterbodies are included as a part of the island where they occur.
    From the resulting layer, select the big island of Puerto Rico and create a separate polygon feature layer from it. This extracts a modified shoreline boundary for the big island of Puerto Rico only. We don’t want to use the modified shorelines created above for other islands that didn’t have an issue of disconnected shoreline segments near large rivers.

Go back to the original Caribbean CUSP lines and convert them to polygons.
Add a dissolve field and dissolve using the dissolve tool. This produces a layer where all inland waterbodies are included as a part of the island where they occur.
From the island boundaries derived from the original CUSP data, remove the polygons that overlap with the big island of Puerto Rico derived from the modified CUSP data. This produces a layer representing all U.S. Caribbean islands except the big island of Puerto Rico.
Merge the modified big island of Puerto Rico layer with the layer for all other islands.
Create and populate a field that has unique IDs for all islands.
Convert the island polygon to a raster using the ArcPy Feature to Raster function. This makes a raster that correctly represents the interior of the islands. However, because the Feature to Raster function for polygons works differently than the Line to Raster function, the shoreline doesn’t perfectly match the result we get when we convert the CUSP lines to a raster. 
Because the Caribbean coastal shoreline condition indicator is created from the CUSP lines, we need the shorelines to match exactly. To reconcile this, go back to the original Caribbean CUSP line data and use the Feature to Raster function again, this time converting the lines to a raster. 
Use the ArcPy Cell Statistics “MAXIMUM” function to combine the two rasters above (one created from the CUSP lines and one created from the CUSP-derived polygons).
Export the raster that represents the extent of Caribbean islands.
Use the Region Group function to give unique values to each island.
Reclassify to make 3 island size classes. The big island of Puerto Rico is the only island in the highest class. The medium island class contains the following islands: Isla Mona, Isla de Vieques, Isla de Culebra, St. Thomas, St. John, and St. Croix. All other islands were put in the smaller class. All other non-island pixels in the Caribbean were given a value of marine.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 2023 Data Download or Caribbean-only Southeast Blueprint 2023 Data Download under > 6_Code. Literature Cited National Oceanic and Atmospheric Administration (NOAA), National Ocean Service, National Geodetic Survey. NOAA Continually Updated Shoreline Product (CUSP): Southeast Caribbean. [https://coast.noaa.gov/digitalcoast/data/cusp.html].

Represents the base zoning districts in Middlesex County. This is the default zoning district. Where overlay zoning exists, this is the underlying zoning. See adZoningOverlay. The layer was initially created using heads up zoning techniques. MCOP staff attempted to interpret paper zoning maps from the municipalities, using NJDOT road layers, aerial photography, and the NJDEP stream midpoint layers as guides. In March and April 2012 the county neared completion of the countywide tax parcel layer, and so a systematic overhaul of the zoning layer was undertaken to substantially improve the accuracy of the linework. Corecting the lines was undertaken by attempting to snap the old linework to the parcel layer using a cut and merge technique. In addition to the above efforts, MCOP staff has attempted to maintain the layer in an ongoing fashion to reflect densities represented in new zoning ordinances and redevelopment areas. This has required some interpretation of plans on the part of staff members. In 2017, base zoning was seperated from base zoning and placed in a seperate feature class.

Washes displays the natural drainage of the area. This data theme contains multiple regulatory classifications, the correct current classification is stored in the CFS_CODE2, CFS_NUM2, and CFS_SHORT2 fields. Lineage: Future plans to merge this layer with layer Wash_02K and the City's layer wash_ci, then rectify. Spatial Domain: Pima County Rectified: orthophoto90 Maintenance Description: The washes feature class is exported from the geodatabase on a nightly basis. The washes annotation is in a separate cover: washanno Naming unknown/unnamed washes from USGS quad....04/14/08 Maintenance Format: GDB Std Export Primary Source Organization: PC Primary Source Document: Orthophotos Primary Source Date: 1990 Primary Source Scale: 12000 Secondary Source Organization: USGS Secondary Source Document: Transportation DLG Secondary Source Date: 1988 Secondary Source Scale: 100000 GIS Contact: Mary Beth Clark MapGuide Layer Name: Washes - All MapGuide Scale Range: 0 - 100000PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Known ErrorsKnown Errors/Qualifications: Orthophoto washes are more detailed and are not edgematched to DLG washes. In the current regulatory scheme all washes between 2,000 CFS and 10,000 CFS are stored in the 5,000 to 10,000 category pending review.Data ContactLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Update FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

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.

The Historic Migration Zone is based on a composite area defined by the channel locations in 1955, 1972, and 2005. The resulting area reflects the zone of channel occupation over a 50-year timeframe. The method for delineating the HMZ is to overlay the digitized polygons for the bankfull channel for each time series, and merge those polygons into a single HMZ polygon. The bankfull channel reflects the active channel area that is comprised of unvegetated substrate, and its boundaries are delineated as the boundary between open channel and woody vegetation stands, terrace margins, or bedrock valley wall. The HMZ contains all unvegetated channel threads that are interpreted to convey water under bankfull conditions (typical spring runoff), and as such, the zone has split flow segments and islands. All islands within the HMZ are included with the merged HMZ polygon.