A table that contains additional geology attribute data that can be used with geology gis data by using a join or relate.

These are Rights-of-Ways (ROW) on Idaho BLM land (and some other Federal agency land) as shown on Bureau of Land Management (BLM) Master Title Plats (MTP). Every GIS ROW feature has a "CASEFILE" value, also known as the serial number of the ROW. This corresponds to the LR2000 database, which is a national BLM database for federal lands information. This GIS ROW feature class can be joined or related to exported information from LR2000 using the "CASEFILE" (GIS) and "SERIAL_NR_FULL" (LR2000) fields. NOTE: the LR2000 information is only available to internal BLM users and is not available to the public as it contains sensitive information. This ROW data for any given area may not be complete due to new ROW activity or because of missed or coincident ROW features during the initial data creation. It is recommended that a thorough inventory of all ROWs in a specific project area be obtained (an LR2000 report can provide this) and the GIS ROW data be checked before using this data for projects needing utmost ROW accuracy. The ROW data that was digitized is what was present on the MTP at the time of the digitizing done for that township. The project was performed over several years. Therefore, the "early" townships digitized are more out of date regarding ROWs compared to the ones more recently digitized. Unfortunately, there is no attribute that indicates the digitizing sequence. Any updates to this ROW feature class should be sent to the BLM Idaho State Office GIS staff for incorporation into the statewide GIS ROW feature classes for improvement over time. For more information contact us at blm_id_stateoffice@blm.gov.

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 March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose

County boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This layer removes the coastal buffer polygons. 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 (this dataset)
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
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
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

• CDTFA_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.
• CDTFA_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. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_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.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• 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.

Service has been deprecated in favour of https://hub.arcgis.com/datasets/f7020453cbe14b2c83379b9a0a2c8194_0/about?locale=en and https://hub.arcgis.com/datasets/f7020453cbe14b2c83379b9a0a2c8194_1/about?locale=en

Le service a été déprécié en faveur de https://hub.arcgis.com/datasets/f7020453cbe14b2c83379b9a0a2c8194_0/about?locale=fr et https://hub.arcgis.com/datasets/f7020453cbe14b2c83379b9a0a2c8194_1/about?locale=frCette couche contient des polygones qui représentent les unités géologiques au Nouveau-Brunswick. Le tableau connexe de relations géologiques des substrats présente des détails relatifs à chacune des unités cartographiées, notamment une brève description sous forme de légende, le nom du groupe, le nom de la formation et l’âge des roches. Cette couche est le résultat de près de 50 ans de cartographie des substrats, et elle continue de changer au fur et à mesure de que nouvelles interprétations sont faites et que plus de données sont recueillies par l’entremise de travaux sur le terrain et d’analyses.This layer is the result of nearly 50 years of bedrock mapping and it continues to change as new interpretations are made and more data is collected through field work and analysis. This layer is meant to be used in conjunction with the Bedrock Geology Relatetable and the Bedrock Geology Lines layer.Using digital data that existed in files that generally followed National Topographic System (NTS) grids at the 1:20,000 and 1:50,000 scale. These files were combined and edited (e.g., edge matching at file boundaries) to create the provincial, continuous coverage layer. New point data (2018 - present) are collected digitally using GPS enabled tablets. Updates to the polygon features are digitized in ArcGIS. Mapping is generally conducted at the 1:20,000 scale.This layer is updated periodically when new information resulting from additional geologic mapping impacts polygon boundaries or associated attributes. This is most likely to occur annually after field projects are completed. Refinements and minor changes are ongoing and may occur on a more frequent basis.A separate table, BedrockGeology_Relate, with additional information about each unit in the feature class is available. This table can be used as a relate or join within your GIS.See attached Excel file, NBGeology Geodatabase Structure.xlsx

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 March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose

County boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. 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 (this dataset)
   • 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
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
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

• CDTFA_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.
• CDTFA_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. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_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.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• 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.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

BLKS00 contains Census 2000 Block boundaries and population by blocks for the state of Maine at 1:100,000 scale. Census Block boundaries are statistical subdivisions of counties for the reporting of decennial census data. The Census 2000 TIGER/Line Files are the primary source for this data set. BLKS00 is built to POLYGON topology and contains the attributes FIPSSTCO, COUNTY, COUSUB00, COUSUB00NA(ME), TRCT00, BLKGRP00, PLC00, PLC00NA(ME), BLK00, BLK00NUM, BLKNAME, STFIDBLK00, POP00, CENTAG, USDSTRCT03, SNDSTRCT03, and HSDSTRCT03. The item STFIDBLK00 is a calculated combination of these items that uniquely identifies a Census Block within the State of Maine. The item CENTAG was added for correct labeling and/or statistics where multiple polygons contain the same block number, as is the case in some coastal communities where islands are depicted. The numeric item POP00 was populated and proofed from the Census 2000 Redistricting Data (P.L. 94-171) Summary File. The item COUSUB00 contains the Federal Information Processing Standard (FIPS) code a single 5 character code field used by the Bureau of the Census to identify the Census County Division to which the block belongs. COUSUBNA has been added to improve convenience in labeling. Likewise TRCT00 (Tract), BLKGRP00 (Block Group), and PLC00 (Designated Place if applicable), and PLC00NA for Designated Place name labeling. All Census geographies, cross tabulated in the dataset, can be mapped using the ID included for each level, i.e. For Census County Subdivisions COUSUB00, Census Tracts STFIDTRCT00, Census Block Groups STFIDBLKGRP00, Census Designate Places PLC00 and Census Blocks STFIDBLK00. Unique-ids for each Census geographic unit can be used to relate or join these datasets to extended Census data files, counts, tabulations, and reference tables. For more information on related files that have been published at the Maine GIS Data Catalog see the "Tables" link at http://megis.maine.gov/catalog/

The Aquinnah MA parcel bounds and related Assess table were compiled by Cartographic Associates in FY19 and forwarded to MassGIS for final processing into the Commonwealth's Level 3 Parcel Data Standard. The related table of building information was provided to the Martha's Vineyard Commission from the Town's assessing department in the year 2020. All related tables join to the feature class based on [Loc_ID].

The Geographic Information Retrieval and Analysis System (GIRAS) was developed in the mid 70s to put into digital form a number of data layers which were of interest to the USGS. One of these data layers was the Hydrologic Units. The map is based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging unit. The hydrologic units are encoded with an eight- digit number that indicates the hydrologic region (first two digits), hydrologic subregion (second two digits), accounting unit (third two digits), and cataloging unit (fourth two digits).

The data produced by GIRAS was originally collected at a scale of 1:250K. Some areas, notably major cities in the west, were recompiled at a scale of 1:100K. In order to join the data together and use the data in a geographic information system (GIS) the data were processed in the ARC/INFO GUS software package. Within the GIS, the data were edgematched and the neatline boundaries between maps were removed to create a single data set for the conterminous United States.

NOTE: A version of this data theme that is more throughly checked (though based on smaller-scale maps) is available here: https://water.usgs.gov/lookup/getspatial?huc2m

HUC, GIRAS, Hydrologic Units, 1:250

For the most current data and information relating to hydrologic unit codes (HUCs) please see http://water.usgs.gov/GIS/huc.html. The Watershed Boundary Dataset (WBD) is the most current data available for watershed delineation. See http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset

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.PurposeCity 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). 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, coastal buffers are removed, leaving 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 Buffers (this dataset)Counties: 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 BuffersWithout 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.

Tempe’s roadways are an important means of transportation for residents, the workforce, students, and visitors. Tempe measures the quality and condition of its roadways using a Pavement Quality Index (PQI). This measure, rated from a low of 0 to a high of 100, is used by the City to plan for maintenance and repairs, and to allocate resources in the most efficient way possible.

This measure is created using pavement quality data maintained in the RoadMatrix Pavement Management Program. About every three years, the City surveys pavement, such as the smoothness of roadways and any signs of distress in the pavement surface. This data is then used to calculate the PQI, which determines roadway maintenance prioritization schedules as well as the most optimal road treatment options (such as placing a filler material in the cracks and treating the entire pavement surface, milling and replacing the top layer of the asphalt pavement, reconstructing the street section)

This page provides data for the performance measure related to PQI. To access geospatial data regarding PQI please visit https://data.tempe.gov/dataset/pavement-quality-index-segments

The performance measure dashboard is available at 1.22 Pavement Quality Index

This resource represents annual citywide average PQI.

This resource is used in the indicators found in the Safe and Secure Communities dashboard.

Additional Information

Source: Stantec/Road Matrix

Contact (author): Isaac Chavira

Contact E-Mail (author): isaac_chavira@tempe.gov

Contact (maintainer): Sue Taaffe

Contact E-Mail (maintainer): sue_taaffe@tempe.gov

Data Source Type: CSV

Preparation Method: Extracted from Roadmatrix and joined to GIS network

Publish Frequency: Annual (Average PQI)/Quarterly (Segment PQI)

Publish Method: Manual

Data Dictionary

Deaths related to smoking for Greater London. Deaths are expressed as the rate per 100,000 for the period 2005 to 2007. data sourced from the Guardian (http://www.guardian.co.uk/world-government-data/search?q=uk+smoking+in+2007&facet_year=2010) and data.gov.uk (http://data.london.gov.uk/datastore/package/deaths-smoking#). Boundary data is from OS Open Data which has been tweaked and augmented to have the ONS codes to join the two datasets (done in ArcGIS). GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-06-27 and migrated to Edinburgh DataShare on 2017-02-21.

PARTY table which contains information about the parties involved – unique VEH_ID field. A common way to work with data of this structure is to create a party-level table or a victim-level table that has data from all its parents joined to each row. For example, if you plan to do analyses at the victim-level, you can start with a VICTIM table and join in values from corresponding PARTY and CRASH tables. Please refer to this link for the related Victim tables. Please refer to this link for the related SWITRS collision data. For more references on joining this table to SWITRS collisions, go to this link at LA County GIS Portal.

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 Namoi (NAM) dataset contains v5 of the Asset database (NAM_asset_database_20160218.mdb), a Geodatabase version for GIS mapping purposes (NAM_asset_database_20160218_GISOnly.gdb), the draft Water Dependent Asset Register spreadsheet (BA-NIC-NAM-130-WaterDependentAssetRegister-AssetList-v20160218.xlsx), a data dictionary (NAM_asset_database_doc_20160218.doc), a folder (Indigenous_doc) containing documentation associated with Indigenous water asset project, and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below.

The Asset database for the Namoi subregion on 18 February 2016 supersedes the previous version of the Asset database v4 "Asset database for the Namoi subregion on 15 January 2015" GUID: c32e70ad-9357-4297-a5dd-e1f1e1f5255f. Updates in this v5 database include:

(1) Total number of registered water assets was increased by 7 due to:

(a) The 6 assets changed M2 test to "Yes" and 1 assets changed reason from the review done by Ecologist group. The original data is included the database as the table tbl_NAM _Species_TEC_decisions_reveiw_23112015

(b) One indigenous water asset from OWS were added. The data and documents from OWS are included in subdirectory Indigenous_doc

(c)The draft new Water Dependent Asset Register file (BA-NIC-NAM-130-WaterDependentAssetRegister-AssetList-v20160218.xlsx) was created

(2) The databases, especially spatial database, were changed such as (a) spatial data are saved in a separated file geodatabase, (b) duplicated attributes fields in spatial data were removed and only ID field is kept in the spatial data. The user can use AID or ElementID to join the table in personal geodatabase with relevant spatial data

The user can join the Table Assetlist (in NAM_asset_database_20160218.mdb) to the spatial data (GM_NAM_AssetList_ln, GM_NAM_AssetList_poly and GM_NAM_AssetList_pt in NAM_asset_database_20160218_GISOnly.gdb) from ArcMap by AID to get those attributes for assets. The user can join the Table Elementlist (in NAM_asset_database_20160218.mdb) to the spatial data (GM_NAM_ElementsList_ln, GM_NAM_ElementsList_poly and GM_NAM_ElementsList_pt in NAM_asset_database_20160218_GISOnly.gdb) from ArcMap by ElementID to get those attributes for elements . Element_to_asset (in NAM_asset_database_20160218.mdb) can join to above the spatial data or above two joined results for more information.

Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of " NAM_asset_database_doc_20160218.doc", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "NAM_asset_database_doc_20160218.doc doc" located in the zip file.

The public version of this asset database can be accessed via the following dataset: Asset database for the Namoi subregion on 18 February 2016 Public (https://data.gov.au/data/dataset/3134fa6b-f876-46dd-b26b-88d46d424185).

Purpose

The Asset List Database was developed to spatially identify water dependent assets found within the Namoi subregion.

The public version of this asset database can be accessed via the following dataset: Asset database for the Namoi subregion on 18 February 2016 Public (https://data.gov.au/data/dataset/3134fa6b-f876-46dd-b26b-88d46d424185).

Dataset History

On 20 April 2015 the title of this database was changed from "Namoi_AssetList_Database_v4_20150115".

This dataset replicates the spatial and tabular content and structure of the previous version of the Namoi asset list ("Asset list for Namoi - CURRENT"; ID: 538c717c-c04a-4720-8bcd-96fbdf7f0d80) with the exception that decisions made by the Namoi Project Team concerning Materiality Test 2 (water dependency) have been incorporated into the AssetList table, which are used to define the water dependent asset register.

Date \t Notes

22/07/2014\tInitial database for asset related tables and feature classes, and imported element data from element list database

5/09/2014\tUpdated database with updated WSP/GWMP/RegRiv assets/elements; additional WSP plus point water volume data and additional RegRiv plus point water volume data

18/11/2014\tMerge some assets with non standard classification to standard classification

18/11/2014\tadd additional point groundwater economic data ( 121 new elements)

18/11/2014\tadd additional point surface water economic data (49 new elements)

15/01/2015\tIncorporate materiality decisions (M2) from project team into AssetList table

18/02/2016\t"(1)Total number of registered water assets was increased by 7 due to:

            (a) The 6 assets changed M2 test to "Yes" and 1 assets changed reason from the review done by Ecologist 

           group. The original data is included the database as the table tbl_NAM _Species_TEC_decisions_reveiw_23112015

           (b) One indigenous water asset from OWS were added. The data and documents from OWS are included in 

           subdirectory Indigenous_doc

           (c)The draft new Water Dependent Asset Register file (BA-NIC-NAM-130-WaterDependentAssetRegister-

            AssetList-v20160218.xlsx) was created

           (2) The databases, especially spatial  database, were changed such as (a) spatial data are saved in a separated file 

            geodatabase, (b) duplicated attributes fields in spatial data were removed and only ID field is kept in the spatial

            data."

Dataset Citation

Bioregional Assessment Programme (2016) Asset database for the Namoi subregion on 18 February 2016. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/22061f2c-e86d-4ca8-9860-c349c2513fd8.

Dataset Ancestors

• Derived From Asset list for Namoi - CURRENT

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From NSW Office of Water Surface Water Licences in NIC linked to locations v1 (22 April 2014)

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Asset database for the Namoi subregion on 15 January 2015

• Derived From Missing SW Licensing Data in the Namoi PAE 20140711

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports

• Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases

• Derived From NSW Office of Water Groundwater licences extract linked to spatial locations NIC v3 (13 March 2014)

• Derived From [Australia - Species of National Environmental Significance

This intersection points feature class represents current intersections in the City of Los Angeles. Few intersection points, named pseudo nodes, are used to split the street centerline at a point that is not a true intersection at the ground level. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way. The right of way information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Intersection layer was created in geographical information systems (GIS) software to display intersection points. Intersection points are placed where street line features join or cross each other and where freeway off- and on-ramp line features join street line features. The intersection points layer is a feature class in the LACityCenterlineData.gdb Geodatabase dataset. The layer consists of spatial data as a point feature class and attribute data for the features. The intersection points relates to the intersection attribute table, which contains data describing the limits of the street segment, by the CL_NODE_ID field. The layer shows the location of the intersection points on map products and web mapping applications, and the Department of Transportation, LADOT, uses the intersection points in their GIS system. The intersection attributes are used in the Intersection search function on BOE's web mapping application NavigateLA. The intersection spatial data and related attribute data are maintained in the Intersection layer using Street Centerline Editing application. The City of Los Angeles Municipal code states, all public right-of-ways (roads, alleys, etc) are streets, thus all of them have intersections. List of Fields:Y: This field captures the georeferenced location along the vertical plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, Y = in the record of a point, while the X = .CL_NODE_ID: This field value is entered as new point features are added to the edit layer, during Street Centerline application editing process. The values are assigned automatically and consecutively by the ArcGIS software first to the street centerline spatial data layer, then the intersections point spatial data layer, and then the intersections point attribute data during the creation of new intersection points. Each intersection identification number is a unique value. The value relates to the street centerline layer attributes, to the INT_ID_FROM and INT_ID_TO fields. One or more street centerline features intersect the intersection point feature. For example, if a street centerline segment ends at a cul-de-sac, then the point feature intersects only one street centerline segment.X: This field captures the georeferenced location along the horizontal plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, X = in the record of a point, while the Y = .ASSETID: User-defined feature autonumber.USER_ID: The name of the user carrying out the edits.SHAPE: Feature geometry.LST_MODF_DT: Last modification date of the polygon feature.LAT: This field captures the Latitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.OBJECTID: Internal feature number.CRTN_DT: Creation date of the polygon feature.TYPE: This field captures a value for intersection point features that are psuedo nodes or outside of the City. A pseudo node, or point, does not signify a true intersection of two or more different street centerline features. The point is there to split the line feature into two segments. A pseudo node may be needed if for example, the Bureau of Street Services (BSS) has assigned different SECT_ID values for those segments. Values: • S - Feature is a pseudo node and not a true intersection. • null - Feature is an intersection point. • O - Intersection point is outside of the City of LA boundary.LON: This field captures the Longitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.

Statewide Download (FGDB) (SHP)Users can also download smaller geographic areas of this feature service in ArcGIS Pro using the Copy Features geoprocessing tool. The address service contains statewide address points and related landmark name alias table and street name alias table.The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The existing New Jersey Statewide Address Point data last published in 2016 has been transformed in the NENA data model to create this new address point data.The initial address points were processed from statewide parcel records joined with the statewide Tax Assessor's (MOD-IV) database in 2015. Address points supplied by Monmouth County, Sussex County, Morris County and Montgomery Township in Somerset County were incorporated into the statewide address points using customized Extract, Transform and Load (ETL) procedures.The previous version of the address points was loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. Subsequent manual and bulk processing corrections and additions have been made, and are ongoing.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

Complete File Geodatabase containing various layers and tables for Boundaries, Census, Environment, Land, Place, PLSS, Transportation, Tables, Utility, and Water datasets. This includes relationship classes (joins) between the Taxlot layer and related table data.OR State Plane NAD83 Projection

In ArcGIS OnLine an active join (one to many) was created between each town's parcel bounds and related assess table. In ArcPro those 6 joined layers were merged together into one feature layer. This file provides faster response time in web apps that permit filtering the data. The properties of the layer are set in such a way that the boundaries will not appear on the map until zoomed in.Please note: Any building related information associated with the parcel may or may not represent ALL buildings on the parcel.All parcel data meets the MassGIS Level 3 Parcel data standard. Each town has a parcel data consultant (either CAI Technologies or CGIS) who compiles their parcel bounds and export assessing data. All users are encouraged to read the 'attribute' section of the MassGIS metadata so there is clear understanding as to what these data represent.

In AGOL, an active 1 to Many join was established between the Level 3 parcel bounds and the respective building info table (obtained from each town's assessor). Those hosted feature layer 'views' for each town were merged into one Island-wide feature data layer in ArcPro and then published to ArcGIS OnLine. This permits certain functionality in the Dashboard and requires customizing fewer feature layer pop-ups in ArcGIS OnLine.In cases where there are multiple buildings on a parcel, the 'join' created stacked parcels. Therefore, when clicking on a parcel that has multiple buildings, the info for each building will appear in its own pop-up (and not as a related record associated with the parcel).Note 1: West Tisbury's assessing program, while perfectly suited for town assessing purposes, is unable to export the detailed building info for all buildings on a parcel. Therefore, only info for one building is provided per parcel. As to which building's info is exported is a random decision by the assessing software program.Note 2: The Year Built is determined by the assessor and it may be influenced/reflective of when the building was last majorly renovated. The Year Built from the assessor's database is utilized for taxation purposes. This date may not accurately reflect historical fact. The end-user is encouraged to research year built through other resources such as the local museum, town historic commission, &/or the registry of deeds.

This point dataset contains information on the quality and condition of species-rich grassland communities recorded during field surveys in the Cairngorms National Park undertaken between 2020 and 2022, as part of a joint project delivered by NatureScot and the Cairngorms National Park Authority. The aim was to establish the location and extent of species-rich grassland (SRG) within enclosed (and formerly enclosed) farm land, up to a maximum altitudinal limit of 500 m, using a combination of remote sensing and targeted field survey. This dataset covers the Livet, Avon and Dee catchments. Patches of unimproved/semi-improved grassland, down to 0.04 ha in size, were identified and delineated by analysing high-resolution aerial photography (involving image segmentation and subsequent classification of the output). This provided a search map of polygons to visit in the field, targeting survey effort towards the areas where species-rich grassland was most likely to occur. The field survey was undertaken by contractors during July to September in 2020 and 2021, and July to October in 2022. For each polygon, grassland communities and their relative proportion cover were described using the National Vegetation Classification (NVC), and species-richness was assessed. Locations of any missed species-rich grassland, occurring outside the search map polygons, were captured in the field and added to the dataset. This information is stored in the associated CNPGrasslandMapping_2020to2022_Polygons dataset. When species-rich grassland was encountered, additional detailed attributes describing the quality and condition of these stands, plus photo attachments, were collected in this point dataset (which can be joined/related to the polygons using the POLY_ID/ PARENT_POLY fields). A rapid assessment of key features relating to species-richness, sward characteristics, management, and presence and abundance of notable species was undertaken following a structured walk through each SRG stand. Attributes are mostly categorical to promote a consistent, standardised response. The dataset contains the following fields: SRG_NVC – species-rich grassland NVC community; SRG_HAB – broad SRG habitat type; SPEC_SQ_M – number of species per square metre (≤10, 11-20, 21-30, >30); FORB_COV – % cover of forbs (≤10, 11-25, 26-50, 51-75, ≥75); SWRD_HT_CM – sward height range (cm) (≤5, 6-20, 21-30, >30); THATCH_ACCU – thatch accumulation (%) (≤10, 11-25, 26-50, 51-75, ≥75); BARE_GRN – bare ground (%) (<1, 1-5, 6-10, >10%); MANAGEMENT –obvious management at time of survey (grazing - sheep, grazing - cattle, grazing - sheep & cattle, grazing - deer, cutting - hay, cutting - silage, cutting - verge, no grazing / cutting, other - describe in comment);GRAZ_INTENS – grazing intensity (at time of survey) (none, low, moderate, high); PRESSURES – negative pressures impacting site condition (none, over-grazed, under-grazed, scrub / tree encroachment, under-grazed & scrub / tree encroachment, bracken encroachment, ruderals, heavy poaching, other - describe in comment); COMMENT – additional notes on quality/condition/management; Notable species, recorded using DAFOR scale (Dominant, Abundant, Frequent, Occasional, Rare, or Absent): CIRS_HETR – Cirsium heterophyllum; GENT_CAMP – Gentianella campestris; GALI_BORE – Galium boreale; HELI_NUMM – Helianthemum nummularium; LINU_CATH – Linum catharticum; LYCH_FLOS – Lychnis flos-cuculi; MEUM_ATHA – Meum athamanticum; PERS_VIVI – Persicaria vivipara; PIMP_SAXI – Pimpinella saxifraga; TROL_EURO – Trollius europaeus; VIOL_LUTE – Viola lutea; AVEN_PRAT – Avenula pratensis; BRIZ_MEDI – Briza media; BOTR_LUNA – Botrychium lunaria; COLE_VIRI – Coeloglossum viride; DACT_FUCH – Dactylorhiza fuchsii; DACT_MACU – Dactylorhiza maculata; DACT_PURP – Dactylorhiza purpurea; DACT_SP – Dactylorhizasp.; GYMN_BORE – Gymnadenia borealis; NEOT_OVAT – Neottia ovata; PLAT_BIFO – Platanthera bifolia; PLAT_CHLO – Platanthera chlorantha; PSUD_ALBI – Psudorchis albida; TOT_OTRCH_AB - Total orchid abundance (all species combined); WAXCAPS – presence of waxcap fungi (present/absent); OTH_NOTABLE – other notable species; PARENT_POLY – unique identifier of polygon in which the point was collected (can be used as join/relate field); GRIDREF – grid reference of point; SURV_YEAR – year of field survey; SURV_DATE – date of field survey; SURVEYOR – field surveyor; CATCHMENT – river catchment area; POINT_ID – unique identifier.

Complete project metadata on spatialdata.gov.scot