Imagine you are sailing down a wide river and observing the changing landscape on either side. Fields give way to forests, tributaries and streams flow into the river, bridges cross over, and you know that one side of the river is managed by a government agency, while the other is subdivided into land ownership parcels of different sizes. The connectedness, adjacency, and proximity between these features can be summed up in one word: topology.Geodatabase topology allows you to define the spatial relationships you want protected in your GIS data. By doing so, no matter how much you edit, twist, bend, or squash your feature data, things stay connected, adjacent, or within the areas they belong. This course is designed to get you started with geodatabase topology.After completing this course, you will be able to:Use visual inspection and topology to identify and correct errors.Build a geodatabase topology.Choose and apply topology rules.

Download In State Plane Projection Here. ** The Street Centerline feature class now follows the NG911/State of Illinois data specifications including a StreetNameAlias table. The download hyperlink above also contains a full network topology for use with the Esri Network Analyst extension ** These street centerlines were developed for a myriad of uses including E-911, as a cartographic base, and for use in spatial analysis. This coverage should include all public and selected private roads within Lake County, Illinois. Roads are initially entered using recorded documents and then later adjusted using current aerial photography. This dataset should satisfy National Map Accuracy Standards for a 1:1200 product. These centerlines have been provided to the United States Census Bureau and were used to conflate the TIGER road features for Lake County. The Census Bureau evaluated these centerlines and, based on field survey of 109 intersections, determined that there is a 95% confidence level that the coordinate positions in the centerline dataset fall within 1.9 meters of their true ground position. The fields PRE_DIR, ST_NAME, ST_TYPE and SUF_DIR are formatted according to United States Postal Service standards. Update Frequency: This dataset is updated on a weekly basis.

This feature dataset includes feature classes for Surface Management Status and Federal Mineral Estate for Wyoming. This dataset is intended to represent the ownership & management information on BLM Master Title Plats(MTPs). Surface management will be identified by the Agency of Jurisdiction, when the surface is Federal. All other lands will be identified as either Private, Local Government, Wind River Indian Reservation (for tribal lands), State, State Parks & Historic Sites, University of Wyoming, or Wyoming Game & Fish Department. Private parcels do not identify the name of the individual owner. Mineral estate identifies only the Federal mineral interest. The feature dataset also includes topology rules established by the BLM Surface Management Agency National Data Standard. Updates for the June 2016 version includes multiple correction surface and mineral status, as well as conformance with the most recent version of CadNSDI PLSS data (current to June 6, 2016).

The Digital Geologic Units of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina consists of geologic units mapped as area (polygon) features. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). The data were captured, grouped and attributed as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.3.1. (available at: https://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The data layer is available as a feature class in a 9.1 personal geodatabase (grsm_geology.mdb). Attributed geologic contact lines that define the geologic unit polygons are present within the Geologic Contacts (GRSMGLGA) data layer. The Geologic Units (GRSMGLG) GIS data layer is also available as a coverage export (.E00) file (GRSMGLG.E00), and as a shapefile (.SHP) file (GRSMGLG.SHP). Each GIS data format has an ArcGIS 9.1 layer (.LYR) file (GRSMGLG_GDB.LYR (geodatabase feature class), GRSMGLG_COV.LYR (coverage), GRSMGLG_SHP.LYR (shapefile) with map symbology that is included with the GIS data. See the Distribution Information section for additional information on data acquisition. The GIS data projection is NAD83, UTM Zone 17N. That data is within the area of interest of Great Smoky Mountains National Park. This dataset is just one component of the Digital Geologic Map of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina. The data layers (feature classes) that comprise the Digital Geologic Map of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina include: GRSMAML (Alteration and Metamorphic Lines), GRSMATD (Geologic Attitude and Observation Points), GRSMFLD (Folds), GRSMFLT (Faults), GRSMGLG (Geologic Units), GRSMGLGA (Geologic Contacts), GRSMGPT (Point Geologic Features), GRSMGSL (Geologic Sample Localities), GRSMMIN (Mine Point Features), GRSMSEC (Cross Section Lines), GRSMSUR (Surficial Geologic Units), GRSMSURA (Surficial Contacts) and GRSMSYM (Fault Symbology). There are three additional ancillary map components, the Geologic Unit Information (GRSMGLG1) Table, the Source Map Information (GRSMMAP) Table and the Map Help File (GRSM_GEOLOGY.HLP). Refer to the NPS GRE Geology-GIS Geodatabase Data Model v. 1.3.1 (available at: https://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm) for detailed data layer (feature class) and table specifications including attribute field parameters, definitions and domains, and implemented topology rules and relationship classes.The corresponding Integration of Resource Management Applications (IRMA) NPS Data Store reference is Great Smoky Mountains National Park Geology.

The Pima County Jurisdictional Boundaries layer (jurisbnd) delineates the boundaries of incorporated and unincorporated areas within Pima County, Arizona. The maintenance of this layer is handled by Pima County. For more detailed information, please refer to the original metadata, found here. PurposeTo show the boundaries of Pima County's jurisdictions, including Tucson, Marana, Oro Valley, Sahuarita, and South Tucson.Dataset ClassificationLevel 0 - OpenKnown Uses--Known Errors/QualificationsThis layer serves as a general reference and does not indicate ownership. Users should exercise caution in interpreting boundaries for legal or ownership purposes.Data ContactPima County Information Technology Department - Geographic Information Systems201 N Stone Ave., 9th FloorTucson, AZ 85701GISdata@pima.govUpdate FrequencyThe layer undergoes regular updates based on annexations by Pima County's GIS team. Updates to annexations are synchronized with adjustments to the jurisdictional boundaries layer, adhering to established topology rules. The jurisbnd layer is maintained as a standalone dataset.

Basins are the USGS defined boundaries imported from the 1:24000 National Hydrography Dataset (NHD). They are defined by a six-digit Hydrologic Unit Code (HUC) in NHD. Basins are the highest level drainage units that fit within the South Florida Water Management District. The next-higher-level, the four-digit Subregion, covers the entire Florida peninsula. The HUC Code is stored in the NHD_HUA_CODE field. The boundary details have been edited to match AHED WATERSHED boundaries. Topology rules are enforced among features of Basin, Subbasin, Watershed, SubWatershed and Rainarea in AHED.

(Link to Metadata) The BNDHASH dataset depicts Vermont village, town, county, and Regional Planning Commission (RPC) boundaries. It is a composite of generally 'best available' boundaries from various data sources (refer to ARC_SRC and SRC_NOTES attributes). However, this dataset DOES NOT attempt to provide a legally definitive boundary. The layer was originally developed from TBHASH, which was the master VGIS town boundary layer prior to the development and release of BNDHASH. By integrating village, town, county, RPC, and state boundaries into a single layer, VCGI has assured vertical integration of these boundaries and simplified maintenance. BNDHASH also includes annotation text for town, county, and RPC names. BNDHASH includes the following feature classes: 1) BNDHASH_POLY_VILLAGES = Vermont villages 2) BNDHASH_POLY_TOWNS = Vermont towns 3) BNDHASH_POLY_COUNTIES = Vermont counties 4) BNDHASH_POLY_RPCS = Vermont's Regional Planning Commissions 5) BNDHASH_POLY_VTBND = Vermont's state boundary 6) BNDHASH_LINE = Lines on which all POLY feature classes are built The master BNDHASH data is managed as an ESRI geodatabase feature dataset by VCGI. The dataset stores village, town, county, RPC, and state boundaries as seperate feature classes with a set of topology rules which binds the features. This arrangement assures vertical integration of the various boundaries. VCGI will update this layer on an annual basis by reviewing records housed in the VT State Archives - Secretary of State's Office. VCGI also welcomes documented information from VGIS users which identify boundary errors. NOTE - VCGI has NOT attempted to create a legally definitive boundary layer. Instead the idea is to maintain an integrated village/town/county/RPC/state boundary layer which provides for a reasonably accurate representation of these boundaries (refer to ARC_SRC and SRC_NOTES). BNDHASH includes all counties, towns, and villages listed in "Population and Local Government - State of Vermont - 2000" published by the Secretary of State. BNDHASH may include changes endorsed by the Legislature since the publication of this document in 2000 (eg: villages merged with towns). Utlimately the Vermont Secratary of State's Office and the VT Legislature are responsible for maintaining information which accurately describes the locations of these boundaries. BNDHASH should be used for general mapping purposes only. * Users who wish to determine which boundaries are different from the original TBHASH boundaries should refer to the ORIG_ARC field in the BOUNDARY_BNDHASH_LINE (line feature with attributes). Also, updates to BNDHASH are tracked by version number (ex: 2003A). The UPDACT field is used to track changes between versions. The UPDACT field is flushed between versions.

(Link to Metadata) The BNDHASH dataset depicts Vermont village, town, county, and Regional Planning Commission (RPC) boundaries. It is a composite of generally 'best available' boundaries from various data sources (refer to ARC_SRC and SRC_NOTES attributes). However, this dataset DOES NOT attempt to provide a legally definitive boundary. The layer was originally developed from TBHASH, which was the master VGIS town boundary layer prior to the development and release of BNDHASH. By integrating village, town, county, RPC, and state boundaries into a single layer, VCGI has assured vertical integration of these boundaries and simplified maintenance. BNDHASH also includes annotation text for town, county, and RPC names. BNDHASH includes the following feature classes: 1) BNDHASH_POLY_VILLAGES = Vermont villages 2) BNDHASH_POLY_TOWNS = Vermont towns 3) BNDHASH_POLY_COUNTIES = Vermont counties 4) BNDHASH_POLY_RPCS = Vermont's Regional Planning Commissions 5) BNDHASH_POLY_VTBND = Vermont's state boundary 6) BNDHASH_LINE = Lines on which all POLY feature classes are built The master BNDHASH data is managed as an ESRI geodatabase feature dataset by VCGI. The dataset stores village, town, county, RPC, and state boundaries as seperate feature classes with a set of topology rules which binds the features. This arrangement assures vertical integration of the various boundaries. VCGI will update this layer on an annual basis by reviewing records housed in the VT State Archives - Secretary of State's Office. VCGI also welcomes documented information from VGIS users which identify boundary errors. NOTE - VCGI has NOT attempted to create a legally definitive boundary layer. Instead the idea is to maintain an integrated village/town/county/RPC/state boundary layer which provides for a reasonably accurate representation of these boundaries (refer to ARC_SRC and SRC_NOTES). BNDHASH includes all counties, towns, and villages listed in "Population and Local Government - State of Vermont - 2000" published by the Secretary of State. BNDHASH may include changes endorsed by the Legislature since the publication of this document in 2000 (eg: villages merged with towns). Utlimately the Vermont Secratary of State's Office and the VT Legislature are responsible for maintaining information which accurately describes the locations of these boundaries. BNDHASH should be used for general mapping purposes only. * Users who wish to determine which boundaries are different from the original TBHASH boundaries should refer to the ORIG_ARC field in the BOUNDARY_BNDHASH_LINE (line feature with attributes). Also, updates to BNDHASH are tracked by version number (ex: 2003A). The UPDACT field is used to track changes between versions. The UPDACT field is flushed between versions.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The vegetation mapping and characterization process began with the delineation of draft polygons from remote sensing imagery. Field crews then visited each polygon to collect qualitative vegetation cover data and further refine polygon boundaries. Concurrently, vegetation data were collected at both randomly and subjectively placed plots. At the end of each field day, crews transferred all edits made to field maps to a set of “master” paper maps that did not go into the field. Each polygon was then edited in ArcMap to reflect any boundary changes collected in the field on paper maps, and each polygon feature was attributed to reflect any changes in polygon identification numbers and tentative association names. The datasets are housed in a file geodatabase structure (.gdb), enabling the establishment of topology rules and relationships. Strict nomenclature was enforced for polygons, such that unique names were assigned to each polygon; these reflected the verified physiognomic formation type with a prefix of representative letters (W = Woodland, SS = shrub savanna, etc.) followed by a number.

Background

The soil map of Israel was first published by Rabinovitch et al. in 1969. It was a massive work that took place 5 years. The map was published in a printed format at a 1:250,000 scale. Until now, a digital version of this map was not available. Accordingly, we carefully digitized the soil map of Rabinovitch and provided the map herein.

Materials and Methods

This dataset contains georeferenced raster layers of the soil map (1:250,00) of Israel published by Ravikovitch (1969). The georectification was done using control points located on the borders of Israel. With this information, it was possible to create polygons over the georeferenced raster layers. This was done using the editing tool of ArcGIS 10.3. For each polygon we assigned the same classification provided by Ravikovitch (1969). Once all the polygons were created, topological corrections were applied using the method of Longley et al., (2015) in order to rectify possible inaccuracies in the digitation. To this end, we used the topology tool of ArcGIS 10.3 applying two rules:

• polygons must not have gaps
• polygons must not overlap.

This publication contains:

• the northern and the southern sections of the Israel map of soils after the georectification in geotiff format
• the final product of the cartographic edition of the Israel map of soils in shapefile format
• a PDF map showing the shapefile layer over the original map after georectification

Please, if you are going to make use of this map, cite this publication properly.

References

• Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015). Geographic information science and systems. John Wiley & Sons.
• Ravikovitch, S. (1969) Manual and Map of Soils of Israel; The Magnes Press, The Hebrew University: Jerusalem, Israel.

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2013 Tuolumne County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Tuolumne County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.0 and 10.2 using 2012 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2013 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted during June 2013. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This parcels dataset is a spatial representation of tax lots for Cape May County, New Jersey that have been extracted from the NJ statewide parcels composite by the NJ Office of Information Technology, Office of GIS (NJOGIS). Parcels at county boundaries have been modified to correspond with the NJ county boundaries and the parcels in adjacent counties.This GIS parcel data set was created by using scanned county tax maps. The scanned images were georeferenced to the 2002 color digitial orthophotos for the State of New Jersey. Software customization was developed to standardize data capture as well as processing. Quality control/quality assurance methods were developed and used throughout the entire data capture and processing. GIS processing was done using ESRI's ArcInfo and topology rules were applied to ensure proper connectivity between polygons.Each parcel contains a field named PAMS_PIN based on a concatenation of the county/municipality code, block number, lot number and qualification code. Using the PAMS_PIN, the dataset can be joined to the MOD-IV database table that contains supplementary attribute information regarding lot ownership and characteristics. Due to irregularities in the data development process, duplicate PAMS_PIN values exist in the parcel records. Users should avoid joining MOD-IV database table records to all parcel records with duplicate PAMS_PINs because of uncertainty regarding whether the MOD-IV records will join to the correct parcel records. There are also parcel records with unique PAMS_PIN values for which there are no corresponding records in the MOD-IV database tables. This is mostly due to the way data are organized in the MOD-IV database.The polygons delineated in the dataset do not represent legal boundaries and should not be used to provide a legal determination of land ownership. Parcels are not survey data and should not be used as such.The MOD-IV system provides for uniform preparation, maintenance, presentation and storage of property tax information required by the Constitution of the State of New Jersey, New Jersey Statutes and rules promulgated by the Director of the Division of Taxation. MOD-IV maintains and updates all assessment records and produces all statutorily required tax lists for property tax bills. This list accounts for all parcels of real property as delineated and identified on each municipality's official tax map, as well as taxable values and descriptive data for each parcel. Tax List records were received as raw data from the Taxation Team of NJOIT which collected source information from municipal tax assessors and created the statewide table. This table was subsequently processed for ease of use with NJ tax parcel spatial data and split into an individual table for each county.***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.

The region is the top tier of local government in New Zealand. There are 16 regions of New Zealand (Part 1 of Schedule 2 of the Local Government Act 2002). Eleven are governed by an elected regional council, while five are governed by territorial authorities (the second tier of local government) who also perform the functions of a regional council and thus are known as unitary authorities. These unitary authorities are Auckland Council, Nelson City Council, Gisborne, Tasman, and Marlborough District Councils. The Chatham Islands Council also perform some of the functions of a regional council, but is not strictly a unitary authority. Unitary authorities act as regional councils for the purposes of a wide range of Acts and regulations. Regional council areas are based on water catchment areas. Regional councils are responsible for the administration of many environmental and public transport matters.Regional Councils were established in 1989 after the abolition of the 22 local government regions. The local government act 2002, requires the boundaries of regions to confirm as far as possible to one or more water catchments. When determining regional boundaries, the local Government commission gave consideration to regional communities of interest when selecting water catchments to included in a region. It also considered factors such as natural resource management, land use planning and environmental matters. Some regional boundaries are conterminous with territorial authority boundaries but there are many exceptions. An example is Taupo District, which is split between four regions, although most of its area falls within the Waikato Region. Where territorial local authorities straddle regional council boundaries, the affected area have been statistically defined in complete area units. Generally regional councils contain complete territorial authorities. The unitary authority of the Auckland Council was formed in 2010, under the Local Government (Tamaki Makarau Reorganisation) Act 2009, replacing the Auckland Regional Council and seven territorial authorities.The seaward boundary of any costal regional council is the twelve mile New Zealand territorial limit. Regional councils are defined at meshblock and area unit level.Regional Councils included in the 2013 digital pattern are:Regional Council CodeRegional Council Name01Northland Region02Auckland Region03Waikato Region04Bay of Plenty Region05Gisborne Region06Hawke's Bay Region07Taranaki Region08Manawatu-Wanganui Region09Wellington Region12West Coast Region13Canterbury Region14Otago Region15Southland Region16Tasman Region17Nelson Region18Marlborough Region99Area Outside RegionAs at 1stJuly 2007, Digital Boundary data became freely available.Deriving of Output FilesThe original vertices delineating the meshblock boundary pattern were digitised in 1991 from 1:5,000 scale urban maps and 1:50,000 scale rural maps. The magnitude of error of the original digital points would have been in the range of +/- 10 metres in urban areas and +/- 25 metres in rural areas. Where meshblock boundaries coincide with cadastral boundaries the magnitude of error will be within the range of 1–5 metres in urban areas and 5 - 20 metres in rural areas. This being the estimated magnitude of error of Landonline.The creation of high definition and generalised meshblock boundaries for the 2013 digital pattern and the dissolving of these meshblocks into other geographies/boundaries were completed within Statistics New Zealand using ESRI's ArcGIS desktop suite and the Data Interoperability extension with the following process: 1. Import data and all attribute fields into an ESRI File Geodatabase from LINZ as a shapefile2. Run geometry checks and repairs.3. Run Topology Checks on all data (Must Not Have Gaps, Must Not Overlap), detailed below.4. Generalise the meshblock layers to a 1m tolerance to create generalised dataset. 5. Clip the high definition and generalised meshblock layers to the coastline using land water codes.6. Dissolve all four meshblock datasets (clipped and unclipped, for both generalised and high definition versions) to higher geographies to create the following output data layers: Area Unit, Territorial Authorities, Regional Council, Urban Areas, Community Boards, Territorial Authority Subdivisions, Wards Constituencies and Maori Constituencies for the four datasets. 7. Complete a frequency analysis to determine that each code only has a single record.8. Re-run topology checks for overlaps and gaps.9. Export all created datasets into MapInfo and Shapefile format using the Data Interoperability extension to create 3 output formats for each file. 10. Quality Assurance and rechecking of delivery files.The High Definition version is similar to how the layer exists in Landonline with a couple of changes to fix topology errors identified in topology checking. The following quality checks and steps were applied to the meshblock pattern:Translation of ESRI Shapefiles to ESRI geodatabase datasetThe meshblock dataset was imported into the ESRI File Geodatabase format, required to run the ESRI topology checks. Topology rules were set for each of the layers. Topology ChecksA tolerance of 0.1 cm was applied to the data, which meant that the topology engine validating the data saw any vertex closer than this distance as the same location. A default topology rule of “Must Be Larger than Cluster Tolerance” is applied to all data – this would highlight where any features with a width less than 0.1cm exist. No errors were found for this rule.Three additional topology rules were applied specifically within each of the layers in the ESRI geodatabase – namely “Must Not Overlap”, “Must Not Have Gaps” and “"Area Boundary Must Be Covered By Boundary Of (Meshblock)”. These check that a layer forms a continuous coverage over a surface, that any given point on that surface is only assigned to a single category, and that the dissolved boundaries are identical to the parent meshblock boundaries.Topology Checks Results: There were no errors in either the gap or overlap checks.GeneralisingTo create the generalised Meshblock layer the “Simplify Polygon” geoprocessing tool was used in ArcGIS, with the following parameters:Simplification Algorithm: POINT_REMOVEMaximum Allowable Offset: 1 metreMinimum Area: 1 square metreHandling Topological Errors: RESOLVE_ERRORSClipping of Layers to CoastlineThe processed feature class was then clipped to the coastline. The coastline was defined as features within the supplied Land2013 with codes and descriptions as follows:11- Island – Included12- Mainland – Included21- Inland Water – Included22- Inlet – Excluded23- Oceanic –Excluded33- Other – Included.Features were clipped using the Data Interoperability extension, attribute filter tool. The attribute filter was used on both the generalised and high definition meshblock datasets creating four meshblock layers. Each meshblock dataset also contained all higher geographies and land-water data as attributes. Note: Meshblock 0017001 which is classified as island, was excluded from the clipped meshblock layers, as most of this meshblock is oceanic. Dissolve meshblocks to higher geographiesStatistics New Zealand then dissolved the ESRI meshblock feature classes to the higher geographies, for both the full and clipped dataset, generalised and high definition datasets. To dissolve the higher geographies, a model was built using the dissolver, aggregator and sorter tools, with each output set to include geography code and names within the Data Interoperability extension. Export to MapInfo Format and ShapfilesThe data was exported to MapInfo and Shapefile format using ESRI's Data Interoperability extension Translation tool. Quality Assurance and rechecking of delivery filesThe feature counts of all files were checked to ensure all layers had the correct number of features. This included checking that all multipart features had translated correctly in the new file.

🇺🇸 미국 English This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legend specific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2015 Calaveras County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of: Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. SPECIAL NOTE FOR CALAVERAS 2015 SURVEY The Calaveras 2015 landuse survey took place prior to the Butte Fire that impacted a large portion of Calaveras County during September 2015. The survey only shows landuse for pre-fire conditions. There was no survey post fire. This data represents a land use survey of Calaveras County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.3 using 2014 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2015 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted from August 31, 2015 through September 10, 2015. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Agricultural fields the staff were unable to access were designated 'E' in the Class field for Entry Denied in accordance with the 2009 Landuse Legend. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This layer is a comprehensive depiction of roads within Island County that have an assigned MOBILITY Road Log Number. Road log numbers are used by the County for storing and managing most roads engineering data. Most importantly, the road log numbers correlate to the CRAB's MOBILITY database. By default Roads_Mobility consists primarily of roads owned by Island County. The centerlines of this layer were copied from Roads_Address. Roads_Address was photogrammetrically generated from 2007 aerial photos of the county. All attributes within Roads_Mobility were conflated from pre-existing centerline layers. Roads_Mobility is a spatial subset of Roads_Address. Roads_Mobility plays a secondary role in a topology (Roads_and_Tranportation_Topology). Topology rules require all centerlines in Roads_Mobility are spatially coincident with features in Roads_Address. Spatial editing of Roads_Mobility should occur within Roads_Address with coincident edits applied to Roads_Mobility polylines.

Watersheds are the next level of drainage areas larger than Subwatersheds. They represent the USGS 10 Digit Hydrologic Unit Code (HUC) drainage areas. Watershed boundaries were updated by incorporating SFWMD subject matter experts advice and newer data from business units when available. The HUC code from USGS is not populated for AHED Watersheds because there is not a one to one correspondence between the two datasets. In AHED, topology rules are enforced among the features of Basin, Subbasin, Watershed, SubWatershed and Rainarea feature classes.

This data represents a land use survey of Alpine County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.0 and 10.2 using 2012 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2013 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted during September 2013. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This data represents a land use survey of the southern and western portions of Mendocino County. The northern boundary of the survey area coincides with the northern boundaries of the following three Detailed Analysis Units(DAUs): Big-Noyo-Ten Mile, Forsythe and Coyote-Russian River. DAUs are the smallest study area, within a Hydrologic Region, for the analysis of water supply and use (California Water Plan Update 2013). The survey was conducted by the California Department of Water Resources, North Central Region Office staff. Land use field boundaries were digitized with ArcGIS 9.3 using 2009 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed using 2010 NAIP and Landsat 5 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted between the end of July and the beginning of October 2010. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning system (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using a customized data entry program developed by DWR to work with ArcMap software. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation of 2009 and 2010 NAIP imagery. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This data represents a land use survey of 2017 San Joaquin County conducted by the California Department of Water Resources, North Central Region Office staff. Land use field boundaries were digitized with ArcGIS 10.5.1 using 2016 NAIP as the base, and Google Earth and Sentinel-2 imagery website were used as reference as well. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were not drawn to represent legal parcel (ownership) boundaries and are not meant to be used as parcel boundaries. The field work for this survey was conducted from July 2017 through August 2017. Images, land use boundaries and ESRI ArcMap software were loaded onto Surface Pro tablet PCs that were used as the field data collection tools. Staff took these Surface Pro tablet into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Agricultural fields the staff were unable to access were designated 'E' in the Class field for Entry Denied in accordance with the 2016 Land Use Legend. The areas designated with 'E' were also interpreted using a combination of Google Earth, Sentinel-2 Imagery website, Land IQ (LIQ) 2017 Delta Survey, and the county of San Joaquin 2017 Agriculture GIS feature class. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Water source information was not collected for this land use survey. Therefore, the water source has been designated as Unknown. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region,Office and at DRA's headquarters office under the leadership of Muffet Wilkerson, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

Subbasins are grouped from Watershed boundaries and are consistent with NHD (National Hydrography Dataset) Subbasins. In areas where AHED Watersheds need major changes, the subsequent change in Subbaisn boundary will be communicated with USGS and NRCS (Natural Resources Conservation Service). Subbasins are USGS drainage areas one level smaller than Basins and each have a unique 8 Digit Hydrologic Unit Code or HUC. Topology rules are enforced among features of Basin, Subbasin, Watershed, SubWatershed and RainArea.