The classification of land according to what activities take place on it or how it is being used; for example, agricultural, industrial, residential, rural, or commercial. Land use information and analysis is a fundamental tool in the planning process.

DVRPC’s 2020 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2020. This dataset supports many of DVRPC's planning analysis goals.

Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.

lu20cat: Land use main category two-digit code.

lu20catn: Land use main category name.

lu20cat

lu20catn

1 - Residential

3 - Industrial

4 - Transportation

5 - Utility

6 - Commercial

7 - Institutional

8 - Military

9 - Recreation

10 - Agriculture

11 - Mining

12 - Wooded

13 - Water

14 - Undeveloped

lu20sub: Land use subcategory five-digit code. (refer to this data dictionary for code description)

lu20subn: Land use subcategory name.

lu20dev: Development status.

mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).

acres: Area of feature, in US acres.

geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).

state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.

The downloadable ZIP file contains model documentation and contact information for the model creator. For more information, or a copy of the project report which provides greater model detail, please contact Ryan Urie - traigo12@gmail.com.This model was created from February through April 2010 as a central component of the developer's master's project in Bioregional Planning and Community Design at the University of Idaho to provide a tool for identifying appropriate locations for various land uses based on a variety of user-defined social, economic, ecological, and other criteria. It was developed using the Land-Use Conflict Identification Strategy developed by Carr and Zwick (2007). The purpose of this model is to allow users to identify suitable locations within a user-defined extent for any land use based on any number of social, economic, ecological, or other criteria the user chooses. The model as it is currently composed was designed to identify highly suitable locations for new residential, commercial, and industrial development in Kootenai County, Idaho using criteria, evaluations, and weightings chosen by the model's developer. After criteria were chosen, one or more data layers were gathered for each criterion from public sources. These layers were processed to result in a 60m-resolution raster showing the suitability of each criterion across the county. These criteria were ultimately combined with a weighting sum to result in an overall development suitability raster. The model is intended to serve only as an example of how a GIS-based land-use suitability analysis can be conceptualized and implemented using ArcGIS ModelBuilder, and under no circumstances should the model's outputs be applied to real-world decisions or activities. The model was designed to be extremely flexible so that later users may determine their own land-use suitability, suitability criteria, evaluation rationale, and criteria weights. As this was the first project of its kind completed by the model developer, no guarantees are made as to the quality of the model or the absence of errorsThis model has a hierarchical structure in which some forty individual land-use suitability criteria are combined by weighted summation into several land-use goals which are again combined by weighted summation to yield a final land-use suitability layer. As such, any inconsistencies or errors anywhere in the model tend to reveal themselves in the final output and the model is in a sense self-testing. For example, each individual criterion is presented as a raster with values from 1-9 in a defined spatial extent. Inconsistencies at any point in the model will reveal themselves in the final output in the form of an extent different from that desired, missing values, or values outside the 1-9 range.This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

Check out the Licenses and Inspections Business Licenses visualization.View metadata for key information about this dataset.Licenses are required for individuals and businesses to engage in select commercial activities. For example, vendors and restaurants require a license in order to sell goods and food and trades-people, such as plumbers and contractors, require a license in order to practice their trade.Information includes license application type, applicant, property for which the license would be issued, application date, issue date, and expiration date. Data is accurate; however, it may be misinterpreted by an unfamiliar user.For questions about this dataset, contact ligisteam@phila.gov. For technical assistance, email maps@phila.gov.

This feature class is updated every business day using Python scripts and the TCID databases. Please disregard the "Date Updated" field as it does not keep in sync with DWR's internal enterprise geodatabase updates.A Water Right change is a transaction that either adds or removes water from the original water right. The dataset covers surface water rights in the Fernley and Fallon areas of Nevada. This change layer does not show where water is used if the manner of use is converted from irrigation to another manner of use such as commercial, wildlife, or municipal. Some examples of water right changes are boundary lines to delineate where water can be used, how much water can be used, and manner of use.The dataset is used to track water right changes over time and to update the water righted dataset which shows the result of the place of use for water rights. Representation:The OnOff field indicates if a change added or removed water to a specific area. The Action Date field indicates when the change took place.The Map Used field identifies which map record was used to generate the place of use area (polygon).Date of Creation – 2015This dataset is updated whenever there is a change to a water right within the Newlands Mapping Project. The water right place of use change is plotted to add or remove acreage from an existing water right.

The Regional DataBase, RDB, is a database and estimation system where countries upload catch and sample data for commercial fish species requested in Regional Coordination Groups' Data Call for coordination of sampling of commercial fish species. To upload data, work on data, raise/estimate data and to download data, a password is required.

Xverum’s Point of Interest (POI) Data is a comprehensive dataset of 230M+ verified locations, covering businesses, commercial properties, and public places across 5000+ industry categories. Our dataset enables retailers, investors, and GIS professionals to make data-driven decisions for business expansion, location intelligence, and geographic analysis.

With regular updates and continuous POI discovery, Xverum ensures your mapping and business location models have the latest data on business openings, closures, and geographic trends. Delivered in bulk via S3 Bucket or cloud storage, our dataset integrates seamlessly into geospatial analysis, market research, and navigation platforms.

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For the automated workflows, we create Jupyter notebooks for each state. In these workflows, GIS processing to merge, extract and project GeoTIFF data was the most important process. For this process, we used ArcPy which is a python package to perform geographic data analysis, data conversion, and data management in ArcGIS (Toms, 2015). After creating state-scale LSS datasets in GeoTIFF format, we convert GeoTIFF to NetCDF using xarray and rioxarray Python packages. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. We used xarray to manipulate data type and add metadata in NetCDF file and rioxarray to save GeoTIFF to NetCDF format. Through these procedures, we created three composite HyddroShare resources to share state-scale LSS datasets. Due to the limitation of ArcGIS Pro license which is a commercial GIS software, we developed this Jupyter notebook on Windows OS.

USDA has purchased a Enterprise Premium license for this Orthoimagery dataset from DigitalGlobe, Inc. Any government, education, not-for-profit agency and public/individuals not engaged in using the "Product for Commercial Exploitation or Commercial Purposes" can use this licensed data. Use of this product for Commercial Purposes by a person/company/organization for a profit or fee is strictly prohibited. Please refer to the separately attached license from DigitalGlobe, Inc. for additional information. Digital orthoimagery combines the image characteristics of a digital image with the geometric qualities of a map. The primary dynamic digital orthophoto is a 50 centimeter ground resolution, image cast to the customer specified projection and datum defined in the Spatial Reference Information section of this metadata document. The overedge is included to facilitate tonal matching for mosaicking and ensure full coverage if the imagery is reprojected. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south.

DigitalGlobe WorldView-2 Satellite Orthoimagery was delivered to USDA in GeoTIFF file format. USDA-NRCS-NGMC processed the DigitalGlobe delivered WorldView-2 (WV-2) Multi-Spectral/Pan data bundle to produce an 8 Band, 16 Bit Pan-Sharpen Mosaic using ERDAS 2011. Bands 2, 3 & 4 were subset from the 8 band, 16-bit mosaic to create a natural color (RGB) mosaic.The resulting mosaic was rescaled from 16-bit to 8-bit depth using ERDAS 2011. The image contrast level was adjusted using the Breakpoint editor and breakpoints were saved and applied to the LUT stretched output file. LizardTech GeoExpress 8 was used to create a compressed Generation 3 MrSID from the RGB mosaic. The MrSID compressed file is delivered with a separate FGDC compliant metadata file. The composite file contains the following three Multi-Spectral bands: Band 2: Blue (450 - 510 nm) Band 3: Green (510 - 580 nm) Band 4: Yellow (585 - 625 nm)For additional information, please contact Hawaii Statewide GIS Program, Office of Planning, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

Power plants of at least 1 MW are required to report data. Counties in gray had no utility scale (commercial) renewable electric generation. Distributed generation (for example, rooftop solar) is not included. Data is classified using the Jenks Natural Breaks method. Projection: NAD 1983 California (Teale) Albers (Intl Feet). Data Sources: California Energy Commission. Energy production data is from the Quarterly Fuel and Energy Report, and the Wind Generation Reporting System databases. Data is for 2021 and is current as of July 8, 2022. For more information, please contact Rebecca Vail at (916) 477-0738 or John Hingtgen at (916) 510-9747.

USDA has purchased a Enterprise Premium license for this Orthoimagery dataset from DigitalGlobe, Inc. Any government, education, not-for-profit agency and public/individuals not engaged in using the "Product for Commercial Exploitation or Commercial Purposes" can use this licensed data. Use of this product for Commercial Purposes by a person/company/organization for a profit or fee is strictly prohibited. Please refer to the separately attached license from DigitalGlobe, Inc. for additional information. Digital orthoimagery combines the image characteristics of a digital image with the geometric qualities of a map. The primary dynamic digital orthophoto is a 50 centimeter ground resolution, image cast to the customer specified projection and datum defined in the Spatial Reference Information section of this metadata document. The overedge is included to facilitate tonal matching for mosaicking and ensure full coverage if the imagery is reprojected. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south.

DigitalGlobe WorldView-2 Satellite Orthoimagery was delivered to USDA in GeoTIFF file format. USDA-NRCS-NGMC processed the DigitalGlobe delivered WorldView-2 (WV-2) Multi-Spectral/Pan data bundle to produce an 8 Band, 16 Bit Pan-Sharpen Mosaic using ERDAS 2011 in three sections. The individual sections were rescaled to 8-bit and the statistics were recalculated before merging into a seamless 8-bit full island mosaic. Bands 2, 3 & 4 were subset from the 8 band, 16-bit mosaic to create a natural color (RGB) mosaic.The resulting mosaic was rescaled from 16-bit to 8-bit depth using ERDAS 2011. The image contrast level was adjusted using the Breakpoint editor and breakpoints were saved and applied to the LUT stretched output file. LizardTech GeoExpress 8 was used to create a compressed Generation 3 MrSID from the RGB mosaic. The MrSID compressed file is delivered with a separate FGDC compliant metadata file. The composite file contains the following three Multi-Spectral bands: Band 2: Blue (450 - 510 nm) Band 3: Green (510 - 580 nm) Band 4: Yellow (585 - 625 nm)

For additional information, please contact Hawaii Statewide GIS Program, Office of Planning, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

Microsoft XL table with coordinates in UTM29N and PSA (particle size analysis) results suitable for GIS integration for commercial seabed sediment samples collected onboard RV Celtic Explorer and RV Celtic Voyager between 2008-2011.

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/ It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

ESYS plc and the Department of Geomatic Engineering at University College London (UCL) have been funded by the British National Space Centre (BNSC) to develop a web GIS service to serve geographic data derived from remote sensing datasets. Funding was provided as part of the BNSC International Co-operation Programme 2 (ICP-2).

Particular aims of the project were to:

1. use Open Geospatial Consortium (OGC, recently renamed from the OpenGIS Consortium) technologies for map and data serving;

2. serve datasets for Europe and Africa, particularly Landsat TM and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data;

3. provide a website giving access to the served data;

4. provide software scripts, etc., and a document reporting the data processing and software set-up methods developed during the project.

ICEDS was inspired in particular by the Committee on Earth Observing Satellites (CEOS) CEOS Landsat and SRTM Project (CLASP) proposal. An express intention of ICEDS (aim 4 in the list above) was therefore that the solution developed by ESYS and UCL should be redistributable, for example, to other CEOS members. This was taken to mean not only software scripts but also the methods developed by the project team to prepare the data and set up the server. In order to be compatible with aim 4, it was also felt that the use of Open Source, or at least 'free-of-cost' software for the Web GIS serving was an essential component. After an initial survey of the Web GIS packages available at the time , the ICEDS team decided to use the Deegree package, a free software initiative founded by the GIS and Remote Sensing unit of the Department of Geography, University of Bonn , and lat/lon . However the Red Spider web mapping software suite was also provided by IONIC Software - this is a commercial web mapping package but was provided pro bono by IONIC for this project and has been used in parallel to investigate the possibilities and limitations opened up by using a commercial package.

http://iceds.ge.ucl.ac.uk/

USDA has purchased a Enterprise Premium license for this Orthoimagery dataset from DigitalGlobe, Inc. Any government, education, not-for-profit agency and public/individuals not engaged in using the "Product for Commercial Exploitation or Commercial Purposes" can use this licensed data. Use of this product for Commercial Purposes by a person/company/organization for a profit or fee is strictly prohibited. Please refer to the separately attached license from DigitalGlobe, Inc. for additional information. Digital orthoimagery combines the image characteristics of a digital image with the geometric qualities of a map. The primary dynamic digital orthophoto is a 50 centimeter ground resolution, image cast to the customer specified projection and datum defined in the Spatial Reference Information section of this metadata document. The overedge is included to facilitate tonal matching for mosaicking and ensure full coverage if the imagery is reprojected. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south.

DigitalGlobe WorldView-2 Satellite Orthoimagery was delivered to USDA in GeoTIFF file format. USDA-NRCS-NGMC processed the DigitalGlobe delivered WorldView-2 (WV-2) Multi-Spectral/Pan data bundle to produce an 8 Band, 16 Bit Pan-Sharpen Mosaic using ERDAS 2011. Bands 2, 3 & 4 were subset from the 8 band, 16-bit mosaic to create a natural color (RGB) mosaic.The resulting mosaic was rescaled from 16-bit to 8-bit depth using ERDAS 2011. The image contrast level was adjusted using the Breakpoint editor and breakpoints were saved and applied to the LUT stretched output file. LizardTech GeoExpress 8 was used to create a compressed Generation 3 MrSID from the RGB mosaic. The MrSID compressed file is delivered with a separate FGDC compliant metadata file. The composite file contains the following three Multi-Spectral bands: Band 2: Blue (450 - 510 nm) Band 3: Green (510 - 580 nm) Band 4: Yellow (585 - 625 nm)

For additional information, please contact Hawaii Statewide GIS Program, Office of Planning, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

Refer to the 'Current Geographic Boundaries Table' layer for a list of all current geographies and recent updates.

This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 2,395 SA2s (2,379 digitised and 16 with empty or null geometries (non-digitised)).

SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations.

The SA2 should:

form a contiguous cluster of one or more SA1s,

excluding exceptions below, allow the release of multivariate statistics with minimal data suppression,

capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area,

be socially homogeneous and capture a community of interest. It may have, for example:

• a shared road network,
• shared community facilities,
• shared historical or social links, or
• socio-economic similarity,

​

form a nested hierarchy with statistical output geographies and administrative boundaries. It must:

• be built from SA1s,
• either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils.

SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents.

In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area.

SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns.

In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area.

To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2.

Zero or nominal population SA2s

To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include:

• SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara.
• SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas
• SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council.
• SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name):

400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency.

SA2 numbering and naming

Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City).

SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change.

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High-definition version

This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre.

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Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

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Digital data

Digital boundary data became freely available on 1 July 2007.

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Further information

To download geographic classifications in table formats such as CSV please use Ariā

For more information please refer to the Statistical standard for geographic areas 2023.

Contact: geography@stats.govt.nz

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Unstable_Slopes/MapServer/3The siteclass data layer was created for use in implementing Forest Practices' Riparian Management Rules. (See WAC 222-30-021 and 222-30-022.)

The siteclass data layer was derived from the DNR soils data layer's site index codes and major tree species codes for western and eastern Washington soils contained in the layer's Soils-Main table and Soils-Pflg (private forest land grade) table. Site index ranges in the Soils_PFLG took precedence over site index ranges in the Soils-Main table where data existed.The siteclass data layer was created for use in implementing new ForestPractices' Riparian Management Rules. (See WAC 222-30-021 and 222-30-022.) The siteclass information was derived from the DNR soils data layer's site indexcodes and major tree species codes for western and eastern Washington soilscontained in the layer's Soils-Main table and Soils-Pflg (private forest landgrade) table. Site index ranges in the Soils_PFLG took precedence over siteindex ranges in the Soils-Main table where data existed.Siteclass codes as derived from the soil survey:For Western Washington, the 50 year site index is used SITECLASS SITE INDEX RANGE I 137+ II 119-136 III 97-118 IV 76-96 V 1-75For Eastern Washington, the 100 year site index is used SITECLASS SITE INDEX RANGE I 120+ II 101-120 III 81-100 IV 61-80 V 1-60In addition to the coding scheme above, the following codes were added forrule compliance: SITECLASS DESCRIPTION 6 (Red Alder) The soils major species code indicated Red Alder 7 (ND/GP) No data), NA, or gravel pit 8 (NC/MFP) Non-commercial or marginal commercial forest land 9 (WAT) Water body(Rule note: If the site index does not exist or indicates red alder,noncommercial, or marginally commercial species, the following apply:If the whole RMZ width is within those categories, use site class V.If those categories occupy only a portion of the RMZ width, then use thesite index for conifer in the adjacent soil polygon.)WADNR SOILS LAYER INFORMATION LAYER: SOILS GEN.SOURCE: State soils mapping program CODE DOCUMENT: State soil surveys CONTACT: NA COVER TYPE: Spatial polygon coverage DATA TYPE: Primary data Information for the SOILS data layer was derived from the Private Forest Land Grading system (PFLG) and subsequent soil surveys. PFLG was a five year mapping program completed in 1980 for the purpose of forest land taxation. It was funded by the Washington State Department of Revenue in cooperation with the Department of Natural Resources, Soil Conservation Service (SCS), USDA Forest Service and Washington State University. State and private lands which had the potential of supporting commercial forest stands were surveyed. Some Indian tribal and federal lands were surveyed. Because this was a cooperative soil survey project, agricultural and non- commercial forest lands were also included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soils delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were added at a later time and are now available through associated lookup tables. SCS soils data on agricultural lands also have subsequently been added to this data layer. Approximately 1100 townships wholly or partially contain digitized soils data (2101 townships would provide complete coverage of the state of Washington). SOILS data are currently stored in the Polygon Attribute Table (.PAT) and INFO expansion files. COORDINATE SYSTEM: WA State Plane South Zone (5626) (N. zone converted to S. zone) COORDINATE UNITS: Feet HORIZONTAL DATUM: NAD27 PROJECTION NAME: Lambert Conformal Conic **** MAJOR CODES USED FOR SITECLASS DATA*****PFLG DATA: ITEM: PFLG.MAJ.SPEC TITLE: Potential major tree species for given soil FORMAT: INPUT OUTPUT DATA DECIMAL ARRAY ARRAY WIDTH WIDTH TYPE PLACES OCCUR. INDEX ------------------------------------------------- 3 3 C 0 0 0 CODE TABLE OR VALUE RANGE: SOIL.MAJ.SPEC.CODE DESCRIPTION: Potentially major tree species for a given soil type. The data carried by this item describes a major commercial tree species that could potentially grow on a specific soil type as identified in the Private Forest Land Grading program (PFLG). Non-tree codes are also included to map non-soil ground cover, e.g. water, gravel pits. ITEM: PFLG.SITE.INDEX TITLE: Mean site index calc.from trees on given soil FORMAT: INPUT OUTPUT DATA DECIMAL ARRAY ARRAY WIDTH WIDTH TYPE PLACES OCCUR. INDEX ------------------------------------------------- 3 3 I 0 0 0 CODE TABLE OR VALUE RANGE: 0-200 DESCRIPTION: Site index data collected for the Private Forest Land Grading soils program (PFLG). It is a designation of the quality of a forest site based on the height of of the tallest trees (dominant and co-dominant trees) in a stand at an arbitrarily chosen age. Usually the age chosen is 50 or 100 years. For example, if the average height attained by the tallest trees in a fully stocked stand at the age of 50 years is 75 feet, the site index is 75 feet. Westside site conditions are estimated by using an index age of 50 years, while eastside site conditions are estimated by using an index age of 100 years.--------------------------------------------------------------------SOILS-MAIN DATA: CODE TABLE NAME: SOIL.MAJ.SPEC.CODE ----------------------------------------------------------------------------- CODE MAP/REPORT MAP CODE DESCRIPTION LABEL SYMB --------- ------------ ---- -------------------------------------------------- AF ALPINE FIR 0 Subalpine fir DF DOUGLAS FIR 0 Douglas fir GF GRAND FIR 0 Grand fir GP GRAVEL PIT 0 Gravel pit LP LODGEPOLE PN 0 Lodgepole pine MFP MAR FOR PROD 0 Marginal forest productivity NA N/A 0 Not applicable NC NON-COMMERC 0 Non-commercial ND NO DATA 0 No data PP PONDEROSA PN 0 Ponderosa pine RA RED ALDER 0 Red alder WAT WATER 0 Water WH W HEMLOCK 0 Western hemlock WL W LARCH 0 Western larch WP W WHITE PINE 0 Western white pine ITEM: SITE.INDEX.SIDE TITLE: Indicates 100 yr or 50 yr soil site index FORMAT: INPUT OUTPUT DATA DECIMAL ARRAY ARRAY WIDTH WIDTH TYPE PLACES OCCUR. INDEX ------------------------------------------------- 1 1 C 0 0 0 CODE FILE OR VALUE RANGE: SITE.INDEX.SIDE.CODE DESCRIPTION: Code used to indicate whether 100 year or 50 year site index tables are used to calculate the site index of a soil type. Note that some site indexes for "eastside" soils are based on the 50 year index table. SITE.INDEX.SIDE Indicates 100 yr or 50 yr soil site index CODE FILE SITE.INDEX.SIDE.CODE IS NOT USED BY OTHER ITEMS CODE MAP/REPORT MAP CODE DESCRIPTION LABEL SYMB --------- ------------ ---- -------------------------------------------------- E 100 YR SITE 0 Soil site index based on 100 year table W 50 YR SITE 0 Soil site index based on 50 year table------------------------------------------------------------------

The map was developed using available parcel polygons attributed with tax assessment data as of project initiation in early 2020, Computer-Assisted Mass Appraisal (CAMA) data dated February 2020, and the Chesapeake Bay Program’s 2017/18 Land Use Land Cover data (2022 edition), subsequently referred to as “CBP LULC.” The map also incorporates land use datasets provided by county and municipal jurisdictions to the extent possible while maintaining standard statewide classification definitions and rules. The product was developed to be consistent with the 2018 National Agriculture Imagery Program (NAIP) imagery and CBP LULC dataset. MDP’s draft updated land use classification scheme is available as a separate document. This product is a beta release for public use and further testing. Methods for developing subsequent releases beyond this 2018 baseline will be refined based on feedback from the user community. Urban Land Uses 11 Low-density residential - Detached single-family/duplex dwelling units, yards, and associated areas. Includes generalized areas with lot sizes of less than five acres but at least one-half acre (0.2 to 2 dwelling units/acre). 12 Medium-density residential - Detached single-family/duplex, attached single-unit row housing, yards, and associated areas Includes generalized areas with lot sizes of less than one-half acre but at least one-eighth acre (2 to 8 dwelling units/acre). 13 High-density residential - Attached single-unit row housing, garden apartments, high-rise apartments/condominiums, mobile home and trailer parks, yards, and associated areas. Includes generalized areas with more than eight dwelling units per acre. This may include subsidized housing. 14 Commercial - Retail and wholesale services. Areas used primarily for the sale of products and services, including associated yards and parking areas. This category may include airports, welcome houses, telecommunication towers, and boat marinas. 15 Industrial - Manufacturing and industrial parks, including associated warehouses, storage yards, research laboratories, and parking areas. Warehouses that are returned by a commercial query should be categorized as industrial. This also includes power plants. 16 Institutional - Elementary and secondary schools, middle schools, junior and senior high schools, public and private colleges and universities, military installations (built-up areas only, including buildings and storage, training, and similar areas), churches, medical and health facilities, correctional facilities, government offices and facilities that are clearly separable from any surrounding natural or agricultural land cover, and other non-profit uses. 17 Extractive - Surface mining operations, including sand and gravel pits, quarries, coal surface mines, and deep coal mines. Status of activity (active vs. abandoned) is not distinguished. 18 Open urban land - Includes parks, open spaces, recreational areas not classified as institutional, golf courses, and cemeteries. Includes only built-up and turf-dominated areas that are clearly separable from any surrounding natural or agricultural land cover. 190 – Very Low Density Residential – Clustered residential parcels that have lot sizes less than 20 acres but at least five acres (0.2 to 0.05 dwelling units/acre) 50 – Water 80 Transportation - Transportation features include impervious roads, roadway rights-of-way, and parcels primarily containing light rail or metro stations and park-and-ride lots. 99 – Other Land - Remaining land not covered under another category. Examples include but are not limited to unbuilt lots, rural land, single-family residential parcels greater than or equal to 20 acres in size, and undeveloped portions of large parcels containing urban uses. May include undeveloped land that is either developable or constrained from further development.Note: Urban Land Use classifications encompass the entire parcel on parcels less than five acres that contain a structure as of 2018 based on the Maryland Department of Planning and Maryland State Department of Assessment and Taxation’s Computer-Assisted Mass Appraisal (CAMA) Building dataset. Elsewhere, the Chesapeake Bay Program’s 2017/18 Land Use Land Cover dataset (2022 edition) is used to delineate the extent of development on a parcel. For more information, see Methodology Documentation.Feature Service Link: https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_LandUse/MapServer/1This copy has been projected to "WGS 1984 Web Mercator (auxiliary sphere)" and, therefore, is for illustrative purposes only. To use the data for geospatial analysis or area calculations, please download the copy projected to "NAD_1983 StatePlane Maryland FIPS 1900" from MDP's website at https://planning.maryland.gov/pages/ourproducts/downloadfiles.aspx.

The Inventory of New Jersey’s The Inventory of New Jersey’s Estuarine Shellfish Resources is conducted on a rotating basis throughout the major Atlantic coastal estuaries of New Jersey. The primary purpose of the work is to estimate the standing stock of hard clams (Mercenaria mercenaria) and describe their relative distribution. Additionally, the survey describes the relative distribution of other commercially important bivalve species and vascular submerged aquatic vegetation (“SAV”), also known as seagrasses. Hard Clam: The substrate is sampled with a hydraulic hard clam dredge designed to retain clams sized 30mm and larger. All live clams collected are counted and measured to the nearest millimeter. The density of clams at each station is reported in clams per square foot. The resulting geospatial data represents the relative distribution of hard clams at either “none” (no clams collected), “low” (0.01 to 2), “moderate” (>0.20 - 2), or “high” (>0.50 clams/ft2) densities. Where no category designation is given, the area is considered a “no data” area relative to this survey. This means that the survey did not sample within this area for reasons including shallow water, obstructions, or the presence of shellfish aquaculture leases. The area may or may not be marked formally as such. However, a “no data” area may contain shellfish resources unknown to the Marine Resources Administration (MRA) or the MRA may have data for the area from other investigations. It does not automatically mean that the area is devoid of shellfish resources. This data represents a one point in time documentation of relative abundance of hard clams, and hard clams may be found presently in areas not previously sampled or at stations where they were not historically collected. Complete reports for each surveyed estuary provide methodology, analysis, charts, and additional pertinent information, and can be found on the NJ Fish and Wildlife’s website. The NJ Coastal Zone Management rules at N.J.A.C. 7:7 define shellfish densities of 0.2 clams per square foot or greater as productive shellfish habitat. The Leasing of Atlantic Coast Bottom for Aquaculture regulations discourages establishing leases in productive shellfish habitat (NJAC 7:25-24.6(d)). Note that this layer does not include delineation of shellfish leases or aquaculture development zones. Those data are provided separately. Data from 1980s were digitized based primarily on the georeferenced images of the 1980s’ map series, in combination with usage of the 1986 NJDEP Landuse/Landcover geospatial dataset to more accurately depict shoreline boundaries. Digitizing was completed using freehand and/or copying/pasting/editing waterbody features from the 1986 NJDEP Landuse/Landcover geospatial dataset. Digitizing was completed at a scale between 1:4,000 to 1:12,000. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. Data from 2000s to present were created based upon survey station tabular data which was then mapped as a point feature class. Several GIS tools were then used to generate polygon features surrounding the stations to represent hard clam distribution (see Process Steps for more detail). Associated Species: When other commercially or recreationally important bivalve species are retained in the sample, they are documented, along with common invertebrate species. Data from the 1980s documents the presence of all other commercially and recreationally important bivalve species that are regulated by the State of New Jersey as well as common (but not all) shellfish predators that were retrained in the dredge while targeting hard clams. Presence indicates the area is productive for the species. The regulated bivalve species are soft clams (Mya arenaria), bay scallops (Argopecten irradians), surf clam (Spisula solidissima), Eastern oyster (Crassostrea virginica), and blue mussel (Mytilus edulis). This data is a point in time observation of production areas and regulated bivalve species may be found presently in areas not previously sampled or at stations where they were not historically collected. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. It is important to note that this data is not a comprehensive evaluation of Eastern oyster populations in the Mullica River, Great Egg Harbor River, or Delaware Bay, which are surveyed separately and specifically for that species. Similarly, although surf clams are occasionally found in estuarine environments, the species primarily dwells in the Atlantic Ocean and separate comprehensive population surveys of state and federal waters are available. For additional species collected (for example sponges, non-commercial shellfish, etc.) please contact the Bureau of Shellfisheries. Historical reports for each surveyed estuary provide methodology, analysis, charts, and additional pertinent information, and can be requested by contacting the Marine Resources Administration. The features were digitized based primarily on the georeferenced images of the 1980s’ map series, in combination with usage of the 1986 NJDEP Land use/Landcover geospatial dataset in order to more accurately depict shoreline boundaries. Digitizing was completed using freehand and/or copying/pasting/editing waterbody features from the 1986 NJDEP Landuse/Landcover geospatial dataset. Digitizing was completed at a scale between 1:4,000 to 1:12,000. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. Data from 2000 to present also documents the presence of all other commercially and recreationally important bivalve species that are regulated by the State of New Jersey as well as common invertebrates, including common bivalve predators. Presence indicates that area is productive for the species listed. The regulated bivalve species are soft clams (Mya arenaria), bay scallops (Argopecten irradians), surf clam (Spisula solidissima), Eastern oyster (Crassostrea virginica), and blue mussel (Mytilus edulis). This data is a one point in time observation of production areas and regulated bivalve species may be found presently in areas not previously sampled or at stations where they were not historically collected. It is important to note that this data is not a comprehensive evaluation of Eastern oyster populations in the Mullica River, Great Egg Harbor River, or Delaware Bay, which are surveyed separately and specifically for that species. Similarly, although surf clams are occasionally found in estuarine environments, the species primarily dwells in the Atlantic Ocean and separate comprehensive population surveys of state and federal waters are available. Further, data on channeled whelk (Busycotypus canaliculatus), knobbed whelk (Busycon carica), Atlantic horseshoe crab (Limulus polyphemus) and blue crab (Callinectes sapidus) are not intended for use in fishery management plans at this time. For additional species collected (for example sponges, non-commercial shellfish, etc.) please contact the Marine Resources Administration. This feature class was created based upon survey station tabular data which was then mapped as a point feature class. Several GIS tools were then used to generate polygon features surrounding the stations to represent each species’ distribution (see Process Steps for more detail). Submerged Aquatic Vegetation: When submerged aquatic vegetation (SAV; seagrass) is retained in the sample, or observed visually from the research vessel, the presence of the vegetation and species is noted. Only presence of the vegetation is provided, without inference regarding coverage, shoot density, or any other characteristic. Only regulated species (per N.J.A.C. 7:7-9.6) of vascular vegetation is presented here. This is primarily eelgrass (Zostera marina) and widgeon grass (Ruppia maritima. However, other regulated species are found in New Jersey. Data from 1980s is a “snapshot in time” of relative distribution of SAV, and SAV may be found presently in areas not previously sampled or at stations where they were not historically collected. Species composition may change over time. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. Where hard copy charts were not previously created (Shrewsbury, Manasquan, and Metedeconk Rivers), a 1,000ft buffer was placed around the survey station where SAV was found. Historical reports for each surveyed estuary provide methodology, analysis, charts, and additional pertinent information, and can be requested by contacting the Marine Resources Administration. The SAV data from the 1980s can confirm the history of SAV in a given area, corroborating other survey years. However, further investigation is necessary if it is the only dataset available for a project. In such cases, please contact the Marine Resources Administration (MRA) as they may have information on the area that was collected during different surveys or is not yet published. Data from 2000s to present is also a “one point in time” documentation of relative distribution of SAV, and SAV may be found presently in areas not previously sampled or at stations where they were not historically collected. Species composition may change over time. Where SAV was found, a 1,000ft