• land values for the past five years (where available)

• the valuing year used to calculate council rates

  </font></li><li><font size='4'>the
  valuation basis 
  
  </font></li><li><font size='4'>the
  property number, address, and zoning information 
  
  </font></li><li><font size='4'>the area
  and boundaries of non strata properties 
  
  </font></li><li><font size='4'>notice of
  any concessions or allowances that apply to the land value. 
  

The map does not show land values for individual strata properties.

• property sales information for individual properties from 2001

  </font></li><li><font size='4'>property
  sales information at a street and suburb level for the last five
  years (where available
  </font></li><li><font size='4'>area for
  non strata properties 
  
  </font></li><li><font size='4'>the
  dealing number and sale date (or contract date) 
  
  </font></li><li><font size='4'>the date
  the property sales information was last updated 
  
  </font></li><li><font size='4'>whether
  the property is strata or non strata, or if the sale is part of a
  multi property sale. 
  

Contact us

Phone : 1800 110 038

Mon-Fri, 8:30am – 5:00pm

Please call TIS National on 131 450 and ask them to call Valuation Services on 1800 110 038.

Metadata

This data provides the geographic location for parcel boundary lines within the jurisdiction of the Carteret County, NC and is based on recorded surveys and deeds. This dataset is maintained by the Carteret County GIS Division. Data is updated on an as needed basis. The description for each field name in the layer is included below.FieldAliasMeaningPIN15Parcel NumberTax Parcel ID Number (PIN4+ PIN5)ROLL_TYPERoll TypeRoll type of property (regularly taxed property or tax-exempt)GISMOTHERMother ParcelMother ParcelGISMAPNUMPIN1First 4 digits of PIN15MAPNAMPIN2PIN1 + 2 digitsGISBLOCKPIN3Digits 7 and 8 of PIN15GISPINPIN4PIN2 +PIN3GISPDOTPIN5Digit numbers 9 to 12 of PIN15CONDO_Condo unit #Condo numberGISPRIDOld Tax IDOld parcel number style based on prior mapping system before NC GRID system mapsOWNERTax Owner 1Tax parcel ownerOWNER2Tax Owner 2Secondary tax parcel ownerSITE_HOUSESite House #Site Address House NumberSITE_DIRSite DirSite Address Street directionalSITE_STSite StreetSite Address Street NameSITE_STTYPSite Street TypeSite Address Street Suffix (e.g., Dr., St., etc.)SITE_APTNOSite AptSite Address UnitSITE_CITYSite CitySite Address City/CommunityPropertyAddressPhysical AddressProperty AddressMAIL_ADDRESS1Mailing address and streetConcatenated mailing addressMAIL_ADDRESS2Mailing unitSecondary mailing addressMAIL_CITYMailing cityMailing address CityMAIL_STATEMailing stateMailing address StateMAIL_ZI4Mailing Zip4Mailing address Zip Code + 4MAIL_ZI5Mailing Zip5Mailing address Zip CodeFullMailingAddressFull Mailing AddressFull mailing address concatenatedDBOOKDeed BookDeed book containing the most recent deed to the propertyDPAGEDeed PageDeed page containing the most recent deed to the propertyDDATEDeed Dateunformatted most recent deed date for the propertyDeedDate_2Formatted Deed DateFormatted most recent deed date for the propertyMapBookPlat Map BookMap BookMapPagePlat Map PageMap PagePLATBOOKPlat BookRecorded Plat BookPLATPAGEPlat PageRecorded Plat PageLEGAL_DESCParcel Legal DescriptionLegal descriptionLegalAcresLegal Acres (new)Deeded or platted acresDeededAcresLegal AcresAcres from recorded deedsCalculatedLandUnitsTaxed AcresTaxed land acreageGISacresCalculated AcresCalculated GIS acresGISacres2Calculated Acres (new)Calculated GIS acresLandCodeTax Land Category CodeLand CodeLandCodeDescriptionTax Land Category DescriptionLand Code DescriptionMUNICIPALITYMunicipality/ETJIf parcel is within the corporate limits or ETJTOWNSHIPTownshipTownship codeRESCUE_DISTTax Rescue DistrictTax rescue district that the parcel is withinFIRE_DISTTax Fire DistrictTax fire district that the parcel is withinJurisdictionTax District CodeTax District CodeNBHDNeighborhood CodeNeighborhood codeNeighborhoodNameNeighborhood NameNeighborhood code descriptionBuildingCount# BuildingsNumber of buildings within the propertyY_BLT_HOUSEYear Built HouseYear house was builtBLT_CONDOYear Built CondoYear condo was builtTOT_SQ_FTTotal Sq FootTotal square footage of structure on propertyHtdSqFtHeated Sq FootHeated square footages of structure on the propertyBldgModelBuilding ModelBuilding ModelBEDROOMS# Bedrooms# BedroomsBATHROOMS# Bathrooms# BathroomsBldgUseBuilding UseBuilding UseConditionBuilding ConditionBuilding ConditionDwellingStyleDescriptionDwelling DescriptionDwelling style descriptionExWllDes1Exterior wall 1Exterior wall type 1 descriptionExWllDes2Exterior wall 2Exterior wall type 2 descriptionExWllTyp1Exterior wall 1 codeExterior wall type 1ExWllTyp2Exterior wall 2 codeExterior wall type 2FondDes1Foundation Description Foundation type 1 descriptionFondTyp1Foundation Description CodeFoundation type 1RCovDes1Roof # 1 Covering DescriptionRoof covering type 1 descriptionRCovDes2Roof # 2 CoveringDescriptionRoof covering type 2 descriptionRCovTyp1Roof # 1 CodeRoof covering type 1RCovTyp2Roof # 2 CodeRoof covering type 2RStrDes1Roof Structure DescriptionRoof structure type 1 descriptionRStrTyp1Roof Structure CodeRoof structure type 1GradeBuilding GradeTax gradeGradeAndCDUBuilding Grade & ConditionBuilding Grade & ConditionHeatDes1Heating/Cooling TypeHeating type 1 descriptionHeatTyp1Heating/Cooling CodeHeating type 1FireplaceCountFireplacesFireplacesSTRUC_VALStructure ValueValue of structure(s) on the propertyLAND_VALUETax Land ValueValue of the land on the propertyOTHER_VALOther Structures ValueOther value of the propertyTotal_EMVTotal Estimated Market ValueTotal estimated tax valueSaleImprovedorVacantSALE_PRICEVacant or Improved SaleRecorded Sale PriceVacant of Improved SaleRecorded sale priceSaleDateRecorded Sale DateRecorded sale dateSubdivision_NameSubdivision NameSubdivision NameSubdivision_Platbk_pagSubdivision Plat Book/PakeSubdivision Plat Book and PageCommissioner_DistrictCommissioner District #Commissioner"s DistrictCommissioner_Name1Commissioner NameCommissioner"s NameCommissioner_InfoCommissioner LinkLink to Commissioner"s contact info Elementary_SchoolElementary DistrictElementary school name/districtMiddle_SchoolMiddle School DistrictMiddle School name/districtHigh_SchoolHigh School DistrictHigh school name/districtIsImprovedProperty ImprovedBuilt on = 1, Vacant = 0IsQualifiedQualified SaleQualified = 1, Not Qualified = 0Use_codeTax Use CodeLand use codeUse_descTax Use Code DescriptionLand use descriptionPerm_De1Permit # 1 DescriptionPermit #1 descriptionPerm_De2Permit # 2 DescriptionPermit #2 descriptionPerm_Is1Permit # 1 Issue DatePermit #1 issue datePerm_Is2Permit # 2 Issue DatePermit #2 issue datePerm_N1Permit # 1 NumberPermit #1Perm_N2Permit #2 NumberPermit #2Perm_Ty1Permit # 1 TypePermit #1 typePerm_Ty2Permit # 2 TypePermit #2 typeACTL_DA1Permit #1 Completion DatePermit #1 - actual completion dateACTL_DA2Permit #2 Completion DatePermit #2 - actual completion dateReviewedDateTax Office Review DateDate ReviewedNoise_lvlBogue Air Field Noise LevelNoise level zones surrounding military air basesRisk_levelBogue Landing Risk LevelRisk level for military air plane accident potential within the AICUZ zonesaicuzBogue Air Field AICUZAir Installation Compatible Use Zone - planning zones pertaining to military air bases and the surrounding real estateOBJECTIDOBJECTIDESRI default unique IDSHAPEShapeESRI default fieldSHAPE.STArea()Shape AreaESRI default fieldSHAPE.STLength()Shape PerimeterESRI default field

This dataset includes raster-based nominal land value data of Istanbul City. It is created using open-source QGIS software with several spatial analyses, such as proximity, terrain, and visibility. The dataset has 10 metres spatial resolution.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

Please note: this data is live (updated nightly) to reflect the latest changes in the City's systems of record.Overview of the Data:This dataset is a polygon feature layer with the boundaries of all tax parcels owned by the City of Rochester. This includes all public parks, and municipal buildings, as well as vacant land and structures currently owned by the City. The data includes fields with features about each property including property type, date of sale, land value, dimensions, and more.About City Owned Properties:The City's real estate inventory is managed by the Division of Real Estate in the Department of Neighborhood and Business Development. Properties like municipal buildings and parks are expected to be in long term ownership of the City. Properties such as vacant land and vacant structures are ones the City is actively seeking to reposition for redevelopment to increase the City's tax base and economic activity. The City acquires many of these properties through the tax foreclosure auction process when no private entity bids the minimum bid. Some of these properties stay in the City's ownership for years, while others are quickly sold to development partners. For more information please visit the City's webpage for the Division of Real Estate: https://www.cityofrochester.gov/realestate/Data Dictionary: SBL: The twenty-digit unique identifier assigned to a tax parcel. PRINTKEY: A unique identifier for a tax parcel, typically in the format of “Tax map section – Block – Lot". Street Number: The street number where the tax parcel is located. Street Name: The street name where the tax parcel is located. NAME: The street number and street name for the tax parcel. City: The city where the tax parcel is located. Property Class Code: The standardized code to identify the type and/or use of the tax parcel. For a full list of codes, view the NYS Real Property System (RPS) property classification codes guide. Property Class: The name of the property class associated with the property class code. Property Type: The type of property associated with the property class code. There are nine different types of property according to RPS: 100: Agricultural 200: Residential 300: Vacant Land 400: Commercial 500: Recreation & Entertainment 600: Community Services 700: Industrial 800: Public Services 900: Wild, forested, conservation lands and public parks First Owner Name: The name of the property owner of the vacant tax parcel. If there are multiple owners, then the first one is displayed. Postal Address: The USPS postal address for the vacant landowner. Postal City: The USPS postal city, state, and zip code for the vacant landowner. Lot Frontage: The length (in feet) of how wide the lot is across the street. Lot Depth: The length (in feet) of how far the lot goes back from the street. Stated Area: The area of the vacant tax parcel. Current Land Value: The current value (in USD) of the tax parcel. Current Total Assessed Value: The current value (in USD) assigned by a tax assessor, which takes into consideration both the land value, buildings on the land, etc. Current Taxable Value: The amount (in USD) of the assessed value that can be taxed. Tentative Land Value: The current value (in USD) of the land on the tax parcel, subject to change based on appeals, reassessments, and public review. Tentative Total Assessed Value: The preliminary estimate (in USD) of the tax parcel’s assessed value, which includes tentative land value and tentative improvement value. Tentative Taxable Value: The preliminary estimate (in USD) of the tax parcel’s value used to calculate property taxes. Sale Date: The date (MM/DD/YYYY) of when the vacant tax parcel was sold. Sale Price: The price (in USD) of what the vacant tax parcel was sold for. Book: The record book that the property deed or sale is recorded in. Page: The page in the record book where the property deed or sale is recorded in. Deed Type: The type of deed associated with the vacant tax parcel sale. RESCOM: Notes whether the vacant tax parcel is zoned for residential or commercial use. R: Residential C: Commercial BISZONING: Notes the zoning district the vacant tax parcel is in. For more information on zoning, visit the City’s Zoning District map. OWNERSHIPCODE: Code to note type of ownership (if applicable). Number of Residential Units: Notes how many residential units are available on the tax parcel (if applicable). LOW_STREET_NUM: The street number of the vacant tax parcel. HIGH_STREET_NUM: The street number of the vacant tax parcel. GISEXTDATE: The date and time when the data was last updated. SALE_DATE_datefield: The recorded date of sale of the vacant tax parcel (if available). Source: This data comes from the department of Neighborhood and Business Development, Bureau of Real Estate.

Map of the standard land values ​​determined by the expert committee for property values ​​in Berlin on January 1st, 2013.

Announce land prices every two years and calculate the changes in land prices announced in various years for the whole country, municipalities directly under the central government, and counties (cities).

The historical land value map of the district Viersen of the year 2001.

The soil benchmark is an average location value for the soil within an area, determined from purchase prices, which has largely identical conditions according to its state of development and the type and extent of structural use. It is based on the square meter of land area of a plot of land with a defined land condition (ground benchmark plot).

Deviations of the individual plot of land with regard to the value-determining properties such as type and measure of structural use, plot size, development status, location characteristics, soil condition and cutting cause deviations of its market value from the soil benchmark.

Land benchmark values (according to §196 BauGB) are average location values for undeveloped, load-free plots of land, taking into account the value-influencing characteristics of the land benchmark plot, such as development and development status, type and measure of structural usability as well as plot size and layout. Special properties of the individual property are not taken into account. Claims against institutions of land-use planning or the building permit authority cannot be derived from the information provided in the land reference value map. Soil benchmarks have no binding effect.

The expert committee derives the land benchmarks from the actual land prices.

Since the soil benchmark map 1 January 2003, the soil benchmark values are created in a 2-year cycle and since 1 January 2011 the soil benchmark values are displayed zonally.

Parcel feature class was created 03/02/2023. It is actively undergoing edits and is updated on a monthly basis.

SCIPS Statistics:

• 378 parcels have no SCIPS table match

• 213 are parcels added to The Villages at Vanden Meadows

• 125 are parcels with no APN recorded in the Tax Map (these are marked with 'No GIS Primary Table APN Match' in the PARCELID field).

• 27 are parcels that have APNs in the tax maps but have been prerecorded as not having a SCIPS match (these are marked with 'Currently Under Review as of 6/30/2022' in the Data Notes field).

• 5 represent land/base parcels that have condos overlaying them- the land parcels have an APN in the tax map.

• 8 remaining have land use codes of VACANT COMMERCIAL, VACANT RESIDENTIAL, TRANSITIONAL LAND, TAXABLE BELOW MINIMUM VALUE, & LIGHT INDUSTRIAL

• 34 SCIPS table records have no parcel match

• 2 SCIPS records with an ACTIVE status have a use description of NEWLY CREATED PARCELS.

• 32 SCIPS records with an ACTIVE status have a use description of one of the following: RIGHT OF WAY, GOVERNMENTAL & MISCELLANEOUS, AGRICULTURAL PROPERTY, VACANT COMMERCIAL LAND, TAXABLE BELOW MINIMUM VALUE, RAW SUBDIVISION LAND, VACANT RESIDENTIAL LAND < 1 AC.

Topology is built to flag overlaps and gaps within the data. However, the current data condition has exceptions. Overlap exceptions include overlapping air parcels depicting multistory buildings, such as condos, business parks, and trailer parks. Gap exceptions include missing right-of-way parcels and open space areas. These exceptions are being corrected where information within the Tax Maps is available.

As of the data's creation date:

• 4568 gaps exist

• 2403 overlaps exist

Fields:

PARCELID - APN Number

DataNotes - Notes from GTG Team

GISAcreage - Measured Acreage Value

Xcentroid - X Centroid Value

Ycentroid - Y Centroid Value

AssessorMap - Link to Tax Map

PropertyChar - Link to Property Characteristics

TaxInfo - Link to Tax Information

asmtnum – PIN

rollyear - Current Tax Roll Year

acres - Recorded Acreage Value

lotsize – Lot Size Square Footage

usecode - Use Code

use_desc - Use Code Description

subdiv – Subdivision Name

qclass – Quality Class

yrblt - Year Built

status – PIN Status

valland - Land Value

valimp – Improvement Value

valtv – Trees & Vine Value

valfme – Fixed Machinery & Equipment Value

valpp – Personal Property Value

valpen – Penalty Value

assessee – Assessee Name

addr1 – Mailing Address 1

addr2 – Mailing A

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Map of the standard land values ​​determined by the expert committee for property values ​​in Berlin on January 1st, 2012.

The article analyses the relation of market prices in the agricultural land market and selected pedological characteristics of traded lands. During the period of 2009–2018 in 12 districts of Slovakia more than 153,000 plots with different pedo-ecological and geographic conditions have been analysed. Based on soil types, texture composition, steepness, gravel content, and depth, corresponding price levels were derived, and soil price maps were developed. The highest valued soils are of chernozem type (EUR 1.64 m−2), loamy soils (EUR 0.86 m−2), soils on flat land (EUR 1.09 m−2), slightly gravelly soils (EUR 1.02 m−2), and deep soils (EUR 1.10 m−2). The land price is evidently highly correlated with its qualitative parameters. Using GIS technologies, the entire territory of Slovakia has been categorized by this means and a so-called basic map of agricultural soil market prices in Slovakia has been created.

To access parcel information:Enter an address or zoom in by using the +/- tools or your mouse scroll wheel. Parcels will draw when zoomed in.Click on a parcel to display a popup with information about that parcel.Click the "Basemap" button to display background aerial imagery.From the "Layers" button you can turn map features on and off.Complete Help (PDF)Parcel Legend:Full Map LegendAbout this ViewerThis viewer displays land property boundaries from assessor parcel maps across Massachusetts. Each parcel is linked to selected descriptive information from assessor databases. Data for all 351 cities and towns are the standardized "Level 3" tax parcels served by MassGIS. More details ...Read about and download parcel dataUpdatesV 1.1: Added 'Layers' tab. (2018)V 1.2: Reformatted popup to use HTML table for columns and made address larger. (Jan 2019)V 1.3: Added 'Download Parcel Data by City/Town' option to list of layers. This box is checked off by default but when activated a user can identify anywhere and download data for that entire city/town, except Boston. (March 14, 2019)V 1.4: Data for Boston is included in the "Level 3" standardized parcels layer. (August 10, 2020)V 1.4 MassGIS, EOTSS 2021

Dataset SummaryPlease note: this data is live (updated nightly) to reflect the latest changes in the City's systems of record.About this data:The operational purpose of the vacant land dataset is to facilitate the tracking and mapping of vacant land for the purposes of promoting redevelopment of lots to increase the City's tax base and spur increased economic activity. These properties are both City owned and privately owned. The vast majority of vacant lots are the result of a demolition of a structure that once stood on the property. Vacant lots are noted in the official tax parcel assessment records with a class code beginning with 3, which denotes the category vacant land.Related Resources:For a searchable interactive mapping application, please visit the City of Rochester's Property Information explorer tool. For further information about the city's property tax assessments, please contact the City of Rochester Assessment Bureau. To access the City's zoning code, please click here.Data Dictionary: SBL: The twenty-digit unique identifier assigned to a tax parcel. PRINTKEY: A unique identifier for a tax parcel, typically in the format of “Tax map section – Block – Lot". Street Number: The street number where the tax parcel is located. Street Name: The street name where the tax parcel is located. NAME: The street number and street name for the tax parcel. City: The city where the tax parcel is located. Property Class Code: The standardized code to identify the type and/or use of the tax parcel. For a full list of codes, view the NYS Real Property System (RPS) property classification codes guide. Property Class: The name of the property class associated with the property class code. Property Type: The type of property associated with the property class code. There are nine different types of property according to RPS: 100: Agricultural 200: Residential 300: Vacant Land 400: Commercial 500: Recreation & Entertainment 600: Community Services 700: Industrial 800: Public Services 900: Wild, forested, conservation lands and public parks First Owner Name: The name of the property owner of the vacant tax parcel. If there are multiple owners, then the first one is displayed. Postal Address: The USPS postal address for the vacant landowner. Postal City: The USPS postal city, state, and zip code for the vacant landowner. Lot Frontage: The length (in feet) of how wide the lot is across the street. Lot Depth: The length (in feet) of how far the lot goes back from the street. Stated Area: The area of the vacant tax parcel. Current Land Value: The current value (in USD) of the tax parcel. Current Total Assessed Value: The current value (in USD) assigned by a tax assessor, which takes into consideration both the land value, buildings on the land, etc. Current Taxable Value: The amount (in USD) of the assessed value that can be taxed. Tentative Land Value: The current value (in USD) of the land on the tax parcel, subject to change based on appeals, reassessments, and public review. Tentative Total Assessed Value: The preliminary estimate (in USD) of the tax parcel’s assessed value, which includes tentative land value and tentative improvement value. Tentative Taxable Value: The preliminary estimate (in USD) of the tax parcel’s value used to calculate property taxes. Sale Date: The date (MM/DD/YYYY) of when the vacant tax parcel was sold. Sale Price: The price (in USD) of what the vacant tax parcel was sold for. Book: The record book that the property deed or sale is recorded in. Page: The page in the record book where the property deed or sale is recorded in. Deed Type: The type of deed associated with the vacant tax parcel sale. RESCOM: Notes whether the vacant tax parcel is zoned for residential or commercial use. R: Residential C: Commercial BISZONING: Notes the zoning district the vacant tax parcel is in. For more information on zoning, visit the City’s Zoning District map. OWNERSHIPCODE: Code to note type of ownership (if applicable). Number of Residential Units: Notes how many residential units are available on the tax parcel (if applicable). LOW_STREET_NUM: The street number of the vacant tax parcel. HIGH_STREET_NUM: The street number of the vacant tax parcel. GISEXTDATE: The date and time when the data was last updated. SALE_DATE_datefield: The recorded date of sale of the vacant tax parcel (if available). Source: This data comes from the department of Neighborhood and Business Development, Bureau of Business and Zoning.

Land benchmark values (according to §196 BauGB) are average location values for undeveloped, load-free plots of land, taking into account the value-influencing characteristics of the land benchmark plot, such as development and development status, type and measure of structural usability as well as plot size and layout.

The land reference values for agricultural, forestry and horticultural land are determined zonally for the entire urban area of the state capital Dresden. The values and value-influencing features can be found in the separate map section (see associated document).

The expert committee derives the land benchmarks from the actual land prices. Soil benchmarks have no binding effect.

Since the soil benchmark map 1 January 2003, the soil benchmark values are created in a 2-year cycle and since 1 January 2011 the soil benchmark values are displayed zonally.

This map contains a hexbin layer that provides users with information on the mean values for city property tax, total property values, improvement values and land values within each hexbin.

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

Accurate and precise measurements of global cropland extent are needed for monitoring the sustainability of agriculture at all scales. Recent advancement in remote sensing and land cover mapping methods have greatly increased the ability to estimate cropland area distribution and trends. Here the FAO presents a map of cropland agreement produced by consolidating information at pixel level from six high-resolutions maps for circa 2020. The following six high resolution layers were used: ESRI 10 meter LU/LC, FROM-GLC, GLAD, GLC-FCS30, Globeland30 and Worldcover.

Two bands are included in the dataset:

1. Simple agreement (values between 1 and 6)
2. Detailed agreement (values between 1 and 63)

The map, developed in the Google Earth Engine platform, combines the 6 land cover/cropland layers to show their cropland agreement on pixel level at a spatial resolution of 30 meters. The simple agreement has pixel values that range from 1 (only 1 dataset classifies as cropland) to 6 (all datasets agree on presence of cropland). Pixels with a value of 0 indicate pixels where all datasets agree on absence of cropland. The second band includes a detailed agreement, showing which combination of the 6 datasets classify a pixel as cropland. The overview table (DetailedAgreement_LookupTable.xlsx) shows what the pixel values of this detailed agreement (from 1 to 63) correspond to.

The dataset has been uploaded in 16 tiles, in the preview below and in the file "ACroplandAgreement_30m_Tiles.png" the extent of each tile can be found.

For more information on FAO statistics on land cover and land use:

FAO. 2022. Land use statistics and indicators. Global, regional and country trends, 2000–2020. FAOSTAT Analytical Brief, no. 48. Rome. https://doi.org/10.4060/cc0963en

FAO. 2021. Land cover statistics. Global, regional and country trends, 2000–2019. FAOSTAT Analytical Brief Series No. 37. Rome.

We are also including a tabular version that’s slightly more comprehensive (would include anything that didn’t join to the parcel basefile due to lot alterations or resubdivisions since 2023 and/or due to parcels comprised of condos). This Excel file can be downloaded HERE, and does not contain the latitude and longitude information.Data Dictionary: Attribute Label Definition Source

TAX_ID Unique 26 character property tax identification number Onondaga County Planning

PRINTKEY Abbreviated tax identification number (section-block-lot) Onondaga County Planning

ADDRESSNUM Property’s physical street address Onondaga County Planning

ADDRESSNAM Property’s physical street name Onondaga County Planning

LAT Latitude Onondaga County Planning

LONG Longitude Onondaga County Planning

TAX_ID_1 City Tax ID number (26 digit number used for parcel mapping) City of Syracuse - Assessment

SBL Property Tax Map Number (Section, Block, Lot) City of Syracuse - Assessment

PNUMBR Property Number (10 digit number) City of Syracuse - Assessment

StNum Parcel street number City of Syracuse - Assessment

StName Parcel street name City of Syracuse - Assessment

FullAddress Street number and street name City of Syracuse - Assessment

Zip Parcel zip code City of Syracuse - Assessment

desc_1 Lot description including dimensions City of Syracuse - Assessment

desc_2 Lot description including dimensions City of Syracuse - Assessment

desc_3 Lot description including dimensions City of Syracuse - Assessment

SHAPE_IND

City of Syracuse - Assessment

LUC_parcel New York State property type classification code assigned by assessor during each roll categorizing the property by use. For more details: https://www.tax.ny.gov/research/property/assess/manuals/prclas.htm City of Syracuse - Assessment

LU_parcel New York State property type classification name City of Syracuse - Assessment

LUCat_Old Legacy land use category that corresponds to the overarching NYS category, i.e. all 400s = commercial, all 300s = vacant land, etc. NA

land_av Land assessed value City of Syracuse - Assessment

total_av Full assessed value City of Syracuse - Assessment

Owner Property owner name (First, Initial, Last, Suffix) City of Syracuse - Assessment

Add1_OwnPOBox Property owner mailing address (PO Box) City of Syracuse - Assessment

Add2_OwnStAdd Property owner mailing address (street number, street name, street direction) City of Syracuse - Assessment

Add3_OwnUnitInfo Property owner mailing address unit info (unit name, unit number) City of Syracuse - Assessment

Add4_OwnCityStateZip Property owner mailing address (city, state or country, zip code) City of Syracuse - Assessment

FRONT Front footage for square or rectangular shaped lots and the effective front feet on irregularly shaped lots in feet City of Syracuse - Assessment

DEPTH Actual depth of rectangular shaped lots in feet (irregular lots are usually measured in acres or square feet) City of Syracuse - Assessment

ACRES Number of acres (where values were 0, acreage calculated as FRONT*DEPTH)/43560) City of Syracuse - Assessment

yr_built Year built. Where year built was "0" or null, effective year built is given. (Effective age is determined by comparing the physical condition of one building with that of other like-use, newer buildings. Effective age may or may not represent the actual year built; if there have been constant upgrades or excellent maintenance this may be more recent than the original year built.) City of Syracuse - Assessment

n_ResUnits Number of residential units NA - Calculated field

IPSVacant Is it a vacant structure? ("Commercial" or "Residential" = Yes; null = No) City of Syracuse - Division of Code Enforcement

IPS_Condition Property Condition Score assigned to vacant properties by housing inspectors during routine vacant inspections (1 = Worst; 5 = Best) City of Syracuse - Division of Code Enforcement

NREligible National Register of Historic Places Eligible ("NR Eligible (SHPO)," or "NR Listed") City of Syracuse - Neighborhood and Business Development

LPSS Locally Protected Site Status ("Eligible/Architecturally Significant" or "Local Protected Site or Local District") City of Syracuse - Neighborhood and Business Development

WTR_ACTIVE Water activity code ("I" = Inactive; "A" = Active) City of Syracuse - Water

RNI Is property located in Resurgent Neighborhood Initiative (RNI) Area? (1 = Yes; 0 = No) City of Syracuse - Neighborhood and Business Development

DPW_Quad Geographic quadrant property is located in. Quadrants are divided Northwest, Northeast, Southwest, and Southeast based on property location in relation to I-81 and I-690. DPW uses the quad designation for some types of staff assignments. City of Syracuse - Department of Public Works

TNT_NAME TNT Sector property is located in City of Syracuse - Neighborhood and Business Development

NHOOD City Neighborhood Syracuse-Onondaga County Planning Agency (SOCPA)

NRSA Is property located in Neighborhood Revitilization Strategy Area (NRSA)? (1 = Yes; 0 = No) City of Syracuse - Neighborhood and Business Development

DOCE_Area Geographic boundary use to assign Division of Code Enforcement cases City of Syracuse - Neighborhood and Business Development

ZONE_DIST_PREV Former zoning district code Syracuse-Onondaga County Planning Agency (SOCPA)

REZONE ReZone designation (adopted June 2023) City of Syracuse - Neighborhood and Business Development

New_CC_DIST Current Common Council District property is located in Onondaga County Board of Elections

CTID_2020 Census Tract ID (2020) U.S. Census Bureau

CTLAB_2020 Census Tract Label (2020) U.S. Census Bureau

CT_2020 Census Tract (2020) U.S. Census Bureau

SpecNhood Is property located in a special Neighborhood historic preservation district? (1 = Yes; 0 or null = No) Syracuse-Onondaga County Planning Agency (SOCPA)

InPD Is property located in preservation district? (1 = Yes; 0 or null = No) Syracuse-Onondaga County Planning Agency (SOCPA)

PDNAME Preservation District name Syracuse-Onondaga County Planning Agency (SOCPA)

ELECT_DIST Election district number Onondaga County Board of Elections

CITY_WARD City ward number Onondaga County Board of Elections

COUNTY_LEG Onondaga County Legislative District number (as of Dec 2022) Onondaga County Board of Elections

NYS_ASSEMB New York State Assembly District number (as of Dec 2022) Onondaga County Board of Elections

NYS_SENATE New York State Senate District number (as of Dec 2022) Onondaga County Board of Elections

US_CONGR United States Congressional District number Onondaga County Board of Elections

Dataset Contact InformationOrganization: Neighborhood & Business DevelopmentPosition:Data Program ManagerCity:Syracuse, NYE-Mail Address:opendata@syrgov.netPlease note there is a data quality issue in this iteration with the preservation district (“InPD,” “PDNAME”) and special neighborhood historic district (“SpecNhood”) fields erroneously showing null results for all parcels.