The justification and targeting of conservation policy rests on reliable measures of public and private benefits from competing land uses. Advances in Earth system observation and modeling permit the mapping of public ecosystem services at unprecedented scales and resolutions, prompting new proposals for land protection policies and priorities. Data on private benefits from land use are not available at similar scales and resolutions, resulting in a data mismatch with unknown consequences. Here I show that private benefits from land can be quantified at large scales and high resolutions, and that doing so can have important implications for conservation policy models. I develop the first high-resolution estimates of fair market value of private lands in the contiguous United States by training tree-based ensemble models on 6 million land sales. The resulting estimates predict conservation cost with up to 8.5 times greater accuracy than earlier proxies. Studies using coarser cost proxies underestimated conservation costs, especially at the expensive tail of the distribution. This might have led to underestimations of policy budgets by factors of up to 37.5 in recent work. More accurate cost accounting will help policy makers acknowledge the full magnitude of contemporary conservation challenges, and can assist with the targeting of public ecosystem service investments. Methods See Methods & Materials in Nolte (2020) PNAS

All datasets except NSW land values and property sales information in this web maps are maintained by Spatial Service. Property NSW provides Land value and property Sales information. Update …Show full descriptionAll datasets except NSW land values and property sales information in this web maps are maintained by Spatial Service. Property NSW provides Land value and property Sales information. Update frequency for each dataset varies depending on the dataset. All these datasets are used in the land values and property sales map web map application. Please see individual metadata for each dataset below. Land value and property sales map can be found HERE.For more information regarding the Land valuation and Property Sales information data please contact : valuationenquiry@property.nsw.gov.auFor all other datasets, please contact ss-sds@customerservice.nsw.gov.au

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.

Map of the standard land values ​​determined by the expert committee for property values ​​in Berlin on January 1st, 2020.

Greene County, Ohio tax map boundaries provided as polygon GIS data. The tax map boundaries were created from the historic tax map books in Greene County. The data is used as a location reference to the original tax map book and page.

The main purposes of this online map are 1. to demonstrate the Web-Based Geographic Information System (GIS) in the District of Columbia Office of Tax and Revenue (OTR) Real Property Tax Administration (RPTA), and 2. to share detailed real property data and information to real property owners, the public, and other government entities. The rich map and interactive application include relevant real property valuation contributing map layers, links to original source agencies, and a variety of search, query, and analysis options to meet the needs of a wide user base. The location and links to the original DC Boundary Stones add a fun, historical,and educational component.The Office of the Chief Financial Officer, DC Office of Tax and Revenue (OTR), Real Property Assessment Division values all real property in the District of Columbia. The public interactive online DC Office of Tax and Revenue Real Property Assessment Lot Map Search application accompanies the OTR Tax Payer Service Center and may be used to search for and view all real property, related assessment areas, assessment data, and detailed assessment information.

Map of the standard land values ​​determined by the expert committee for property values ​​in Berlin on January 1st, 2018.

The DC Office of the Chief Financial Officer (OCFO), Office of Tax and Revenue (OTR), Real Property Tax Administration (RPTA) values all real property in the District of Columbia. This public interactive Real Property Assessment map application accompanies the OCFO MyTax DC and OTR websites. Use this mapping application to search for and view all real property, assessment valuation data, assessment neighborhood areas and sub-areas, detailed assessment information, and many real property valuation reports by various political and administrative areas. View by other administrative areas such as DC Wards, ANCs, DC Squares, and by specific real property characteristics such as property type and/or sale date. If you have questions, comments, or suggestions regarding the Real Property Assessment Map, contact the Real Property Assessment Division GIS Program at (202) 442-6484 or maps.title@dc.gov.

• land \tvalues for the past five years (where available) \t

  \t
• the \tvaluing year used to calculate council rates \t \t
• the \tvaluation basis \t \t
• the \tproperty number, address, and zoning information \t \t
• the area \tand boundaries of non strata properties \t \t
• notice of \tany concessions or allowances that apply to the land value. \t

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

• property \tsales information for individual properties from 2001 \t \t
• property \tsales information at a street and suburb level for the last five \tyears (where available \t
• area for \tnon strata properties \t \t
• the \tdealing number and sale date (or contract date) \t \t
• the date \tthe property sales information was last updated \t \t
• whether \tthe property is strata or non strata, or if the sale is part of a \tmulti property sale. \t

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 map application contains a hexbin layer that provides users with information on the mean values for city commercial property tax, total property values, improvement values and land values within each hexbin. The City of Dallas Commercial Hex map is fed into this application. This dashboard is featured on the Commercial page of the City of Dallas Tax Appraisal Hub site.

Property assessment parcels for New Brunswick.

The DC Office of the Chief Financial Officer (OCFO), Office of Tax and Revenue (OTR), Real Property Tax Administration (RPTA) values all real property in the District of Columbia. This public interactive Real Property Assessment map application accompanies the OCFO MyTax DC and OTR websites. Use this mapping application to search for and view all real property, assessment valuation data, assessment neighborhood areas and sub-areas, detailed assessment information, and many real property valuation reports by various political and administrative areas. View by other administrative areas such as DC Wards, ANCs, DC Squares, and by specific real property characteristics such as property type and/or sale date. If you have questions, comments, or suggestions regarding the Real Property Assessment Map, contact the Real Property Assessment Division GIS Program at (202) 442-6484 or maps.title@dc.gov.

Parcels and property data maintained and provided by Lee County Property Appraiser are converted to points. Property attribute data joined to parcel GIS layer by Lee County Government GIS. This dataset is generally used in spatial analysis.Process description: Parcel polygons, condominium points and property data provided by the Lee County Property Appraiser are processed by Lee County's GIS Department using the following steps:Join property data to parcel polygons Join property data to condo pointsConvert parcel polygons to points using ESRI's ArcGIS tool "Feature to Point" and designate the "Source" field "P".Load Condominium points into this layer and designate the "Source" field "C". Add X/Y coordinates in Florida State Plane West, NAD 83, feet using the "Add X/Y" tool.Projected coordinate system name: NAD_1983_StatePlane_Florida_West_FIPS_0902_FeetGeographic coordinate system name: GCS_North_American_1983

 Name
 Type
 Length
 Description


 STRAP
 String
 25
 17-digit Property ID (Section, Township, Range, Area, Block, Lot)


 BLOCK
 String
 10
 5-digit portion of STRAP (positions 9-13)


 LOT
 String
 8
 Last 4-digits of STRAP


 FOLIOID
 Double
 8
 Unique Property ID


 MAINTDATE
 Date
 8
 Date LeePA staff updated record


 MAINTWHO
 String
 20
 LeePA staff who updated record


 UPDATED
 Date
 8
 Data compilation date


 HIDE_STRAP
 String
 1
 Confidential parcel ownership


 TRSPARCEL
 String
 17
 Parcel ID sorted by Township, Range & Section


 DORCODE
 String
 2
 Department of Revenue. See https://leepa.org/Docs/Codes/DOR_Code_List.pdf


 CONDOTYPE
 String
 1
 Type of condominium: C (commercial) or R (residential)


 UNITOFMEAS
 String
 2
 Type of Unit of Measure (ex: AC=acre, LT=lot, FF=frontage in feet)


 NUMUNITS
 Double
 8
 Number of Land Units (units defined in UNITOFMEAS)


 FRONTAGE
 Integer
 4
 Road Frontage in Feet


 DEPTH
 Integer
 4
 Property Depth in Feet


 GISACRES
 Double
 8
 Total Computed Acres from GIS


 TAXINGDIST
 String
 3
 Taxing District of Property


 TAXDISTDES
 String
 60
 Taxing District Description


 FIREDIST
 String
 3
 Fire District of Property


 FIREDISTDE
 String
 60
 Fire District Description


 ZONING
 String
 10
 Zoning of Property


 ZONINGAREA
 String
 3
 Governing Area for Zoning


 LANDUSECOD
 SmallInteger
 2
 Land Use Code


 LANDUSEDES
 String
 60
 Land Use Description


 LANDISON
 String
 5
 BAY,CANAL,CREEK,GULF,LAKE,RIVER & GOLF


 SITEADDR
 String
 55
 Lee County Addressing/E911


 SITENUMBER
 String
 10
 Property Location - Street Number


 SITESTREET
 String
 40
 Street Name


 SITEUNIT
 String
 5
 Unit Number


 SITECITY
 String
 20
 City


 SITEZIP
 String
 5
 Zip Code


 JUST
 Double
 8
 Market Value


 ASSESSED
 Double
 8
 Building Value + Land Value


 TAXABLE
 Double
 8
 Taxable Value


 LAND
 Double
 8
 Land Value


 BUILDING
 Double
 8
 Building Value


 LXFV
 Double
 8
 Land Extra Feature Value


 BXFV
 Double
 8
 Building Extra Feature value


 NEWBUILT
 Double
 8
 New Construction Value


 AGAMOUNT
 Double
 8
 Agriculture Exemption Value


 DISAMOUNT
 Double
 8
 Disability Exemption Value


 HISTAMOUNT
 Double
 8
 Historical Exemption Value


 HSTDAMOUNT
 Double
 8
 Homestead Exemption Value


 SNRAMOUNT
 Double
 8
 Senior Exemption Value


 WHLYAMOUNT
 Double
 8
 Wholly Exemption Value


 WIDAMOUNT
 Double
 8
 Widow Exemption Value


 WIDRAMOUNT
 Double
 8
 Widower Exemption Value


 BLDGCOUNT
 SmallInteger
 2
 Total Number of Buildings on Parcel


 MINBUILTY
 SmallInteger
 2
 Oldest Building Built


 MAXBUILTY
 SmallInteger
 2
 Newest Building Built


 TOTALAREA
 Double
 8
 Total Building Area


 HEATEDAREA
 Double
 8
 Total Heated Area


 MAXSTORIES
 Double
 8
 Tallest Building on Parcel


 BEDROOMS
 Integer
 4
 Total Number of Bedrooms


 BATHROOMS
 Double
 8
 Total Number of Bathrooms / Not For Comm


 GARAGE
 String
 1
 Garage on Property 'Y'


 CARPORT
 String
 1
 Carport on Property 'Y'


 POOL
 String
 1
 Pool on Property 'Y'


 BOATDOCK
 String
 1
 Boat Dock on Property 'Y'


 SEAWALL
 String
 1
 Sea Wall on Property 'Y'


 NBLDGCOUNT
 SmallInteger
 2
 Total Number of New Buildings on ParcelTotal Number of New Buildings on Parcel


 NMINBUILTY
 SmallInteger
 2
 Oldest New Building Built


 NMAXBUILTY
 SmallInteger
 2
 Newest New Building Built


 NTOTALAREA
 Double
 8
 Total New Building Area


 NHEATEDARE
 Double
 8
 Total New Heated Area


 NMAXSTORIE
 Double
 8
 Tallest New Building on Parcel


 NBEDROOMS
 Integer
 4
 Total Number of New Bedrooms


 NBATHROOMS
 Double
 8
 Total Number of New Bathrooms/Not For Comm


 NGARAGE
 String
 1
 New Garage on Property 'Y'


 NCARPORT
 String
 1
 New Carport on Property 'Y'


 NPOOL
 String
 1
 New Pool on Property 'Y'


 NBOATDOCK
 String
 1
 New Boat Dock on Property 'Y'


 NSEAWALL
 String
 1
 New Sea Wall on Property 'Y'


 O_NAME
 String
 30
 Owner Name


 O_OTHERS
 String
 120
 Other Owners


 O_CAREOF
 String
 30
 In Care Of Line


 O_ADDR1
 String
 30
 Owner Mailing Address Line 1


 O_ADDR2
 String
 30
 Owner Mailing Address Line 2


 O_CITY
 String
 30
 Owner Mailing City


 O_STATE
 String
 2
 Owner Mailing State


 O_ZIP
 String
 9
 Owner Mailing Zip


 O_COUNTRY
 String
 30
 Owner Mailing Country


 S_1DATE
 Date
 8
 Most Current Sale Date > $100.00


 S_1AMOUNT
 Double
 8
 Sale Amount


 S_1VI
 String
 1
 Sale Vacant or Improved


 S_1TC
 String
 2
 Sale Transaction Code


 S_1TOC
 String
 2
 Sale Transaction Override Code


 S_1OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_2DATE
 Date
 8
 Previous Sale Date > $100.00


 S_2AMOUNT
 Double
 8
 Sale Amount


 S_2VI
 String
 1
 Sale Vacant or Improved


 S_2TC
 String
 2
 Sale Transaction Code


 S_2TOC
 String
 2
 Sale Transaction Override Code


 S_2OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_3DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_3AMOUNT
 Double
 8
 Sale Amount


 S_3VI
 String
 1
 Sale Vacant or Improved


 S_3TC
 String
 2
 Sale Transaction Code


 S_3TOC
 String
 2
 Sale Transaction Override Code


 S_3OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_4DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_4AMOUNT
 Double
 8
 Sale Amount


 S_4VI
 String
 1
 Sale Vacant or Improved


 S_4TC
 String
 2
 Sale Transaction Code


 S_4TOC
 String
 2
 Sale Transaction Override Code


 S_4OR_NUM
 String
 13

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.

Global maps of agricultural expansion potential at a 300 m resolution

This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study.

Abstract:

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.

Data description:

We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively.

Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*". The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.

Repository content:

Full resolution layers: - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with <50% crops category at the spatial resolution of 10 arc-seconds. - "readme.txt" is the text file with the basic description and the metadata for the repository.

Aggregated layers: This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below).

File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif”

• "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.

• "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.

• "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops.

Raster metadata:

Driver: GTiff Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs

Notes on use:

Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.

Internal view of the parcel layer. This view contains all the attributes that can be seen by County employees.There are approximately 51,300 real property parcels in Napa County. Parcels delineate the approximate boundaries of property ownership as described in Napa County deeds, filed maps, and other source documents. GIS parcel boundaries are maintained by the Information Technology Services GIS team. Assessor Parcel Maps are created and maintained by the Assessor Division Mapping Section. Each parcel has an Assessor Parcel Number (APN) that is its unique identifier. The APN is the link to various Napa County databases containing information such as owner name, situs address, property value, land use, zoning, flood data, and other related information. Data for this map service is sourced from the Napa County Parcels dataset which is updated nightly with any recent changes made by the mapping team. There may at times be a delay between when a document is recorded and when the new parcel boundary configuration and corresponding information is available in the online GIS parcel viewer.From 1850 to early 1900s assessor staff wrote the name of the property owner and the property value on map pages. They began using larger maps, called “tank maps” because of the large steel cabinet they were kept in, organized by school district (before unification) on which names and values were written. In the 1920s, the assessor kept large books of maps by road district on which names were written. In the 1950s, most county assessors contracted with the State Board of Equalization for board staff to draw standardized 11x17 inch maps following the provisions of Assessor Handbook 215. Maps were originally drawn on linen. By the 1980’s Assessor maps were being drawn on mylar rather than linen. In the early 1990s Napa County transitioned from drawing on mylar to creating maps in AutoCAD. When GIS arrived in Napa County in the mid-1990s, the AutoCAD images were copied over into the GIS parcel layer. Sidwell, an independent consultant, was then contracted by the Assessor’s Office to convert these APN files into the current seamless ArcGIS parcel fabric for the entire County. Beginning with the 2024-2025 assessment roll, the maps are being drawn directly in the parcel fabric layer.Parcels in the GIS parcel fabric are drawn according to the legal description using coordinate geometry (COGO) drawing tools and various reference data such as Public Lands Survey section boundaries and road centerlines. The legal descriptions are not defined by the GIS parcel fabric. Any changes made in the GIS parcel fabric via official records, filed maps, and other source documents are uploaded overnight. There is always at least a 6-month delay between when a document is recorded and when the new parcel configuration and corresponding information is available in the online parcel viewer for search or download.Parcel boundary accuracy can vary significantly, with errors ranging from a few feet to several hundred feet. These distortions are caused by several factors such as: the map projection - the error derived when a spherical coordinate system model is projected into a planar coordinate system using the local projected coordinate system; and the ground to grid conversion - the distortion between ground survey measurements and the virtual grid measurements. The aim of the parcel fabric is to construct a visual interpretation that is adequate for basic geographic understanding. This digital data is intended for illustration and demonstration purposes only and is not considered a legal resource, nor legally authoritative.SFAP & CFAP DISCLAIMER: Per the California Code, RTC 606. some legal parcels may have been combined for assessment purposes (CFAP) or separated for assessment purposes (SFAP) into multiple parcels for a variety of tax assessment reasons. SFAP and CFAP parcels are assigned their own APN number and primarily result from a parcel being split by a tax rate area boundary, due to a recorded land use lease, or by request of the property owner. Assessor parcel (APN) maps reflect when parcels have been separated or combined for assessment purposes, and are one legal entity. The goal of the GIS parcel fabric data is to distinguish the SFAP and CFAP parcel configurations from the legal configurations, to convey the legal parcel configurations. This workflow is in progress. Please be advised that while we endeavor to restore SFAP and CFAP parcels back to their legal configurations in the primary parcel fabric layer, SFAP and CFAP parcels may be distributed throughout the dataset. Parcels that have been restored to their legal configurations, do not reflect the SFAP or CFAP parcel configurations that correspond to the current property tax delineations. We intend for parcel reports and parcel data to capture when a parcel has been separated or combined for assessment purposes, however in some cases, information may not be available in GIS for the SFAP/CFAP status of a parcel configuration shown. For help or questions regarding a parcel’s SFAP/CFAP status, or property survey data, please visit Napa County’s Surveying Services or Property Mapping Information. For more information you can visit our website: When a Parcel is Not a Parcel | Napa County, CA

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.

The datasets were produced during the doctoral research titled "A modelling framework to estimate the effects of future transport interventions on land values". Specifically, the datasets were produced and prepared with the purpose of training a geostatistical model and construct a residential land value map for Guatemala City using a predictive approach. The file "gc_log" contains a set of spatial points representing parcel centroids of the observations that were used to train a regression-kriging model. A spatialized variable selection was implemented in order to retain only important variables. The "grid_prediction.csv" contains a set of spatial points representing the centroids of an hexagonal tessellation containing values for all the required predictors of land value. The "research_code.R" file contains the scripts developed to implement the spatial variable selection, train the regression-kriging model and construct the land value map. Date: 2020-05-29

This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the Verponding registration in Amsterdam. The verponding or the ‘Verpondings-quohieren van den 8sten penning’ was a tax in the Netherlands on the 8th penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide verponding registration started in 1647 and continued into the early 19th century. With the introduction of the cadastre system in 1810, the verponding came to an end.

The original tax registration is kept in the Amsterdam City Archives (Archief nr. 5044) and the four registration books transcribed in this dataset are Archief 5044, inventory 255, 273, 281, 284. The verponding was collected by districts (wijken). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.

This dataset digitises four registration books of the verponding between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.

Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the verponding. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced verponding can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.

GIS specifications:

Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)

Historical map tiles URL (From Amsterdam Time Machine)

NB: This verponding dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution.

Contributors

Historical and archival research: Weixuan Li, Bart Reuvekamp

Plotting of geo-referenced points: Bart Reuvekamp

Spatial analysis: Weixuan Li

Mapping software: QGIS

Acknowledgements: Virtual Interiors project, Daan de Groot

LULC class typology differed between the two LULC maps and so not all categories are comparable (e.g. Rubber class is present for the modified-LULC map but absent from the FROM-GLC map).Area (km2) and proportion of study area (%) taken up by different classes for each LULC map.