Map Index Sheets from Block and Lot Grid of Property Assessment and based on aerial photography, showing 1983 datum with solid line and NAD 27 with 5 second grid tics and italicized grid coordinate markers and outlines of map sheet boundaries. Each grid square is 3500 x 4500 feet. Each Index Sheet contains 16 lot/block sheets, labeled from left to right, top to bottom (4 across, 4 down): A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S. The first (4) numeric characters in a parcelID indicate the Index sheet in which the parcel can be found, the alpha character identifies the block in which most (or all) of the property lies.

Open areas within parking lots not designated for parking in Easton, Massachusetts. Compiled from 2017 vector mapping project conducted by WSP. The aerial photographic mission was carried out on April 12, 2017. The vector data was collected at scale of 1"= 40'.

Historic land uses on lots that were vacant, privately owned, and zoned for manufacturing in 2009. Information came from a review of several years of historical Sanborn maps over the past 100 years. When the SPEED 1.0 mapping application was created in 2009, OER had its vendor examine historic land use maps on vacant, privately-owned, industrially-zoned tax lots. Up to seven years of maps for each lot were examined, and information was recorded that indicated industrial uses or potential environmental contamination such as historic fill. Data for an additional 139 lots requested by community-based organizations was added in 2014. Each record represents the information from a map from a particular year on a particular tax lot at that time. Limitations of funding determined the number of lots included and entailed that not all years were examined for each lot.

Extensive land use and geographic data at the tax lot level in GIS format (ESRI Shapefile). Contains more than seventy fields derived from data maintained by city agencies, merged with tax lot features from the Department of Finance’s Digital Tax Map, clipped to the shoreline.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Lot boundary Lines in the City of Pflugerville, TX and its extr-territorial jurisdiction. This data updates automatically when SDE is edited. If downloaded from Open Data, this data is the most current to the day of download.

Work in progress: data might be changed

The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies.

Vehicle Detections

Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock.

The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/807618b6-5c38-4456-88a1-cb47500081ff/download/detection_map.png" alt="Overview map of detected vehicles" title="Overview map of detected vehicles"> Figure 1: Overview map of detected vehicles

Parking Areas

The public parking areas were digitized manually using aerial images and the detected vehicles in order to exclude irregular parking spaces as far as possible. They were also tagged as to whether they were aligned parallel to the road and assigned to a use at the time of recording, as some are used for construction sites or outdoor catering, for example. Depending on the intended use, they can be filtered individually.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/16b14c61-d1d6-4eda-891d-176bdd787bf5/download/parking_area_example.png" alt="Example parking area occupation pattern" title="Visualization of example parking areas on top of an aerial image [by LGLN]"> Figure 2: Visualization of example parking areas on top of an aerial image [by LGLN]

Parking Occupancy

For modelling the parking occupancy, single slots are sampled as center points every 5 m from the parking areas. In this way, they can be integrated into a street/routing graph, for example, as prepared in Wage et al. (2023). Own representations can be generated from the parking area and vehicle detections. Those parking points were intersected with the vehicle boxes to identify occupancy at the respective epochs.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/ca0b97c8-2542-479e-83d7-74adb2fc47c0/download/datenpub-bays.png" alt="Overview map of parking slots' average load" title="Overview map of parking slots' average load"> Figure 3: Overview map of average parking lot load

However, unoccupied spaces cannot be determined quite as trivially the other way around, since no detected vehicle can result just as from no measurement/observation. Therefore, a parking space is only recorded as unoccupied if a vehicle was detected at the same time in the neighborhood on the same parking lane and therefore it can be assumed that there is a measurement.

To close temporal gaps, interpolations were made by hour for each parking slot, assuming that between two consecutive observations with an occupancy the space was also occupied in between - or if both times free also free in between. If there was a change, this is indicated by a proportional value. To close spatial gaps, unobserved spaces in the area are drawn randomly from the ten closest occupation patterns around.

This results in an exemplary occupancy pattern of a synthetic day. Depending on the application, the value could be interpreted as occupancy probability or occupancy share.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/184a1f75-79ab-4d0e-bb1b-8ed170678280/download/occupation_example.png" alt="Example parking area occupation pattern" title="Example parking area occupation pattern"> Figure 4: Example parking area occupation pattern

References

• F. Bock, D. Eggert and M. Sester (2015): On-street Parking Statistics Using LiDAR Mobile Mapping, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 2015, pp. 2812-2818. https://doi.org/10.1109/ITSC.2015.452
• A. Leichter, U. Feuerhake, and M. Sester (2021): Determination of Parking Space and its Concurrent Usage Over Time Using Semantically Segmented Mobile Mapping Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 185–192. https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-185-2021
• O. Wage, M. Heumann, and L. Bienzeisler (2023): Modeling and Calibration of Last-Mile Logistics to Study Smart-City Dynamic Space Management Scenarios. In 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility (SuMob ’23), November 13, 2023, Hamburg, Germany. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3615899.3627930

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.

Updated WeeklyThis data set represents tax lots for Jackson County and includes account info (ownership, assessed and real market values, and building information). Data is updated on a weekly basis from assessor tax parcel information. Where there are several different owners on an individual parcel, multiple parcels polygons will exist since parcel to ownership is not a one to one relationship. Some other fields like year built are populated with the first record if multiple exists. For question about parcel updates or issues please contact the Assessor's Office at (541) 774-6059.

To access the tax lot layer you will need to contact the county Assessor's office. ORMAP is a statewide digital cadastral base map that is publicly accessible, continually maintained, supports the Oregon property tax system, supports a multi-purpose land information system, strives to comply with appropriate state and national standards, and will continue to be improved over time.

Polygon geometry with attributes displaying the recorded boundaries or lot lines of property in East Baton Rouge Parish, Louisiana.Metadata

This layer contains the boundaries and IDs of the Maryland tax maps produced by Maryland Department of Planning. Tax maps, also known as assessment maps, property maps or parcel maps, are a graphic representation of real property showing and defining individual property boundaries in relationship to contiguous real property.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://geodata.md.gov/imap/rest/services/PlanningCadastre/MD_PropertyData/MapServer/2

This layer is displayed on the Minimum lot size overlay map in City Plan version 6 as and identifies designated minimum lot sizes. The layer is also available in Council’s City Plan interactive mapping tool. For further information on City Plan, please visit http://www.goldcoast.qld.gov.au/planning-and-building/city-plan-2015-19859.html

The Zoning Tax Lot Database is a comma-separated values (CSV) file that contains up-to-date zoning by parcel. The Database includes the zoning designations and zoning map associated with a specific tax block and lot. The Database is updated on a monthly basis to reflect rezoning and corrections to the file.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Connecticut Parcels for Protected Open Space Mapping is a polygon feature-based layer that includes basic parcel-level information for some towns in Connecticut. This parcel layer includes information provided by individual municipalities. These parcel data are incomplete and out of date. The accuracy, currency and completeness of the data reflect the content of the data at the time DEEP acquired the data from the individual municipalities. Attribute information is comprised of values such as town name and map lot block number. These data are not updated by CT DEEP and should only be used as a general reference. Critical decisions involving parcel-level information should be based on more recently acquired information from the respective municipalities. These parcels are not to be considered legal boundaries such as boundaries determined from certain classified survey maps or deed descriptions. Parcel boundaries shown in this layer are based on information from municipalities used for property tax purposes. Largely due to differences in horizontal accuracy among various data layers, do not expect these parcel boundaries to line up exactly with or be properly postioned relative to features shown on other layers available from CT DEEP such as scanned USGS topography quadrangle maps, roads, hydrography, town boundaries, and even orthophotograpy.

The data in the parcel layer was obtained from individual Connecticut municipalities. An effort was made to collect data once from each municipality. The data acquisition date for each set of municipally-supplied parcel data was not recorded and CT DEEP does not keep this information up-to-date. Consequently, these data are out-of-date, incomplete and do not reflect the current state of property ownership in these municipalities. These parcels are not to be considered legal boundaries such as boundaries determined from certain classified survey maps or deed descriptions. Parcel boundaries shown in this layer are based on information from municipalities used for property tax purposes. Parcel boundaries and attribute information have not been updated in this layer since the time the information was originally acquired by CT DEEP. For example, property boundaries are incorrect where subdivisions have occurred. Also, field attribute values are populated only if the information was supplied to CT DEEP. For example, parcels in some towns lack location (street name) information or possibly map lot block values. Therefore, field attributes are inconsistent, may include gaps, and do not represent complete sets of values among all towns. They should not be compared and analyzed across towns. It is emphasized that critical decisions involving parcel-level information be based on more recently obtained information from the respective municipalities. These data are only suitable for general reference purposes. Be cautious when using these data. Many Connecticut municipalities provide access to more up-to-date and more detailed property ownership information on the Internet. This dataset includes parcel information for the following towns: Andover, Ansonia, Ashford, Avon, Beacon Falls, Berlin, Bethany, Bethel, Bethlehem, Bloomfield, Bolton, Branford, Bridgewater, Brookfield, Brooklyn, Canaan, Canterbury, Canton, Chaplin, Cheshire, Chester, Clinton, Colchester, Colebrook, Columbia, Cornwall, Coventry, Cromwell, Danbury, Darien, Deep River, Derby, East Granby, East Haddam, East Hampton, East Hartford, East Lyme, East Windsor, Eastford, Ellington, Enfield, Essex, Farmington, Franklin, Glastonbury, Granby, Greenwich, Griswold, Groton, Guilford, Haddam, Hamden, Hartford, Hebron, Kent, Killingly, Killingworth, Lebanon, Ledyard, Lisbon, Litchfield, Lyme, Madison, Manchester, Mansfield, Marlborough, Meriden, Middlebury, Middlefield, Middletown, Milford, Monroe, Montville, Morri

See full Data Guide here. This layer includes polygon features that depict protected open space for towns of the Protected Open Space Mapping (POSM) project, which is administered by the Connecticut Department of Energy and Environmental Protection, Land Acquisition and Management. Only parcels that meet the criteria of protected open space as defined in the POSM project are in this layer. Protected open space is defined as: (1) Land or interest in land acquired for the permanent protection of natural features of the state's landscape or essential habitat for endangered or threatened species; or (2) Land or an interest in land acquired to permanently support and sustain non-facility-based outdoor recreation, forestry and fishery activities, or other wildlife or natural resource conservation or preservation activities. Includes protected open space data for the towns of Andover, Ansonia, Ashford, Avon, Beacon Falls, Canaan, Clinton, Berlin, Bethany, Bethel, Bethlehem, Bloomfield, Bridgewater, Bolton, Brookfield, Brooklyn, Canterbury, Canton, Chaplin, Cheshire, Colchester, Colebrook, Columbia, Cornwall, Coventry, Cromwell, Danbury, Derby, East Granby, East Haddam, East Hampton, East Hartford, East Windsor, Eastford, Ellington, Enfield, Essex, Farmington, Franklin, Glastonbury, Goshen, Granby, Griswold, Groton, Guilford, Haddam, Hampton, Hartford, Hebron, Kent, Killingworth, Lebanon, Ledyard, Lisbon, Litchfield, Madison, Manchester, Mansfield, Marlborough, Meriden, Middlebury, Middlefield, Middletown, Monroe, Montville, Morris, New Britain, New Canaan, New Fairfield, New Milford, New Hartford, Newington, Newtown, Norfolk, North, Norwich, Preston, Ridgefield, Shelton, Stonington, Oxford, Plainfield, Plainville, Pomfret, Portland, Prospect, Putnam, Redding, Rocky Hill, Roxbury, Salem, Salisbury, Scotland, Seymour, Sharon, Sherman, Simsbury, Somers, South Windsor, Southbury, Southington, Sprague, Sterling, Suffield, Thomaston, Thompson, Tolland, Torrington, Union, Vernon, Wallingford, Windham, Warren, Washington, Waterbury, Watertown, West Hartford, Westbrook, Weston, Wethersfield, Willington, Wilton, Windsor, Windsor Locks, Wolcott, Woodbridge, Woodbury, and Woodstock. Additional towns are added to this list as they are completed. The layer is based on information from various sources collected and compiled during the period from March 2005 through the present. These sources include but are not limited to municipal Assessor's records (the Assessor's database, hard copy maps and deeds) and existing digital parcel data. The layer represents conditions as of the date of research at each city or town hall. The Protected Open Space layer includes the parcel shape (geometry), a project-specific parcel ID based on the Town and Town Assessor's lot numbering system, and system-defined (automatically generated) fields. The Protected Open Space layer has an accompanying table containing more detailed information about each feature (parcel). This table is called Protected Open Space Dat, and can be joined to Protected Open Space in ArcMap using the parcel ID (PAR_ID) field. Detailed information in the Protected Open Space Data attribute table includes the Assessor's Map, Block and Lot numbers (the Assessor's parcel identification numbering system), the official name of the parcel (such as the park or forest name if it has one), address and owner information, the deed volume and page numbers, survey information, open space type, the unique parcel ID number (Par_ID), comments collected by researchers during city/town hall visits, and acreage. This layer does not include parcels that do not meet the definition of open space as defined above. Features are stored as polygons that represent the best available locational information, and are "best fit" to the land base available for each.

The Connecticut Department of Environmental Protection's (CTDEP) Permanently Protected Open Space Phase Mapping Project Phase 1 (Protected Open Space Phase1) layer includes permanently protected open space parcels in towns in Phase 1 that meet the CTDEP's definition for this project, the Permanently Protected Open Space Mapping (CT POSM) Project. The CTDEP defines permanently protected open space as (1) Land or interest in land acquired for the permanent protection of natural features of the state's landscape or essential habitat for endangered or threatened species; or (2) Land or an interest in land acquired to permanently support and sustain non facility-based outdoor recreations, forestry and fishery activities, or other wildlife or natural resource conservation or preservation activities.

Towns in Phase 1 of the CT POSM project are situated along the CT coast and portions of the Thames River and are the following: Branford, Bridgeport, Chester, Clinton, Darien, Deep River, East Haven, East Lyme, Essex, Fairfield, Greenwich, Groton, Guilford, Hamden, Ledyard, Lyme, Madison, Milford, Montville, New Haven, New London, North Branford, North Haven, Norwalk, Norwich, Old Lyme, Old Saybrook, Orange, Preston, Shelton, Stamford, Stonington, Stratford, Waterford, West Haven, Westbrook, Westport.

For the purposes of the project a number of categories or classifications of open space have also been created. These include: Land Trust, Land Trust with buidlings, Private, Private with buildings, Utility Company, Utility Company with buildings, Federal, State, Municipal, Municipal with buildings, Conservation easement, and non-DEP State land. The layer is based on information from various sources collected and compiled during the period from August 2002 trhough October 2003. These sources include municipal Assessor's records (the Assessor's database, hard copy maps and deeds) and existing digital parcel data. The layer represents conditions on the date of research at each city or town hall.

The Protected Open Space Phase1 layer includes the parcel shape (geometry), a project-specific parcel ID based on the Town and Town's Assessor lot numbering system, and system-defined (automatically generated) fields. In addition, the Protected_Open_Space_Phase1 layer has an accompanying table containing more detailed information about each parcel's collection, standardization and storage. This table is called Protected Open Space Phase1 Data and can be joined to Protected Open Space Phase1 in ArcMap using the parcel ID (PAR_ID) field. Detailed information includes the Assessor's Map, Block and Lot numbers (the Assessor's parcel identification numbering system), the official name of the parcel (such as the park or forest name if it has one), address and owner information, the deed volume and page numbers, survey information, open space type, the project-specific parcel ID number (Par_ID), comments collected by researchers during city/town hall visits, acreage collected during site reconaissance and the data source. This layer does not include parcels that do not meet the definition of open space as defined above. Features are stored as polygon feature type that represent the best available locational information, i.e. "best fit" to the land base available for each.

Phase 1 of the Protected Open Space Mapping (POSM) Project was accomplished by a contractor using only a querying process to identify open space. The contractor obtained assessor's data from the various towns and created programs to cull open space parcels strictly by query processes. We have found many errors and omissions in the data, but at this point in the project we cannot revisit all the coastal towns. Therefore, this data is being sent with a disclaimer for accuracy. You are welcome to use it but not to publish it. Please note that we do not include any water company parcels despite them being listed as part of our criteria because we must first obtain written clarification and clearance from the U.S. Department of Homeland Security.

We have since changed our data collection method for Phase 2 of this project. DEP staff now visit each town hall and thoroughly research the land records. The project is expected to be complete by 2010.

A web map used to visualize available digital parcel data for Organized Towns and Unorganized Territories throughout the state of Maine. Individual towns submit parcel data on a voluntary basis; the data are compiled by the Maine Office of GIS for dissemination by the Maine GeoLibrary, and where available, the web map also includes assessor data contained in the Parcels_ADB related table.This web map is intended for use within the Maine Geoparcel Viewer Application; it is not intended for use as a standalone web map.Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. Maine Parcels Organized Towns and Maine Parcels Organized Towns ADB are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, sometimes many years apart, which affects the currency of Maine GeoLibrary parcels data. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions.

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of surface (paved or unpaved)

EditDate type: Stringwidth: 4precision: 0

These are lands still within the District of Columbia that has never been subdivided into either Record or Tax Lots through the two offices that manage land records (OS & RPTA), this land is referred to as Parcels, expressed as fractions (Ex Parcel 117/36). In this example, the number “36” would be the 36th out conveyance from original Parcel 117. The tracking of parcels was started in 1905 when, by Act of Congress, all the District’s unsubdivided properties which were mostly rural farms at the time were given parcel numbers. Their boundaries were also depicted (in many cases approximated), in large books in DCRA's Office of the Surveyor. Until the late 1960s, building permits were routinely issued by the city for new construction on Parcels, but today all Parcels, like Tax Lots, must be converted into subdivision Lots of Record before permits will be issued for exterior work. Parcels are only found in the old “County of Washington,” north of Florida Ave and east of the Anacostia River. There are no Parcels found within the original city limits or Georgetown. Parcels are not in Squares. There are examples where parcel land may be physically located in the middle of a city Square, but Parcels are not considered part of a Square until they are duly subdivided by the D.C. Surveyor’s Office.

Subdivision Lots based off of the legal description recorded with the Commissioner of the Revenue. Subdivisions are enumerated in the lot-block-section system (plat system), which refers to specific parcels of land identified by a lot number or letter within a block, or subdivision plat, in which the lot is located.Subdivision Lots are maintained within the Parcel Feature and is dissolved out weekly. The Parcel feature is the geographic representation of cadastral records within the County as recorded in deeds and plats. The current parcel set is based off of the 1979 double circle maps by Wingate Appraisal & Mapping and digitized in the early 1990's. The data is continuously updated, as new land records become available. New parcels are added in a “best fit” methodology giving preference to the most current source. This feature is co-managed in AutoCAD and ArcMap. In Arc this polygon feature is part of an Editing topology along with our Zoning feature and our Administration feature. This prevents self-intersection and gaps, while ensuring complete coverage among the participating features.

This dataset represents parcels not mapped or sourced in Vector Property Map. Please refer to the common ownership lots layer in https://opendata.dc.gov for the most current data on ownership. Property Owner Points. This dataset contains points that represent the approximate location of real property lots within the District of Columbia. Each property point is generated based on a corresponding record maintained within the Office of Tax and Revenue (OTR) Real Property Tax Administration's (RPTA) real property database. Each point contains the full attribution of database fields derived from ITS public release extract. The initial data conversion effort was begun in 1997 as a means to provide RPTA with a digital mapping system which could be maintained to reflect ongoing changes to property lots and ownership. The initial step was to scan RPTA tax square maps from aperture cards at an effective paper resolution of 400 DPI. The resulting images were then georeferenced to DC's 0.2-meter resolution 1995 digital orthophotos. During the georeferencing process, the images were not warped; they were simply scaled and rotated to best fit the orthophotos. The DC tax assessor provided a database of active tax accounts which were placed interactively by an operator using the georeferenced square image and the orthophoto. Centroids were placed on the primary structure visible in the orthophoto within the raster property polygon. The placement was performed within ArcView 3.2 using a customized data production application. Accounts which could not be placed in the first pass were then reviewed by another operator to attempt to find their correct location. The placed points were QC'd through a spatial overlay with the square index to assure a match between the square field value within the property database and the actual square polygon into which the point was placed. Spot checking was then performed to confirm that the centroids fell within the correct raster lot. The centroids were delivered to OTR as a single citywide AutoCAD DWG file. Attribute features with square, suffix, and lot numbers (SSLs) were included as an AutoCAD block.