A georeferenced and tagged dataset of photographs is presented. Over 2,000 photographs from the Ebro Delta Natural Park, N-E Spain, have been treated. Raw data come from Wikiloc, a Volunteered Geographic Information source, and have been cleansed and systematized. The photographs have been classified according to their image content. An automatic first analysis was performed using 8-bit software. For uncertain tags, a second supervised analysis was performed. Classification into eight types and thirty-seven subtypes was conducted by considering landscape and social reactions. Data have been treated with the ArcGis 10.2. Geographic Information System. This dataset is useful for understanding ecosystem services by means of users’ photographs.

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 Pacific Time.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Rows included for Citywide case counts Rows are included for the Citywide case counts and incidence rate every day. These Citywide rows can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling bases.

Related dataset See the dataset of the most recent cumulative counts for all geographic areas here: https://data.sfgov.org/COVID-19/COVID-19-Cases-and-Deaths-Summarized-by-Geography/tpyr-dvnc

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by geography over time are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

Population Density : This vector dataset provides the population density by commune in Cambodia, as provided by Cambodian Demographic Census 2008 (Ministry of Planning, National Institute of Statistics). Dataset were provided to Open Development Cambodia (ODC) in vector format by Save Cambodia's Wildlife's Atlas Working Group.Urban Density in Cambodia (2011) : This vector dataset provides the urban density in Cambodia, as given by the United Nations Population Fund (UNFPA). Dataset were provided to Open Development Cambodia (ODC) by Save Cambodia's Wildlife's Atlas Working Group.Population Projections for 2030 in Cambodia (2010) : This dataset provides projected population of 2030, projected annual growth rate in each province in Cambodia, given by National Institute of Statistics and the United Nations. Data were provided to Open Development Cambodia (ODC) in vector format by Save Cambodia's Wildlife's Atlas Working Group.River networks of Cambodia : Vector polyline data of river networks in Cambodia. Attributes include: name of river, name of basin, name of sub-basin, Strahler number.Canals in Cambodia (2008) : This dataset is included geographical locations of canals and types of canal such as earthen, levee and masonry. The data is released by Department of Geography of Ministry of Land Management, Urban Planning, and Construction of Cambodia, and then it is contributed by Office for the Coordination of Humanitarian Affairs (OCHA) and shared on Humanitarian Data Exchange (HDX). ODC's map and data team has collected the data from HDX website in Shapefile format and re-published it on ODC's website.Special economic zone in Cambodia (2006-2019) : This dataset describes the information of special economic zone (SEZ) in Cambodia from 2006 to 2019. The total number of 42 SEZ is recorded. The data was collected from many sources by ODC’s mappers such as the royal gazette of Cambodia's government, and reports of the governmental ministries in hard and soft copies of pdf format. Geographic data is encoded in the WGS 84, Zone 48 North coordinate reference system.Road and railway networks in Cambodia (2012- 2019) : Road networks are produced by Open Street Map. ODC's map and data team extracted the data in vector format. Moreover, the polyline data of railway​ given by Save Cambodia's Wildlife's Atlas Working Group in Cambodia for two statuses such as existing, proposed new lines in Cambodia.Forest cover in Cambodia (2015-2018) : This forest cover is extracted from the Forest Monitoring System (https://rlcms-servir.adpc.net/en/forest-monitor/) which is developed by SERVIR-Mekong and the Global Land Analysis and Discovery Lab (GLAD) from University of Maryland. The definition of forest for this dataset is the tree canopy greater than 10% with height more than 5 meters.Schools in flood-prone area 2013 (information 2012-2014) : This dataset is created by clipping between Cambodia flood-prone areas in 2013 dataset and Basic information of school dataset to identify schools are under the flood extend in 2013. The basic information of school contains the spatial location of school, the attribute information in 2014, and total enrollment in 2012.Basic map of Cambodia (2014) : These datasets contain three different types of administrative boundary levels: provincial, district and commune which were contributed by Office for the Coordination of Humanitarian Affairs (OCHA) to Humanitarian Data Exchange (HDX). The datasets were obtained from the Department of Geography of Ministry of Land Management, Urban Planning and Construction (MLMUPC) in 2008 and then unofficially updated in 2014 by referring to Sub-decrees on administrative modifications. Most Recent Changes: New province added (Tbong Khmum), with underlying districts and communes.Land cover in Cambodia (2012- 2016) : The land cover is extracted from the Regional Land Cover Monitoring System (https://rlcms-servir.adpc.net/en/landcover/) which is developed by SERVIR-Mekong. The primitives are calculated from remote sensing indices which were made from yearly Landsat surface reflectance composites. The training data were collected by combining field information with high-resolution satellite imagery.Cropland in Cambodia : This dataset contains information of cropland and location of croplands in Cambodia which was downloaded from World Food Programme GeoNode (WFPGeoNode) using data in 2013 from​ the Department of Land and Geography of the Ministry of Land Management, Urban Planning and Construction.Community Fisheries Map for Cambodia (2011) : This dataset provides 2011 geographic boundaries, size and the number of villages covered by each community fishery for which coordinates are available in Cambodia, as given by the Fisheries Administration. For those community fisheries sites without coordinates, locations are given as the center points of communes and metrics are taken from the Commune Database of 2011. Geographic data is encoded in the WGS 84 coordinate reference system. Data were provided to ODC in vector format by Save Cambodia's Wildlife's Atlas Working Group.Digital Elevation Model (DEM 12.5 m) in 2010 : This raster dataset provides the Digital Elevation Model in the world. Dataset were provided to ASF Data Search Vertex by EarthData. This dataset has high resolution terrain at 12.5 meter. Alaska Satellite Facility (ASF) : making remote-sensing data accessible. ASF operates the NASA archive of synthetic aperture radar (SAR) data from a variety of satellites and aircraft, providing these data and associated specialty support services to researchers in support of NASA’s Earth Science Data and Information System (ESDIS) project.Function Area : This dataset are produced by Open Street Map. The data extracted the data in vector format (point feature).Tourism area (Museum, Attraction) : This dataset are produced by Open Street Map. The data extracted the data in vector format (point feature).Entity : Royal Government of Cambodia, Ministry of Planning, National Institute of Statistics; Cambodian Demographic Census 2008. Phnom Penh, 2008; Save Cambodia's Wildlife; In Atlas of Cambodia: maps on socio-economic development and environment;Time period : 2006-2018Frequency of update : Always up-to-dateGeo-coverage() : NationalGeo-coverage: National() : Cambodia

Global prevalence of non-perennial rivers and streamsJune 2021prepared by Mathis L. Messager (mathis.messager@mail.mcgill.ca)Bernhard Lehner (bernhard.lehner@mcgill.ca)1. Overview and background 2. Repository content3. Data format and projection4. License and citations4.1 License agreement4.2 Citations and acknowledgements1. Overview and backgroundThis documentation describes the data produced for the research article: Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5In this study, we developed a statistical Random Forest model to produce the first reach-scale estimate of the global distribution of non-perennial rivers and streams. For this purpose, we linked quality-checked observed streamflow data from 5,615 gauging stations (on 4,428 perennial and 1,187 non-perennial reaches) with 113 candidate environmental predictors available globally. Predictors included variables describing climate, physiography, land cover, soil, geology, and groundwater as well as estimates of long-term naturalised (i.e., without anthropogenic water use in the form of abstractions or impoundments) mean monthly and mean annual flow (MAF), derived from a global hydrological model (WaterGAP 2.2; Müller Schmied et al. 2014). Following model training and validation, we predicted the probability of flow intermittence for all river reaches in the RiverATLAS database (Linke et al. 2019), a digital representation of the global river network at high spatial resolution.The data repository includes two datasets resulting from this study:1. a geometric network of the global river system where each river segment is associated with:i. 113 hydro-environmental predictors used in model development and predictions, andii. the probability and class of flow intermittence predicted by the model.2. point locations of the 5,516 gauging stations used in model training/testing, where each station is associated with a line segment representing a reach in the river network, and a set of metadata.These datasets have been generated with source code located at messamat.github.io/globalirmap/.Note that, although several attributes initially included in RiverATLAS version 1.0 have been updated for this study, the dataset provided here is not an established new version of RiverATLAS. 2. Repository contentThe data repository has the following structure (for usage, see section 3. Data Format and Projection; GIRES stands for Global Intermittent Rivers and Ephemeral Streams):— GIRES_v10_gdb.zip/ : file geodatabase in ESRI® geodatabase format containing two feature classes (zipped) |——— GIRES_v10_rivers : river network lines |——— GIRES_v10_stations : points with streamflow summary statistics and metadata— GIRES_v10_shp.zip/ : directory containing ten shapefiles (zipped) Same content as GIRES_v10_gdb.zip for users that cannot read ESRI geodatabases (tiled by region due to size limitations). |——— GIRES_v10_rivers_af.shp : Africa |——— GIRES_v10_rivers_ar.shp : North American Arctic |——— GIRES_v10_rivers_as.shp : Asia |——— GIRES_v10_rivers_au.shp : Australasia|——— GIRES_v10_rivers_eu.shp : Europe|——— GIRES_v10_rivers_gr.shp : Greenland|——— GIRES_v10_rivers_na.shp : North America|——— GIRES_v10_rivers_sa.shp : South America|——— GIRES_v10_rivers_si.shp : Siberia|——— GIRES_v10_stations.shp : points with streamflow summary statistics and metadata— Other_technical_documentations.zip/ : directory containing three documentation files (zipped)|——— HydroATLAS_TechDoc_v10.pdf : documentation for river network framework|——— RiverATLAS_Catalog_v10.pdf : documentation for river network hydro-environmental attributes|——— Readme_GSIM_part1.txt : documentation for gauging stations from the Global Streamflow Indices and Metadata (GSIM) archive— README_Technical_documentation_GIRES_v10.pdf : full documentation for this repository3. Data format and projectionThe geometric network (lines) and gauging stations (points) datasets are distributed both in ESRI® file geodatabase and shapefile formats. The file geodatabase contains all data and is the prime, recommended format. Shapefiles are provided as a copy for users that cannot read the geodatabase. Each shapefile consists of five main files (.dbf, .sbn, .sbx, .shp, .shx), and projection information is provided in an ASCII text file (.prj). The attribute table can be accessed as a stand-alone file in dBASE format (.dbf) which is included in the Shapefile format. These datasets are available electronically in compressed zip file format. To use the data files, the zip files must first be decompressed.All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326).4. License and citations4.1 License agreement This documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.4.2 Citations and acknowledgements.Citations and acknowledgements of this dataset should be made as follows:Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5 We kindly ask users to cite this study in any published material produced using it. If possible, online links to this repository (https://doi.org/10.6084/m9.figshare.14633022) should also be provided.

The Modern treaties (formerly known as the Post-1975 treaties) dataset contains geographic boundaries as well as basic attribute data representing signed agreements that were negotiated between Indigenous groups, the Government of Canada, and provincial and territorial governments after 1975. These boundaries represent the areas of Canada where Indigenous land rights and title have not been addressed by treaty or through other legal means. These boundaries represent the final result of a negotiated First Nation’s claimed area. These boundaries are usually not surveyed but help to delineate the geographic extent of the rights of Indigenous beneficiaries defined within the agreement. The Modern treaties dataset includes: 1) Overall Treaty Area (OTA) which is the broad area to which the agreement applies, often composed of the sum of the specific geographies defined within the treaty. 2) Treaty Settlement Lands that is wholly Indigenous-owned and forms part of the “land and cash” settlements that are integral to the treaty. This category belongs to the Department of Natural Resources Canada and can be found on the Open data website as ‘‘Aboriginal Lands’’ classified as ‘‘Land Claim’’. 3) Treaty-Specified Rights Areas which category pertains to areas (lands or waters) where specific rights, activities or responsibilities apply pursuant to the treaty. 4) Other Treaty-related Geography is the catch-all category for any remaining geographies that do not fit within the aforementioned categories but do form part of the OTA, such as lands or marine areas designated for conservation or for specific projects. For more information about Modern treaties, visit https://www.rcaanc-cirnac.gc.ca/eng/1100100028574/1529354437231#chp4. The Modern treaties dataset is one of multiple datasets representing treaties and agreements between the Crown and Indigenous peoples. The Crown-Indigenous treaties and agreements geospatial datasets represent the geographic boundaries of the solemn agreements between the Crown and Indigenous peoples that set out promises, obligations and benefits for parties. The following datasets are also available: 1) The Historic treaties (formerly known as the Pre-1975 treaties) dataset, which represents most signed treaties that were negotiated between Indigenous peoples and the Crown between 1725 and 1929. 2) The Indigenous agreements dataset, which represents established protocols in place for consultation processes, self-government agreements, and other signed agreements between Indigenous groups and the Crown which do not fall into the aforementioned categories. The Modern treaties dataset is Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC) and Indigenous Services Canada (ISC)’s primary source for Modern treaties geographic boundaries on maps. This dataset can also be viewed in the Aboriginal and Treaty Rights Information System (ATRIS). This web-based system provides access to information to inform governments, industry and other interested parties in determining their consultation obligations and in carrying out their consultation research. For more information, visit https://www.rcaanc-cirnac.gc.ca/eng/1100100014686/1609421785838.

Refer to the current geographies boundaries table for a list of all current geographies and recent updates.

This dataset is the definitive version of the annually released ward boundaries as at 1 January 2025, as defined by the territorial authorities and/or Local Government Commission and maintained by Stats NZ. This version contains 224 wards, excluding ‘area outside ward’.

Wards are defined under the Local Electoral Act 2001 and result from dividing a territorial authority for electoral purposes. Wards were originally set up within any territorial authority with a population of at least 20,000. The ward system was designed to allow for the recognition of communities within a territorial authority and to increase community involvement in the local government system.

Territorial authorities can now choose whether they would like to maintain electoral wards. As a result, the number of wards has steadily decreased since they were first created in 1989. Ward boundaries are reviewed in the year before the three-yearly local government elections.

Wards are defined at meshblock level, and do not coincide with the statistical area 1 (SA1), statistical area 2 (SA2), or statistical area 3 (SA3) geographies.

Numbering

Wards are numbered based on their corresponding territorial authority. Each ward has a unique five-digit number. The first three digits represent the territorial authority that the ward lies within. The following two digits are sequential and represent the number of wards within a territorial authority. For example, Westland District (057) has three wards, which are coded 05701, 05702, and 05703.

Some territorial authorities do not use wards. In the classification, these territorial authorities use ‘99’ for the last two digits of the ward code, and the descriptor “Area Outside Ward”.

​

High-definition version

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

​

Macrons

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

​

Digital data

Digital boundary data became freely available on 1 July 2007.

​

Further information

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

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

Contact: geography@stats.govt.nz

Key Table Information.Table Title.All Sectors: County Business Patterns, including ZIP Code Business Patterns, by Legal Form of Organization and Employment Size Class for the U.S., States, and Selected Geographies: 2023.Table ID.CBP2023.CB2300CBP.Survey/Program.Economic Surveys.Year.2023.Dataset.ECNSVY Business Patterns County Business Patterns.Source.U.S. Census Bureau, 2023 Economic Surveys, Business Patterns.Release Date.2025-06-26.Release Schedule.County Business Patterns (CBP) data, including ZIP Code Business Patterns (ZBP) data are released annually around the month of June. For more information about CBP data releases, see County Business Patterns Updates..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2023, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2017 North American Industry Classification System (NAICS). For more information, see County Business Patterns Methodology..Methodology.Data Items and Other Identifying Records.Number of establishmentsAnnual payroll ($1,000)First-quarter payroll ($1,000)Number of employees (during the pay period including March 12)Noise range for annual payroll, first-quarter payroll, and number of employees during the pay period including March 12Definitions of data items can be found in the table by clicking on the column header and selecting “Column Notes” or by accessing the County Business Patterns Glossary..Unit(s) of Observation.The units for CBP are employer establishments with paid employees extracted from the Business Register, Census Bureau's source of information on employer establishments. An establishment is a single physical location at which business is conducted or services or industrial operations are performed. An establishment is not necessarily equivalent to a company or enterprise, which may consist of one or more establishments. For more information, see County Business Patterns Methodology..Geography Coverage.The data are shown at the U.S., State, County, Metropolitan and Micropolitan Statistical Areas, Combined Statistical Area, 5-digit ZIP code, and Congressional District levels. Also available are data for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) at the state and county equivalent levels.Four additional employment-size classes (1,000 to 1,499 employees, 1,500 to 2,499 employees, 2,500 to 4,999 employees, and 5,000 or more employees) are available at the CSA, MSA, and county-levels.For information about geographic classification, see Program Methodology..Industry Coverage.The data are shown at the 2- through 6-digit NAICS code levels for all sectors with published data, and for NAICS code 00 (Total for all sectors).ZBP data by employment size class, shown at the 2- through 6-digit NAICS code levels, only contains data on the number of establishments. ZBP data shown for NAICS code 00 (Total across all sectors) contains data on the number of establishments, total employment, first quarter payroll, and annual payroll.For information about industry coverage, see Program Methodology..Business Characteristics.Data are classified by Legal Form of Organization (U.S. and state level only) and employment size category of the establishment (1,000 to 1,499 employees, 1,500 to 2,499 employees, 2,500 to 4,999 employees, and 5,000 or more employees). Definitions of data items can be found in the table by clicking on the column header and selecting “Column Notes” or by accessing the County Business Patterns Glossary..Sampling.There is no sampling done for County Business Patterns. CBP data are derived from a complete tabulation of all establishments on the Census Bureau’s Business Register that meet the in-scope criteria for being included in CBP. For more information about methodology and data limitations, see County Business Patterns Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7503949, Disclosure Review Board (DRB) approval number: CBDRB-FY25-0158). Beginning with reference year 2007, CBP and ZBP data are released using the Noise Infusion disclosure avoidance methodology to protect confidentiality. To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. In accordance with U.S. Code, Title 13, Section 9, no data are published that would disclose the operations of an individual employer. For more information on the coverage, disclosure avoidance, and methodology of the CBP and ZBP data products see Program Methodology..Technical Documentation/Methodology.For detailed information see, Program Methodology..Weigh...

This dataset represents a compilation of data from various government agencies throughout the City of New York. The underlying geography is derived from the Tax Lot Polygon feature class that is part of the Department of Finance's Digital Tax Map (DTM). The tax lots have been clipped to the shoreline, as defined by NYCMap planimetric features. The attribute information is from the Department of City Planning's PLUTO data. The attribute data pertains to tax lot and building characteristics and geographic, political and administrative information for each tax lot in New York City.The Tax Lot Polygon feature class and PLUTO are derived from different sources. As a result, some PLUTO records do not have a corresponding tax lot in the Tax Lot polygon feature class at the time of release. These records are included in a separate non-geographic PLUTO Only table. There are a number of reasons why there can be a tax lot in PLUTO that does not match the DTM; the most common reason is that the various source files are maintained by different departments and divisions with varying update cycles and criteria for adding and removing records. The attribute definitions for the PLUTO Only table are the same as those for MapPLUTO. DCP Mapping Lots includes some features that are not on the tax maps. They have been added by DCP for cartographic purposes. They include street center 'malls', traffic islands and some built streets through parks. These features have very few associated attributes.To report problems, please open a GitHub issue or email DCPOpendata@planning.nyc.gov.DATES OF INPUT DATASETS:Department of City Planning - E-Designations: 2/5/2021Department of City Planning - Zoning Map Index: 7/31/2019Department of City Planning - NYC City Owned and Leased Properties: 11/15/2020Department of City Planning - NYC GIS Zoning Features: 2/5/2021Department of City Planning - Polictical and Administrative Districts: 11/17/2020Department of City Planning - Geosupport version 20D: 11/17/2020Department of Finance - Digital Tax Map: 1/30/2021Department of Finance - Mass Appraisal System (CAMA): 2/10/2021Department of Finance - Property Tax System (PTS): 2/6/2021Landmarks Preservation Commission - Historic Districts: 2/4/2021Landmarks Preservation Commission - Individual Landmarks: 2/4/2021Department of Information Telecommunications & Technology - Building Footprints: 2/10/2021Department of Parks and Recreation - GreenThumb Garden Info: 1/4/2021