The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. Read more about previewing data. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Have a look at our walk-through of how to make a chart in the catalogue. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and

Coordinate system Update:Notably, this dataset will be provided in NAD 83 Connecticut State Plane (2011) (EPSG 2234) projection, instead of WGS 1984 Web Mercator Auxiliary Sphere (EPSG 3857) which is the coordinate system of the 2023 dataset and will remain in Connecticut State Plane moving forward.Ownership Suppression and Data Access:The updated dataset now includes parcel data for all towns across the state, with some towns featuring fully suppressed ownership information. In these instances, the owner’s name will be replaced with the label "Current Owner," the co-owner’s name will be listed as "Current Co-Owner," and the mailing address will appear as the property address itself. For towns with suppressed ownership data, users should be aware that there was no "Suppression" field in the submission to verify specific details. This measure was implemented this year to help verify compliance with Suppression.New Data Fields:The new dataset introduces the "Land Acres" field, which will display the total acreage for each parcel. This additional field allows for more detailed analysis and better supports planning, zoning, and property valuation tasks. An important new addition is the FIPS code field, which provides the Federal Information Processing Standards (FIPS) code for each parcel’s corresponding block. This allows users to easily identify which block the parcel is in.Updated Service URL:The new parcel service URL includes all the updates mentioned above, such as the improved coordinate system, new data fields, and additional geospatial information. Users are strongly encouraged to transition to the new service as soon as possible to ensure that their workflows remain uninterrupted. The URL for this service will remain persistent moving forward. Once you have transitioned to the new service, the URL will remain constant, ensuring long term stability.For a limited time, the old service will continue to be available, but it will eventually be retired. Users should plan to switch to the new service well before this cutoff to avoid any disruptions in data access.The dataset has combined the Parcels and Computer-Assisted Mass Appraisal (CAMA) data for 2024 into a single dataset. This dataset is designed to make it easier for stakeholders and the GIS community to use and access the information as a geospatial dataset. Included in this dataset are geometries for all 169 municipalities and attribution from the CAMA data for all but one municipality. Pursuant to Section 7-100l of the Connecticut General Statutes, each municipality is required to transmit a digital parcel file and an accompanying assessor’s database file (known as a CAMA report), to its respective regional council of governments (COG) by May 1 annually. These data were gathered from the CT municipalities by the COGs and then submitted to CT OPM. This dataset was created on 10/31/2024 from data collected in 2023-2024. Data was processed using Python scripts and ArcGIS Pro, ensuring standardization and integration of the data.CAMA Notes:The CAMA underwent several steps to standardize and consolidate the information. Python scripts were used to concatenate fields and create a unique identifier for each entry. The resulting dataset contains 1,353,595 entries and information on property assessments and other relevant attributes.CAMA was provided by the towns.Spatial Data Notes:Data processing involved merging the parcels from different municipalities using ArcGIS Pro and Python. The resulting dataset contains 1,290,196 parcels.No alteration has been made to the spatial geometry of the data.Fields that are associated with CAMA data were provided by towns.The data fields that have information from the CAMA were sourced from the towns’ CAMA data.If no field for the parcels was provided for linking back to the CAMA by the town a new field within the original data was selected if it had a match rate above 50%, that joined back to the CAMA.Linking fields were renamed to "Link".All linking fields had a census town code added to the beginning of the value to create a unique identifier per town.Any field that was not town name, Location, Editor, Edit Date, or a field associated back to the CAMA, was not used in the creation of this Dataset.Only the fields related to town name, location, editor, edit date, and link fields associated with the towns’ CAMA were included in the creation of this dataset. Any other field provided in the original data was deleted or not used.Field names for town (Muni, Municipality) were renamed to "Town Name".The attributes included in the data: Town Name OwnerCo-OwnerLinkEditorEdit DateCollection year – year the parcels were submittedLocationMailing AddressMailing CityMailing StateAssessed TotalAssessed LandAssessed BuildingPre-Year Assessed Total Appraised LandAppraised BuildingAppraised OutbuildingConditionModelValuationZoneState UseState Use DescriptionLand Acre Living AreaEffective AreaTotal roomsNumber of bedroomsNumber of BathsNumber of Half-BathsSale PriceSale DateQualifiedOccupancyPrior Sale PricePrior Sale DatePrior Book and PagePlanning RegionFIPS Code *Please note that not all parcels have a link to a CAMA entry.*If any discrepancies are discovered within the data, whether pertaining to geographical inaccuracies or attribute inaccuracy, please directly contact the respective municipalities to request any necessary amendmentsAdditional information about the specifics of data availability and compliance will be coming soon.If you need a WFS service for use in specific applications : Please Click Here

The dataset has combined the Parcels and Computer-Assisted Mass Appraisal (CAMA) data for 2023 into a single dataset. This dataset is designed to make it easier for stakeholders and the GIS community to use and access the information as a geospatial dataset. Included in this dataset are geometries for all 169 municipalities and attribution from the CAMA data for all but one municipality. Pursuant to Section 7-100l of the Connecticut General Statutes, each municipality is required to transmit a digital parcel file and an accompanying assessor’s database file (known as a CAMA report), to its respective regional council of governments (COG) by May 1 annually. These data were gathered from the CT municipalities by the COGs and then submitted to CT OPM. This dataset was created on 12/08/2023 from data collected in 2022-2023. Data was processed using Python scripts and ArcGIS Pro, ensuring standardization and integration of the data.CAMA Notes:The CAMA underwent several steps to standardize and consolidate the information. Python scripts were used to concatenate fields and create a unique identifier for each entry. The resulting dataset contains 1,353,595 entries and information on property assessments and other relevant attributes.CAMA was provided by the towns.Canaan parcels are viewable, but no additional information is available since no CAMA data was submitted.Spatial Data Notes:Data processing involved merging the parcels from different municipalities using ArcGIS Pro and Python. The resulting dataset contains 1,247,506 parcels.No alteration has been made to the spatial geometry of the data.Fields that are associated with CAMA data were provided by towns.The data fields that have information from the CAMA were sourced from the towns’ CAMA data.If no field for the parcels was provided for linking back to the CAMA by the town a new field within the original data was selected if it had a match rate above 50%, that joined back to the CAMA.Linking fields were renamed to "Link".All linking fields had a census town code added to the beginning of the value to create a unique identifier per town.Any field that was not town name, Location, Editor, Edit Date, or a field associated back to the CAMA, was not used in the creation of this Dataset.Only the fields related to town name, location, editor, edit date, and link fields associated with the towns’ CAMA were included in the creation of this dataset. Any other field provided in the original data was deleted or not used.Field names for town (Muni, Municipality) were renamed to "Town Name".The attributes included in the data: Town Name OwnerCo-OwnerLinkEditorEdit DateCollection year – year the parcels were submittedLocationMailing AddressMailing CityMailing StateAssessed TotalAssessed LandAssessed BuildingPre-Year Assessed Total Appraised LandAppraised BuildingAppraised OutbuildingConditionModelValuationZoneState UseState Use DescriptionLiving AreaEffective AreaTotal roomsNumber of bedroomsNumber of BathsNumber of Half-BathsSale PriceSale DateQualifiedOccupancyPrior Sale PricePrior Sale DatePrior Book and PagePlanning Region*Please note that not all parcels have a link to a CAMA entry.*If any discrepancies are discovered within the data, whether pertaining to geographical inaccuracies or attribute inaccuracy, please directly contact the respective municipalities to request any necessary amendmentsAs of 2/15/2023 - Occupancy, State Use, State Use Description, and Mailing State added to datasetAdditional information about the specifics of data availability and compliance will be coming soon.

CAMA_2004_HARF File Geodatabase Feature Class Thumbnail Not Available Tags Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, CAMA, Database, Structure, Appraisal Summary Detailed structural information for parcels. Description The CAMA (Computer Assisted Mass Appraisal) Database is created on a yearly basis using data obtained from the State Department of Assessments and Taxation (SDAT). Each yearly download contains additional residential housing characteristics as available for parcels included in the CAMA Database and the CAMA supplementary databases for each jurisdiction.. Documentation for CAMA, including thorough definitions for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the CAMA Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the CAMA Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the CAMA Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the CAMA Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 194 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." Credits Maryland Department of Planning Use limitations BES use only. Extent West -76.568860 East -76.081594 North 39.726323 South 39.392952 Scale Range There is no scale range for this item.

AT_2004_HOWA File Geodatabase Feature Class Thumbnail Not Available Tags Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, A&T, Database, Assessors, Taxation Summary Serves as a basis for performing various analyses based on parcel data. Description Assessments & Taxation (A&T) Database from MD Property View 2004 for Howard County. The A&T Database contains parcel data from the State Department of Assessments and Taxation; it incorporates parcel ownership and address information, parcel valuation information and basic information about the land and structure(s) associated with a given parcel. These data form the basis for the 2004 Database, which also includes selected Computer Assisted Mass Appraisal (CAMA) characteristics, text descriptions to make parcel code field data more readily accessible and logical True/False fields which identify parcels with certain characteristics. Documentation for A&T, including a thorough definition for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the A&T Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the A&T Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the A&T Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the A&T Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 1160 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." Credits Maryland Department of Planning Use limitations BES use only. Extent West -77.186932 East -76.699458 North 39.373967 South 39.099693 Scale Range There is no scale range for this item.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

CAMA_2004_ANNE File Geodatabase Feature Class Thumbnail Not Available Tags Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, CAMA, Database, Structure, Appraisal Summary Detailed structural information for parcels. Description The CAMA (Computer Assisted Mass Appraisal) Database is created on a yearly basis using data obtained from the State Department of Assessments and Taxation (SDAT). Each yearly download contains additional residential housing characteristics as available for parcels included in the CAMA Database and the CAMA supplementary databases for each jurisdiction.. Documentation for CAMA, including thorough definitions for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the CAMA Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the CAMA Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the CAMA Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the CAMA Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 236 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." Credits Maryland Department of Planning Use limitations BES use only. Extent West -76.838483 East -76.395283 North 39.238726 South 38.708591 Scale Range There is no scale range for this item. You are currently using the Item Description metadata style. Change your metadata

description: This dataset contains point features representing the approximate location of tax parcels contained in County Assessor tax rolls. Individual county data was integrated into this statewide publication by the Arkansas Geographic Information Office (AGIO). The Computer Aided Mass Appraisal (CAMA) systems maintained in each county are used to populate the database attributes for each centroid feature. The entity attribute structure conforms to the Arkansas Cadastral Mapping Standard. The digital cadastral data is provided as a publication version that only represents a snapshot of the production data at the time it was received from the county. Published updates may be made to counties throughout the year. These will occur after new data is digitized or updates to existing data are finished. Production versions of the data exist in the various counties where daily and weekly updates occur. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This column reflects the date when AGIO received the data from the county. Only parcels with an associated Computer Assisted Mass Appraisal (CAMA) record are provided. This means a CAMA record may exist, but no point geometry or vice-versa. Cadastral data is dynamic by its nature; therefore it is impossible for any county to ever be considered complete. The data is NOT topologically enforced. As a statewide integrator, AGIO publishes the data but does not make judgment calls about where points or polygon lines are meant to be located. Therefore each county data set is published without topology rules being enforced. GIS Technicians use best practices such as polygon closure and vertex snapping, however, topology is not built for each county. Users should be aware, by Arkansas Law (15-21-504 2 B) digital cadastral data does not represent legal property boundary descriptions, nor is it suitable for boundary determination of the individual parcels included in the cadastre. Users requiring a boundary determination should consult an Arkansas Registered Land Surveyor (http://www.arkansas.gov/pels/search/search.php) on boundary questions. The digital cadastral data is intended to be a graphical representation of the tax parcel only. Just because a county is listed does NOT imply the data represents county wide coverage. AGIO worked with each county to determine a level of production that warranted the data was ready to be published. For example, in some counties only the north part of the county was covered or in other cases only rural parcels are covered and yet in others only urban parcels. The approach is to begin incremental publishing as production blocks are ready, even though a county may not have county wide coverage. Each case represents a significant amount of data that will be useful immediately. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This date reflects when the data was received from the county. Digital cadastral data users should be aware the County Assessor Mapping Program adopted a phased approach for developing cadastral data. Phase One includes the production of a parcel centroid for each parcel that bears the attributes prescribed by the state cadastral mapping standard. Phase Two includes the production of parcel polygon geometry and bears the standard attributes. The Arkansas standard closely mirrors the federal Cadastral Core Data Standard established by the Federal Geographic Data Committee, Subcommittee for Cadastral Data. Counties within this file include: Arkansas, Ashley, Baxter, Boone, Carroll, Chicot, Clark, Clay, Columbia, Conway, Craighead, Crawford, Cross, Desha, Faulkner, Franklin, Hot Spring, Howard, Izard, Jackson, Jefferson, Lafayette, Lincoln, Little River, Logan, Lonoke, Madison, Mississippi, Montgomery, Nevada, Newton, Perry, Pike, Poinsett, Polk, Pope, Pulaski, Randolph, Saline, Sebastian, Stone, Van Buren, Washington and White.; abstract: This dataset contains point features representing the approximate location of tax parcels contained in County Assessor tax rolls. Individual county data was integrated into this statewide publication by the Arkansas Geographic Information Office (AGIO). The Computer Aided Mass Appraisal (CAMA) systems maintained in each county are used to populate the database attributes for each centroid feature. The entity attribute structure conforms to the Arkansas Cadastral Mapping Standard. The digital cadastral data is provided as a publication version that only represents a snapshot of the production data at the time it was received from the county. Published updates may be made to counties throughout the year. These will occur after new data is digitized or updates to existing data are finished. Production versions of the data exist in the various counties where daily and weekly updates occur. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This column reflects the date when AGIO received the data from the county. Only parcels with an associated Computer Assisted Mass Appraisal (CAMA) record are provided. This means a CAMA record may exist, but no point geometry or vice-versa. Cadastral data is dynamic by its nature; therefore it is impossible for any county to ever be considered complete. The data is NOT topologically enforced. As a statewide integrator, AGIO publishes the data but does not make judgment calls about where points or polygon lines are meant to be located. Therefore each county data set is published without topology rules being enforced. GIS Technicians use best practices such as polygon closure and vertex snapping, however, topology is not built for each county. Users should be aware, by Arkansas Law (15-21-504 2 B) digital cadastral data does not represent legal property boundary descriptions, nor is it suitable for boundary determination of the individual parcels included in the cadastre. Users requiring a boundary determination should consult an Arkansas Registered Land Surveyor (http://www.arkansas.gov/pels/search/search.php) on boundary questions. The digital cadastral data is intended to be a graphical representation of the tax parcel only. Just because a county is listed does NOT imply the data represents county wide coverage. AGIO worked with each county to determine a level of production that warranted the data was ready to be published. For example, in some counties only the north part of the county was covered or in other cases only rural parcels are covered and yet in others only urban parcels. The approach is to begin incremental publishing as production blocks are ready, even though a county may not have county wide coverage. Each case represents a significant amount of data that will be useful immediately. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This date reflects when the data was received from the county. Digital cadastral data users should be aware the County Assessor Mapping Program adopted a phased approach for developing cadastral data. Phase One includes the production of a parcel centroid for each parcel that bears the attributes prescribed by the state cadastral mapping standard. Phase Two includes the production of parcel polygon geometry and bears the standard attributes. The Arkansas standard closely mirrors the federal Cadastral Core Data Standard established by the Federal Geographic Data Committee, Subcommittee for Cadastral Data. Counties within this file include: Arkansas, Ashley, Baxter, Boone, Carroll, Chicot, Clark, Clay, Columbia, Conway, Craighead, Crawford, Cross, Desha, Faulkner, Franklin, Hot Spring, Howard, Izard, Jackson, Jefferson, Lafayette, Lincoln, Little River, Logan, Lonoke, Madison, Mississippi, Montgomery, Nevada, Newton, Perry, Pike, Poinsett, Polk, Pope, Pulaski, Randolph, Saline, Sebastian, Stone, Van Buren, Washington and White.

The Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format …Show full descriptionThe Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format is required by most, grid or raster, spatial modelling tools such as the Multi-criteria Analysis Shell for Spatial Decision Support (MCAS-S). Grid Garage contains 36 tools designed to save you time by batch processing repetitive GIS tasks as well diagnosing problems with data and capturing a record of processing step and any errors encountered. Grid Garage provides tools that function using a list based approach to batch processing where both inputs and outputs are specified in tables to enable selective batch processing and detailed result reporting. In many cases the tools simply extend the functionality of standard ArcGIS tools, providing some or all of the inputs required by these tools via the input table to enable batch processing on a 'per item' basis. This approach differs slightly from normal batch processing in ArcGIS, instead of manually selecting single items or a folder on which to apply a tool or model you provide a table listing target datasets. In summary the Grid Garage allows you to: List, describe and manage very large volumes of geodata. Batch process repetitive GIS tasks such as managing (renaming, describing etc.) or processing (clipping, resampling, reprojecting etc.) many geodata inputs such as time-series geodata derived from satellite imagery or climate models. Record any errors when batch processing and diagnose errors by interrogating the input geodata that failed. Develop your own models in ArcGIS ModelBuilder that allow you to automate any GIS workflow utilising one or more of the Grid Garage tools that can process an unlimited number of inputs. Automate the process of generating MCAS-S TIP metadata files for any number of input raster datasets. The Grid Garage is intended for use by anyone with an understanding of GIS principles and an intermediate to advanced level of GIS skills. Using the Grid Garage tools in ArcGIS ModelBuilder requires skills in the use of the ArcGIS ModelBuilder tool. Download Instructions: Create a new folder on your computer or network and then download and unzip the zip file from the GitHub Release page for each of the following items in the 'Data and Resources' section below. There is a folder in each zip file that contains all the files. See the Grid Garage User Guide for instructions on how to install and use the Grid Garage Toolbox with the sample data provided.

CAMA_2004_BACI File Geodatabase Feature Class Thumbnail Not Available Tags Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, CAMA, Database, Structure, Appraisal Summary Detailed structural information for parcels. Description The CAMA (Computer Assisted Mass Appraisal) Database is created on a yearly basis using data obtained from the State Department of Assessments and Taxation (SDAT). Each yearly download contains additional residential housing characteristics as available for parcels included in the CAMA Database and the CAMA supplementary databases for each jurisdiction.. Documentation for CAMA, including thorough definitions for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the CAMA Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the CAMA Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the CAMA Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the CAMA Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 235 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." Credits Maryland Department of Planning Use limitations BES use only. Extent West -76.713415 East -76.526101 North 39.374324 South 39.200707 Scale Range There is no scale range for this item.

Parcel data from Fulton County for the most recent tax year. This layer can be used in software programs and apps so only the data layer needs to be updated instead of each individual program or app. Fulton Update Date attribute is the Last Updated Date from the Fulton County GIS Open Data site. From Fulton:This dataset represents the boundary of each parcel of land in Fulton County recorded for the purpose of aiding in the appraisal of real property and the determination of property tax. A parcel dataset is created each year in association with that year's tax digest. The parcel dataset for any given year is not considered final until the completion of the digest, which generally occurs around mid-year. Until the completion of the digest, the parcel dataset is considered to be a work in progress. Any necessary corrections and omissions may continue to be made even after the completion of the digest. The parcel dataset in its published form incorporates information from the CAMA (computer-aided mass appraisal) database. The CAMA information included with the published dataset is selected based on its value to the typical consumer of the data and includes the parcel identification number, the property address, property owner, owner's mailing address, tax district, assessed and appraised value for land and improvements, the number of livable units, acreage, property class and land use class.

This dataset represents Lake Sediment Geochemical Analyses for the province of Saskatchewan.

During the intense level of activity directed toward the exploration for uranium in the 1970s, the Saskatchewan Geological Survey and the Geological Survey of Canada funded the collection of several thousand samples of sediments and waters from lakes around the Athabasca Sandstone. All sediment samples were analyzed for U, Cu, Ni, Pb, Zn, Co, Fe and Mn. Selected samples were analyzed for a wide range of additional elements. All lake waters were analyzed for U, F-, and pH, and several hundred samples were analyzed for additional elements and parameters. The Summary Table that precedes this text shows the numbers of samples and elements, and the source of data from which the 8,939 samples listed in the 9 Tables are derived. Over 20 years ago the data in these listings were coded into the Saskatchewan Geological Survey’s ‘Geochemical Data File’, designed in the 1970s (Dunn, 1978b, 1979), and developed by SaskComp (the computer programming department of the Saskatchewan government at that time). The only database listed in the present report that was not in the Geochemical Data File was GSC Open File #779, jointly produced by the SGS and GSC (Coker and Dunn, 1981, 1983) and containing data from detailed surveys of the IAEA/NEA Athabasca Test Area (adjacent to Wollaston Lake). The old Geochemical Data File was state-of-the-art at the time, and data have been available for public scrutiny since inception in 1977. Demonstrations of the File were given at the SGS Open House meetings in 1977 and 1978. The explosive development of personal computers during the past 20 years has made the original Geochemical Data File something of a dinosaur, and the data have been difficult to access and manipulate. The present data file is a compilation that has resulted from detailed evaluation, streamlining, editing and breakdown of the data into simplified Excel files that can easily be manipulated by anyone with a modest knowledge of computers. These data are of historic value and their re-evaluation could assist in current uranium exploration programs. Of particular value is their use in environmental studies, since they represent a 1970s snapshot of the chemistry of the northern Saskatchewan environment prior to mine developments. At the start of sample collection in 1975 Key Lake had not been drained and the only mine site was the pit at Rabbit Lake. This compilation has divided the data into 9 tables, each presented as a shape file. There are 6 shape files of lake sediment data (1LS - 6LS) and 3 shape files of lake water data (4LW - 6LW). Lake water samples were from the same sites as the lake sediments listed in files 4LS - 6LS, hence they have been given the same numeric designation. The data are mostly compatible among the Tables. However, although analytical methods and quality control protocols were similar, they were sufficiently different to warrant treating the data as separate listings. For any regional plotting of data extracted from all Tables these differences should be considered when interpreting distribution patterns. Of particular relevance is that all sediment samples were analyzed for U by neutron activation, with the exception of 158 samples (Table 2LS) where determinations were by fluorometry. These data sets should be fully compatible, because the two techniques provide similar values. Comparison of U data from sediment samples collected and analyzed over four years, then reanalyzed as one batch has shown excellent precision and accuracy (Coker and Dunn, 1981). All U in water determinations were by fluorometry, and all F- by selective ion electrode. Loss on ignition (LOI) data were determined by ignition at 500o C for 4 hours. Table 1LS This data set comprises samples collected by SGS between 1975 and 1978. Samples were digested in aqua regia and all trace elements, except U (see above), were determined by atomic absorption spectrometry (AA). **Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse. They are therefore identical and share the same refresh schedule.

The zip file contains a large tiff mosaic stitched together from a series of aerial photographs of the Calhoun CZO area taken in 1933, when the area was being acquired by the US Forest Service. USFS archaeologist Mike Harmon delivered the black-and-white photographs, known to him as the 'Sumter National Forest Purchase Aerials', to us in a box. The photographs include most of the Enoree District of the Sumter National Forest, including the entirety of the Calhoun CZO, not just the long-term plots and small watersheds. The photographs were scanned and georectified, then color-balanced and stitched together following 'seams' - high-contrast features such as rivers and roads ('seamlined'). In addition to the main tiff are four files that can be used to properly geolocate the composite image in ArcGIS.

The multilayer pdf file includes a smaller version of the seamlined 1933 aerial photography mosaic raster layer, as well as this aerial mosaic transparent over slope map (for a 3D-like 1933 image raster). Other layers include contours, roads, boundaries, sampling locations, 1.5 m DEM, 1.5m slope, 1m 2013 NAIP aerial imagery, and 2014 canopy height. The pdf file includes both 'interfluve order' and 'landshed order.' These two layers mean the same thing, but the landshed is the area unit around the interfluve that is used for statistics; this dataset has been QC'ed. The Interfluve Order network was used to delineate the landsheds and agrees with it >95% of the time, but has a few inaccuracies (it was automated by the computer) that were fixed manually. Use the network for viewing and considering the landscape at large, but for the specific interfluve order, check the color of the 'Landshed Order' dataset to verify its accuracy.

Date Range Comments: The exact date these photos were taken is unknown, but the year is thought to be 1933.The flight date is prior to the USFS land purchases for the Enoree District of the Sumter National Forest; the photos are thus known as the "pre-purchase photos").

Parcel data from Fulton County for the most recent tax year. This layer can be used in software programs and apps so only the data layer needs to be updated instead of each individual program or app. Fulton Update Date attribute is the Last Updated Date from the Fulton County GIS Open Data site. From Fulton:This dataset represents the boundary of each parcel of land in Fulton County recorded for the purpose of aiding in the appraisal of real property and the determination of property tax. A parcel dataset is created each year in association with that year's tax digest. The parcel dataset for any given year is not considered final until the completion of the digest, which generally occurs around mid-year. Until the completion of the digest, the parcel dataset is considered to be a work in progress. Any necessary corrections and omissions may continue to be made even after the completion of the digest. The parcel dataset in its published form incorporates information from the CAMA (computer-aided mass appraisal) database. The CAMA information included with the published dataset is selected based on its value to the typical consumer of the data and includes the parcel identification number, the property address, property owner, owner's mailing address, tax district, assessed and appraised value for land and improvements, the number of livable units, acreage, property class and land use class.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This is the authors’ version of the work. It is based on a poster presented at the Wageningen Conference on Applied Soil Science, http://www.wageningensoilmeeting.wur.nl/UK/ Cite as: Bosco, C., de Rigo, D., Dewitte, O., Montanarella, L., 2011. Towards the reproducibility in soil erosion modeling: a new Pan-European soil erosion map. Wageningen Conference on Applied Soil Science “Soil Science in a Changing World”, 18 - 22 September 2011, Wageningen, The Netherlands. Author’s version DOI:10.6084/m9.figshare.936872 arXiv:1402.3847

Towards the reproducibility in soil erosion modeling:a new Pan-European soil erosion map

Claudio Bosco ¹, Daniele de Rigo ¹ ² , Olivier Dewitte ¹, Luca Montanarella ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy

Soil erosion by water is a widespread phenomenon throughout Europe and has the potentiality, with his on-site and off-site effects, to affect water quality, food security and floods. Despite the implementation of numerous and different models for estimating soil erosion by water in Europe, there is still a lack of harmonization of assessment methodologies. Often, different approaches result in soil erosion rates significantly different. Even when the same model is applied to the same region the results may differ. This can be due to the way the model is implemented (i.e. with the selection of different algorithms when available) and/or to the use of datasets having different resolution or accuracy. Scientific computation is emerging as one of the central topic of the scientific method, for overcoming these problems there is thus the necessity to develop reproducible computational method where codes and data are available. The present study illustrates this approach. Using only public available datasets, we applied the Revised Universal Soil loss Equation (RUSLE) to locate the most sensitive areas to soil erosion by water in Europe. A significant effort was made for selecting the better simplified equations to be used when a strict application of the RUSLE model is not possible. In particular for the computation of the Rainfall Erosivity factor (R) the reproducible research paradigm was applied. The calculation of the R factor was implemented using public datasets and the GNU R language. An easily reproducible validation procedure based on measured precipitation time series was applied using MATLAB language. Designing the computational modelling architecture with the aim to ease as much as possible the future reuse of the model in analysing climate change scenarios is also a challenging goal of the research.

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AT_2004_ANNE File Geodatabase Feature Class Thumbnail Not Available Tags Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, A&T, Database, Assessors, Taxation Summary Serves as a basis for performing various analyses based on parcel data. Description Assessments & Taxation (A&T) Database from MD Property View 2004 for Anne Arundel County. The A&T Database contains parcel data from the State Department of Assessments and Taxation; it incorporates parcel ownership and address information, parcel valuation information and basic information about the land and structure(s) associated with a given parcel. These data form the basis for the 2004 Database, which also includes selected Computer Assisted Mass Appraisal (CAMA) characteristics, text descriptions to make parcel code field data more readily accessible and logical True/False fields which identify parcels with certain characteristics. Documentation for A&T, including a thorough definition for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the A&T Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the A&T Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the A&T Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the A&T Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 897 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." Credits Maryland Department of Planning Use limitations BES use only. Extent West -76.838738 East -76.395283 North 39.238726 South 38.708588 Scale Range There is no scale range for this item.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

AT_2004_BACO File Geodatabase Feature Class Thumbnail Not Available Tags Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, A&T, Database, Assessors, Taxation Summary Serves as a basis for performing various analyses based on parcel data. Description Assessments & Taxation (A&T) Database from MD Property View 2004 for Baltimore County. The A&T Database contains parcel data from the State Department of Assessments and Taxation; it incorporates parcel ownership and address information, parcel valuation information and basic information about the land and structure(s) associated with a given parcel. These data form the basis for the 2004 Database, which also includes selected Computer Assisted Mass Appraisal (CAMA) characteristics, text descriptions to make parcel code field data more readily accessible and logical True/False fields which identify parcels with certain characteristics. Documentation for A&T, including a thorough definition for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the A&T Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the A&T Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the A&T Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the A&T Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 5870 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." Credits Maryland Department of Planning Use limitations BES use only. Extent West -76.897802 East -76.335214 North 39.726520 South 39.192552 Scale Range There is no scale range for this item.