The Geodatabase to Shapefile Warning Tool examines feature classes in input file geodatabases for characteristics and data that would be lost or altered if it were transformed into a shapefile. Checks include:
1) large files (feature classes with more than 255 fields or over 2GB), 2) field names longer than 10 characters
string fields longer than 254 characters, 3) date fields with time values 4) NULL values, 5) BLOB, guid, global id, and raster field types, 6) attribute domains or subtypes, and 7) annotation or topology

The results of this inspection are written to a text file ("warning_report_[geodatabase_name]") in the directory where the geodatabase is located. A section at the top provides a list of feature classes and information about the geodatabase as a whole. The report has a section for each valid feature class that returned a warning, with a summary of possible warnings and then more details about issues found.

The tool can process multiple file geodatabases at once. A separate text file report will be created for each geodatabase. The toolbox was created using ArcGIS Pro 3.7.11.

For more information about this and other related tools, explore the Geospatial Data Curation toolkit

Urban Tree Canopy Assessment. This was created using the Urban Tree Canopy Syracuse 2010 (All Layers) file HERE.The data for this map was created using LIDAR and other spatial analysis tools to identify and measure tree canopy in the landscape. This was a collaboration between the US Forest Service Northern Research Station (USFS), the University of Vermont Spatial Laboratory, and SUNY ESF. Because the full map is too large to be viewed in ArcGIS Online, this has been reduced to a vector tile layer to allow it to be viewed online. To download and view the shapefiles and all of the layers, you can download the data HERE and view this in either ArcGIS Pro or QGIS.Data DictionaryDescription source  USDA Forest ServiceList of values  Value 1 Description Tree CanopyValue 2 Description Grass/ShrubValue 3 Description Bare SoilValue 4 Description WaterValue 5 Description BuildingsValue 6 Description Roads/RailroadsValue 7 Description Other PavedField Class Alias Class Data type String Width 20Geometric objects  Feature class name landcover_2010_syracusecity Object type  complex Object count 7ArcGIS Feature Class Properties Feature class name landcover_2010_syracusecity Feature type  Simple Geometry type Polygon Has topology FALSE Feature count 7 Spatial index TRUE Linear referencing  FALSEDistributionAvailable format  Name ShapefileTransfer options  Transfer size 163.805Description Downloadable DataFieldsDetails for object landcover_2010_syracusecityType Feature Class Row count  7 Definition  UTCField FIDAlias FID Data type OID Width  4 Precision 0 Scale 0Field descriptionInternal feature number.Description source ESRIDescription of valueSequential unique whole numbers that are automatically generated.Field ShapeAlias Shape Data type Geometry Width 0 Precision 0 Scale 0Field description Feature geometry.Description source  ESRIDescription of values Coordinates defining the features.Field CodeAlias Code Data type Number Width 4Overview Description  Metadata DetailsMetadata language  English Metadata character set utf8 - 8 bit UCS Transfer FormatScope of the data described by the metadata  dataset Scope name  datasetLast update 2011-06-02ArcGIS metadata properties Metadata format ArcGIS 1.0 Metadata style North American Profile of ISO19115 2003Created in ArcGIS for the item 2011-06-02 16:48:35 Last modified in ArcGIS for the item 2011-06-02 16:44:43Automatic updates Have been performed Yes Last update 2011-06-02 16:44:43Item location history  Item copied or moved 2011-06-02 16:48:35 From T:\TestSites\NY\Syracuse\Temp\landcover_2010_syracusecity To \T7500\F$\Export\LandCover_2010_SyracuseCity\landcover_2010_syracusecity

GIS data digitised from 2 DigitalGlobe images at a scale of 1:1000. The features were digitised using ArcGIS Pro and were created within a topology to ensure the spatial integrity of the data. Line data include coastlines, ice fronts and grounding lines. Polygon data include continent, island, ice tongue and rock features.

The images and data are of the Bølingen Islands and surrounding area, in the Prydz Bay region of Antarctica. (18FEB23042505-P2AS-017311657010_01_P001.TIL; 18FEB23042504-M2AS-017311657010_01_P001.TIL) (24MAR16035205-P2AS-017311660010_01_P001.TIL; 24MAR16035205-M2AS-017311660010_01_P001.TIL) Copyright 2024 DigitalGlobe Incorporated, Longmont CO USA 80503-6493

Urban Tree Canopy Assessment. Created using LIDAR and other spatial analysis tools to identify and measure tree canopy in the landscape. This was a collaboration between the US Forest Service Northern Research Station (USFS), the University of Vermont Spatial Laboratory, and SUNY ESF. Includes all layers, but is too large to be viewed in ArcGIS Online. To view all of the layers, you can download the data and view this in either ArcGIS Pro or QGIS.Data DictionaryDescription source  USDA Forest ServiceList of values  Value 1 Description Tree CanopyValue 2 Description Grass/ShrubValue 3 Description Bare SoilValue 4 Description WaterValue 5 Description BuildingsValue 6 Description Roads/RailroadsValue 7 Description Other PavedField Class Alias Class Data type String Width 20Geometric objects  Feature class name landcover_2010_syracusecity Object type  complex Object count 7ArcGIS Feature Class Properties Feature class name landcover_2010_syracusecity Feature type  Simple Geometry type Polygon Has topology FALSE Feature count 7 Spatial index TRUE Linear referencing  FALSEDistributionAvailable format  Name ShapefileTransfer options  Transfer size 163.805Description Downloadable DataFieldsDetails for object landcover_2010_syracusecityType Feature Class Row count  7 Definition  UTCField FIDAlias FID Data type OID Width  4 Precision 0 Scale 0Field descriptionInternal feature number.Description source ESRIDescription of valueSequential unique whole numbers that are automatically generated.Field ShapeAlias Shape Data type Geometry Width 0 Precision 0 Scale 0Field description Feature geometry.Description source  ESRIDescription of values Coordinates defining the features.Field CodeAlias Code Data type Number Width 4Overview Description  Metadata DetailsMetadata language  English Metadata character set utf8 - 8 bit UCS Transfer FormatScope of the data described by the metadata  dataset Scope name  datasetLast update 2011-06-02ArcGIS metadata properties Metadata format ArcGIS 1.0 Metadata style North American Profile of ISO19115 2003Created in ArcGIS for the item 2011-06-02 16:48:35 Last modified in ArcGIS for the item 2011-06-02 16:44:43Automatic updates Have been performed Yes Last update 2011-06-02 16:44:43Item location history  Item copied or moved 2011-06-02 16:48:35 From T:\TestSites\NY\Syracuse\Temp\landcover_2010_syracusecity To \T7500\F$\Export\LandCover_2010_SyracuseCity\landcover_2010_syracusecity

This data represents the graphic portrayal of land parcels and their spatial relationships throughout York County, South Carolina. Land parcel boundaries are also the basis for and define coincident boundaries for other layers, such as zoning, subdivisions, public safety response (ORI -Police, Fire, EMS) and Jurisdiction.Boundaries are established from a variety of sources including cadastral plats, subdivision plats, deeds, land contracts, right-of-way plats, and others. Each feature represents a parcel of land that is inventoried by a unique identifier, referred to as a “Tax Map Id” number. This dataset also includes multi-unit structures which have separate tax accounts for each unit, such as condominium units, represented as stacked polygon features. The parent parcel number [ParentTaxID] for the land parcel is distinguished from the child parcel [TaxMapID] for the condo unit. This data does not include mobile home data. Attributes include data stored within the Esri Fabric data model combined with those from the CAMA data. Examples of relevant attributes include:the [TaxMapID], [ParcelID] and [AprAccNum] can be used to uniquely identify each parcel. the [MailAddr1], [MailAddr2], [MailApt], [MailCity], [MailState], [MailZip] can be used as the full tax billing address for the owner.The [Owner1], [Owner2], [Owner3] describe the owner.the [YearBuilt] offers the oldest year a building was built on the property, reference this web map for info on potential lead pipes on premises;the area of the parcel in acres [GISSizeAC] as calculated from the parcel geometry and also the [deededAcres] from recorded documents, and ;the date that the parcel boundary was last edited [DATE_MODIFIED].How were parcels compiled? This layer was initially developed as an ink-on-mylar property maps maintained by the County from the early 1970's through around 2001.In the 1990s, the county procured services to convert parcels from source documents, however the product delivered in 2000 used a methodology which lost fidelity of source documents. Since then, county staff adhered to this same methodology in their daily work. Between 2001 and 2015 staff used an Esri topology to maintain parcel data in ArcMap. In 2015 the county migrated to Parcel Fabric (ArcMap) and then in 2021 to Pro (2.6/10.8.1 Enterprise) Parcel Fabric. In May of 2021 the county began outsourcing maintenance of parcel edits. This has worked well and was initiated in part to ensure a higher standard of editing practice was adhered to, but also to fulfil a shortage of skilled staff in the job market. County parcel mapping staff remain responsible for simple transactions (merge, split), compilation of materials to create vendor edit request task, and QC or review of vendor work. In Q4 2021, County Staff performed a needs assessment to review alignment issues between parcels and other layers and the internal business requirements for data alignment to parcels. They determined boundary layers must remain coincident with parcels, which are used in decision making by citizens and across many areas of government. Also, it was determined that our parcels had many errors from 20 years of edits in a non-Fabric data model and the previous editing practices. The county will be remapping parcels using ARP grant funding in the 2023-2024 timeframe. Upon delivery in 2024, data maintenance practices will ensure ongoing alignment with parcels.Year BuiltTo obtain the year built for structures on a property, use the 'Buildings' table available through our open data portal.Once you have downloaded the 'Buildings' table and this parcels layer, consider processing the building records in some way to join or perform a relate as there could be many buildings on one parcel, using the following fields:Parcel.AprAccNum = BuildingTable.PropertyID(Note: 98,227 parcels have 1 building, 647 parcels have 2 buildings, 272 have 3 or more)Data SchemaReview the Parcel schema document (PDF) to gain a better understand of the data fields. Access the file geodatabase source data in SC State Plane coordinate system

This dataset consists of topographic features across the East Antarctic coastal region, extending from 33°E to 168°E and from the coast inland to approximately 84°S in some areas.

The features were digitised using ArcGIS Pro and were created within a topology to ensure the spatial integrity of the data. Line data include coastlines, ice fronts and grounding lines. Polygon data include continent features, islands, ice shelfs, ice tongues, icebergs, rocks and lakes.

The features were digitised at a scale of 1:25,000 using Sentinel2 imagery: earthexplorer.usgs.gov, 'Copernicus Sentinel data [2023]'. Note: Individual Sentinel 2 data source images are referenced in the data attribute tables with the exception of the coastline polygon dataset which was derived from the coastline line dataset.

Grounding lines were derived from ASAID_Grounding_line_continent_Sc_dep : Rignot, E., J. Mouginot, and B. Scheuchl. 2016. MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://nsidc.org/data/nsidc-0498/versions/2

The ASAID data were edited using ICESat2 data: Derived Grounding Zone for Antarctic Ice Shelves, United States Antarctic Program Data Center (USAP-DC) www.usap-dc.org ; http://www.usap-dc.org/view/dataset/609469 as well as Sentinel2 imagery and The Reference Elevation Model of Antarctica version 2 (REMA 2): Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P., The Reference Elevation Model of Antarctica, The Cryosphere, 13, 665-674, https://doi.org/10.5194/tc-13-665-2019 , 2019 DEM(s) courtesy of the Polar Geospatial Center.

The Antarctic Iceberg Data were sourced from U.S.National Ice Centre (usicecenter.gov/Products/AntarcIcebergs) in CSV format. The CSV data used in this project is dated 3 Feb 2023. The point data were used to locate and digitise icebergs using Sentinel 2 imagery at a scale of 1:25000.

The 25K topographic features are stored in the Australian Antarctic Division Enterprise GIS and are available for download using the provided links.

This data represents a land use survey of San Joaquin County conducted by the California Department of Water Resources, North Central Region Office staff. Land use field boundaries were digitized with ArcGIS 10.5.1 using 2016 NAIP as the base, and Google Earth and Sentinel-2 imagery website were used as reference as well. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were not drawn to represent legal parcel (ownership) boundaries and are not meant to be used as parcel boundaries. The field work for this survey was conducted from July 2017 through August 2017. Images, land use boundaries and ESRI ArcMap software were loaded onto Surface Pro tablet PCs that were used as the field data collection tools. Staff took these Surface Pro tablet into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Agricultural fields the staff were unable to access were designated 'E' in the Class field for Entry Denied in accordance with the 2016 Land Use Legend. The areas designated with 'E' were also interpreted using a combination of Google Earth, Sentinel-2 Imagery website, Land IQ (LIQ) 2017 Delta Survey, and the county of San Joaquin 2017 Agriculture GIS feature class. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Water source information was not collected for this land use survey. Therefore, the water source has been designated as Unknown. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DRA's headquarters office under the leadership of Muffet Wilkerson, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors. The 2017 San Joaquin County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Regional Assistance (DRA). Land use boundaries were digitized, and land use was mapped by staff of DWR’s North Central Region using 2016 United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) one-meter resolution digital imagery, Sentinel-2 satellite imagery, and the Google Earth website. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DRA headquarters, and North Central Region. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses.

Analysis of ‘San Joaquin County Land Use Survey 2017’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/34320867-1a92-4422-98e2-4f68d26cff40 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---