Maintaining accurate data is a concern of all GIS users. The geodatabase offers you the ability to create geographic features that represent the real world. As the real world changes, you must update these features and their attributes. When creating or updating data, you can add behavior to your features and other objects to minimize the potential for errors.After completing this course, you will be able to:Define the two types of attribute domains and discuss how they differ.Create attribute domains and use them when editing data.Create subtypes and use them when editing data.Explain the difference between an attribute domain and a subtype.

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 script will prompt the user for a path to a file geodatabase or a sde geodatabase connection file. Then the script will loop through the feature classes\tables and document details about the attribute rules. All of the data gathered is written to a csv file. This is a Jupyter Notebook written using arcpy.Sources used to develop this notebook:Iterate through SDE to find and export FCs with Attribute Rules with python?Attribute Rule propertiesA Python script to Automate Attribute Rules Deployment

The data release for the geologic and structure maps of the Wallace 1 x 2 degrees quadrangle, Montana and Idaho, is a Geologic Map Schema (GeMS)-compliant version that updates the GIS files for the geologic map published in U.S. Geological Survey (USGS) Miscellaneous Investigations Series Map I-1509-A (Harrison and others, 2000). The updated digital data present the attribute tables and geospatial features (points, lines and polygons) in the format that meets GeMS requirements. This data release presents the geologic map as shown on the plates and captured in geospatial data for the published map. Minor errors, such as mistakes in line decoration or differences between the digital data and the map image, are corrected in this version. The database represents the geology for the 16,754 square kilometer, geologically complex Wallace quadrangle in northern Idaho and western Montana, at a publication scale of 1:250,000. The map covers primarily Lake, Mineral, Sanders and Shoshone Counties, but also includes minor parts of Flathead, Lincoln, and Missoula Counties. These GIS data supersede those in the interpretive report: Harrison, J.E., Griggs, A.B., Wells, J.D., Kelley, W.N., Derkey, P.D., and EROS Data Center, 2000, Geologic and structure maps of the Wallace 1- x 2- degree quadrangle, Montana and Idaho: a digital database: U.S. Geological Survey Miscellaneous Investigations Series Map I-1509-A, https://pubs.usgs.gov/imap/i1509a/.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

Xverum’s Point of Interest (POI) Data is a comprehensive dataset containing 230M+ verified locations across 5000 business categories. Our dataset delivers structured geographic data, business attributes, location intelligence, and mapping insights, making it an essential tool for GIS applications, market research, urban planning, and competitive analysis.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

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Extensive POI Coverage: ✅ 230M+ Points of Interest worldwide, covering 5000 business categories. ✅ Includes retail stores, restaurants, corporate offices, landmarks, and service providers.

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Rich Business Insights: ✅ Detailed business attributes, including company name, category, and subcategories. ✅ Contact details, including phone number and website (if available). ✅ Consumer review insights, including rating distribution and total number of reviews (additional feature). ✅ Operating hours where available.

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Esri's ArcGIS Online tools provide three methods of filtering larger datasets using attribute or geospatial information that are a part of each individual dataset. These instructions provide a basic overview of the step a GeoHub end user can take to filter out unnecessary data or to specifically hone in a particular location to find data related to this location and download the specific information filtered through the search bar, as seen on the map or using the attribute filters in the Data tab.

Designates boundaries to establish extent of livestock distribution and management within pastures. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

These tables list all GIS attributes associated with both the land cover and urban tree canopy results, along with attribute definitions, and associated raster values from the input raster datasets.

Building polygons were created in February 2013 by Geoscience Australia by manually digitising the outline of each building off the 2011 orthophotography. Digitisation was done from scratch off the 2011 orthophotography within Quantum GIS. Using the ArcMap 'zonal statistics' tool the minimum, mean and maximum heights were found for each building polygon from the 2011 digital elevation model and the 2011 digital surface model (DSM). This information was then joined to the building polygon attribute table. To find the building height from ground to roof, the difference between the Mean DSM and mean DEM was calculated and added as a field to the attribute table. To find the maximum height of each building the difference between the Maximum DSM and Mean DEM was calculated. Polygon area, perimeter, and x and y coordinates of each building were also attached as attributes. Accuracy is high as the layer was based on the 2011 orthophotography. Error may have been introduced through the digitisation process. Building lean in the orthophotography may also contribute to polygons which are slightly inaccurately placed. Height attribute accuracy is inaccurate for building polygons which have tree cover above them, as the tree elevation would influence the digital surface model. Particularly the Max_height field may include tree heights rather than building heights in some cases. Attribute accuracy could be improved by using the raw 2011 lidar data (.las files) which are classified at 'buildings' to attach heights. This method was tested and was extremely time consuming - only the height_max field was significantly improved. Disclaimer

UPDATED As of Sept 13, 2022 we have changed some of our attributes. The most significant changes involve fields that began with Primary Tax Payer and Alternate Tax Payer now start with Owner and Tax Payer, respectively.Attributed Parcel Polygons have boundaries that represent property descriptions (legal descriptions) and land ownership. The attribute fields are based on ones provided in Ramsey County Users Group shapefiles, ParPts and OnLineCore, and MetroGIS Parcel Datasets. These fields are populated from a compilation of records and information from various state, county and city offices, and other sources. This data is joined to Ramsey County's Parcel data using a property identification number (PIN) which is assigned to both polygons and points and is the primary link to county tax and assessment data. In 1986, parcels were digitized from hand drawn 1:2400 half and 1:1200 quarter section maps. In 1995, the parcel data layer was converted to ArcINFO format and held in the ArcStorm data base. Parcels were converted in 2005 to an ESRI ArcGIS GeoDatabase. In 2013, Attributes, which were previously joined only to Parcel Points, were added to Parcel Polygons. Parcel Points are used to represent the one-to-many relationship that Common Interest Communities (CIC), Apartment Ownerships or Condominiums, as well as Manufactured Homes and Apartment Units have with the Parcel Polygons. Polygons associated with CICs, Apartment Ownerships and Condominiums use the smallest Parcel Point PIN as their PIN and have attribute data limited information that is not unit specific (e.g., tax payer name and address information, tax and estimated market values are omitted). There are a few cases where polygons exist but there is no associated point, these include common areas associated with a CIC, parks or water; no official PIN exists for these polygons and there is no attribute data.

The data release for geologic maps of Ravalli Group and other Mesoproterozoic Belt Supergroup strata in northern Idaho and northwestern Montana is a digital, Geologic Map Schema (GeMS)-compliant version of maps published in U.S. Geological Survey (USGS) Open-File Report 2001-438 (Boleneus and others, 2001). The new digital data include attribute tables and geospatial features (points, lines, and polygons) in the format that meets GeMS requirements. This data release presents the geologic maps as shown on the plates and captured in geospatial data for the published maps. The database represents the geology for the 2.7 million acre, geologically complex study area in eleven plates at a publication scale of 1:48,000, and two plates at a publication scale of 1:12,000. The maps cover primarily Sanders, Shoshone, Kootenai, and Lincoln Counties, but also include minor parts of Benewah and Bonner Counties. Geologic mapping was undertaken between 1979 and 1984 by ASARCO Inc. as part of the ...

The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.

The Lake County Data Extract provides parcel characteristics and tax information for all properties located in Lake County, Illinois. Casual users can find the standalone Tax Parcel Boundary Data above and Parcel Attribute Data here. The information contained in the extract is "as is", and Lake County does not in any way guarantee the correctness or accuracy of the information contained therein.

This dataset is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information.

This dataset consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Note: This metadata record was created by MnGeo to serve as a generic record for all SSURGO data sets within Minnesota. See the individual county metadata records created by NRCS for county-specific information; these records are included in the data set download files.

This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate systems from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. (See section 5 of the metadata). The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties. Summary attribute information is in the Attributes Overview. Detailed information about the attributes can be found in the MetroGIS Regional Parcels Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = http://www.hennepin.us/gisopendata
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

The GIS of Mexican States, Municipalities and Islands consists of attribute and boundary data for 1990. The attribute data include population, language, education, literacy, housing Units and land cover classification from the 1990 Mexican population and housing census. The boundary data associated with the United States-Mexico border are consistent with the U.S. Census Bureau TIGER95 data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Road's data is maintained as a line layer for representing the centerline of a real-world roadway. This dataset is referred to as the RoadCenterLine layer in the GIS Data Layers Registry in NENA-STA-010 [3] and in NENA documents going forward. GIS road centerline arc-node topology is associated with attribute data containing information on street names, address ranges, jurisdictional boundaries, and other attributes.

The multiple attribute mapping process provides a vector based inventory of the landscape in terms of slope, terrain, landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey, soil erosion condition, and rockiness. Mass movement and soil conservation measures are mapped where they exist, as is a selected range of weed species. These characteristics of the land are part of the larger set of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. This set of codes are termed the Standard Classification for Attributes of Land (SCALD). The value of the attribute mapping is that the data objectively characterises the land and can be used for a range of land uses and land management purposes. This system of mapping maximises the efficiency of GIS operation by describing a number of attributes into one polygon, avoiding problems caused by overlaying of different data sets. Mapping is carried out at 1:25000 scale using base maps from the NSW Land Information Centre medium scale topographic series. Outputs are most useful at the sub-catchment or regional scale but not at property level. The data are extremely valuable at the river basin scale for integrated catchment planning programmes The information can, however, be useful as a first level of information in property planning exercises.