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".

Abstract Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information about landslide occurrence across the entire U.S. This data release is an update of previous versions 1 (Jones and others, 2019) and 2 (Belair and others, 2022). Changes relative to version 2 are summarized in us_ls_v3_changes.txt. It provides an integrated database of the landslides from these inventories (refer to US_Landslide_v3_gpkg) with a selection of uniform attributes, including links to the original digital inventory files (whenever available) (“Inv_URL”). The data release includes digital inventories created by both USGS and non-USGS authors. The original inventory is denoted by an abbreviation in the “Inventory” attribute. The full citation for each abbreviation can be found in us_ls_v3_references.csv. The date of the landslide event is included as a minimum and maximum (“Date_Min” and “Date_Max”) to accommodate events that happen within a range of dates. The date value is inherently difficult to interpret or discern due to the nature of landsliding, where some landslides move for long periods of time or move intermittently, and some areas can exhibit multiple landslide events. To preserve the constituent inventories as much as possible, we include all entries even if they are not considered landslides, such as “gullies” or “avalanche chutes.” We include a landslide type attribute when that information is available (“LS_Type”). The landslide classification system used in the original inventories is not always known or stated in the metadata, but many mapping entities use the schema from Cruden and Varnes (1996) or the updated schema from Hungr and others (2014). Given the wide range of landslide information sources in this data compilation, we provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide (entry) (“Confidence”). The confidence level reflects the resolution and quality of input data, as well as the method used for identification and mapping. This confidence does not reflect a formal accuracy assessment of field attributes. Relative to the previous data releases (version 1 and 2), this update (v3) includes more inventories, updated confidence rules, a new landslide type attribute, a new unique identifier (“USGS_ID”), new machine-readable date fields, and an ancillary database containing all fields from the original inventories (refer to US_Landslide_v3_ancillary). Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort. When possible, please cite the constituent inventories as well as this data release. This data release includes: (1) a landslide point file and polygon file in multiple forms (US_Landslide_v3_gpkg, US_Landslide_v3_shp, US_Landslide_v3_csv), (2) an ancillary database with original fields (US_Landslide_v3_ancillary), (3) a spreadsheet that summarizes the confidence rules, their justification, and any extra analyses (us_ls_v3_analyses.csv), (4) a summary file of the changes made between version 2 and version 3 (us_ls_v3_changes.txt), (5) a file containing the references of the constituent inventories (us_ls_v3_references.csv), (6) and a readme (README.txt). Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Data fields Field Names Definitions USGS_ID Unique USGS identifier for each landslide entry. Date_Min Minimum possible date of landslide occurrence. If date is known to the day, Date_Min will have a value while Date_Max is empty. Time zone is assumed to be local, except for Inventories ‘USGS Earthquake-Triggered Ground Failure’ and ‘USGS Seismogenic Mass Movements’ which are in UTC. Date_Max Maximum possible date of landslide occurrence. If date is known to the day, Date_Max will be empty while Date_Min has a value. Time zone is assumed to be local, except for Inventories ‘USGS Earthquake-Triggered Ground Failure’ and ‘USGS Seismogenic Mass Movements’ which are in UTC. Fatalities Number of fatalities caused by landslide event. Confidence Confidence in landslide (entry) extent, nature, and location. LS_Type Landslide (entry) type. Classification schema of original inventories is often not specified. Inventory Name of original source inventory. Inv_URL URL or link to original source inventory. Info_Source Information source or sub-layer from original source inventory. Notes Unformatted notes field, includes additional information. Lat_N Latitude of point or polygon centroid in WGS 1984 Lon_W Longitude of point or polygon centroid in WGS 1984 Confidence attributes Confidence Definitions 1 Possible landslide (feature) in the area 2 Probable landslide (feature) in the area 3 Likely landslide (feature) at or near this location 5 Moderate confidence in extent or nature of landslide (feature) at this location 8 High confidence in extent or nature of landslide (feature) References Belair, G.M., Jones, E.S., Slaughter, S.L., and Mirus, B.B., 2022, Landslide Inventories across the United States version 2: U.S. Geological Survey data release, https://doi.org/10.5066/P9FZUX6N. Cruden, D.M. and Varnes, D.J., 1996, Landslide Types and Processes, in Turner, K.A. and Schuster R. L., eds., Landslides Investigation and Mitigation: Transportation Research Board, U.S. National Research Council Special Report 247, U.S. National Academy of Sciences, Chapter 3, p. 36-75. ESRI, 2023, ArcGIS Pro (Version 3.1.3), Redlands, CA: Environmental Systems Research Institute, Retrieved from https://www.esri.com/en-us/arcgis/products/arcgis-pro/resources. Hungr, O., Leroueil, S., and Picarelli, L., 2014, The Varnes classification of landslide types, an update, Landslides, 11(2), p. 167-194, https://doi.org/10.1007/s10346-013-0436-y. Jones, E.S., Mirus, B.B, Schmitt, R.G., Baum, R.L., Burns, W.J., Crawford, M., Godt, J.W., Kirschbaum, D.B., Lancaster, J.T., Lindsey, K.O., McCoy, K.E., Slaughter, S., and Stanley, T.A., 2019, Landslide Inventories across the United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9E2A37P. Python Software Foundation, 2023, Python Language Reference, version 3.9, Retrieved from http://www.python.org. QGIS.org, 2022, QGIS Geographic Information System (Version 3.28.4-Firenze), QGIS Association, Retrieved from http://www.qgis.org.

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

• Owner

• Co-Owner

• Link

• Editor

• Edit Date

• Collection year – year the parcels were submitted

• Location

• Mailing Address

• Mailing City

• Mailing State

• Assessed Total

• Assessed Land

• Assessed Building

• Pre-Year Assessed Total

• Appraised Land

• Appraised Building

• Appraised Outbuilding

• Condition

• <span

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. were derived from the NVC. NatureServe developed a preliminary list of potential vegetation types. These data were combined with existing plot data (Cully 2002) to derive an initial list of potential types. Additional data and information were gleaned from a field visit and incorporated into the final list of map units. Because of the park’s small size and the large amount of field data, the map units are equivalent to existing vegetation associations or local associations/descriptions (e.g., Prairie Dog Colony). In addition to vegetation type, vegetation structures were described using three attributes: height, coverage density, and coverage pattern. In addition to vegetation structure and context, a number of attributes for each polygon were stored in the associated table within the GIS database. Many of these attributes were derived from the photointerpretation; others were calculated or crosswalked from other classifications. Table 2.7.2 shows all of the attributes and their sources. Anderson Level 1 and 2 codes are also included (Anderson et al. 1976). These codes should allow for a more regional perspective on the vegetation types. Look-up tables for the names associated with the codes is included within the geodatabase and in Appendix D. The look-up tables contain all the NVC formation information as well as alliance names, unique IDs, and the ecological system codes (El_Code) for the associations. These El_Codes often represent a one-to-many relationship; that is, one association may be related to more than one ecological system. The NatureServe conservation status is included as a separate item. Finally, slope (degrees), aspect, and elevation were calculated for each polygon label point using a digital elevation model and an ArcView script. The slope figure will vary if one uses a TIN (triangulated irregular network) versus a GRID (grid-referenced information display) for the calculation (Jenness 2005). A grid was used for the slope figure in this dataset. Acres and hectares were calculated using XTools Pro for ArcGIS Desktop.

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: Soil Survey Geographic Database (SSURGO) DownloaderItem Type: Web Mapping Application URLSummary: Download ready-to-use project packages with over 170 attributes derived from the SSURGO (Soil Survey Geographic Database) dataset.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: https://nmcdc.maps.arcgis.com/home/item.html?id=cdc49bd63ea54dd2977f3f2853e07fff link to Esri web mapping applicationFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=305ef916da574a71877edb15c3f47f08#overviewUID: 26Data Requested: Ag CensusMethod of Acquisition: Esri web mapDate Acquired: 6/16/22Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDINGDOCUMENTATION FROM DATA SOURCE URL: This application provides quick access to ready-to-use project packages filled with useful soil data derived from the SSURGO dataset.To use this application, navigate to your study area and click the map. A pop-up window will open. Click download and the project package will be copied to your computer. Double click the downloaded package to open it in ArcGIS Pro. Alt + click on the layer in the table of contents to zoom to the subbasin.Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryThe map packages were created from the October 2021 SSURGO snapshot. The dataset covers the 48 contiguous United States plus Hawaii and portions of Alaska. Map packages are available for Puerto Rico and the US Virgin Islands. A project package for US Island Territories and associated states of the Pacific Ocean can be downloaded by clicking one of the included areas in the map. The Pacific Project Package includes: Guam, the Marshall Islands, the Northern Marianas Islands, Palau, the Federated States of Micronesia, and American Samoa.Not all areas within SSURGO have completed soil surveys and many attributes have areas with no data. The soil data in the packages is also available as a feature layer in the ArcGIS Living Atlas of the World.AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Map Unit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Map Unit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some map units have a null value for soil order, a

This layer presents a summary of burnable vegetation classes across all National Park Service units. An analysis was performed to summarize both the National Land Cover Dataset (2021 CONUS/2016 ALASKA) (NLCD) and Landfire FBFM40 (2020) datasets by each NPS unit. Geoprocessing Steps:NLCD and FBFM40 data were extracted to each NPS unit from the sources noted below. Following the raster extraction, the output was converted to a polygon feature class and intersected with the NPS Boundary Dataset to associate each landcover pixel associated NPS unit. Lastly, a new GIS Acres field was added to the dataset and calculated. Following geoprocessing within ArcGIS Pro, Safe Software FME Workbench was utilized to aggregate and summarize the raw processing data. All data was aggregated by NPS Unit, Landcover Type and Landcover Source. Final acres were calculated for each aggregation.Source:National Land Cover Database (NLCD) NLCD 2021 Land Cover (CONUS) - Downloaded here.NLCD 2016 Land Cover (ALASKA) - Downloaded here.Landfire 40 Scott and Burgan Fire Behavior Fuel Models (FBFM40)CONUS 2020 (LF2.2.0) - Downloaded here.ALASKA 2020 (LF2.2.0) - Downloaded here.Attributes:Unit Code: The unique identifier for the NPS Unit being summarized.NPS Region: The region in which the NPS unit is located.Vegetation Class: The type of vegetation class.FBFM40

Coded ValueDescription

-9999No Data NB1Urban/developed NB2Snow/ice NB3Agriculture NB8Water NB9Barren GR1Short, sparse, dry climate grass GR2Low load, dry climate grass GR3Low load, very coarse, humid climate grass GR4Moderate load, dry climate grass GR5Low load, humid climate grass GR6Moderate load, humid climate grass GR7High load, dry climate grass GR8High load, very coarse, humid climate grass GR9Very high load, humid climate grass GS1Low load, dry climate grass-shrub GS2Moderate load, dry climate grass-shrub GS3Moderate load, humid climate grass-shrub GS4High load, humid climate grass-shrub SH1Low load, dry climate shrub SH2Moderate load, dry climate shrub SH3Moderate load, humid climate shrub SH4Low load, humid climate timber-shrub SH5High load, humid climate grass-shrub SH6Low load, humid climate shrub SH7Very high load, dry climate shrub SH8High load, humid climate shrub SH9Very high load, humid climate shrub TU1Light load, dry climate timber-grass-shrub TU2Moderate load, humid climate timber-shrub TU3Moderate load, humid climate timber-grass-shrub TU4Dwarf conifer with understory TU5Very high load, dry climate timber-shrub TL1Low load, compact conifer litter TL2Low load, broadleaf litter TL3Moderate load, confider litter TL4Small downed logs TL5High load, confider litter TL6Moderate load, broadleaf litter TL7Large downed logs TL8Long-needle litter TL9Very high road, broadleaf litter SB1Low load, activity fuel SB2Moderate load, activity fuel or low load, blowdown SB3High load, activity fuel or moderate load, blowdown SB4High load, blowdown

Land Cover Data Source: The source of data, either NLCD or FBFM40.GIS Acres: Unit of measure calculated in acres using GIS.FMP Status: Indicates whether the NPS unit has an existing Fire Management Plan.

This data is utilized in the Lesson 1.1 What is Climate activity on the MI EnviroLearning Hub Climate Change page.Station data accessed was accessed from NOAA. Data was imported into ArcGIS Pro where Coordinate Table to Point was used to spatially enable the originating CSV. This feature service, which incorporates Census Designated Places from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics, was used to spatially join weather stations to the nearest incorporated area throughout Michigan.Email Egle-Maps@Michigan.gov for questions.Former name: MichiganStationswAvgs19912020_WithinIncoproatedArea_UpdatedName Display Name Field Name Description

STATION_ID MichiganStationswAvgs19912020_W Station ID where weather data is collected

STATION MichiganStationswAvgs19912020_1 Station name where weather data is collected

ELEVATION MichiganStationswAvgs19912020_6 Elevation above mean sea level-meters

MLY-PRCP-NORMAL MichiganStationswAvgs19912020_8 Long-term averages of monthly precipitation total-inches

MLY-TAVG-NORMAL MichiganStationswAvgs19912020_9 Long-term averages of monthly average temperature -F

OID MichiganStationswAvgs1991202_10 Object ID for weather dataset

Join_Count MichiganStationswAvgs1991202_11 Spatial join count of weather station data to specific weather station

TARGET_FID MichiganStationswAvgs1991202_12 Spatial Join ID

Current place ANSI code MichiganStationswAvgs1991202_13 Census codes for identification of geographic entities (used for join)

Geographic Identifier MichiganStationswAvgs1991202_14 Geographic identifier (used for join)

Current class code MichiganStationswAvgs1991202_15 Class (CLASSFP) code defines the current class of a geographic entity

Current functional status MichiganStationswAvgs1991202_16 Status of weather station

Area of Land (Square Meters) MichiganStationswAvgs1991202_17 Area of land in square meters

Area of Water (Square Meters) MichiganStationswAvgs1991202_18 Area of water in square meters

Current latitude of the internal point MichiganStationswAvgs1991202_19 Latitude

Current longitude of the internal point MichiganStationswAvgs1991202_20 Longitude

Name MichiganStationswAvgs1991202_21 Location name of weather station

Current consolidated city GNIS code MichiganStationswAvgs1991202_22 Geographic Names Information System for an incorporated area

OBJECTID MichiganStationswAvgs1991202_23 Object ID for point dataset

Named Landforms of the World v3 (NLWv3) is an update to Named Landforms of the World v2 (NLWv2), which will remain available as the compilation that best matches the work of E.M. Bridges and Richard E. Murphy. In NLWv3, we added attributes describing each landform's volcanism based on the Smithsonian Institution's Global Volcanism Program's data (GVP). We designed NLWv3 layers for two purposes:Label maps with broadly accepted names for physiographic features. Use the polygons as a basis to add fields (attributes) to observation data or other small features to facilitate rich and relevant descriptions that indicate how other features relate to named physiographic features. Three workflows are recommended: (1) For point features, Identity and then Join Field; (2) Zonal Statistics as Table and then Join Field, and when many such attributes are being produced, (3) when adding multiple different attributes, the recently added Zonal Characterization tool and then Join Field. While we gained ability to estimate the area of Earth's volcanic landforms, we also learned that volcanoes are relatively short-lived as landforms. The GVP provided two inventories, one for the Holocene Epoch, which is the most recent 11,700 years (since the last ice age), and for the Pleistocene Epoch, which precedes the Holocene, and lasted about 2.6 million years. There were only 7.8% more volcanoes included for the Pleistocene, even though the Pleistocene is 222 times longer. That means most older volcanoes have disappeared through natural erosional and depositional processes. In the NLWv3, we consider volcanic landforms as being one of many types of landforms, including calderas, clusters and complexes, shields, stratovolcanoes, or minor volcanic features such as lava domes and fissure vents. Not all of the GVP features, particularly fissure vents and remnants of calderas, are large enough to be mapped as polygons in the NLWv3. Similarly complexes and volcanic fields typically had greater areas and included many individual cinder cones and calderas. ContinentCount of Volcanic LandformsArea km2 of Volcanic Landforms (% of land area)Europe7822,888 (0.23%)Antarctica4234,035 (0.27%)Australia14757,422 (0.65%)South America37081,475 (0.46%)Small Volcanic Islands559124,310 (8.52%)Africa282147,116 (0.50%)Asia698227,486 (0.53%)North America622295,340 (1.23%)Global Totals2,7981,000,073 (0.67%)Overview of UpdatesCorresponding landform polygons were assigned attributes for the GVP's ID, name, province, and region. See details in the volcanic attributes section below. Additionally, an describing volcanism for each GVP feature was derived from these and several other GVP attributes to provide a reader-friendly characterization of each feature.Landforms of Antarctica. Given recent analysis of Antarctica and the GVP data it became possible to provide rudimentary landform features for Antarctica. See details in the Antarctica section below.Refined the definition of Murphy's Isolated Volcanics classification. If the volcanic landform occurred outside of a orogenic, rifting, or subducting zone, it could not be considered isolated, as this is where volcanos are expected to occur. Only volcanoes occurring in areas with no tectonic activity are considered Isolated Volcanics, and these typically occur in mid-continent or mid-tectonic plate. See details in the Isolated Volcanic Areas section below.Edits to tectonic process attributes in selected areas. The Global Volcanism Program point locations for volcanoes includes an attribute for the underlying tectonic process. The concept matched the existing tectonic process in the NLWv2 and we compared the values. When the values differed, we reviewed research and made changes. See details in the Tectonic Process section below.Minor boundary changes at the province and lower levels in the western mountains of North and South America. See details in the Boundary Change Locations section below.Technical CharacteristicsThe NLWv2 and NLWv3 are derived the same raster datasets used to produce the 2018 version of the World Terrestrial Ecosystems (WTEs), which when combined have a lowest-common-denominator resolution, a.k.a. minimum-mapping-unit of 1-km. This means that some features, such as small islands are not included and complex coastlines are simplified and only included as land if the 1-km cell contains at least 50% land. Because the coastlines included in the original datasets varied by as much as 3-km from the actual coastline, nearly always due to missing land, we manually corrected many of the worst cases in NLWv2 using the 12 to 30-meter resolution World Hillshade layer as a guide. In NLWv3, we continued this work by adding 247 volcanic islands, some of which were smaller than 1-km in area. We estimate these islands to have been about one percent of the smaller islands of the world. In NLWv3, we also refined the coastlines of volcanic coastal areas, particularly in Oceania and Japan. For NLWv4, we plan to continue this refinement work intending that future versions of NLW will have a progressively refined, medium resolution coastline, though we do not intend to capture the full detail of the Global Islands dataset produced from 30-m Landsat. Detailed Description of UpdatesVolcanic AttributesWe combined the Holocene and Pleistocene spreadsheets containing the coordinates and attributes for each volcano, then added a column for the geologic age before exporting as a .CSV file and importing into ArcGIS Pro. We used the XY Table to Points tool to create point features. We ultimately found that nearly ten percent of the point locations lacked sufficient precision to fall within the correct landform polygon, so we manually reviewed each point and assigned the Volcano ID to each polygon.We were able to assign 2,394 of 2,662 GVP volcanic features to landform polygons. 198 GVP features were not used because they represented undersea features and 75 GVP features did not have apparent landforms; either being very small or indistinguishable from surrounding topography. Of the 2,394 assigned GVP features, 48% are Holocene age features and 52% are Pleistocene age features. We found that 225 GVP features were not located within a landform feature that topographically represented a volcanic landform feature, e.g., a caldera or stratovolcano. This was usually due to insufficient precision of the coordinates provided, which sometimes were rounded to the nearest integer of latitude and longitude and could be over 50-km distant from the landform's location.AttributeDescriptionVolcano ID (SI)The six-digit unique ID for the Global Volcanism Program features.Volcano Name (SI)The Name of the volcanic feature as provided by the Global Volcanism Program. Volcanic Region (SI)The Name of the volcanic region as provided by the Global Volcanism Program. Volcanic Province (SI)The Name of the volcanic province as provided by the Global Volcanism Program. VolcanismA consistently formatted description volcanism for the landform feature based on the age, last eruption, landform type, and type of material. This information was not consistently available from the Global Volcanism Program, and we used a Python script to determine the condition of the Global Volcanism Program's data and then include whatever information was available. AntarcticaSeveral recent analyses of Antarctica complemented the GVP point features. In particular, the British Antarctic Survey's 2019 Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet shows sufficiently detailed land surface elevation beneath the ice sheets to support identifying topographic landform classes. We georeferenced the elevation image and combined that with Bridge's geomorphological divisions and provinces to divide the continent into landforms. More work needs to be done to make these landform polygons as rich and accurately defined as those in NLWv2. Isolated Volcanic AreasNLWv2 has 333 Isolated Volcanic landforms. We intentionally expanded on Murphy's map which could not show many of the smaller landforms and areas due to the 1:50,000,000 scale (poster sized map of the world). Murphy's map only included isolated volcanic areas in three locations: north-central Africa, Hawaii, and Iceland. In NLWv2, we used the Global Lithological Map to identify several areas on each continent and used the example of Hawaii to include many other known volcanic islands. In most ways, Isolated Volcanics denoted geographic isolation from other mountain systems. NLWv3 contains 2,798 volcanic landform features, and 185 have Murphy's Isolated Volcanic structure class because they do not occur within a region with the tectonic process of orogenic, subduction, or rifting. These Isolated Volcanic landform features are located mostly in mid-tectonic plate regions of Africa, the Arabian Peninsula, and on islands, particularly in the southern hemisphere, with a few in North America and Asia. NLWv3 contains 2,603 volcanic landform features, occurring on all continents and islands within all oceans. Tectonic ProcessThe GVP data included a tectonic setting attribute that was compiled independently of the NLWv2 tectonic setting variable. When these differed, we reviewed and if needed update the tectonic setting variable in the NLWv3. This also exposed several regions of landforms requiring updates to the Structure class. These areas included Japan, northeast Asia, the Aleutian Islands, and Alaska to either Orogenic or None. We independently verified these regions using Orogeny and Mantle Dynamics: role of tectonic erosion and second continent in the mantle transition zone which indicated specific orogenic and subducting areas, disagreeing with our original assessment and the GVP attribution for tectonic setting. Tectonic ProcessHolocene Volcanic Features Pleistocene Volcanic FeaturesNone (Isolated)7797Orogenic329497Subduction Zone639655Rifted Area275130Boundary Change LocationsThe

This stormwater forecast script tool was developed by the Natural Resources Department at the Atlanta Regional Commission.WHAT IS THE STORMWATER FORECAST?In 2022, the District developed a novel water quantity-based indicator, the Stormwater Forecast, to support watershed managers with ongoing challenges related to water quality, streambank erosion, and nuisance flooding.The Stormwater Forecast is a planning-level estimate of the total potential storage volume required by Stormwater Control Measures to manage runoff from development at a basin scale under both current and future conditions. Based on current development patterns, the results of the Stormwater Forecast show the 15-county Metro Atlanta region should be managing up to 27 billion cubic feet of runoff volume with Stormwater Control Measures, and if regulations remain the same the total volumes are estimated to increase by up to 100 percent by 2040. STORMWATER FORECAST USER GUIDEThe Stormwater Forecast User Guide outlines steps for calculating stormwater runoff volumes for an area of interest using the Stormwater Forecast and performing a Stormwater Forecast Gap Analysis using the custom stormwater runoff volume results.STORMWATER FORECAST GEOPROCESSING PACKAGEThe Stormwater Forecast Geoprocessing Package contains the Stormwater Forecast Script Tool and a geodatabase with the following four parameters needed to execute the tool. AreaofInterestStormwaterForecastDevelopedAreaNLCD_Imperviousness_2019.tifThe Stormwater Forecast Script Tool provides users with an automated calculation method for calculating custom stormwater runoff volumes within an area of interest using the Stormwater Forecast.FIELD ABBREVIATIONS AND DESCRIPTIONS FOR STORMWATER FORECAST RESULTSUnique_ID = Unique Identification Characters for Stormwater Forecast SubcatchmentNHD_Sub_ID = National Hydrography Dataset Subcatchment Identification Numbers HUC_12 = Hydrologic Unit Code-12 Identification Numbers County = County Name HUC_8 = Hydrologic Unit Code-8 Identification Numbers MRB = HUC-8 Major River Basin Name Area_Dev_a = 2019 Developed Area, in acresImpv_Area = 2019 Total Impervious Area within Developed Area, in acresAOI_19_WQ = 2019 Water Quality Volume for Area of Interest, in cubic feet AOI_19_CP = 2019 Channel Protection Volume for Area of Interest, in cubic feetAOI_19_OF = 2019 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_30_WQ = 2030 Water Quality Volume for Area of Interest, in cubic feet AOI_30_CP = 2030 Channel Protection Volume for Area of Interest, in cubic feetAOI_30_OF = 2030 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_40_WQ = 2040 Water Quality Volume for Area of Interest, in cubic feet AOI_40_CP = 2040 Channel Protection Volume for Area of Interest, in cubic feetAOI_40_OF = 2040 Overbank Flood Protection Volume for Area of Interest, in cubic feetRequired Software: Esri’s ArcGIS Pro and Esri’s Spatial Analyst and Image Analyst Extensions

Version 3 of the Named Landforms of the World (NLWv3) is an update of version 2 of the Named Landforms of the World (NLWv2). NLWv2 will remain available as the compilation that best matches the work of E.M. Bridges and Richard E. Murphy. In NLWv3, we added attributes that describe each landform's volcanism based on data from the Smithsonian Institution's Global Volcanism Program (GVP). We designed NLWv3 layers for two purposes:Label maps with broadly accepted names for physiographic features. Use the polygons as a basis to add fields (attributes) to observation data or other small features to facilitate rich and relevant descriptions that indicate how other features relate to named physiographic features. Three workflows are recommended: (1) For point features, Identity and then Join Field; (2) Zonal Statistics as Table and then Join Field, and when many such attributes are being produced, (3) when adding multiple different attributes, the recently added Zonal Characterization tool and then Join Field. While we gained ability to estimate the area of Earth"s volcanic landforms, we also learned that volcanoes are relatively short-lived as landforms. The GVP provided two inventories, one for the Holocene Epoch, which is the most recent 11,700 years (since the last ice age), and for the Pleistocene Epoch, which precedes the Holocene, and lasted about 2.6 million years. There were only 7.8% more volcanoes included for the Pleistocene, even though the Pleistocene is 222 times longer. That means most older volcanoes have disappeared through natural erosional and depositional processes. In the NLWv3, we consider volcanic landforms as being one of many types of landforms, including calderas, clusters and complexes, shields, stratovolcanoes, or minor volcanic features such as lava domes and fissure vents. Not all of the GVP features, particularly fissure vents and remnants of calderas, are large enough to be mapped as polygons in the NLWv3. Similarly, complexes and volcanic fields typically had greater areas and included many individual cinder cones and calderas. ContinentCount of Volcanic LandformsArea km2 of Volcanic Landforms (% of land area)Europe7822,888 (0.23%)Antarctica4234,035 (0.27%)Australia14757,422 (0.65%)South America37081,475 (0.46%)Small Volcanic Islands559124,310 (8.52%)Africa282147,116 (0.50%)Asia698227,486 (0.53%)North America622295,340 (1.23%)Global Totals2,7981,000,073 (0.67%) Overview of UpdatesCorresponding landform polygons were assigned attributes for the GVP"s ID, name, province, and region. See details in the volcanic attributes section below. Additionally, an describing volcanism for each GVP feature was derived from these and several other GVP attributes to provide a reader-friendly characterization of each feature.Landforms of Antarctica. Given recent analysis of Antarctica and the GVP data it became possible to provide rudimentary landform features for Antarctica. See details in the Antarctica section below.Refined the definition of Murphy"s Isolated Volcanics classification. If the volcanic landform occurred outside of a orogenic, rifting, or subducting zone, it could not be considered isolated, as this is where volcanos are expected to occur. Only volcanoes occurring in areas with no tectonic activity are considered Isolated Volcanics, and these typically occur in mid-continent or mid-tectonic plate. See details in the Isolated Volcanic Areas section below.Edits to tectonic process attributes in selected areas. The Global Volcanism Program point locations for volcanoes includes an attribute for the underlying tectonic process. The concept matched the existing tectonic process in the NLWv2 and we compared the values. When the values differed, we reviewed research and made changes. See details in the Tectonic Process section below.Minor boundary changes at the province and lower levels in the western mountains of North and South America. See details in the Boundary Change Locations section below.Technical CharacteristicsThe NLWv2 and NLWv3 are derived the same raster datasets used to produce the 2018 version of the World Terrestrial Ecosystems (WTEs), which when combined have a lowest-common-denominator resolution, a.k.a. minimum-mapping-unit of 1-km. This means that some features, such as small islands are not included and complex coastlines are simplified and only included as land if the 1-km cell contains at least 50% land. Because the coastlines included in the original datasets varied by as much as 3-km from the actual coastline, nearly always due to missing land, we manually corrected many of the worst cases in NLWv2 using the 12 to 30-meter resolution World Hillshade layer as a guide. In NLWv3, we continued this work by adding 247 volcanic islands, some of which were smaller than 1-km in area. We estimate these islands to have been about one percent of the smaller islands of the world. In NLWv3, we also refined the coastlines of volcanic coastal areas, particularly in Oceania and Japan. For NLWv4, we plan to continue this refinement work intending that future versions of NLW will have a progressively refined, medium resolution coastline, though we do not intend to capture the full detail of the Global Islands dataset produced from 30-m Landsat. Detailed Description of Updates Volcanic AttributesWe combined the Holocene and Pleistocene spreadsheets containing the coordinates and attributes for each volcano, then added a column for the geologic age before exporting as a .CSV file and importing into ArcGIS Pro. We used the XY Table to Points tool to create point features. We ultimately found that nearly ten percent of the point locations lacked sufficient precision to fall within the correct landform polygon, so we manually reviewed each point and assigned the Volcano ID to each polygon.We were able to assign 2,394 of 2,662 GVP volcanic features to landform polygons. 198 GVP features were not used because they represented undersea features and 75 GVP features did not have apparent landforms; either being very small or indistinguishable from surrounding topography. Of the 2,394 assigned GVP features, 48% are Holocene age features and 52% are Pleistocene age features. We found that 225 GVP features were not located within a landform feature that topographically represented a volcanic landform feature, e.g., a caldera or stratovolcano. This was usually due to insufficient precision of the coordinates provided, which sometimes were rounded to the nearest integer of latitude and longitude and could be over 50-km distant from the landform"s location. AttributeDescriptionVolcano ID (SI)The six-digit unique ID for the Global Volcanism Program features.Volcano Name (SI)The Name of the volcanic feature as provided by the Global Volcanism Program. Volcanic Region (SI)The Name of the volcanic region as provided by the Global Volcanism Program. Volcanic Province (SI)The Name of the volcanic province as provided by the Global Volcanism Program. VolcanismA consistently formatted description volcanism for the landform feature based on the age, last eruption, landform type, and type of material. This information was not consistently available from the Global Volcanism Program, and we used a Python script to determine the condition of the Global Volcanism Program"s data and then include whatever information was available. AntarcticaSeveral recent analyses of Antarctica complemented the GVP point features. In particular, the British Antarctic Survey"s 2019 Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet shows sufficiently detailed land surface elevation beneath the ice sheets to support identifying topographic landform classes. We georeferenced the elevation image and combined that with Bridge"s geomorphological divisions and provinces to divide the continent into landforms. More work needs to be done to make these landform polygons as rich and accurately defined as those in NLWv2. Isolated Volcanic AreasNLWv2 has 333 Isolated Volcanic landforms. We intentionally expanded on Murphy"s map which could not show many of the smaller landforms and areas due to the 1:50,000,000 scale (poster sized map of the world). Murphy"s map only included isolated volcanic areas in three locations: north-central Africa, Hawaii, and Iceland. In NLWv2, we used the Global Lithological Map to identify several areas on each continent and used the example of Hawaii to include many other known volcanic islands. In most ways, Isolated Volcanics denoted geographic isolation from other mountain systems. NLWv3 contains 2,798 volcanic landform features, and 185 have Murphy"s Isolated Volcanic structure class because they do not occur within a region with the tectonic process of orogenic, subduction, or rifting. These Isolated Volcanic landform features are located mostly in mid-tectonic plate regions of Africa, the Arabian Peninsula, and on islands, particularly in the southern hemisphere, with a few in North America and Asia. NLWv3 contains 2,603 volcanic landform features, occurring on all continents and islands within all oceans. Tectonic ProcessThe GVP data included a tectonic setting attribute that was compiled independently of the NLWv2 tectonic setting variable. When these differed, we reviewed and if needed update the tectonic setting variable in the NLWv3. This also exposed several regions of landforms requiring updates to the Structure class. These areas included Japan, northeast Asia, the Aleutian Islands, and Alaska to either Orogenic or None. We independently verified these regions using Orogeny and Mantle Dynamics: role of tectonic erosion and second continent in the mantle transition zone which indicated specific orogenic and subducting areas, disagreeing with our original assessment and the GVP attribution for tectonic setting. Tectonic ProcessHolocene Volcanic Features Pleistocene Volcanic FeaturesNone (Isolated)7797Orogenic329497Subduction

The Old Cambridge Canal Wells Geospatial Data layer is a point type geometry layer depicting observation wells along Cambridge canal in Nebraska. It was created from legacy data developed in ArcMap that was then imported into ArcGIS Pro. There are two attribute fields for this layer: ID, and Location. This data did not undergo a quality assurance and quality control process and as a result there are some errors and inconsistencies in the data.The schema was created by Frenchman Cambridge Irrigation District. Frenchman Cambridge Irrigation District collected the data using a GPS unit for the S&T Project 19042: Developing a Collaborative Environment for Sharing Geographic Information Systems (GIS) Data Between Reclamation and Irrigation Districts.RISE Catalog Item 128534: https://data.usbr.gov/catalog/7980/item/128534To download data, please use the RISE Geospatial Open Data site: https://rise-usbr.opendata.arcgis.com/datasets/ab8e2de29ccb4b64a2f013e2b75807d6