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🌆 About This Dataset

This synthetic dataset simulates 300 global cities across 6 major geographic regions, designed specifically for unsupervised machine learning and clustering analysis. It explores how economic status, environmental quality, infrastructure, and digital access shape urban lifestyles worldwide.

🎯 Perfect For:

• 📊 K-Means, DBSCAN, Agglomerative Clustering
• 🔬 PCA & t-SNE Dimensionality Reduction
• 🗺️ Geospatial Visualization (Plotly, Folium)
• 📈 Correlation Analysis & Feature Engineering
• 🎓 Educational Projects (Beginner to Intermediate)

📦 What's Inside?

🔥 Key Features

✅ Realistic Correlations: Income strongly predicts rent (+0.8), internet access (+0.7), and happiness (+0.6)
✅ Regional Diversity: Each region has distinct economic and environmental characteristics
✅ Clustering-Ready: Naturally separable into 4-5 lifestyle archetypes
✅ Beginner-Friendly: No data cleaning required, includes example code
✅ Documented: Comprehensive README with methodology and use cases

🚀 Quick Start Example

import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Load and prepare
df = pd.read_csv('city_lifestyle_dataset.csv')
X = df.drop(['city_name', 'country'], axis=1)
X_scaled = StandardScaler().fit_transform(X)

# Cluster
kmeans = KMeans(n_clusters=5, random_state=42)
df['cluster'] = kmeans.fit_predict(X_scaled)

# Analyze
print(df.groupby('cluster').mean())

🎓 Learning Outcomes

After working with this dataset, you will be able to: 1. Apply K-Means, DBSCAN, and Hierarchical Clustering 2. Use PCA for dimensionality reduction and visualization 3. Interpret correlation matrices and feature relationships 4. Create geographic visualizations with cluster assignments 5. Profile and name discovered clusters based on characteristics

📚 Ideal For These Projects

• 🏆 Kaggle Competitions: Practice clustering techniques
• 📝 Academic Projects: Urban planning, sociology, environmental science
• 💼 Portfolio Work: Showcase ML skills to employers
• 🎓 Learning: Hands-on practice with unsupervised learning
• 🔬 Research: Urban lifestyle segmentation studies

🌍 Expected Clusters

🛠️ Technical Details

• Format: CSV (UTF-8)
• Size: ~300 rows × 10 columns
• Missing Values: 0%
• Data Types: 2 categorical, 8 numerical
• Target Variable: None (unsupervised)
• Correlation Strength: Pre-validated (r: 0.4 to 0.8)

📖 What Makes This Dataset Special?

Unlike random synthetic data, this dataset was carefully engineered with: - ✨ Realistic correlation structures based on urban research - 🌍 Regional characteristics matching real-world patterns - 🎯 Optimal cluster separability (validated via silhouette scores) - 📚 Comprehensive documentation and starter code

🏅 Use This Dataset If You Want To:

✓ Learn clustering without data cleaning hassles
✓ Practice PCA and dimensionality reduction
✓ Create beautiful geographic visualizations
✓ Understand feature correlation in real-world contexts
✓ Build a portfolio project with clear business insights

📊 Acknowledgments

This dataset was designed for educational purposes in machine learning and data science. While synthetic, it reflects real patterns observed in global urban development research.

Happy Clustering! 🎉

Coordinator, Geospatial Technical Services Job #: 1358 Jurisdiction: CMM Division: City Planning & Community Development Department: Sustainable Infrastructure

Public Open Space Geographic Information System data collection for Perth and Peel Metropolitan Areas

The public open space (POS) dataset contains polygon boundaries of areas defined as publicly available and open. This geographic information system (GIS) dataset was collected in 2011/2012 using ArcGIS software and aerial photography dated from 2010-2011. The data was collected across the Perth Metro and Peel Region.

POS refer to all land reserved for the provision of green space and natural environments (e.g. parks, reserves, bushland) that is freely accessible and intended for use for recreation purposes (active or passive) by the general public. Four types of “green and natural public open spaces” are distinguished: (1) Park; (2) Natural or Conservation Area; (3) School Grounds; and (4) Residual. Areas where the public are not permitted except on payment or which are available to limited and selected numbers by membership (e.g. golf courses and sports centre facilities) or setbacks and buffers required by legislation are not included.

Initially, potential POSs were identified from a combination of existing geographic information system (GIS) spatial data layers to create a generalized representation of ‘green space’ throughout the Perth metropolitan and Peel regions. Base data layers include: cadastral polygons, metropolitan and regional planning scheme polygons, school point locations, and reserve vesting polygons. The ‘green’ space layer was then visually updated and edited to represent the true boundaries of each POS using 2010-2011 aerial photography within the ArcGIS software environment. Each resulting ’green’ polygon was then classified using a decision tree into one of four possible categories: park, natural or conservation area, school grounds, or residual green space.

Following the classification process, amenity and other information about each POS was collected for polygons classified as “Park” following a protocol developed at the Centre for the Built Environment and Health (CBEH) called POSDAT (Public Open Space Desktop Auditing Tool). The parks were audited using aerial photography visualized using ArcGIS software. . The presence or absence of amenities such as sporting facilities (e.g. tennis courts, soccer fields, skate parks etc) were audited as well as information on the environmental quality (i.e. presence of water, adjacency to bushland, shade along paths, etc), recreational amenities (e.g. presence of BBQ’, café or kiosks, public access toilets) and information on selected features related to personal safety.

The data is stored in an ArcGIS File Geodatabase Feature Class (size 4MB) and has restricted access.

Data creation methodology, data definitions, and links to publications based on this data, accompany the dataset.

Manager, Geospatial Solutions Job #: 1636 Jurisdiction: Out-of-Scope Department: Sustainable Infrastructure / Geospatial Solutions

The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products. This item was updated on Apr 14, 2023 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneAnalysis: Optimized for analysis What can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location. This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme. Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See the Living Atlas Imagery Layers Optimized for Analysis Group for a complete list of imagery layers optimized for analysis. Developing the World Terrestrial EcosystemsWorld Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes. Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields. The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation More information about World Terrestrial Ecosystems can be found in this Story Map.

This dataset maps Public Safety Answering Points (PSAPs) throughout the Mid South Region. A PSAP is a dedicated call center that receives 9-1-1 calls for police, fire, or emergency medical services. These facilities operate continuously — 24 hours a day, 7 days a week — ensuring uninterrupted emergency coverage.How It WorksWhen a 9-1-1 call is placed, trained telecommunicators at the PSAP either:Dispatch appropriate emergency responders directly, orTransfer the call to another public or private safety agency when specialized response is needed. Dataset ContentsThe feature layer includes:PSAP name and facility informationAgency affiliation and service jurisdictionAttributes supporting coverage analysis and interoperability Use CasesThis dataset supports:Operational awareness – visualizing where PSAPs are located and how jurisdictions are servedInteragency coordination – enabling effective response during large-scale incidents and disastersNext Generation 9-1-1 (NG9-1-1) – preparing for accurate, location-based routing of callsStrategic planning – identifying service overlaps, coverage gaps, and opportunities for system improvement Why It MattersPSAPs are the backbone of emergency communications. By centralizing and mapping this data, public safety leaders, GIS analysts, and emergency planners can strengthen regional readiness, improve response times, and ensure reliable access to life-saving services across the Mid South.This integrates the i3 Forest Guide principles:Forest canopy (overview) → What is this dataset about?Tree trunks (orientation) → How does it work?Branches (details) → What’s in it?Undergrowth (purpose) → Why does it matter?

The California State Places Boundary data.

This dataset offers high-resolution boundary definitions, which allow users to analyze and visualize California’s state limits within mapping and spatial analysis projects.

The shapefile is part of a ZIP archive containing multiple related files that together define and support the boundary data. These files include:

.shp (Shape): This is the core file containing the vector data for California’s Places boundaries, representing the geographic location and geometry of the state outline.

.shx (Shape Index): A companion index file for the .shp file, allowing for quick spatial queries and efficient data access.

.dbf (Attribute Table): A database file that stores attribute data linked to the geographic features in the .shp file, such as area identifiers or classification codes, in a tabular format compatible with database applications.

.prj (Projection): This file contains projection information, specifying the coordinate system and map projection used for the data, essential for aligning it accurately on maps.

.cpg (Code Page): This optional file indicates the character encoding for the attribute data in the .dbf file, which is useful for ensuring accurate text representation in various software.

.sbn and .sbx (Spatial Index): These files serve as a spatial index for the shapefile, allowing for faster processing of spatial queries, especially for larger datasets.

.xml (Metadata): A metadata file in XML format, often following FGDC or ISO standards, detailing the dataset’s origin, structure, and usage guidelines, providing essential information about data provenance and quality.

The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by USGS GAP, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The State, Regional and LCC geodatabases contain two feature classes. The PADUS1_3_FeeEasement feature class and the national MPA feature class. Legitimate and other protected area overlaps exist in the full inventory, with Easements loaded on top of Fee. Parcel data within a protected area are dissolved in this file that powers the PAD-US Viewer. As overlaps exist, GAP creates separate analytical layers to summarize area statistics for "GAP Status Code" and "Owner Name". Contact the PAD-US Coordinator for more information. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. The geodatabase includes: 1) Geographic boundaries of public land ownership and voluntarily provided private conservation lands (e.g., Nature Conservancy Preserves); 2) The combination land owner, land manager, management designation or type, parcel name, GIS Acres and source of geographic information of each mapped land unit 3) GAP Status Code conservation measure of each parcel based on USGS National Gap Analysis Program (GAP) protection level categories which provide a measurement of management intent for long-term biodiversity conservation 4) IUCN category for a protected area's inclusion into UNEP-World Conservation Monitoring Centre's World Database for Protected Areas. IUCN protected areas are defined as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" and are categorized following a classification scheme available through USGS GAP; 5) World Database of Protected Areas (WDPA) Site Codes linking the multiple parcels of a single protected area in PAD-US and connecting them to the Global Community. As legitimate and other overlaps exist in the combined inventory GAP creates separate analytical layers to obtain area statistics for "GAP Status Code" and "Owner Name". PAD-US version 1.3 Combined updates include: 1) State, local government and private protected area updates delivered September 2011 from PAD-US State Data Stewards: CO (Colorado State University), FL (Florida Natural Areas Inventory), ID (Idaho Fish and Game), MA (The Commonwealth's Office of Geographic Information Systems, MassGIS), MO (University of Missouri, MoRAP), MT (Montana Natural Heritage Program), NM (Natural Heritage New Mexico), OR (Oregon Natural Heritage Program), VA (Department of Conservation and Recreation, Virginia Natural Heritage Program). 2) Select local government (i.e. county, city) protected areas (3,632) across the country (to complement the current PAD-US inventory) aggregated by the Trust for Public Land (TPL) for their Conservation Almanac that tracks the conservation finance movement across the country. 3) A new Date of Establishment field that identifies the year an area was designated or otherwise protected, attributed for 86% of GAP Status Code 1 and 2 protected areas. Additional dates will be provided in future updates. 4) A national wilderness area update from wilderness.net 5) The Access field that describes public access to protected areas as defined by data stewards or categorical assignment by Primary Designation Type. . The new Access Source field documents local vs. categorical assignments. See the PAD-US Standard Manual for more information: gapanalysis.usgs.gov/padus 6) The transfer of conservation measures (i.e. GAP Status Codes, IUCN Categories) and documentation (i.e. GAP Code Source, GAP Code Date) from PAD-US version 1.2 or categorical assignments (see PAD-US Standard) when not provided by data stewards 7) Integration of non-sensitive National Conservation Easement Database (NCED) easements from August 2011, July 2012 with PAD-US version 1.2 easements. Duplicates were removed, unless 'Stacked' = Y and multiple easements exist. 8) Unique ID's transferred from NCED or requested for new easements. NCED and PAD-US are linked via Source UID in the PAD-US version 1.3 Easement feature class. 9) Official (member and eligible) MPAs from the NOAA MPA Inventory (March 2011, www.mpa.gov) translated into the PAD-US schema with conservation measures transferred from PAD-US version 1.2 or categorically assigned to new protected areas. Contact the PAD-US Coordinator for documentation of categorical GAP Status Code assignments for MPAs. 10) Identified MPA records that overlap existing protected areas in the PAD-US Fee feature class (i.e. PADUS Overlap field in MPA feature class). For example, many National Wildlife Refuges and National Parks are also MPAs and are represented in the PAD-US MPA and Fee feature classes.

This dataset represents a water shortage vulnerability analysis performed by DWR using modified PLSS sections pulled from the Well Completion Report PLSS Section Summaries. The attribute table includes water shortage vulnerability indicators and scores from an analysis done by CA Department of Water Resources, joined to modified PLSS sections. Several relevant summary statistics from the Well Completion Reports are included in this table as well. This data is from the 2024 analysis.

Water Code Division 6 Part 2.55 Section 8 Chapter 10 (Assembly Bill 1668) effectively requires California Department of Water Resources (DWR), in consultation with other agencies and an advisory group, to identify small water suppliers and “rural communities” that are at risk of drought and water shortage. Following legislation passed in 2021 and signed by Governor Gavin Newsom, the Water Code Division 6, Section 10609.50 through 10609.80 (Senate Bill 552 of 2021) effectively requires the California Department of Water Resources to update the scoring and tool periodically in partnership with the State Water Board and other state agencies. This document describes the indicators, datasets, and methods used to construct this deliverable.  This is a statewide effort to systematically and holistically consider water shortage vulnerability statewide of rural communities, focusing on domestic wells and state small water systems serving between 4 and 14 connections. The indicators and scoring methodology will be revised as better data become available and stake-holders evaluate the performance of the indicators, datasets used, and aggregation and ranking method used to aggregate and rank vulnerability scores. Additionally, the scoring system should be adaptive, meaning that our understanding of what contributes to risk and vulnerability of drought and water shortage may evolve. This understanding may especially be informed by experiences gained while navigating responses to future droughts.”

A spatial analysis was performed on the 2020 Census Block Groups, modified PLSS sections, and small water system service areas using a variety of input datasets related to drought vulnerability and water shortage risk and vulnerability. These indicator values were subsequently rescaled and summed for a final vulnerability score for the sections and small water system service areas. The 2020 Census Block Groups were joined with ACS data to represent the social vulnerability of communities, which is relevant to drought risk tolerance and resources. These three feature datasets contain the units of analysis (modified PLSS sections, block groups, small water systems service areas) with the model indicators for vulnerability in the attribute table. Model indicators are calculated for each unit of analysis according to the Vulnerability Scoring documents provided by Julia Ekstrom (Division of Regional Assistance).

All three feature classes are DWR analysis zones that are based off existing GIS datasets. The spatial data for the sections feature class is extracted from the Well Completion Reports PLSS sections to be aligned with the work and analysis that SGMA is doing. These are not true PLSS sections, but a version of the projected section lines in areas where there are gaps in PLSS. The spatial data for the Census block group feature class is downloaded from the Census. ACS (American Communities Survey) data is joined by block group, and statistics calculated by DWR have been added to the attribute table. The spatial data for the small water systems feature class was extracted from the State Water Resources Control Board (SWRCB) SABL dataset, using a definition query to filter for active water systems with 3000 connections or less. None of these datasets are intended to be the authoritative datasets for representing PLSS sections, Census block groups, or water service areas. The spatial data of these feature classes is used as units of analysis for the spatial analysis performed by DWR.

These datasets are intended to be authoritative datasets of the scoring tools required from DWR according to Senate Bill 552. Please refer to the Drought and Water Shortage Vulnerability Scoring: California's Domestic Wells and State Smalls Systems documentation for more information on indicators and scoring. These estimated indicator scores may sometimes be calculated in several different ways, or may have been calculated from data that has since be updated. Counts of domestic wells may be calculated in different ways. In order to align with DWR SGMO's (State Groundwater Management Office) California Groundwater Live dashboards, domestic wells were calculated using the same query. This includes all domestic wells in the Well Completion Reports dataset that are completed after 12/31/1976, and have a 'RecordType' of 'WellCompletion/New/Production or Monitoring/NA'.

Please refer to the Well Completion Reports metadata for more information. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.4, dated September 14, 2022. DWR makes no warranties or guarantees — either expressed or implied— as to the completeness, accuracy, or correctness of the data.

DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

The California State Boundary data from the US Census Bureau's 2023 MAF/TIGER database provides detailed geographic boundary data designed for use in Geographic Information System applications.

This dataset offers high-resolution boundary definitions, which allow users to analyze and visualize California’s state limits within mapping and spatial analysis projects.

The shapefile is part of a ZIP archive containing multiple related files that together define and support the boundary data. These files include:

.shp (Shape): This is the core file containing the vector data for California’s boundary, representing the geographic location and geometry of the state outline.

.shx (Shape Index): A companion index file for the .shp file, allowing for quick spatial queries and efficient data access.

.dbf (Attribute Table): A database file that stores attribute data linked to the geographic features in the .shp file, such as area identifiers or classification codes, in a tabular format compatible with database applications.

.prj (Projection): This file contains projection information, specifying the coordinate system and map projection used for the data, essential for aligning it accurately on maps.

.cpg (Code Page): This optional file indicates the character encoding for the attribute data in the .dbf file, which is useful for ensuring accurate text representation in various software.

.sbn and .sbx (Spatial Index): These files serve as a spatial index for the shapefile, allowing for faster processing of spatial queries, especially for larger datasets.

.xml (Metadata): A metadata file in XML format, often following FGDC or ISO standards, detailing the dataset’s origin, structure, and usage guidelines, providing essential information about data provenance and quality.

This comprehensive set of files ensures compatibility with most GIS software and allows users to perform a wide range of spatial analyses with detailed information on California’s boundary as defined by the U.S. Census Bureau's 2023 MAF/TIGER database.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines. Dataset SummaryPhenomenon Mapped: LandformsGeographic Extent: GlobalProjection: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereUnits: MetersCell Size: 231.91560581932 metersPixel Depth: 8-bit unsigned integerAnalysis: Restricted single source analysis. Maximum size of analysis is 30,000 x 30,000 pixels.Source: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/ In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS. The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plains Smooth plains with some local relief Irregular plains with moderate relief Irregular plains with low hills Scattered moderate hills Scattered high hills Scattered low mountains Scattered high mountains Moderate hills High hills Tablelands with moderate relief Tablelands with considerable relief Tablelands with high relief Tablelands with very high relief Low mountains High mountains To produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain"s texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class. The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them: What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The Sheeprocks (UT) was revised to resync with the UT habitat change as reflected in the Oct 2017 habitat data, creating the most up-to-date version of this dataset. Data submitted by Wyoming in February 2018 and by Montana and Oregon in May 2016 were used to update earlier versions of this feature class. The biologically significant unit (BSU) is a geographicalspatial area within Greater Sage-Grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. This BSU unit, or subset of this unit is used in the calculation of the anthropogenic disturbance threshold and in the adaptive management habitat trigger. BSU feature classes were submitted by individual statesEISs and consolidated by the Wildlife Spatial Analysis Lab. They are sometimes referred to as core areascore habitat areas in the explanations below, which were consolidated from metadata submitted with BSU feature classes. These data provide a biological tool for planning in the event of human development in sage-grouse habitats. The intended use of all data in the BLMs GIS library is to support diverse activities including planning, management, maintenance, research, and interpretation. While the BSU defines the geographic extent and scale of these two measures, how they are calculated differs based on the specific measures to reflect appropriate assessment and evaluation as supported by scientific literature.

    There are 10 BSUs for the Idaho and Southwestern Montana GRSG EIS sub-region. For the Idaho and Southwestern Montana Greater Sage-Grouse Plan Amendment FEIS the biologically significant unit is defined as: a geographicalspatial area within greater sage-grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. Idaho: BSUs include all of the Idaho Fish and Game modeled nesting and delineated winter habitat, based on 2011 inventories within Priority andor Important Habitat Management Area (Alternative G) within a Conservation Area. There are eight BSUs for Idaho identified by Conservation Area and Habitat Management Area: Idaho Desert Conservation Area - Priority, Idaho Desert Conservation Area - Important, Idaho Mountain Valleys Conservation Area - Priority, Idaho Mountain Valleys Conservation Area - Important, Idaho Southern Conservation Area - Priority, Idaho Southern Conservation Area - Important, Idaho West Owyhee Conservation Area - Priority, and Idaho West Owyhee Conservation Area - Important. Raft River : Utah portion of the Sawtooth National Forest, 1 BSU. All of this areas was defined as Priority habitat in Alternative G. Raft River - Priority.

    Montana: All of the Priority Habitat Management Area. 1 BSU. SW Montana Conservation Area - Priority. Montana BSUs were revised in May 2016 by the MT State Office. They are grouped together and named by the Population in which they are located: Northern Montana, Powder River Basin, Wyoming Basin, and Yellowstone Watershed. North and South Dakota BSUs have been grouped together also.

    California and Nevadas BSUs were developed by Nevada Department of Wildlifes Greater Sage-Grouse Wildlife Staff Specialist and Sagebrush Ecosystem Technical Team Representative in January 2015. Nevadas Biologically Significant Units (BSUs) were delineated by merging associated PMUs to provide a broader scale management option that reflects sage grouse populations at a higher scale. PMU boundarys were then modified to incorporate Core Management Areas (August 2014; Coates et al. 2014) for management purposes. (Does not include Bi-State DPS.) 

    Within Colorado, a Greater Sage-Grouse GIS data set identifying Preliminary Priority Habitat (PPH) and Preliminary General Habitat (PGH) was developed by Colorado Parks and Wildlife. This data is a combination of mapped grouse occupied range, production areas, and modeled habitat (summer, winter, and breeding). PPH is defined as areas of high probability of use (summer or winter, or breeding models) within a 4 mile buffer around leks that have been active within the last 10 years. Isolated areas with low activity were designated as general habitat. PGH is defined as Greater sage-grouse Occupied Range outside of PPH. Datasets used to create PPH and PGH: Summer, winter, and breeding habitat models. Rice, M. B., T. D. Apa, B. L. Walker, M. L. Phillips, J. H. Gammonly, B. Petch, and K. Eichhoff. 2012. Analysis of regional species distribution models based on combined radio-telemetry datasets from multiple small-scale studies. Journal of Applied Ecology in review. Production Areas are defined as 4 mile buffers around leks which have been active within the last 10 years (leks active between 2002-2011). Occupied range was created by mapping efforts of the Colorado Division of Wildlife (now Colorado Parks and Wildlife –CPW) biologists and district officers during the spring of 2004, and further refined in early 2012. Occupied Habitat is defined as areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. This information was derived from field personnel. A variety of data capture techniques were used including the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing atvarious scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Update August 2012: This dataset was modified by the Bureau of Land Management as requested by CPW GIS Specialist, Karin Eichhoff. Eichhoff requested that this dataset, along with the GrSG managment zones (population range zones) dataset, be snapped to county boundaries along the UT-CO border and WY-CO border. The county boundaries dataset was provided by Karin Eichhoff. In addition, a few minor topology errors were corrected where PPH and PGH were overlapping. Update October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh†polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond†were removed from GrSG habitat. Colorado Greater Sage Grouse managment zones based on CDOW GrSG_PopRangeZones20120609.shp. Modified and renumbered by BLM 06092012. The zones were modified again by the BLM in August 2012. The BLM discovered areas where PPH and PGH were not included within the zones. Several discrepancies between the zones and PPH and PGH dataset were discovered, and were corrected by the BLM. Zones 18-21 are linkages added as zones by the BLM. In addition to these changes, the zones were adjusted along the UT-CO boundary and WY-CO boundary to be coincident with the county boundaries dataset. This was requested by Karin Eichhoff, GIS Specialist at the CPW. She provided the county boundaries dataset to the BLM. Greater sage grouse GIS data set identifying occupied, potential and vacantunknown habitats in Colorado. The data set was created by mapping efforts of the Colorado Division of Wildlife biologist and district officers during the spring of 2004, and further refined in the winter of 2005. Occupied Habitat: Areas of suitable habitat known to be used by sage-grouse within the last 10 years from the

Attribute reclassification for fixed Cmin and varying amplitude.

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years since 1992. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2019Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: AnnualWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

This dynamic World Elevation Terrain layer returns float values representing ground heights in meters and compiles multi-resolution data from many authoritative data providers from across the globe. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns.Note: This layer combine data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Climate plays a major role in determining the distribution of plants and animals. Bioclimatology, the study of climate as it affects and is affected by living organisms, is key to understanding the patterns of forests and deserts on the landscape, where productive agricultural lands may be found, and how changes in the climate will affect rare species. This layer is part of the Ecophysiographic Project and is one of the four input layers used to create the World Ecological Land Units Map. This layer provides access to a 250m cell-sized raster with a bioclimatic stratification. The source dataset was a 30-arcsecond resolution raster (equivalent to 0.86 km2 at the equator or about a 920m pixel size). The layer has the following attributes: Temperature Description - Seven classes based on the number of growing degree days (the monthly mean temperature multiplied by number of days in the month summed for all months). The 1950 to 2000 monthly average temperature was used to calculate growing degree days. Values in this field and associated number of growing degree days are: Temperature DescriptionGrowing Degree DaysVery Hot9,000 – 13,500Hot7,000 – 9,000Warm4,500 – 7,000Cool2,500 – 4,500Cold1,000 – 2,500Very Cold300 – 1,000Arctic0 - 300 Aridity Description - Six classes based on an index of aridity calculated by dividing precipitation by evapotranspiration. Precipitation and evapotranspiration are average values from 1950 to 2000. Aridity DescriptionAridity IndexVery Wet1.5 – 70Wet1.0 – 1.5Moist0.6 – 1.0Semi-dry0.3 – 0.6Dry0.1 – 0.3Very Dry0.01 – 0.1 Bioclimate Class - a 2-part description that combines the value of the Temperature Description field and the Aridity Description field. The alias for this field is ELU Bioclimate Reclass. This layer was created by modifying the dataset documented in the publication: Metzger and others. 2012. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Dataset SummaryAnalysis: Optimized for analysis What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. A service is available providing access to the data table associated with this layer. The data table services can be used by developers to quickly and efficiently query the data and to create custom applications. For more information see the World Ecophysiographic Tables. Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See the Living Atlas Imagery Layers Optimized for Analysis Group for a complete list of imagery layers optimized for analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The downloadable ZIP file contains model documentation and contact information for the model creator. For more information, or a copy of the project report which provides greater model detail, please contact Ryan Urie - traigo12@gmail.com.This model was created from February through April 2010 as a central component of the developer's master's project in Bioregional Planning and Community Design at the University of Idaho to provide a tool for identifying appropriate locations for various land uses based on a variety of user-defined social, economic, ecological, and other criteria. It was developed using the Land-Use Conflict Identification Strategy developed by Carr and Zwick (2007). The purpose of this model is to allow users to identify suitable locations within a user-defined extent for any land use based on any number of social, economic, ecological, or other criteria the user chooses. The model as it is currently composed was designed to identify highly suitable locations for new residential, commercial, and industrial development in Kootenai County, Idaho using criteria, evaluations, and weightings chosen by the model's developer. After criteria were chosen, one or more data layers were gathered for each criterion from public sources. These layers were processed to result in a 60m-resolution raster showing the suitability of each criterion across the county. These criteria were ultimately combined with a weighting sum to result in an overall development suitability raster. The model is intended to serve only as an example of how a GIS-based land-use suitability analysis can be conceptualized and implemented using ArcGIS ModelBuilder, and under no circumstances should the model's outputs be applied to real-world decisions or activities. The model was designed to be extremely flexible so that later users may determine their own land-use suitability, suitability criteria, evaluation rationale, and criteria weights. As this was the first project of its kind completed by the model developer, no guarantees are made as to the quality of the model or the absence of errorsThis model has a hierarchical structure in which some forty individual land-use suitability criteria are combined by weighted summation into several land-use goals which are again combined by weighted summation to yield a final land-use suitability layer. As such, any inconsistencies or errors anywhere in the model tend to reveal themselves in the final output and the model is in a sense self-testing. For example, each individual criterion is presented as a raster with values from 1-9 in a defined spatial extent. Inconsistencies at any point in the model will reveal themselves in the final output in the form of an extent different from that desired, missing values, or values outside the 1-9 range.This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

BSUs were revised in May 2016. New data submitted by Montana and Oregon was used to update the earlier version of this feature class. The biologically significant unit (BSU) is a geographical/spatial area within Greater Sage-Grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. This BSU unit, or subset of this unit is used in the calculation of the anthropogenic disturbance threshold and in the adaptive management habitat trigger. BSU feature classes were submitted by individual states/EISs and consolidated by the Wildlife Spatial Analysis Lab. They are sometimes referred to as core areas/core habitat areas in the explanations below, which were consolidated from metadata submitted with BSU feature classes. These data provide a biological tool for planning in the event of human development in sage-grouse habitats. The intended use of all data in the BLM's GIS library is to support diverse activities including planning, management, maintenance, research, and interpretation. While the BSU defines the geographic extent and scale of these two measures, how they are calculated differs based on the specific measures to reflect appropriate assessment and evaluation as supported by scientific literature.There are 10 BSUs for the Idaho and Southwestern Montana GRSG EIS sub-region. For the Idaho and Southwestern Montana Greater Sage-Grouse Plan Amendment FEIS the biologically significant unit is defined as: a geographical/spatial area within greater sage-grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. Idaho: BSUs include all of the Idaho Fish and Game modeled nesting and delineated winter habitat, based on 2011 inventories within Priority and/or Important Habitat Management Area (Alternative G) within a Conservation Area. There are eight BSUs for Idaho identified by Conservation Area and Habitat Management Area: Idaho Desert Conservation Area - Priority, Idaho Desert Conservation Area - Important, Idaho Mountain Valleys Conservation Area - Priority, Idaho Mountain Valleys Conservation Area - Important, Idaho Southern Conservation Area - Priority, Idaho Southern Conservation Area - Important, Idaho West Owyhee Conservation Area - Priority, and Idaho West Owyhee Conservation Area - Important. Raft River : Utah portion of the Sawtooth National Forest, 1 BSU. All of this areas was defined as Priority habitat in Alternative G. Raft River - Priority. Montana: All of the Priority Habitat Management Area. 1 BSU. SW Montana Conservation Area - Priority. Montana BSUs were revised in May 2016 by the MT State Office. They are grouped together and named by the Population in which they are located: Northern Montana, Powder River Basin, Wyoming Basin, and Yellowstone Watershed. North and South Dakota BSUs have been grouped together also. Within Colorado, a Greater Sage-Grouse GIS data set identifying Preliminary Priority Habitat (PPH) and Preliminary General Habitat (PGH) was developed by Colorado Parks and Wildlife. This data is a combination of mapped grouse occupied range, production areas, and modeled habitat (summer, winter, and breeding).PPH is defined as areas of high probability of use (summer or winter, or breeding models) within a 4 mile buffer around leks that have been active within the last 10 years. Isolated areas with low activity were designated as general habitat.PGH is defined as Greater sage-grouse Occupied Range outside of PPH.Datasets used to create PPH and PGH:Summer, winter, and breeding habitat models. Rice, M. B., T. D. Apa, B. L. Walker, M. L. Phillips, J. H. Gammonly, B. Petch, and K. Eichhoff. 2012. Analysis of regional species distribution models based on combined radio-telemetry datasets from multiple small-scale studies. Journal of Applied Ecology in review. Production Areas are defined as 4 mile buffers around leks which have been active within the last 10 years (leks active between 2002-2011). Occupied range was created by mapping efforts of the Colorado Division of Wildlife (now Colorado Parks and Wildlife –CPW) biologists and district officers during the spring of 2004, and further refined in early 2012. Occupied Habitat is defined as areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. This information was derived from field personnel. A variety of data capture techniques were used including the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing atvarious scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Update August 2012: This dataset was modified by the Bureau of Land Management as requested by CPW GIS Specialist, Karin Eichhoff. Eichhoff requested that this dataset, along with the GrSG managment zones (population range zones) dataset, be snapped to county boundaries along the UT-CO border and WY-CO border. The county boundaries dataset was provided by Karin Eichhoff. In addition, a few minor topology errors were corrected where PPH and PGH were overlapping. Update October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat. Colorado Greater Sage Grouse managment zones based on CDOW GrSG_PopRangeZones20120609.shp. Modified and renumbered by BLM 06/09/2012. The zones were modified again by the BLM in August 2012. The BLM discovered areas where PPH and PGH were not included within the zones. Several discrepancies between the zones and PPH and PGH dataset were discovered, and were corrected by the BLM. Zones 18-21 are linkages added as zones by the BLM. In addition to these changes, the zones were adjusted along the UT-CO boundary and WY-CO boundary to be coincident with the county boundaries dataset. This was requested by Karin Eichhoff, GIS Specialist at the CPW. She provided the county boundaries dataset to the BLM. Greater sage grouse GIS data set identifying occupied, potential and vacant/unknown habitats in Colorado. The data set was created by mapping efforts of the Colorado Division of Wildlife biologist and district officers during the spring of 2004, and further refined in the winter of 2005. Occupied Habitat: Areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. Vacant or Unknown Habitat: Suitable habitat for sage-grouse that is separated (not contiguous) from occupied habitats that either: 1) Has not been adequately inventoried, or 2) Has not had documentation of grouse presence in the past 10 years Potentially Suitable Habitat: Unoccupied habitats that could be suitable for occupation of sage-grouse if practical restoration were applied. Soils or other historic information (photos, maps, reports, etc.) indicate sagebrush communities occupied these areas. As examples, these sites could include areas overtaken by pinyon-juniper invasions or converted rangelandsUpdate October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat and management zones, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat.California and Nevada's BSUs were developed by Nevada Department of Wildlife's Greater Sage-Grouse Wildlife Staff Specialist and Sagebrush Ecosystem Technical Team Representative in January 2015. Nevada's Biologically Significant Units (BSUs) were delineated by merging associated PMUs to provide a broader scale management option that reflects sage grouse populations at a higher scale. PMU boundarys were then modified to incorporate Core Management Areas (August 2014; Coates et al. 2014) for management purposes. (Does not include Bi-State DPS.) Oregon submitted updated BSU boundaries in May 2016, which were incorporated into this latest version. In Oregon, Core Areas were defined by the Oregon Department of Fish and Wildlife (ODFW) to be used by BLM to map Oregon PACs, Priority Areas of Conservation for the Greater Sage-grouse RMP Amendments. The data was projected to R6 Albers and will be used as is. Core Area Approach to Habitat Mitigation for Greater Sage-Grouse in Oregon: The goal of these recommendations is to protect essential habitats to meet habitat and population objectives identified in this Plan. The objective of these

This dataset is a combination of the General and Priority habitat component files that were provided to the FS. The following is the metdata associated with that data. This dataset does not include linkages, and has been isolated to NFS lands within the official NWCO FS GRSG EIS boundaries.Greater sage-grouse GIS data set identifying Preliminary Priority Habitat (PPH) and Preliminary General Habitat (PGH) within Colorado. This data is a combination of mapped grouse occupied range, production areas, and modeled habitat (summer, winter, and breeding).PPH is defined as areas of high probability of use (summer or winter, or breeding models) within a 4 mile buffer around leks that have been active within the last 10 years. Isolated areas with low activity were designated as general habitat.PGH is defined as Greater sage-grouse Occupied Range outside of PPH.Datasets used to create PPH and PGH:Summer, winter, and breeding habitat models. Rice, M. B., T. D. Apa, B. L. Walker, M. L. Phillips, J. H. Gammonly, B. Petch, and K. Eichhoff. 2012. Analysis of regional species distribution models based on combined radio-telemetry datasets from multiple small-scale studies. Journal of Applied Ecology in review.Production Areas are defined as 4 mile buffers around leks which have been active within the last 10 years (leks active between 2002-2011).Occupied range was created by mapping efforts of the Colorado Division of Wildlife (now Colorado Parks and Wildlife –CPW) biologists and district officers during the spring of 2004, and further refined in early 2012. Occupied Habitat is defined as areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. This information was derived from field personnel. A variety of data capture techniques were used including the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing atvarious scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35).Update August 2012: This dataset was modified by the Bureau of Land Management as requested by CPW GIS Specialist, Karin Eichhoff. Eichhoff requested that this dataset, along with the GrSG managment zones (population range zones) dataset, be snapped to county boundaries along the UT-CO border and WY-CO border. The county boundaries dataset was provided by Karin Eichhoff. In addition, a few minor topology errors were corrected where PPH and PGH were overlapping.Update October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat.

Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This service provides a tract-level dataset illustrating the outcome of analysis techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural.

To create this data, analysts first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. Analysts then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. The approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike.

If aggregating tract-level probabilities to larger areas, users are strongly encouraged to use occupied household counts as weights.

We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may:

prefer to use an uncontrolled classification, or

prefer to create more than three categories.

To accommodate these uses, our final tract-level output dataset includes the "raw" probability an average household would describe their neighborhood as urban, suburban, and rural. These probability values can be used to create an uncontrolled classification or additional categories.

The final classification is controlled to AHS national estimates (26.9% urban; 52.1% suburban, 21.0% rural).

  For more information about the 2017 AHS Neighborhood Description Study click on the following visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. 

Data Dictionary: DD_Urbanization Perceptions Small Area Index.