Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

Interesting, largely unexplored data analysis and information retrieval opportunities exist for GIS data. In their current form, traditional data usage patterns for data persisted in shapefiles or spatially-enabled relational databases are limited. Opportunities exist to achieve ESIP’s Winter 2019 theme of ‘increasing the use and value of Earth science data and information’ by transforming geospatial data from their original formats into their Resource Description Framework (RDF) manifestation. This work establishes an innovative workflow enabling the publication for Geospatial data persisted in geospatially enabled databases (PostGIS and MonetDB), ESRI shapefiles and XML, GML, KML, JSON, GeoJSON and CSV documents as graphs of linked open geospatial data. This affords the capability to identify implicit connections between related data that wasn't previously linked e.g. automating the detection of features present within large hydrography datasets as well as smaller regional examples and resolving features in a consistent fashion. This previously unavailable capability is achieved through the use of a semantic technology stack which leverages well matured standards within the Semantic Web space such as RDF as the data model, GeoSPARQL as the data access language and International Resource Identifier’s (IRI) for uniquely identifying and referencing entities such as rivers, streams and other water bodies. In anticipation of NASA’s forthcoming Surface Water Ocean Topography (SWOT – https://swot.jpl.nasa.gov) mission, which once launched in 2021 will make NASA’s first-ever global survey of Earth’s surface water, this work uses Hydrography data products (USGS’s National Hydrography Dataset and other topically relevant examples) as the topic matter. The compelling result is a new, innovative data analysis and information retrieval capability which will increases the use and value of Earth science data (GIS) and information. This presentation was given at the Earth Science Information Partners (ESIP) Winter Meeting in January 2019.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

Global Human Footprint Index represents the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset SummaryThe Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers of human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced–the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).Recommended CitationWildlife Conservation Society - WCS, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M61H5F. Accessed DAY MONTH YEAR.

California is doubling down on efforts to achieve carbon neutrality and build resilience to the impacts of climate change. While the impacts vary in different regions of California, every area of the state is already experiencing climate change impacts. The best available science tells us that impacts will continue into the future and will include increases in annual temperatures, changes to precipitation patterns such as longer and more intense droughts, increases in wildfire areas and severity, sea level rise, ocean warming, and the spread of invasive species.

The Climate Explorer contains interactive viewers allowing users to explore predicted changes in temperature and precipitation, sea level rise and storm severity, and opportunities to implement nature-based solutions, which are actions that work with and enhance nature to help address societal challenges on California’s landscapes.

The temperature and precipitation viewer provides access to a subset of the data developed for the 4th California Climate Assessment and made available through Cal-Adapt.

The Sea Level Rise viewer includes data from the U.S. Geological Survey’s Coastal Storm Modeling System (CoSMoS), with more variables available for exploration at Our Coast, Our Future.

CDFW BIOS GIS Dataset, Contact: Laura Ryley, Description: This dataset offers a link to the California segment of the Surf Your Watershed service offered by the United States Environmental Protection Agency (EPA). Surf Your Watershed is a service to help locate, use, and share watershed environmental information. A driving force behind Surf Your Watershed is to get environmental information into the hands of active citizens and groups to help those people connect and share information, ideas, and assistance.

OVERVIEWThis site is dedicated to raising the level of spatial and data literacy used in public policy. We invite you to explore curated content, training, best practices, and datasets that can provide a baseline for your research, analysis, and policy recommendations. Learn about emerging policy questions and how GIS can be used to help come up with solutions to those questions.EXPLOREGo to your area of interest and explore hundreds of maps about various topics such as social equity, economic opportunity, public safety, and more. Browse and view the maps, or collect them and share via a simple URL. Sharing a collection of maps is an easy way to use maps as a tool for understanding. Help policymakers and stakeholders use data as a driving factor for policy decisions in your area.ISSUESBrowse different categories to find data layers, maps, and tools. Use this set of content as a driving force for your GIS workflows related to policy. RESOURCESTo maximize your experience with the Policy Maps, we’ve assembled education, training, best practices, and industry perspectives that help raise your data literacy, provide you with models, and connect you with the work of your peers.

The State Lands Commission has prepared the Significant Lands Inventory (report) for the California Legislature as a general identification and classification of those unconveyed State school lands and tide and submerged lands which possess significant environmental values. The publication incorporates evaluated and pertinent comments received on the initial draft report which was circulated statewide in February 1975.The absence of a particular digitized waterway in the dataset does not mean that the State does not claim ownership of that parcel or waterway, or that such specific parcel or waterway has no significant environmental values. This dataset is not intended to establish ownership, only to identify those parcels which possess significant environmental values. Staff was unable to physically inventory all of the considered lands; instead, the advice and participation of those with known environmental expertise was utilized as additional to staff survey.Tide and submerged lands are digitized in the WaterBody and WaterLine feature classes; WaterLines for coastal areas, WaterBody for inland areas. Tide and submerged lands under the jurisdiction of the State Lands Commission are those sovereign lands received from the Federal Government by virtue of California's admission to the Union on an equal footing with the original States. Such lands, and State interest therein, are generally the lands waterward of the ordinary high water mark of the Pacific Ocean (seaward to a three-mile limit); tidal bays, sloughs, estuaries; and, navigable lakes and streams within the State.School Lands are digitized in the SchoolLand feature class. State school lands under the jurisdiction of the Commission are largely composed of the 16th and 36th sections of each township. The Federal Government transferred these lands to the State in 1853, in order to establish a financial foundation for a public school system. In cases where the 16th and 36th sections were mineral in character, incomplete as to acreage total, or already claimed or granted by the Federal Government, the State was permitted to select other lands "in lieu" of the specific sections.The public trust of commerce, navigation and fisheries which the State retains on patented sovereign lands should also be considered included in this inventory. Wherever a waterway, or body of water, is listed or mapped, the common trust state interest in patented sovereign lands, if any, is also included.The State Lands Commission emphasized when it adopted this report at its December 1, 1975 meeting that all tide and submerged lands are significant by the nature of their public ownership. Only because of the methodology used for this report are all of these waterways not specifically listed in this inventory.It is the intent of the State Lands Commission that the Significant Lands Inventory be periodically updated. This dataset should be considered informational, to assist the Legislature, the Commission, and the public in considering the environmental aspects of a proposed project and the significant values to be protected therein.

The geospatial data reflected in the protected area layer mostly pertain to natural and wilderness areas where development of utility-scale renewable energy is prohibited and were heavily based on RETI 1.0 blackout areas.1 The protected area layer is distinguished for solar PV technology by the BLM greater sage grouse habitat management area which provides separate exclusion areas for the different technology types. Tables 1 and 2 below lists the data sources and precise selection query for each dataset, if applicable, that make up the protected area layer.Table 1: Datasets used in the Protected Area Layer

Dataset

Example Designations

Citation or hyperlink

PAD-US (CBI Edition)

National Parks, GAP Status 1 and 2, State Parks, Open Spaces, Natural Areas

“PAD-US (CBI Edition) Version 2.1b, California”. Conservation Biology Institute. 2016. https://databasin.org/datasets/64538491f43e42ba83e26b849f2cad28.

Conservation Easements

California Conservation Easement Database (CCED), 2022a. 2022. www.CALands.org. Accessed December 2022.

Inventoried Roadless Areas

“Inventoried Roadless Areas.” US Forest Service. Dec 12, 2022. https://www.fs.usda.gov/detail/roadless/2001roadlessrule/maps/?cid=stelprdb5382437

BLM National Landscape Conservation System

Wilderness Areas, Wilderness Study Areas, National Monuments, National Conservation Lands, Conservation Lands of the California Desert, Scenic Rivers

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-areas

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-study-areas

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-national-monuments-nca-forest-reserves-other-poly/

Greater Sage Grouse Habitat Conservation Areas (BLM)

For solar technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’) For wind technology: BLMP_Managm = ‘PHMA’

“Nevada and Northeastern California Greater Sage-Grouse Approved Resource Management Plan Amendment.” US Department of the Interior Bureau of Land Management Nevada State Office. 2015. https://eplanning.blm.gov/public_projects/lup/103343/143707/176908/NVCA_Approved_RMP_Amendment.pdf

Other BLM Protected Areas

Areas of Critical Environmental Concern (ACECs), Recreation Areas (SRMA, ERMA, OHV Designated Areas), including Vinagre Wash Special Recreation Management Area, National Scenic Areas, including Alabama Hills National Scenic Area

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-off-highway-vehicle-designations

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-areas-of-critical-environmental-concern

BLM, personal communication, November 2, 2022.

Mono Basin NFSA

https://pcta.maps.arcgis.com/home/item.html?id=cf1495f8e09940989995c06f9e290f6b#overview

Terrestrial 30x30 Conserved Areas

Gap Status 1 and 2

CA Nature. 30x30 Conserved Areas, Terrestrial. 2021. https://www.californianature.ca.gov/datasets/CAnature::30x30-conserved-areas-terrestrial/ Accessed September 2022.

CPAD

Open Spaces and Parks under city or county level

California Protected Areas Database (CPAD), 2022b. 2022. https://www.calands.org/cpad/. Accessed February 22, 2023.

USFS Special Interest Management Areas

Research Natural Areas, Recreation Areas, National Recreational Trail, Experimental Forest, Scenic Area

https://data-usfs.hub.arcgis.com/datasets/usfs::special-interest-management-areas-feature-layer/about

Proposed Protected Area

Molok Luyuk Extension (Berryessa Mtn NM Expansion)

CalWild, personal communication, January 19, 2023.

Table 2: Query Definition for Components of Protected Areas Dataset SQL Query PAD-US (CBI Edition) p_des_tp IN ('Wild, Scenic and Recreation River', 'Area of Critical Environmental Concern', 'Ecological Reserve', 'National Conservation Area', 'National Historic Site', 'National Historical Park', 'National Monument', 'National Park General Public Land', 'National Preserve', 'National Recreation Area', 'National Scenic Area', 'National Seashore', 'Wilderness Study Area', 'Wilderness Area', 'Wildlife Management Area', 'State Wildlife Management Area', 'State Park', 'State Recreation Area', 'State Nature Preserve/Reserve', 'State Natural Area', 'State Ecological Reserve', 'State Cultural/Historic Area', 'State Beach', 'Special Management Area', 'National Wildlife Refuge', 'Natural Area', 'Nature Preserve', 'Research Natural Area') Or s_des_tp IN ('Natioanal Monument', 'National Monument', 'National Park General Public Land', 'National Preserve', 'National Recreation Area', 'National Scenic Area', 'National Seashore', 'National Conservation Area', 'Area of Critical Environmental Concern', 'National Wildlife Refuge', 'State Park', 'State Wildlife Area', 'State Wildlife Management Area', 'State Wildlife Refuge', 'State Ecological Reserve', 'Wild, Scenic and Recreation River', 'Wilderness Area', 'Wildlife Management Area') Or t_des_tp IN ('National Monument', 'National Park General Public Land', 'National Recreation Area', 'Area of Critical Environmental Concern', 'National Conservation Area', 'State Wildlife Management Area', 'Wild, Scenic and Recreation River', 'Wildlife Management Area') Or p_loc_ds IN ('Ecological Reserve', 'Research and Educational Land') Or gap_sts IN ('1', '2') Or own_type = 'Private Conservation Land' Or (own_type = 'Local Land' And (p_des_tp LIKE '%"Open Space"%' Or p_des_tp LIKE '%Park%' Or p_des_tp LIKE '%Recreation Area%' Or p_des_tp LIKE '%Natural Area%')) Or (p_des_tp = 'Other State Land' And (p_loc_ds IN ('State Vehicular Recreation Area', 'BLM Resource Management Area', 'Resource Management Area') And gap_sts <> '2')) CPAD AGNCY_LEV IN ('City', 'County') And ACCESS_TYP = 'Open Access' And (UNIT_NAME LIKE '%Park%' OR UNIT_NAME LIKE '%Open Space%' OR UNIT_NAME LIKE '%park%' OR UNIT_NAME LIKE '%Recreation Area%' OR UNIT_NAME LIKE '%Natural Area%' OR GAP2_acres > 0 OR GAP1_acres >0) Greater Sage- Grouse Habitat Conservation Areas (BLM) For Solar Technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’) For Wind Technology: BLM_Managm = ‘PHMA’ This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.For a complete description of the creation of this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.[1] Final RETI Phase 2A report, available at https://ww2.energy.ca.gov/2009publications/RETI-1000-2009-001/RETI-1000-2009-001-F-REV2.PDF.

Change Log: Version 1.1 (January 22, 2024 10:29 AM) Layer revised to allow for gaps to remain when combining all components of the protected area layer.

Based primarily on the most recent release of LANDFIRE v2.0.0, this generalized land cover dataset provides full coverage of California including to the three nautical mile limit offshore. It represents a ground condition of 2016 divided into 30mx30m cells across the entire state. The state is grouped into the following land cover classes: forests, shrublands and chaparral, grasslands, croplands, wetlands, seagrasses and seaweeds, developed lands, and sparsely vegetated lands. The mapped area has been extended offshore to three nautical miles. Lakes, reservoirs, rivers, and oceans that do not overlay seagrasses and seaweeds are identified in as “open water.” LANDFIRE v.2.0.0 provides the source for much of the land cover and is an integrated dataset with many layers. The Existing Vegetation Type (EVT) and Biophysical Settings (BPS) layers provide inputs to this data set. The EVT layer contains data on life form (tree, shrub, herb, developed, agriculture, sparse, barren, snow-ice, or water), a named vegetation type, and notes on recent disturbance. These are used to assign a likely generalized land cover type to each pixel. This result is then refined using the BPS layer to suggest the land cover that might exist in recently disturbed (fire or logging) areas absent that disturbance. These results are then supplemented through the creation of a seagrasses and seaweeds dataset by combining data on the presence of eelgrass and kelp canopy and replacing the water category with seagrasses and seaweeds where it is present.These data result from the integration of remote sensing (satellite imagery analysis), with field data, using computer algorithms under the oversight of the LANDFIRE team or the teams developing the seagrass and kelp maps. Errors are expected in all data and while every attempt is made to minimize and understand them, they cannot be eliminated. As a result, the cells in the data represent an estimate of what is on the ground at that specific location. Validation techniques used in the production of the data help identify and allow for correction of gross errors, but individual pixels, or even small groupings of them may differ from real world conditions. Similarly, while efforts are made to be consistent with the selection of the source satellite data, the difference between seasons or a wet versus dry year do impact the final maps, notably water and wetlands.Data SourcesLANDFIRE: LANDFIRE Existing Vegetation Type layer.(2013 - 2021). U.S. Department of Interior, Geological Survey.[Online]. Available: https://landfire.gov/version_download.php [Accessed: February 3, 2021].LANDFIRE: LANDFIRE Biophysical Setting layer.(2013 - 2021). U.S. Department of Interior, Geological Survey.[Online]. Available: https://landfire.gov/version_download.php [Accessed: February 3, 2021].Bell, T, K. Cavanaugh, D. Siegel. 2020. SBC LTER: Time series of quarterly NetCDF files of kelp biomass in the canopy from Landsat 5, 7 and 8, since 1984 (ongoing) ver 13. Environmental Data Initiative. https://doi.org/10.6073/pasta/5d3fb6fd293bd403a0714d870a4dd7d8. Accessed 2021-04-08. (Data extraction performed by T. Bell April 8, 2021)Eelgrass Survey GIS Data version 2.0 (2017, updated 2020), National Marine Fisheries Service West Coast Region. Available: https://www.sfei.org/data/eelgrass-survey-gis-data#sthash.u94SjLu7.afUwqGJA.dpbs [Accessed: April 6, 2021)

Sea-level rise (SLR) due to climate change is a serious global threat: The scientific evidence is now overwhelming. Continued growth of greenhouse gas emissions and associated global warming could well promote SLR of 1m in this century, and unexpectedly rapid breakup of the Greenland and West Antarctic ice sheets might produce a 3-5m SLR. In this research, we have assessed the consequences of continued SLR for 84 coastal developing countries. Geographic Information System (GIS) software has been used to overlay the best available, spatially-disaggregated global data on critical impact elements (land, population, agriculture, urban extent, wetlands, and GDP), with the inundation zones projected for 1-5m SLR. This research was carried out by the World Bank in 2006, and was funded by the Canadian Trust Fund (TF030569) sponsored by the Canadian International Development Agency (CIDA).

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. http://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains diffuse horizontal irradiation (DIF) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characeristics: DIF LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 198.94 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Lesson 1. An Introduction to working with multispectral satellite data in ArcGIS Pro In which we learn: • How to unpack tar and gz files from USGS EROS • The basic map interface in ArcGIS • How to add image files • What each individual band of Landsat spectral data looks like • The difference between: o Analysis-ready data: surface reflectance and surface temperature o Landsat Collection 1 Level 3 data: burned area and dynamic surface water o Sentinel2data o ISRO AWiFS and LISS-3 data Lesson 2. Basic image preprocessing In which we learn: • How to composite using the composite band tool • How to represent composite images • All about band combinations • How to composite using raster functions • How to subset data into a rectangle • How to clip to a polygon Lesson 3. Working with mosaic datasets In which we learn: o How to prepare an empty mosaic dataset o How to add images to a mosaic dataset o How to change symbology in a mosaic dataset o How to add a time attribute o How to add a time dimension to the mosaic dataset o How to view time series data in a mosaic dataset Lesson 4. Working with and creating derived datasets In which we learn: • How to visualize Landsat ARD surface temperature • How to calculate F° from K° using ARD surface temperature • How to generate and apply .lyrx files • How to calculate an NDVI raster using ISRO LISS-3 data • How to visualize burned areas using Landsat Level 3 data • How to visualize dynamic surface water extent using Landsat Level 3 data

National Monuments Service - Archaeological Survey of Ireland. Published by Department of Housing, Local Government and Heritage. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This Archaeological Survey of Ireland dataset is published from the database of the National Monuments Service Sites and Monuments Record (SMR). This dataset also can be viewed and interrogated through the online Historic Environment Viewer: https://heritagedata.maps.arcgis.com/apps/webappviewer/index.html?id=0c9eb9575b544081b0d296436d8f60f8

A Sites and Monuments Record (SMR) was issued for all counties in the State between 1984 and 1992. The SMR is a manual containing a numbered list of certain and possible monuments accompanied by 6-inch Ordnance Survey maps (at a reduced scale). The SMR formed the basis for issuing the Record of Monuments and Places (RMP) - the statutory list of recorded monuments established under Section 12 of the National Monuments (Amendment) Act 1994. The RMP was issued for each county between 1995 and 1998 in a similar format to the existing SMR. The RMP differs from the earlier lists in that, as defined in the Act, only monuments with known locations or places where there are believed to be monuments are included.

The large Archaeological Survey of Ireland archive and supporting database are managed by the National Monuments Service and the records are continually updated and supplemented as additional monuments are discovered. On the Historic Environment viewer an area around each monument has been shaded, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes. This data has been released for download as Open Data under the DPER Open Data Strategy and is licensed for re-use under the Creative Commons Attribution 4.0 International licence. http://creativecommons.org/licenses/by/4.0

Please note that the centre point of each record is not indicative of the geographic extent of the monument. The existing point centroids were digitised relative to the OSI 6-inch mapping and the move from this older IG-referenced series to the larger-scale ITM mapping will necessitate revisions. The accuracy of the derived ITM co-ordinates is limited to the OS 6-inch scale and errors may ensue should the user apply the co-ordinates to larger scale maps. Records that do not refer to 'monuments' are designated 'Redundant record' and are retained in the archive as they may relate to features that were once considered to be monuments but which on investigation proved otherwise. Redundant records may also refer to duplicate records or errors in the data structure of the Archaeological Survey of Ireland.

This dataset is provided for re-use in a number of ways and the technical options are outlined below. For a live and current view of the data, please use the web services or the data extract tool in the Historic Environment Viewer. The National Monuments Service also provide an Open Data snapshot of its national dataset in CSV as a bulk data download. Users should consult the National Monument Service website https://www.archaeology.ie/ for further information and guidance on the National Monument Act(s) and the legal significance of this dataset.

Open Data Bulk Data Downloads (version date: 23/08/2023)

The Sites and Monuments Record (SMR) is provided as a national download in Comma Separated Value (CSV) format. This format can be easily integrated into a number of software clients for re-use and analysis. The Longitude and Latitude coordinates are also provided to aid its re-use in web mapping systems, however, the ITM easting/northings coordinates should be quoted for official purposes. ERSI Shapefiles of the SMR points and SMRZone polygons are also available The SMRZones represent an area around each monument, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes.

GIS Web Service APIs (live views):

For users with access to GIS software please note that the Archaeological Survey of Ireland data is also available spatial data web services. By accessing and consuming the web service users are deemed to have accepted the Terms and Conditions. The web services are available at the URL endpoints advertised below:

SMR; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMROpenData/FeatureServer

SMRZone; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMRZoneOpenData/FeatureServer

Historic Environment Viewer - Query Tool

The "Query" tool can alternatively be used to selectively filter and download the data represented in the Historic Environment Viewer. The instructions for using this tool in the Historic Environment Viewer are detailed in the associated Help file: https://www.archaeology.ie/sites/default/files/media/pdf/HEV_UserGuide_v01.pdf...

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled land use classes for each year. See additional information about land use in the Entity_and_Attribute_Information section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.References:Breiman, L. (2001). Machine Learning (Vol. 45, Issue 3, pp. 261-277). https://doi.org/10.1023/a:1017934522171 Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012 Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010 Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031 Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Weiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoServiceFor complete information, please visit https://data.gov.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.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 this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

This data release includes GIS datasets supporting the Colorado Legacy Mine Lands Watershed Delineation and Scoring tool (WaDeS), a web mapping application available at https://geonarrative.usgs.gov/colmlwades/. Water chemistry data were compiled from the U.S. Geological Survey (USGS) National Water Information System (NWIS), U.S. Environmental Protection Agency (EPA) STORET database, and the USGS Central Colorado Assessment Project (CCAP) (Church and others, 2009). The CCAP study area was used for this application. Samples were summarized at each monitoring station and hardness-dependent chronic and acute toxicity thresholds for aquatic life protections under Colorado Regulation No. 31 (CDPHE, 5 CCR 1002-31) for cadmium, copper, lead, and/or zinc were calculated. Samples were scored according to how metal concentrations compared with acute and chronic toxicity thresholds. The results were used in combination with remote sensing derived hydrothermal alteration (Rockwell and Bonham, 2017) and mine-related features (Horton and San Juan, 2016) to identify potential mine remediation sites within the headwaters of the central Colorado mineral belt. Headwaters were defined by watersheds delineated from a 10-meter digital elevation dataset (DEM), ranging in 5-35 square kilometers in size. Python and R scripts used to derive these products are included with this data release as documentation of the processing steps and to enable users to adapt the methods for their own applications. References Church, S.E., San Juan, C.A., Fey, D.L., Schmidt, T.S., Klein, T.L. DeWitt, E.H., Wanty, R.B., Verplanck, P.L., Mitchell, K.A., Adams, M.G., Choate, L.M., Todorov, T.I., Rockwell, B.W., McEachron, Luke, and Anthony, M.W., 2012, Geospatial database for regional environmental assessment of central Colorado: U.S. Geological Survey Data Series 614, 76 p., https://doi.org/10.3133/ds614. Colorado Department of Public Health and Environment (CDPHE), Water Quality Control Commission 5 CCR 1002-31. Regulation No. 31 The Basic Standards and Methodologies for Surface Water. Effective 12/31/2021, accessed on July 28, 2023 at https://cdphe.colorado.gov/water-quality-control-commission-regulations. Horton, J.D., and San Juan, C.A., 2022, Prospect- and mine-related features from U.S. Geological Survey 7.5- and 15-minute topographic quadrangle maps of the United States (ver. 8.0, September 2022): U.S. Geological Survey data release, https://doi.org/10.5066/F78W3CHG. Rockwell, B.W. and Bonham, L.C., 2017, Digital maps of hydrothermal alteration type, key mineral groups, and green vegetation of the western United States derived from automated analysis of ASTER satellite data: U.S. Geological Survey data release, https://doi.org/10.5066/F7CR5RK7.