ArcGIS has many analysis and geoprocessing tools that can help you solve real-world problems with your data. In some cases, you are able to run individual tools to complete an analysis. But sometimes you may require a more comprehensive way to create, share, and document your analysis workflow.In these situations, you can use a built-in application called ModelBuilder to create a workflow that you can reuse, modify, save, and share with others.In this course, you will learn the basics of working with ModelBuilder and creating models. Models contain many different elements, many of which you will learn about. You will also learn how to work with models that others create and share with you. Sharing models is one of the major advantages of working with ModelBuilder and models in general. You will learn how to prepare a model for sharing by setting various model parameters.After completing this course, you will be able to:Identify model elements and states.Describe a prebuilt model's processes and outputs.Create and document models for site selection and network analysis.Define model parameters and prepare a model for sharing.

This ArcGIS model inserts a file name into a feature class attribute table. The tool allows an user to identify features by a field that reference the name of the original file. It is useful when an user have to merge multiple feature classes and needs to identify which layer the features come from.

The Habitat Suitability Analysis was built using ArcGIS Pro's ModelBuilder tool. This program does not have an option to save the model's inputs as a relative file path. As a result, the model may not run because it's searching for each layer's original file path. If this happens, we have included a file titled Habitat_Suitability_Analysis_Script that outlines the processes we used to build the model. This submission contains three folders and three supplemental files. The folder titled "Data" includes all of the raw data and data input in the Habitat Suitability Analysis. The folder titled "Scripts" describes the steps to build the Habitat Suitability Analysis model in ArcGIS Pro. The Results folder contains the Habitat Suitability Analysis model and the data that was input into the model. The supplemental files are a file titled "Dryad_Folder_Contents" which describes the contents of every folder in this submission, and a file titled "Habitat_Suitability_Analysis_README" which contain...

The Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format is required by most, grid or raster, spatial modelling tools such as the Multi-criteria Analysis Shell for Spatial Decision Support (MCAS-S). Grid Garage contains 36 tools designed to save you time by batch processing repetitive GIS tasks as well diagnosing problems with data and capturing a record of processing step and any errors encountered.

Grid Garage provides tools that function using a list based approach to batch processing where both inputs and outputs are specified in tables to enable selective batch processing and detailed result reporting. In many cases the tools simply extend the functionality of standard ArcGIS tools, providing some or all of the inputs required by these tools via the input table to enable batch processing on a 'per item' basis. This approach differs slightly from normal batch processing in ArcGIS, instead of manually selecting single items or a folder on which to apply a tool or model you provide a table listing target datasets. In summary the Grid Garage allows you to:

• List, describe and manage very large volumes of geodata.
• Batch process repetitive GIS tasks such as managing (renaming, describing etc.) or processing (clipping, resampling, reprojecting etc.) many geodata inputs such as time-series geodata derived from satellite imagery or climate models.
• Record any errors when batch processing and diagnose errors by interrogating the input geodata that failed.
• Develop your own models in ArcGIS ModelBuilder that allow you to automate any GIS workflow utilising one or more of the Grid Garage tools that can process an unlimited number of inputs.
• Automate the process of generating MCAS-S TIP metadata files for any number of input raster datasets.

The Grid Garage is intended for use by anyone with an understanding of GIS principles and an intermediate to advanced level of GIS skills. Using the Grid Garage tools in ArcGIS ModelBuilder requires skills in the use of the ArcGIS ModelBuilder tool.

Download Instructions: Create a new folder on your computer or network and then download and unzip the zip file from the GitHub Release page for each of the following items in the 'Data and Resources' section below. There is a folder in each zip file that contains all the files. See the Grid Garage User Guide for instructions on how to install and use the Grid Garage Toolbox with the sample data provided.

Pacific lamprey (Entosphenus tridentata) are native fish to the Columbia River Basin. Over the past 60 years, anthropogenic disturbances have contributed to a 95% decline of historical population numbers. Member-tribes of the Columbia River Inter-Tribal Fish Commission have acknowledged the importance of Pacific lamprey to the Columbia River ecosystem and expressed concern about the loss of an essential tribal cultural resource. As a result, the Columbia River Inter-Tribal Fish Commission created the Tribal Pacific Lamprey Restoration Plan to halt their decline, re-establish the species, and restore the population to sustainable, harvestable levels throughout their historical range. Limited knowledge about the movement and preferred habitat of larval Pacific lamprey, such as optimal habitat conditions, demographic information, and species resilience, results in challenges to monitor and protect the species. Pacific lamprey is known to use the mainstem Columbia River to migrate between their spawning grounds and the Pacific Ocean. However, dams, levees, and culverts within the Columbia River Estuary and adjacent tributaries have restricted the lamprey's access to spawning grounds and other upstream habitats. These restrictions have prompted conservation and restoration efforts to better understand how Pacific lamprey utilizes the Columbia River Estuary. Here, we address these knowledge gaps in an effort to aid restoration initiatives by completing a Habitat Suitability Analysis to determine where optimal larval Pacific lamprey habitat may exist in the Columbia River Estuary. The project identified the spatial and temporal distribution of suitable habitat for larval Pacific lamprey and generated recommendations to address habitat-related knowledge gaps and further evaluate anthropogenic threats to their recovery. The results of the Habitat Suitability Analysis suggest that habitat conditions in the Columbia River itself are unable to support larval lamprey year-round, but may provide suitable habitat on a seasonal basis due to spatial and temporal limitations. However, we stress that our analyses were necessarily limited to aquatic conditions and that the temperature of the water column used in our analyses may differ from the temperature within fine sediments, where larval lamprey burrow. Our results imply that suitable lamprey habitat is present at times throughout the year in the Columbia River Estuary, and these locations can be used to support habitat restoration and conservation strategies for improving the species' recovery. Anthropogenic threats to the Columbia River continue to alter habitat conditions, including average water temperature, salinity, and sedimentation. Laboratory experiments have provided insight into the potential impacts of changing temperature and salinity on larval Pacific lamprey, where elevated water temperatures can affect their development and elevated salinity levels can result in larval mortality. In addition, anthropogenic disturbances such as dams, levees, and culverts have cut off the Columbia River Estuary's floodplain habitats from the mainstem Columbia River, decreased sedimentation rates, and separated adult lamprey from the floodplains and tributaries that they use to spawn. The presence of these barriers in the region can inhibit the distribution of fine sediments in the river, limiting where larval lamprey burrow and develop. The burrowing behavior of larval lamprey has yet to fully be investigated in the Columbia River Estuary. Limited research may be due to the lack of resources for studying Pacific lamprey's life cycle, habitat, and population dynamics since they are not federally designated as an endangered species, like resident salmonid species. This has further added to the challenge of understanding the species and restoring its population to sustainable numbers. To the best of our knowledge, this project is the first to explore spatial and temporal trends of suitable larval Pacific lamprey habitat conditions in the Columbia River Estuary. The Habitat Suitability Analysis provides technical information about the presence and distribution of suitable conditions to address habitat-related uncertainties. The member-tribes of the Columbia River Inter-Tribal Fish Commission and their collaborators can incorporate the information into current and future Pacific lamprey restoration, conservation, and education programs to enhance general understanding of lamprey populations throughout the Columbia River Basin. Key recommendations are provided to address additional knowledge gaps and prioritize future restoration projects in the Columbia River Basin including the refinement of the Habitat Suitability Analysis, evaluation of barrier effects on Pacific lamprey passage, and assessment of climate change scenarios on larval lamprey habitat. The Habitat Suitability Analysis uses salinity, temperature, and geomorphology data to identify suitable larval Pacific lamprey ...

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.

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.

This permit conversion tool converts ePermit .xls files to quarter-quarter or lat/long locations. Also included is a public lands survey geodatabase necessary to run the POU tool. This Model Builder toolset is available for ArcGIS 10.1-5. The March 2018 update provided here tests for field types and processes the fields accordingly.

Site Wind Right (SWR) is The Nature Conservancy’s approach to promoting smart, renewable wind energy in the right places, that is, where wind development is unlikely to encounter significant wildlife-related conflict, project delays and cost overruns. It represents a collaborative effort involving more than 50 scientists and GIS professionals from across the organization who built upon published studies of wind and wildlife interactions.This map shows the “wind belt” of the United States, a utility-scale, land-based 80-meter resolution wind speed map. Used to illustrate high wind resource areas within the central U.S., this map contains modified NREL and AWS Truepower wind maps (http://energy.gov/eere/wind/windexchange).

The interactive online map uses GIS technology and pulls from more than 100 data sets on wind resources, wildlife habitat, current land use and infrastructure to help inform siting decisions across 17 states in the Central US. The region encompasses nearly 80 percent of the country’s current and planned onshore wind capacity. The map was created to identify appropriate areas for wind develop, and is an important source of information for screening early in the project siting process. It can also be used to support application of the US Fish and Wildlife Service Land-Based Wind Energy Guidelines, specifically Tier 1 and Tier 2 evaluations.

Combining the SWR map with other land suitability factors suggests that over 1,000 gigawatts of wind energy may be developed exclusively in areas of low conservation impact. The results of this analysis indicate that it is possible to accelerate a clean, low-impact energy future, one that supports energy, climate and conservation goals.

Region: Great Plains/Midwest U.S.Partners: Local, state, and federal wildlife agencies, partners, and conservation professionals

Website: nature.org/sitewindrightContacts: Chris Hise [chise@tnc.org]; Nathan Cummins [ncummins@tnc.org]

Software: ArcMap (10.1-10.6), ArcGIS Pro (2.2), ModelBuilder, ArcGIS Online

Data sources: U.S. Fish and Wildlife Service, U.S. Geological Survey, The Nature Conservancy, State DNR and Heritage programs, and other data. For details, see methods paper, Appendix A (nature.org/sitewindright)

Primary care physician Preliminary test gravity model results for ZIP Codes. Results derived using ArcGIS ModelBuilder. Class breaks (<1,000 1,000 - 2,000 >2,000). Prepared March, 2015 - more information at http://www.unm.edu/~lspear/health_stuff.html.

This dataset is a compilation of existing and new publicly-available geoscience data that were used to conduct a geothermal play fairway analysis (PFA) in north-western Argentina in the Jujuy and Salta provinces. The 'Model_Input_Datasets' folder includes the original ArcGIS shapefiles and rasters that were used to build the geological favorability models (attribute models) for heat, permeability and fluid. Detailed metadata for each dataset (e.g. provenance; use constraints etc.) can be viewed for each file in ArcCatalog. The 'Area_One' shapefile represents the study area boundary that was used to define the processing extent for the PFA models. The favorability models were built using the ModelBuilder tool in ESRI ArcGIS (this model was run using ESRI ArcMap version 10.7.1). The 'Python_Scripts' folder includes python scripts for building each of the three attribute models (heat, permeability, fluid) and the total combined geothermal favorability model using either the sum (voter) or product (veto) methods. The 'ProcessingNotes_for_PFA_model_development.v2' file (.xlsx or .pdf format) includes a description of the various steps used to weight individual data attribute fields, data layers themselves, and overall model development. This file complements the python scripts.

This dataset accompanies a paper submitted to Geothermics by Lindsey et al., 2021, 'Geothermal play fairway analysis in north-western Argentina'.

Mountain lions need room to roam, and the rugged mountains northwest of Los Angeles provide this protected species the space it needs to hunt and breed. The problem? Their population is suffering as these creatures are killed crossing the roads intersecting their habitat. The solution? Build bridges over these roads enabling mountain lions to proliferate in safety. In this lesson, you'll use ArcGIS Pro to create the geoprocessing models identifying the best locations for these overpasses.

In this lesson you will build skills in the these areas:

• Building a model in ModelBuilder
• Creating a suitability model
• Processing rasters

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

The hypsometric integral (HI) is one of the most commonly used measures that geomorphologists use to describe the shape of the Earth’s surface. A hypsometric integral is usually calculated by plotting the cumulative height and the cumulative area under that height for individual watersheds and then taking the area under that curve to get the hypsometric integral. In a GIS hypsometric integral is calculated by slicing watersheds into elevation bands and plotting the cumulative area for each band. Due to the iterative nature that is required for calculating hypsometric integral it tends to be one of the harder to calculate watershed variables, and thus the need for an automated tool. Although there are instructions online for how to calculate HI in ArcGIS this tool automates the processes and doesn’t require users to do their own plotting or export results to spreadsheets.

This toolbox contains two models. Hypsometric Integral (for shapefiles only) is the main model that most users will want to run. Hypsometric Integral (submodel) is a model that is nested within the Hypsometric Integral (for shapefiles only) model and doesn’t need to be run by itself. The tool computes the hypsometric integral for a given watershed. A new shapefile will be created representing the same watershed the user inputs, but includes a new field, "HI," representing hypsometric integral percentages.

In some instances the Hypsometric Integral (for shapefiles) will show up with a red X and won’t be useable. The workaround for this is to open the Hypsometric Integral (for shapefiles) tool in edit mode (ModelBuilder) delete the Hypsometric Integral (submodel) and drag in your version of the Hypsometric Integral (submodel). Re-connect the following parameters: input DEM, Input Watershed, TempWorkspace, and then connect the output (HI Values for all Watersheds) to the Append tool. Click save.

1. The word document "Source and processing of GIS-RS input data" contains the information of the used Sentinel-2 and DEM data and details of the followed pre-processing steps.2. Calculation of weighted average: the Geodatabase and the Toolbox can be opened in ArcGis. The model in the "WeightedAverage" tbx contains the steps to calculate the values of four vegetation indices for each pixel portion inside each plot. The resulting attribute table can be copied to excel and values/plot can be calculated using the proportional area of each polygon in proportion to the plot area. Notes: a) some very small polygons will require manual filling of the VIs value. b) Check the new location (folder) of the inputs and outputs for each step of the model (model builder).3. The following geo files are input for the model builder in ArcGis (model in the tbx) that need to be transformed into a geodatabase "WeightedAverage":- Shapefiles: all files named "Buffer_Plots_2017", "FishNet10m", "FishNet_DEM12.5", "PlotPolygones", "FishDEM30".- Raster files: all files named "SAVI_Clip", "bi_Clip", "ireci_Clip", "mtci_Clip", "ndi45_Clip", "Slope30utm", "Slope_Degree".4. Models. The R script ModelsP1 contains the information on the model selection process and parameter estimation using K-fold using the examples of two models for two ecosystem services. Use the provided excel file as input. Date Copyrighted: 2020-06-01 Date Submitted: 2020-08-19

Site Wind Right (SWR) is The Nature Conservancy’s approach to promoting smart, renewable wind energy in the right places, that is, where wind development is unlikely to encounter significant wildlife-related conflict, project delays and cost overruns. It represents a collaborative effort involving more than 50 scientists and GIS professionals from across the organization who built upon published studies of wind and wildlife interactions.This map shows the “wind belt” of the United States, a utility-scale, land-based 80-meter resolution wind speed map. Used to illustrate high wind resource areas within the central U.S., this map contains modified NREL and AWS Truepower wind maps (http://energy.gov/eere/wind/windexchange).

The interactive online map uses GIS technology and pulls from more than 100 data sets on wind resources, wildlife habitat, current land use and infrastructure to help inform siting decisions across 17 states in the Central US. The region encompasses nearly 80 percent of the country’s current and planned onshore wind capacity. The map was created to identify appropriate areas for wind develop, and is an important source of information for screening early in the project siting process. It can also be used to support application of the US Fish and Wildlife Service Land-Based Wind Energy Guidelines, specifically Tier 1 and Tier 2 evaluations.

Combining the SWR map with other land suitability factors suggests that over 1,000 gigawatts of wind energy may be developed exclusively in areas of low conservation impact. The results of this analysis indicate that it is possible to accelerate a clean, low-impact energy future, one that supports energy, climate and conservation goals.

Region: Great Plains/Midwest U.S.Partners: Local, state, and federal wildlife agencies, partners, and conservation professionals

Website: nature.org/sitewindrightContacts: Chris Hise [chise@tnc.org]; Nathan Cummins [ncummins@tnc.org]

Software: ArcMap (10.1-10.6), ArcGIS Pro (2.2), ModelBuilder, ArcGIS Online

Data sources: U.S. Fish and Wildlife Service, U.S. Geological Survey, The Nature Conservancy, State DNR and Heritage programs, and other data. For details, see methods paper, Appendix A (nature.org/sitewindright)

Abstract Soil erosion is a major problem observed in terrestrial ecosystems. Monitoring and identifying potential areas for erosion becomes extremely important for the better management of these areas. The main aim of this study was to develop a Geographic Information System script tool based on the universal soil loss equation (USLE), which calculates soil loss in three large agricultural sub-basins. Algorithms were implemented in the graphical interface of ModelBuilder and later in Python programming language, thus allowing the creation of a specific script to calculate soil loss in an automatic way. The "USLE Paracatu Watershed" script was validated and proved to be effective in estimating erosion in the three sub-basins with an average processing time of half second per km2. This can be added via "ArcToolbox" toolbox in ArcGIS software, so that the user only has to add the variables of the USLE equation and the software will process the algorithms in an automatic way, generating the final map with the soil loss value (t/ha.year). The friendly interface of the script allows it to be used in any area, only requiring the user to enter the updated data of parameters that compose the equation.

The vector grid system provides a spatial and statistical infrastructure that allows the integration of environmental and socio-economic data. Its exploitation allows the crossing of different spatial data within the same grid units. Project results obtained using this grid system can be more easily linked. This grid system forms the geographic and statistical infrastructure of the Southern Quebec Land Accounts of the Institute of Statistics of Quebec (ISQ). It forms the geospatial and statistical context for the development of ecosystem accounting in Quebec. **In order to improve the vector grid system and the Land Accounts of Southern Quebec and to better anticipate the future needs of users, we would like to be informed of their use (field of application, objectives of use, territory, association with other products, etc.). You can write to us at maxime.keith@stat.gouv.qc.ca **. This grid system allows the spatial integration of various data relating, for example, to human populations, the economy or the characteristics of land. The ISQ wishes to encourage the use of this system in projects that require the integration of several data sources, the analysis of this data at different spatial scales and the monitoring of this data over time. The fixed geographic references of the grids simplify the compilation of statistics according to different territorial divisions and facilitate the monitoring of changes over time. In particular, the grid system promotes the consistency of data at the provincial level. The spatial intersection of the grid and the spatial data layer to be integrated makes it possible to transfer the information underlying the layer within each cell of the grid. In the case of the Southern Quebec Land Accounts, the spatial intersection of the grid and each of the three land cover layers (1990s, 2000s and 2010s) made it possible to report the dominant coverage within each grid cell. The set of matrix files of Southern Quebec Land Accounts is the result of this intersection. **Characteristics: ** The product includes two vector grids: one formed of cells of 1 km² (or 1,000 m on a side), which covers all of Quebec, and another of 2,500 m² cells (or 50 m on a side, or a quarter of a hectare), which fits perfectly into the first and covers Quebec territory located south of the 52nd parallel. Note that the nomenclature of this system, designed according to a Cartesian plan, was developed so that it was possible to integrate cells with finer resolutions (up to 5 meters on a side). In its 2024 update, the 50 m grid system is divided into 331 parts with a side of 50 km in order to limit the number of cells per part of the grid to millions and thus facilitate geospatial processing. This grid includes a total of approximately 350 million cells or 875,000 km2. It is backwards compatible with the 50m grid broadcast by the ISQ in 2018 (spatial structure and unique identifiers are identical, only the fragmentation is different). **Attribute information for 50 m cells: ** * ID_m50: unique code of the cell; * CO_MUN_2022: geographic code of the municipality of January 2022; * CERQ_NV2: code of the natural region of the ecological reference framework of Quebec; * CL_COUV_T50: unique code of the cell; * CL_COUV_T00, CL_COUV_T01: codes for coverage classes Terrestrial maps from the years 1990, 2000 and 2010. Note: the 2000s are covered by two land cover maps: CL_COUV_T01A and CL_COUV_T01b. The first inventories land cover prior to reassessment using the 2010s map, while the second shows land cover after this reassessment process. **Complementary entity classes: ** * Index_grille50m: index of the parts of the grid; * Decoupage_mun_01_2022: division of municipalities; * Decoupage_MRC_01_2022: division of geographical MRCs; * Decoupage_RA_01_2022: division of administrative regions. Source: System on administrative divisions [SDA] of the Ministry of Natural Resources and Forests [MRNF], January 2022, allows statistical compilations to be carried out according to administrative divisions hierarchically superior to municipalities. * Decoupage_CERQ_NV2_2018: division of level 2 of the CERQ, natural regions. Source: Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks [MELCCFP]. Geospatial processes delivered with the grid (only with the FGDB data set) : * ArcGIS ModelBuilder allowing the spatial intersection and the selection of the dominant value of the geographic layer to populate the grid; * ModelBuilder allowing the statistical compilation of results according to various divisions. Additional information on the grid in the report Southern Quebec Land Accounts published in October 2018 (p. 46). View the results of the Southern Quebec Land Accounts on the interactive map of the Institut de la Statistique du Québec.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This map shows a simple summary of the social vulnerability of populations in the United States. Using Census 2010 information, the map answers the question “Where are the areas of relatively greater potential impact from disaster events within the U.S.?” from the perspective of social vulnerability to hazards. In other words, all areas of the U.S. are assessed relative to each other. Local and regional assessments of social vulnerability should apply the same model to their multi-county or multi-state region. For emergency response planning and hazard mitigation, populations can be assessed by their vulnerability to various hazards (fire, flood, etc). Physical vulnerability refers to a population’s exposure to specific potential hazards, such as living in a designated flood plain. There are various methods for calculating the potential or real geographic extents for various types of hazards. Social vulnerability refers to sensitivity to this exposure due to population and housing characteristics: age, low income, disability, home value or other factors. The social vulnerability score presented in this web service is based upon a 2000 article from the Annals of the Association of American Geographers which sums the values of 8 variables as a surrogate for "social vulnerability". For example, low-income seniors may not have access to a car to simply drive away from an ongoing hazard such as a flood. A map of the flood’s extent can be overlaid on the social vulnerability layer to allow planners and responders to better understand the demographics of the people affected by the hazard. This map depicts social vulnerability at the block group level. A high score indicates an area is more vulnerable. This web service provides a simplistic view of social vulnerability. There are more recent methods and metrics for determining and displaying social vulnerability, including the Social Vulnerability Index (SoVI) which capture the multi-dimensional nature of social vulnerability across space. See www.sovius.org for more information on SoVI. The refereed journal article used to guide the creation of the model in ModelBuilder was: Cutter, S. L., J. T. Mitchell, and M. S. Scott, 2000. "Revealing the Vulnerability of People and Places: A Case Study of Georgetown County, South Carolina." Annals of the Association of American Geographers 90(4): 713-737. Additionally, a white paper used to guide creation of the model in ModelBuilder was "Handbook for Conducting a GIS-Based Hazards Assessment at the County Level" by Susan L. Cutter, Jerry T. Mitchell, and Michael S. Scott.Off-the-shelf software and data were used to generate this index. ModelBuilder in ArcGIS 10.1 was used to connect the data sources and run the calculations required by the model.-------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

This map shows a simple summary of the social vulnerability of populations in the United States. Using Census 2010 information, the map answers the question “Where are the areas of relatively greater potential impact from disaster events within the U.S.?” from the perspective of social vulnerability to hazards. In other words, all areas of the U.S. are assessed relative to each other. Local and regional assessments of social vulnerability should apply the same model to their multi-county or multi-state region. For emergency response planning and hazard mitigation, populations can be assessed by their vulnerability to various hazards (fire, flood, etc). Physical vulnerability refers to a population’s exposure to specific potential hazards, such as living in a designated flood plain. There are various methods for calculating the potential or real geographic extents for various types of hazards. Social vulnerability refers to sensitivity to this exposure due to population and housing characteristics: age, low income, disability, home value or other factors. The social vulnerability score presented in this web service is based upon a 2000 article from the Annals of the Association of American Geographers which sums the values of 8 variables as a surrogate for "social vulnerability". For example, low-income seniors may not have access to a car to simply drive away from an ongoing hazard such as a flood. A map of the flood’s extent can be overlaid on the social vulnerability layer to allow planners and responders to better understand the demographics of the people affected by the hazard. This map depicts social vulnerability at the block group level. A high score indicates an area is more vulnerable. This web service provides a simplistic view of social vulnerability. There are more recent methods and metrics for determining and displaying social vulnerability, including the Social Vulnerability Index (SoVI) which capture the multi-dimensional nature of social vulnerability across space. See www.sovius.org for more information on SoVI. The refereed journal article used to guide the creation of the model in ModelBuilder was: Cutter, S. L., J. T. Mitchell, and M. S. Scott, 2000. "Revealing the Vulnerability of People and Places: A Case Study of Georgetown County, South Carolina." Annals of the Association of American Geographers 90(4): 713-737. Additionally, a white paper used to guide creation of the model in ModelBuilder was "Handbook for Conducting a GIS-Based Hazards Assessment at the County Level" by Susan L. Cutter, Jerry T. Mitchell, and Michael S. Scott.Off-the-shelf software and data were used to generate this index. ModelBuilder in ArcGIS 10.1 was used to connect the data sources and run the calculations required by the model.-------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

This map shows a simple summary of the social vulnerability of populations in the United States. Using Census 2010 information, the map answers the question “Where are the areas of relatively greater potential impact from disaster events within the U.S.?” from the perspective of social vulnerability to hazards. In other words, all areas of the U.S. are assessed relative to each other. Local and regional assessments of social vulnerability should apply the same model to their multi-county or multi-state region. For emergency response planning and hazard mitigation, populations can be assessed by their vulnerability to various hazards (fire, flood, etc). Physical vulnerability refers to a population’s exposure to specific potential hazards, such as living in a designated flood plain. There are various methods for calculating the potential or real geographic extents for various types of hazards. Social vulnerability refers to sensitivity to this exposure due to population and housing characteristics: age, low income, disability, home value or other factors. The social vulnerability score presented in this web service is based upon a 2000 article from the Annals of the Association of American Geographers which sums the values of 8 variables as a surrogate for "social vulnerability". For example, low-income seniors may not have access to a car to simply drive away from an ongoing hazard such as a flood. A map of the flood’s extent can be overlaid on the social vulnerability layer to allow planners and responders to better understand the demographics of the people affected by the hazard. This map depicts social vulnerability at the block group level. A high score indicates an area is more vulnerable. This web service provides a simplistic view of social vulnerability. There are more recent methods and metrics for determining and displaying social vulnerability, including the Social Vulnerability Index (SoVI) which capture the multi-dimensional nature of social vulnerability across space. See www.sovius.org for more information on SoVI. The refereed journal article used to guide the creation of the model in ModelBuilder was: Cutter, S. L., J. T. Mitchell, and M. S. Scott, 2000. "Revealing the Vulnerability of People and Places: A Case Study of Georgetown County, South Carolina." Annals of the Association of American Geographers 90(4): 713-737. Additionally, a white paper used to guide creation of the model in ModelBuilder was "Handbook for Conducting a GIS-Based Hazards Assessment at the County Level" by Susan L. Cutter, Jerry T. Mitchell, and Michael S. Scott.Off-the-shelf software and data were used to generate this index. ModelBuilder in ArcGIS 10.1 was used to connect the data sources and run the calculations required by the model.-------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.