Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhoodHow do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store.Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access.As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying.The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access.Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a

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

STATION_ID MichiganStationswAvgs19912020_W Station ID where weather data is collected

STATION MichiganStationswAvgs19912020_1 Station name where weather data is collected

ELEVATION MichiganStationswAvgs19912020_6 Elevation above mean sea level-meters

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

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

OID MichiganStationswAvgs1991202_10 Object ID for weather dataset

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

TARGET_FID MichiganStationswAvgs1991202_12 Spatial Join ID

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

Geographic Identifier MichiganStationswAvgs1991202_14 Geographic identifier (used for join)

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

Current functional status MichiganStationswAvgs1991202_16 Status of weather station

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

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

Current latitude of the internal point MichiganStationswAvgs1991202_19 Latitude

Current longitude of the internal point MichiganStationswAvgs1991202_20 Longitude

Name MichiganStationswAvgs1991202_21 Location name of weather station

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

OBJECTID MichiganStationswAvgs1991202_23 Object ID for point dataset

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into tree and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Trees is useful in applications such as high-quality 3D basemap creation, urban planning, forestry workflows, and planning climate change response.Trees could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Tree in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputThe model is trained with classified LiDAR that follows the LINZ base specification. The input data should be similar to this specification.Note: The model is dependent on additional attributes such as Intensity, Number of Returns, etc, similar to the LINZ base specification. This model is trained to work on classified and unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Wellington CityTesting dataset - Tawa CityValidation/Evaluation dataset - Christchurch City Dataset City Training Wellington Testing Tawa Validating ChristchurchModel architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.991200 0.975404 0.983239 High Vegetation 0.933569 0.975559 0.954102Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 80%, Test: 20%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-121.69 m to 26.84 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-15 to +15 Maximum points per block8192 Block Size20 Meters Class structure[0, 5]Sample resultsModel to classify a dataset with 5pts/m density Christchurch city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

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

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

This space-time cube contains basic population and housing variables for Public Use Microdata Areas (PUMAs), annually from 2010 to 2023. The variables are from the American Community Survey (ACS) 1-year estimates.A space-time cube is a powerful data structure used to visualize and analyze spatio-temporal data in ArcGIS Pro. Some examples of what you can do with this space-time cube: Create a compelling three-dimensional visualization of homeownership rate through timeFind emerging hot spots of specific race or Hispanic origin groupsIdentify change points of vacant housing unitsForecast future population valuesTo access this space-time cube, click Download, then unzip the downloaded folder. The folder contains a space-time cube (.nc), a file geodatabase (.gdb) containing the PUMA boundaries, and a csv file (.csv) describing the ACS variables in the space-time cube.To view a short tutorial on getting started with this space-time cube, read this blog article. To learn more about how to create and work with space-time cubes in ArcGIS Pro, view the learning path.placeholderSpace Time Cube ContentsSpatial unit and extent: 2020 vintage Public Use Microdata Areas (PUMA) boundaries for the entire United States, Puerto Rico, and Guam. Downloaded from US Census TIGER geodatabases National Sub-State Geography Database, with water and coastlines erased using 2023 500k TIGER Cartographic Boundary Shapefiles. Temporal interval and extent: one year interval, between 2010 and 2023 .Data source: ACS 1-year estimates downloaded from data.census.gov for each year between 2010 and 2023 (except 2020). Table(s) B01001, B03002, B05003, B05011, B19049, B25002, B25003, B25058, B25077.Variables: includes 32 variables on the following themes: population, race and Hispanic origin, foreign-born, housing occupancy, and housing tenure. To view a full listing of the variables, consult the .csv file contained within the downloaded folder.Processing Notes and Usage Tips The space-time cube contains variables that are directly sources from ACS, plus variables that have been calculated using ACS variables. The calculated variables can be identified by the “_calc_” stub in the field name. The spreadsheet contained within the downloaded folder provides more information on each variable source and calculation. It also contains field aliases, which can optionally be used to add aliases to the space-time cube layer or any other feature classes which are derived from the space-time cube (see blog article for information on how to do this). The field aliases were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. The ACS did not publish 1 year estimates for 2020. The variable values for this year were imputed using the temporal trend method of the Create Space Time Cube from Defined Locations tool, which uses the Interpolated Univariate Spline method from the SciPy Interpolation package. This can introduce some unexpected artifacts in the values for this year, for example: count statistics may include decimal places or may become negative, and variables that should sum together to reach the total of another variable may not. Therefore it is advised to take caution when making any conclusions from analysis which are focused around this year. The PUMA boundaries change after each decennial census. For the time series of this space-time cube, there was a boundary change between 2011 and 2012 (from the 2000 census to 2010), and another between 2021 and 2022 (from the 2010 census to 2020). Therefore, apportionment was required for all years between 2010 and 2021 to be able to accurately create a time series based on the 2020 PUMA geographies. A weighted apportionment approach was used, applying either population or housing weights depending on the variable. Apportionment enables us to create longer time-series or time-series which are more current, however it also adds an additional source of error to the ACS estimates. A version of this space-time cube without apportionment, for 2012 to 2021, is provided at LINK TO OTHER CUBE. ACS update the population controls after every decennial census, which can sometimes cause slight shifts in values. For this space-time cube, these happened between from 2011 and 2012, and 2021 and 2022. Therefore it is advised to take caution when making any conclusions from analysis which are focused around these years. A version of this space-time cube without these effects, for 2012 to 2021, is provided at LINK TO OTHER CUBE. In order to have access to the latest functionality, it is recommended to use the most recent version of ArcGIS Pro to work with the space-time cube. In particular, in ArcGIS Pro 3.5, significant enhancements were made to space-time cube visualization workflows. Native space-time cube analysis and visualization is not currently supported in ArcGIS Online. However once visualization or analysis has taken place in ArcGIS Pro, the resulting space-time cube layer can be published as a Web Scene, which can be visualized in Scene Viewer.ACS InformationInformation about the United States Census Bureau's American Community Survey (ACS): About the Survey Geography & ACS Technical Documentation News & UpdatesPlease cite the Census and ACS when using this data.Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

This World Elevation TopoBathy service combines topography (land elevation) and bathymetry (water depths) from various authoritative sources from across the globe. Heights are orthometric (sea level = 0), and bathymetric values are negative downward from sea level. The source data of land elevation in this service is same as in the Terrain layer. When possible, the water areas are represented by the best available bathymetry. Height/Depth units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select additional functions, applied on the server, that return rendered data. For visualizations such as hillshade or elevation tinted hillshade, consider using the appropriate server-side function defined on this service. Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns. NOTE: This image services combine data from different sources and resample the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the max extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percentage Hillshade Multi-Directional Hillshade Elevation Tinted HillshadeSlope MapMosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 is included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request. This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks. Disclaimer: Bathymetry data sources are not to be used for navigation/safety at sea.

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhood How do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app. Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state). On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access. As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car? How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access. Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer. MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in. The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery

Rising sea levels (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much coastal land area as inundation from SLR alone, and 5,297 state-managed sites of contamination may be vulnerable to inundation from GWR in a 1-meter SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the salt water interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are more exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area. Methods Data Dryad This data set includes data from the California State Water Resources Control Board (WRCB), the California Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and the US Census. National Assessment Data Processing: For this portion of the project, ArcGIS Pro and RStudio software applications were used. Data processing for superfund site contaminants in the text and supplementary materials was done in RStudio using R programming language. RStudio and R were also used to clean population data from the American Community Survey. Packages used include: Dplyr, data.table, and tidyverse to clean and organize data from the EPA and ACS. ArcGIS Pro was used to compute spatial data regarding sites in the risk zone and vulnerable populations. DEM data processed for each state removed any elevation data above 10m, keeping anything 10m and below. The Intersection tool was used to identify superfund sites within the 10m sea level rise risk zone. The Calculate Geometry tool was used to calculate the area within each coastal state that was occupied by the 10m SLR zone and used again to calculate the area of each superfund site. Summary Statistics were used to generate the total proportion of superfund site surface area / 10m SLR area for each state. To generate population estimates of socially vulnerable households in proximity to superfund sites, we followed methods similar to that of Carter and Kalman (2020). First, we generated buffers at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate Intersection, the estimated population of each census block group within each buffer zone was calculated. Summary Statistics were used to generate total numbers for each state. Bay Area Data Processing: In this regional study, we compared the groundwater elevation projections by Befus et al (2020) to a combined dataset of contaminated sites that we built from two separate databases (Envirostor and GeoTracker) that are maintained by two independent agencies of the State of California (DTSC and WRCB). We used ArcGIS to manage both the groundwater surfaces, as raster files, from Befus et al (2020) and the State’s point datasets of street addresses for contaminated sites. We used SF BCDC (2020) as the source of social vulnerability rankings for census blocks, using block shapefiles from the US Census (ACS) dataset. In addition, we generated isolines that represent the magnitude of change in groundwater elevation in specific sea level rise scenarios. We compared these isolines of change in elevation to the USGS geological map of the San Francisco Bay region and noted that groundwater is predicted to rise farther inland where Holocene paleochannels meet artificial fill near the shoreline. We also used maps of historic baylands (altered by dikes and fill) from the San Francisco Estuary Institute (SFEI) to identify the number of contaminated sites over rising groundwater that are located on former mudflats and tidal marshes. The contaminated sites' data from the California State Water Resources Control Board (WRCB) and the Department of Toxic Substances (DTSC) was clipped to our study area of nine-bay area counties. The study area does not include the ocean shorelines or the north bay delta area because the water system dynamics differ in deltas. The data was cleaned of any duplicates within each dataset using the Find Identical and Delete Identical tools. Then duplicates between the two datasets were removed by running the intersect tool for the DTSC and WRCB point data. We chose this method over searching for duplicates by name because some sites change names when management is transferred from DTSC to WRCB. Lastly, the datasets were sorted into open and closed sites based on the DTSC and WRCB classifications which are shown in a table in the paper's supplemental material. To calculate areas of rising groundwater, we used data from the USGS paper “Projected groundwater head for coastal California using present-day and future sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J., & Erikson, L. (2020). We used the hydraulic conductivity of 1 condition (Kh1) to calculate areas of rising groundwater. We used the Raster Calculator to subtract the existing groundwater head from the groundwater head under a 1-meter of sea level rise scenario to find the areas where groundwater is rising. Using the Reclass Raster tool, we reclassified the data to give every cell with a value of 0.1016 meters (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of that little can leach into pipes and infrastructure. We then used the Raster to Poly tool to generate polygons of areas of groundwater rise.

Please note, the updated version of this toolbox is now available for download on this page. The COVID-19-Modeling-v1.zip file contains version 5 of the toolbox with updated documentation. Version 5 of the toolbox updates the CHIME Model v1.1.5 tool. The COVID-19Surge (CDC) model is unchanged in this version.More information about the toolbox can be found in the toolbox document. More information about the CHIME Model v1.1.5 tool, including the change log, can be found in the tool documentation and this video.More information about the COVID-19Surge (CDC) tool is included in the tool documentation and this video. CHIME Model v1.1.5 ToolVersion 4 - Updated 11 MAY 2020An implementation of Penn Medicine’s COVID-19 Hospital Impact Model for Epidemics (CHIME) for use in ArcGIS Pro 2.3 or later. This tool leverages SIR (Susceptible, Infected, Recovered) modeling to assist hospitals, cities, and regions with capacity planning around COVID-19 by providing estimates of daily new admissions and current inpatient hospitalizations (census), ICU admissions, and patients requiring ventilation. Version 4 of this tool is based on CHIME v1.1.5 (2020-05-07). Learn more about how CHIME works.Version 4 contains the following updates:Updated the CHIME tool from CHIME v1.1.2 to CHIME v1.1.5.Added a new parameter called Date of Social Distancing Measures Effect to specify the date when social distancing measures started showing their effects.Added a new parameter called Recovery to specify the number of recovered cases at the start of the model.COVID-19Surge (CDC) ToolVersion 1 - Released 04 MAY 2020An implementation of Centers for Disease Control and Prevention’s (CDC) COVID-19Surge for use in ArcGIS Pro 2.3 or later. This tool leverages SIICR (Susceptible, Infected, Infectious, Convalescing, Recovered) modeling to assist hospitals, cities, and regions with capacity planning around COVID-19 by providing estimates of daily new admissions and current inpatient hospitalizations (census), ICU admissions, and patients requiring ventilation based on the extent to which mitigation strategies such as social distancing or shelter-in-place recommendations are implemented. This tool is based on COVID-19Surge. Learn more about how COVID-19Surge works.Potential ApplicationsThe illustration above depicts the outputs of the COVID-19Surge (CDC) tool of the COVID-19 Modeling toolbox.A hospital systems administrator needs a simple model to project the number of patients the hospitals in the network will need to accommodate in the next 90 days due to COVID-19. You know the population served by each hospital, the date and level of current social distancing, the number of people who have recovered, and the number of patients that are currently hospitalized with COVID-19 in each facility. Using your hospital point layer, you run the CHIME Model v1.1.5 tool.An aid agency wants to estimate where and when resources will be required in the counties you serve. You know the population and number of COVID-19 cases today and 14 days ago in each county. You run the COVID-19Surge (CDC) tool using your county polygon data, introducing an Intervention Policy and New Infections Per Case (R0) driven by fields to account for differences in anticipated social distancing policies and effectiveness between counties.A county wants to understand how the lessening or removal of interventions may impact hospital bed availability within the county. You run the CHIME Model v1.1.5 and COVID-19Surge (CDC) tool, checking Add Additional Web App Fields in Summary in both tools. You display the published results from each tool in the Capacity Analysis configurable app so estimates can be compared between models.This toolbox requires any license of ArcGIS Pro 2.3 or higher in order to run. Steps for upgrading ArcGIS Pro can be found here.For questions, comments and support, please visit our COVID-19 GeoNet community.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

NB: The Aotearoa Contour lines are currently in beta and should not be used in production environments.Last updatedSee the vector basemap item details here.See the hillshade item details here.See the contour line item details here.ProjectionNew Zealand Transverse Mercator 2000 (NZTM2000). This vector tile layer provides a detailed basemap for New Zealand in the NZ Transverse Mercator projection. The style is based on the classic Esri Topographic style. This vector tile layer provides unique capabilities for customization and high-resolution display.This map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries for added context.This is a multisource layer, meaning features from the Aotearoa Contour Lines and NZ Topographic Relief layers are combined into one style file. Multisource layers allow for the drawing order of features to be customised, therefore one layer doesn't need to be draped over the other - features from each layer can instead be integrated together in the proper display order. For example, by moving the layers for Buildings and Roads above the contour lines in the style's JSON file, contour lines will be drawn underneath these features in the map. See Esri's blog on multisource vector tile layers for further context. It also includes 10m interval Contour Lines derived from 1m Digital Elevation Model merged with LINZ contours, where 1m DEM is not available yet.The 1m contours have been processed for visualisation purposes using the Focal Statistics tool (30x30m) prior to creating the contours in ArcGIS Pro, then they have been simplified using Simplify Line with the point remove (Douglas Peucker) method with a tolerance of 0.5m.This map uses the NZ Hillshade layer.Data sourcesLand Information New ZealandContour Lines, Buildings, Parcels, Place Names, Water-bodies, Vegetation, Protected Areas, Airports, Railways & Islands.Statistics New ZealandAdministrative Boundaries & Urban AreasNational Institute of Water & Atmospheric Research (NIWA)River Environment Classification© OpenStreetMap ContributorsRoad Centrelines, Buildings, Landuse Types, Areas of Interest and Points of Interest.Natural EarthBathymetry & Marine LabelsThis map is offered by Eagle Technology (Official Esri Distributor). Eagle Technology offers layers and maps that can be used in the ArcGIS platform. The Content team at Eagle Technology updates the layers on a regular basis and regularly adds new content to the Living Atlas. By using this content and combining it with other data you can create new information products quickly and easily.If you have any questions or remarks about the content, please let us now at livingatlas@eagle.co.nzCustomize this MapBecause this map is delivered as a vector tile layer, users can customize the map to change its content and symbology, including fonts. Users are able to turn on and off layers and change symbols for layers.An easy way to change the style of this map is to use the Vector Basemap Style Editor:https://developers.arcgis.com/vector-tile-style-editor/

World Terrestrial Ecosystems are areas of climate, landform and land cover that form the basic components of terrestrial ecosystem structure. This map is the first-of-its-kind effort to characterize and map global terrestrial ecosystems at a much finer spatial resolution (250 m) than existing ecoregionalizations, and a much finer thematic resolution than existing global land cover products.This pro package was updated on February 26, 2024 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. This map is important because the ecologically relevant distinctions are authoritatively defined and modeled using globally consistent objectively derived data.World Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized (default) global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes.In this ArcGIS Pro Package you will see three sources of authoritative information:The World Climate Regions, which establish the macroclimate regimeWorld Landforms, which modify the macroclimates into mesoclimates and microclimatesWorld Vegetation/Land Cover, which identify the major plant formations occurring in a place in response to the climate and landforms.This map allows you to query of any 250 m pixel on the land surface of the Earth, and returns the values of all the input parameters and the name of the World Terrestrial Ecosystem at that location.Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the four components. Values for each of the four input layers are listed in the table below. Every point in this map is symbolized by a combination of values for each of these fields.This layer provides access to a cached map service created by Esri in partnership with U.S. Geological Survey's Climate and Land Use Change Program and The Nature Conservancy. The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation. You can access and view World Terrestrial Ecosystems Image File. You can access and have an high-level understanding of this dataset from the Introduction to World Terrestrial Ecosystems Story Map.

Version 3 of the Named Landforms of the World (NLWv3) is an update of version 2 of the Named Landforms of the World (NLWv2). NLWv2 will remain available as the compilation that best matches the work of E.M. Bridges and Richard E. Murphy. In NLWv3, we added attributes that describe each landform's volcanism based on data from the Smithsonian Institution's Global Volcanism Program (GVP). We designed NLWv3 layers for two purposes:To label maps with broadly accepted names for physiographic features. To add landform attributes to other layers. For example, species observation data or other small features to enable rich and relevant descriptions for how those features relate to landforms. To accomplish this, typically, we use overlay tools such as Identity. For background, version 2 provided features with the physiographic and geomorphologic characteristics for the world's named landforms. This means it was more than just showing the land versus water or mountains versus plains; it also included the underlying structure and processes that created the landforms. We begin with the largest landform regions, which are continents, followed by tectonic plates, then divisions, provinces, sections, and finally, individual landforms. In adding the GVP volcanic landforms to NLWv3, we learned that volcanoes are relatively short-lived as landforms, with most not enduring for two million years. For context, the age of the rocks in most of the Earth's mountain ranges is in the tens to hundreds of millions of years. The full collection of layers and maps for NLWv3 are available in an ArcGIS Online Group named Named Landforms Of the World v3 (NLWv3) Layers and Maps. The GVP included two inventories--one for the Holocene Epoch, which are the volcanoes that formed during most recent 11,700 years (since the last ice age). The other is for the Pleistocene Epoch, which precedes the Holocene, and lasted about 2.6 million years. While the Pleistocene epoch is 222 times longer than the Holocene, it only has 7.8% more volcanoes. Most of the volcanoes that formed during the Pleistocene have disappeared through natural erosional and depositional processes. In NLWv3, volcanic landforms include calderas, clusters and complexes, shields, stratovolcanoes, and minor volcanic features such as cinder cones, lava domes, and fissure vents. Not all the GVP features, particularly fissure vents and remnants of calderas, are large enough to be mapped as polygons in NLWv3. Similarly, complexes and volcanic fields typically had greater areas and included many individual cinder cones and calderas. ContinentCount of Volcanic LandformsArea km2 of Volcanic Landforms (% of land area)Europe7822,888 (0.23%)Antarctica4234,035 (0.27%)Australia14757,422 (0.65%)South America37081,475 (0.46%)Small Volcanic Islands559124,310 (8.52%)Africa282147,116 (0.50%)Asia698227,486 (0.53%)North America622295,340 (1.23%)Global Totals2,7981,000,073 (0.67%)This table shows the distribution of volcanic landforms and their surface areas. Overview of UpdatesCorresponding landform polygons now include attributes for the GVP's ID, name, province, and region. Details are provided below in the volcanic attributes section. Additionally, a text description of volcanism for each GVP feature was derived from these attributes to provide a reader-friendly characterization of each volcanic landform.Landforms of Antarctica. Given recent analysis of Antarctica and the use of GVP data, rudimentary landform features for Antarctica have been added. See details in the Antarctica section below.Refined the definition of Murphy's Isolated Volcanics classification. If the volcanic landform occurred outside of an orogenic, rifting, or subducting zone, only then did we consider it isolated. The areas along tectonic plate boundaries are where volcanoes typically occur. Only volcanoes occurring in areas with no tectonic activity are considered isolated. These typically occur in mid-continent or mid-tectonic plate. See details in the Isolated Volcanic Areas section.Edits to tectonic process attributes in selected areas. The GVP point locations for volcanoes include an attribute for the underlying tectonic process. The concept matched the existing tectonic process in the NLWv2, and we compared the values. When the values differed, we reviewed research and made changes. See details in the Tectonic Process section below.Minor boundary changes at the province, section, and landform level in the western mountains of North and South America. Details are provided below in the Boundary Change Locations section. Technical CharacteristicsThe NLWv2 and NLWv3 are derived from the same raster datasets used to produce the 2018 version of the World Terrestrial Ecosystems (WTEs), which, when combined, have a lowest-common-denominator resolution (minimum mapping unit) of 1 km. Some features, such as very small islands, were not included in NLWv3, and complex coastlines were simplified and were only included if the 1-km cell contained at least 50% land. Because the coastlines in the raster datasets varied by as much as 3 km from the actual coastline, nearly always due to missing land. Many of the worst such cases in NLWv2 were manually corrected using the 12-30-meter resolution World Hillshade layer as a guide. In NLWv3, we continued this work by adding 247 volcanic islands, some of which were smaller than 1 km in area. We estimate that these islands comprise about one percent of the world's smaller islands. In NLWv3, we also refined the coastlines of volcanic coastal areas, particularly in Oceania and Japan. For NLWv4, we plan to continue this refinement work, intending that future versions of NLW will have a progressively refined, medium-resolution coastline. However, we do not intend to capture the full detail of the Global Islands dataset, which was produced from 30-m Landsat data. Detailed Description of Updates Volcanic AttributesThe GVP Excel spreadsheets for the Holocene and Pleistocene epochs, which contained the coordinates and attributes for each volcano, were combined. A column for the geologic age was added before saving the spreadsheet as a .CSV file and importing into ArcGIS Pro. The XY Table to Points tool was used to create point features. Nearly ten percent of the point locations that lacked sufficient precision to fall within the correct landform polygon were revised manually in order to assign the correct Volcano ID to each polygon.2,394 of the 2,662 GVP volcanic features were assigned to landform polygons. 198 GVP features were not assigned because they represented undersea features, and 75 GVP features did not have apparent corresponding landform polygons because they were either too small or indistinguishable from surrounding topography. Of the 2,394 assigned GVP features, 48% are Holocene Age features and 52% are Pleistocene epoch features. 225 GVP features did not fall within within a landform feature that represented topographically a volcanic landform feature, such as a caldera or stratovolcano. This was usually due to insufficient precision of the GVP coordinates, which sometimes were rounded to the nearest integer of latitude and longitude and could therefore be over 50km away from the landform's location. AttributeDescriptionVolcano ID (SI)The six-digit unique ID for the Global Volcanism Program features.Volcano Name (SI)The Name of the volcanic feature as provided by the Global Volcanism Program. Volcanic Region (SI)The Name of the volcanic region as provided by the Global Volcanism Program. Volcanic Province (SI)The Name of the volcanic province as provided by the Global Volcanism Program. VolcanismA consistently formatted description volcanism for the landform feature based on the age, last eruption, landform type, and type of material. This information was not consistently available from the Global Volcanism Program, and we used a Python script to determine the condition of the Global Volcanism Program"s data and then include whatever information was available. AntarcticaSeveral recent analyses of Antarctica complemented the GVP point features. In particular, the British Antarctic Survey's 2019 Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet show sufficiently detailed land surface elevation beneath the ice sheets to support identifying topographic landform classes. We georeferenced the elevation image and combined it with Bridge's geomorphological divisions and provinces to divide the continent into different landform polygons. Additional work is needed to make these landform polygons as rich and accurately defined as those in NLWv2. Isolated Volcanic AreasThere are 333 Isolated Volcanic landforms in NLWv2. We intentionally expanded on Murphy"s map which could not show many of the smaller landforms and areas due to the 1:50,000,000 scale (poster sized map of the world). Murphy"s map only included isolated volcanic areas in three locations: north-central Africa, Hawaii, and Iceland. In NLWv2, we used the Global Lithological Map to identify several areas on each continent and used the example of Hawaii to include many other known volcanic islands. In most ways, Isolated Volcanics denoted geographic isolation from other mountain systems. NLWv3 includes 2,798 volcanic landform features, and 185 have been assigned Murphy's Isolated Volcanic structure class because they do not occur within a region with the tectonic process of orogenic, subduction, or rifting. These Isolated Volcanic landform features are located mostly in mid-tectonic plate regions of Africa, the Arabian Peninsula, and on islands, particularly in the southern hemisphere, with a few in North America and Asia. NLWv3 contains 2,603 volcanic landform features, occurring on all continents and on islands within all oceans. Tectonic ProcessThe GVP data included a tectonic setting attribute that was compiled independently of the NLWv2 tectonic setting variable. When these

Pathways to Removing Obstacles to Housing (PRO Housing) Pathways to Removing Obstacles to Housing, or PRO Housing, is a competitive grant program being administered by HUD. PRO Housing seeks to identify and remove barriers to affordable housing production and preservation.

Under the Need rating factor, applicants will be awarded ten (10) points if their application primarily serves a ‘priority geography’. Priority geography means a geography that has an affordable housing need greater than a threshold calculation for one of three measures. The threshold calculation is determined by the need of the 90th-percentile jurisdiction (top 10%) for each factor as computed comparing only jurisdictions with greater than 50,000 population. Threshold calculations are done at the county and place level and applied respectively to county and place applicants. An application can also quality as a priority geography if it serves a geography that scores in the top 5% of its State for the same three measures. The measures are as follows:

Affordable housing not keeping pace, measured as (change in population 2019-2009 divided by 2009 population) – (change in number of units affordable and available to households at 80% HUD Area Median Family Income (HAMFI) 2019-2009 divided by units affordable and available at 80% HAMFI 2009). Insufficient affordable housing, measured as number of households at 80% HAMFI divided by number of affordable and available units for households at 80% HAMFI. Widespread housing cost burden or substandard housing, measured as number of households with housing problems at 100% HAMFI divided by number of households at 100% HAMFI. Housing problems is defined as: cost burden of at least 50%, overcrowding, or substandard housing.

For more information on Pro Housing, please visit: https://www.hud.gov/program_offices/comm_planning/pro_housing

This links to a .ZIP file contains Montana Spatial Data Infrastructure (MSDI) and other pertinent data layers clipped to the Montana Yellowstone River 2022 Spring Flood Disaster Subset Area of Interest polygon. The Area of Interest includes areas immediately adjacent to the flooded tributaries of the Yellowstone River in Carbon, Park, Stillwater, Sweet Grass, Treasure and Yellowstone Counties. The data layers are current as of July 2022. The .ZIP file also contains ArcMap layer files, map templates, and metadata for the source geodatabase data.For datasets clipped to the county or statewide use the Montana Data Bundler: https://msl.mt.gov/GIS/BundlerInside the zip are: A 2022MontanaFlood_DataList.docx that lists all GIS data included in this archive.A ReadMe.docx that details the data organization, instructions on how to set he map file paths, how to change the display map extents, and how to connect to web GIS services.ArcMap Layer Symbology Files (.lyr)GIS Layer MetadataMap Project Templates (ArcMap 10.7 and ArcGIS Pro 2.9 are included; other versions available upon request)File Geodatabase with data layers clipped to the Spring 2022 Flood Yellowstone River Area of InterestData Included:Montana Spatial Data Infrastructure (MSDI) DataAdministrative Boundaries - County Boundaries - Municipalities-Cities, TownsCadastral - Ownership - Public Lands - Conservation Easements Geographic Names - MT_NamesNational Hydrography Dataset - WBDHUC8-HUC8SubBasin - WBDHUC10-HUC10Watershed - WBDHUC12-HUC12Subwatershed - NHDFlowline - NHDWaterbody - NHDAreaCADNSDI (Public Land Survey database) - PLSSFirstDivision-Sections - PLSSTownship-TownshipsStructure/Address PointsTransportation - Bridges - Railroads - Roads Wetland and RiparianMTNHP Landcover - Landcover 2017 - Landcover 2021 (version 1)Elevation - NED 10 meter digital elevation model (DEM) - NED-Continuous, Integer rasters - Aspect-Continuous, Integer rasters (10 meter) - Slope-Continuous, Integer rasters (10 meter) - LiDAR-Derived Building Footprints - LiDAR Building Footprint Boundary - LiDAR ProjectsSoils (NRCS SSURGO) - Soils Map units - Soils Points - Soils LinesUSDA Forest ServiceLandfire – Existing Vegetation Type (EVT)Landfire – Existing Vegetation Height (EVH)Landfire – Existing Vegetation Cover (EVC)USDA NASS DataCropLand Data Layer 2021Department of Revenue Data2020 DOR Final Land Units (FLU)MiscellaneousBuilding Footprints (Microsoft)USGS 24k Topo Quads

Beta Notice: This item is currently in beta and is intended for early access, testing, and feedback. It is not recommended for production use, as functionality and content are subject to change without notice.This layer displays global downscaled CMIP6 ISIMIP3b projections of annual precipitation. The data is hosted by the Swiss Federal Institute for Forest, Snow, and Landscape Research WSL. It is built to provide free access to high resolution climate data for research and application. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change.WSL produced projections of 19 traditional bioclimate predictors (defined by the USGS) as well as several additional variables as part of CHELSA BIOCLIM+ and provides the following description in their documentation: "High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. The CHELSA (Climatologies at high resolution for the earth’s land surface areas) data (Karger et al. 2017) consists of downscaled model output temperature and precipitation estimates at a horizontal resolution of 30 arc sec. The temperature algorithm is mainly based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction." Dataset SummaryPhenomenon Mapped: Annual precipitation: accumulated precipitation amount over 1 yearGeographic Extent: GlobalData Projection: GCS WGS84Cell Size: 30 arc seconds (~1 km)Units: kg m-2 year-1 (mm/year)Time Extent: averages over 2011-2040, 2041-2070, and 2071-2100.Pixel Type: 32 Bit FloatSource: CHELSA BIOCLIM+Data vintage: 5/30/2025Publication Date: 6/4/2025 Climate ScenariosThe CMIP6 ISIMIP3b climate experiments use Shared Socioeconomic Pathways (SSPs) to model future climate scenarios. Each SSP pairs a human/community behavior component with the traditional RCP greenhouse gas forcings. Three SSPs are included in these services: SSP1-2.6, SSP3-7.0 and SSP5-8.5. From the IPCC AR6 Summery for Policymakers: SSPScenarioEstimated warming(2041–2060)Estimated warming(2081–2100)Very likely range in °C(2081–2100)SSP1-2.6low GHG emissions:CO2 emissions cut to net zero around 20751.7 °C1.8 °C1.3 – 2.4SSP3-7.0high GHG emissions:CO2 emissions double by 21002.1 °C3.6 °C2.8 – 4.6SSP5-8.5very high GHG emissions:CO2 emissions triple by 20752.4 °C4.4 °C3.3 – 5.7Processing the Climate DataCHELSA provides 30-year averaged outputs for the various SSPs from 5 global climate models: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The 5 models are average into a multi-model ensemble for each variable and time period.Accessing the Multidimensional InformationThe time and SSP scenarios are built into the layer using a multidimensional raster. In ArcGIS Online, use the Multidimensional tab to select the time period and SSP scenario of your choice. In ArcGIS Pro, use the Multidimensional Filter to select the time period and SSP scenario of your choice. Each SSP scenario includes the baseline period (1981-2010) which shows the same values in each SSP scenario. Note that the time periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100 are denoted in the multidimensional raster by their mid-point years as 1995, 2025, 2055, and 2085.What can you do with this layer?These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools.Known Quality IssuesEach model is downscaled from ~100km resolution to ~1km resolution by CHELSA. This inevitably introduces some artifacts into the data.ReferencesBrun, P., Zimmerman, N.E., Hari, C., Pellissier, L., Karger, D.N. (2022) Global climate-related predictors at kilometre resolution for the past and future Earth System Science Data. 14, 5573-5603 https://doi.org/10.5194/essd-14-5573-2022Brun, P., Zimmermann, N.E., Hari, C., Pellissier, L., Karger, D.N. (2022). CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution. EnviDat. https://doi.org/10.16904/envidat.332Related LayersCHELSA Bioclimate Projections: Annual Mean Temperature (Bio1)CHELSA Bioclimate Projections: Mean Diurnal Range (Bio2)CHELSA Bioclimate Projections: Isothermality (Bio3)CHELSA Bioclimate Projections: Temperature Seasonality (Bio4)CHELSA Bioclimate Projections: Max Temperature of Warmest Month (Bio5)CHELSA Bioclimate Projections: Min Temperature of Coldest Month (Bio6)CHELSA Bioclimate Projections: Annual Temperature Range (Bio7)CHELSA Bioclimate Projections: Mean Temperature of Wettest Quarter (Bio8)CHELSA Bioclimate Projections: Mean Temperature of Driest Quarter (Bio9)CHELSA Bioclimate Projections: Mean Temperature of Warmest Quarter (Bio10)CHELSA Bioclimate Projections: Mean Temperature of Coldest Quarter (Bio11)CHELSA Bioclimate Projections: Annual Precipitation (Bio12)CHELSA Bioclimate Projections: Precipitation of Wettest Month (Bio13)CHELSA Bioclimate Projections: Precipitation of Driest Month (Bio14)CHELSA Bioclimate Projections: Precipitation Seasonality (Bio15)CHELSA Bioclimate Projections: Precipitation of Wettest Quarter (Bio16)CHELSA Bioclimate Projections: Precipitation of Driest Quarter (Bio17)CHELSA Bioclimate Projections: Precipitation of Warmest Quarter (Bio18)CHELSA Bioclimate Projections: Precipitation of Coldest Quarter (Bio19)CHELSA Bioclimate Projections: Growing Degree Days Above 10CCHELSA Bioclimate Projections: Net Primary ProductivityCHELSA Bioclimate Projections: Snow Cover DaysCHELSA Bioclimate Projections: Snow Water EquivalentQuestions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.