The SACS study area is subdivided into 22 planning reaches (Figure 4 1) derived from three datasets and visual edits based on coastal geomorphology and professional judgment. Datasets include the following:- The Nature Conservancy Ecoregions—boundaries of areas that The Nature Conservancy has prioritized for conservation- State boundaries- Maximum inland limit of Category 5 storm surge inundation represented by the NOAA Sea, Lake, and Overland Surges from Hurricanes (SLOSH) modelThe GIS process to develop the Planning Reaches entailed the follow:The most landward extent of the SLOSH model was manually measured. Based on that measurement a single sided buffer was generated contiguous to the Coast for the AOR. The buffer was manually edited to include some areas that fell outside the buffer distance, specifically in Northern North Carolina and around Mobile Alabama. The Union tool was then used in ArcGIS desktop to overlay Ecoregions and State boundary files. Then the intersect tool was used to overlay the SLOSH buffer with the Union file. The result of the Intersect was then manually cut along the lines defined by the coastal geomorphology using lines defined in the “Manual_Edit_lines” feature. The resulting feature class was then provided with names based on the state two-digit acronym and a sequential number.

AnalysisFEMA's National Flood Hazard Layer (NFHL) and the CDC's Social Vulnerability Index (SVI) were cross referenced to produce a Place Vulnerability Analysis for Hudson County, NJ. Using ArcGIS Pro, the location of interest (Hudson County) was first determined and the Flood Hazard and SVI layers were clipped to this extent. A new feature class, intersecting the two, was then created using the Intersect Tool. The output of this process was the Hudson County Place Vulnerability Layer. Additional Layers were added to the map to assess important special needs infrastructure, community lifelines, and additional hazard risks within the most vulnerable areas of the county.LayersWildfire Hazard Potential: Shows the average wildfire hazard potential for the US on a scale of 1-5. The layer was obtained using ESRI's Living Atlas. Source: https://napsg.maps.arcgis.com/home/item.html?id=ce92e9a37f27439082476c369e2f4254 NOAA Storm Events Database 1950-2021: Shares notable storm events throughout the US recorded by NOAA between the years of 1950-2021. The layer was obtained using ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=88cc0d5e55f343c28739af1a091dfc91 Category 1 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 1 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=49badb9332f14079b69cfa49b56809dc Category 2 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 2 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=b4e4f410fe9746d5898d98bb7467c1c2 Category 3 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 3 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=876a38efe537489fb3bc6b490519117f U.S. Sea Level Rise Projections: Shows different sea level rise projections within the United States. The layer was obtained via ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=8943e6e91c304ba2997d83b597e32861Power Plants: Includes all New Jersey power plants about 1 Megawatt capacity. The layer was obtained via the NJDEP Bureau of GIS website. Source: https://njdep.maps.arcgis.com/home/item.html?id=282eb9eb22cc40a99ed509a7aa9f7c90Solid & Hazardous Waste Facilities: Includes hazardous waste facilities, medical waste facilities, incinerators, recycling facilities, and landfill sites within New Jersey. Obtained via the NJDEP Bureau of GIS website. Source: https://njdep.maps.arcgis.com/home/item.html?id=896615180fb04d8eafda0df9df9a1d73Solid Waste Landfill Sites over 35 Acres: Includes solid waste landfill sites in New Jersey that are larger than 35 acres. Obtained via the NJDEP Bureau of GIS website. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=2b4eab598df94ffabaa8d92e3e46deb4NJ Transit Rail Lines: A layer showing segments of the NJ Transit Rail System and terminals. Data was obtained via the NJ Transit GIS Department. Source: https://www.arcgis.com/home/item.html?id=e6701817be974795aecc7f7a8cc42f79Medical Emergency Response Structures: Contains emergency response centers within the U.S. based off National Geospatial Data Asset data from the U.S. Geological Survey. The layer was obtained using ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=2c36dbb008844081b017da6fd3d0d28bSchools: Shows the location of New Jersey schools, including public, private and charter schools. Obtained via the New Jersey Office of GIS. Source: https://njdep.maps.arcgis.com/home/item.html?id=d8223610010a4c3887cfb88b904545ffChild Care Centers: Shows the location of active child care centers in New Jersey. The layer was obtained via the NJ Bureau of GIS website. Source: https://njdep.maps.arcgis.com/home/item.html?id=0bc9fe070d4c49e1a6555c3fdea15b8aNursing Homes: A layer containing the locations of nursing homes and assisted care facilities in the United States. Obtained via the HIFLD website. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=78c58035fb3942ba82af991bb4476f13cCDC's Social Vulnerability Index (SVI) - ATSDR's Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=05709059044243ae9b42f469f0e06642

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.

Overview:

Living in a rural county, I have often felt the isolation many Tennesseans are forced to face when it comes to accessing medical care. While my family's average drive time ranges from 30 minutes to over an hour to access healthcare, many Tennesseans living in more remote counties are forced drive several times farther.

The story map, "Standing Alone," follows three individuals who have each been differently affected by the disparity in rural Tennessee healthcare. Through their stories, I wanted to peel back the layers of the Tennessee healthcare crisis with geospatial analysis, highlighting underserved counties and advocating for healthcare reform. When it comes to healthcare, no one deserves to be standing alone.

Methods:

Map Showing Rural and Urban Areas: The “USA Urban Areas” and the “USA Counties” layers, both feature layers created by Esri, were added to the map from the Living Atlas. The USA Counties layered was filtered to only counties inside Tennessee. The Derive New Locations analysis tool was then used to find “USA Urban Areas” that intersect the filtered “USA Counties” layer, producing the “Tennessee Urban Areas” layer. Additionally, the Derive New Locations analysis tool was used to find “USA Counties” that do not intersect “USA Urban Areas,” creating the “Tennessee Rural Areas” layer. Custom pop-ups were formatted for the layers. Map Showing Life Expectancy per Tennessee County: The layer, “County Health Rankings 2021” by esri_demographics, was added from the Living Atlas and filtered to show only Tennessee counties. The layer was styled with “Counts and Amounts (Color)” style to show the average life expectancy in years for individuals living in each Tennessee county. The layer “Tennessee Urban Areas”, mentioned above, was also added to the map, and custom pop-ups were created for both layers. Map Showing Percent of Population Living Below the Poverty Level: The layer, “ACS Poverty Status Variables – Boundaries, created by Esri, was added from the Living Atlas and filtered to show only Tennessee counties. This layer was then joined with the Life Expectancy layer created for the Map Showing Life Expectancy per Tennessee county using the Join Features analysis tool, and the resulting layer was styled using “Counts and Amounts (Color)” style to show the percent of population whose income in the past 12 months is below the poverty level. Lastly, the “Tennessee Urban Areas” layer was added to the map, and custom pop-ups were configured for the layers. Map Showing Dr. Copeland’s Office and the Cumberland River Hospital: Addresses and labels for each location were added to an ArcGIS StoryMaps Express Map. Map Showing Rural Counties with Medically Underserved Populations: Using data from the Health Resources Administration’s Find MUA/P (Medically Underserved Area/Population) tool, data showing rural counties with medically underserved populations was inserted in a custom .csv layer and uploaded as a layer. This layer was joined to “USA Counties” using the Join Features analysis tool, and the resulting layer was styled using the “Location (Single symbol)” style. Custom pop-ups were also added to this layer. Maps Showing Ms. Crouch’s Search for Emergency Medical Services: These maps were created by inserting addresses or cities of each location into an ArcGIS StoryMaps Express Map. Map Showing Fentress County Ambulance Station: This map was created by inserting the address of Fentress County Ambulance Service and the location of each city into an ArcGIS StoryMaps Express Map. Map Showing Sum of Ambulance Units per County: Using data from the Tennessee Health Department, a custom .csv layer with the total number of ambulances per EMS station was created and uploaded as a layer. This layer was joined to the “USA Counties Layer” using the Join Features analysis tool, and the resulting layer was styled using the “Counts and Amounts (Size)” style to show the sum of ambulances in each county. Custom pop-ups were added for this layer. Map Showing Hospitals That Have Closed Since 2010: A custom .csv file was created using data from a Tennessee Healthcare Campaign report, and this data was uploaded as a layer showing the location of each hospital that has closed since 2010. The “Tennessee Urban Areas” layer and the “Tennessee Rural Areas” layer were also added to this map. Lastly, custom pop-ups were configured for these layers. Map Showing Drive Time Areas to Trauma Hospitals: Using data from the Tennessee Health Department, a custom .csv file was uploaded as a layer showing the locations of Tennessee trauma hospitals. A drive time buffer was created using the Create Drive-Time Areas analysis tool to map locations 15, 30, 45, and 60 minutes away from a trauma hospital. The “USA Counties” layer was added from the Living Atlas, and the Derive New Locations analysis tool was used to find locations over 60 minutes away from a trauma hospital. Finally, custom pop-ups were added to the layers. Map Showing COVID-19 Case Rate per Hundred Thousand for Each State: Using data from the Centers for Disease Control, a custom .csv file was created and uploaded as a layer, which was joined to “USA Counties” using the Join Features analysis tool. The resulting layer was styled using the “Counts and Amounts (Color)” style to display the case rate per hundred thousand, and customized pop-ups were made for the layer. Map Showing COVID-19 Death Rate per Hundred Thousand for Each State: Using the same layer created in for the Map Showing COVID-19 Case Rate per Hundred Thousand for Each State, the layer was changed to show the death rate per hundred. Customized pop-ups were also added. Map Showing Percent of Deadly COVID-19 Cases in Tennessee: Using data from the Tennessee Health Department, a custom .csv was created, and the percentage of deadly COVID-19 was calculated. This file was uploaded as a layer, which was joined to “USA Counties” using the Join Features analysis tool and styled using “Counts and Amounts (Color)”. Finally, customized pop-ups were added to the map. Map Showing Percent Difference Between National Vaccination Average and County Rates: Using the same data as the Map Showing Percent of Deadly COVID-19 Cases in Tennessee, a custom attribute was created to show the percent difference between county vaccination rates and the national average. The map was styled using the “Counts and Amounts (Color)”, and customized pop-ups were created for the map.

The following methods were used to create the graphics in this story map.

Thumbnail of Clay County: This thumbnail was created using the "Blank White Vector Basemap" by j_nelson. Two copies of the "USA Counties" layer by Esri were added to the map, with one layer outlining all the counties in Tennessee and the other layer highlighting Clay County. A screen shot of this map was uploaded to the story map as an image.Thumbnail of Fentress County: This thumbnail was also created using the "Blank White Vector Basemap" by j_nelson. Two copies of the "USA Counties" layer by Esri were added to the map, with one layer outlining all the counties in Tennessee and the other layer highlighting Fentress County. Finally, a screen shot of this map was uploaded to the story map as an image.

All remaining graphics were custom images created in Microsoft PowerPoint.

Sources and Acknowledgements:

This map was created for the 2022 ArcGIS Online Competition for US High Schools.

I would like to give special thanks to my geomentor and my parents, whose help and guidance were invaluable during the creation of this story map.All sources for information, data, and photographs are included as links throughout the story map.

This feature layer was created by calculating the percentage of impervious surface within a census tract polygon using the tabulate intersection geoprocessing tool. Impervious surface data used in the analysis included is from 2015 through 2023. Updated annually as new data becomes available. If there are any questions about this data, please email dwsdGIS@detroitmi.gov

EMODnet Vessel Density Map were created by Cogea in 2019 in the framework of EMODnet Human Activities, an initiative funded by the EU Commission. The maps are based on AIS data purchased by CLS and show shipping density in 1km*1km cells of a grid covering all EU waters (and some neighbouring areas). Density is expressed as hours per square kilometre per month. A set of AIS data had to be purchased from CLS, a commercial provider. The data consists of messages sent by automatic tracking system installed on board ships and received by terrestrial and satellite receivers alike. The dataset covers the whole 2017 for an area covering all EU waters. A partial pre-processing of the data was carried out by CLS: (i) The only AIS messages delivered were the ones relevant for assessing shipping activities (AIS messages 1, 2, 3, 18 and 19). (ii) The AIS DATA were down-sampled to 3 minutes (iii) Duplicate signals were removed. (iv) Wrong MMSI signals were removed. (v) Special characters and diacritics were removed. (vi) Signals with erroneous speed over ground (SOG) were removed (negative values or more than 80 knots). (vii) Signals with erroneous course over ground (COG) were removed (negative values or more than 360 degrees). (viii) A Kalman filter was applied to remove satellite noise. The Kalman filter was based on a correlated random walk fine-tuned for ship behaviour. The consistency of a new observation with the modeled position is checked compared to key performance indicators such as innovation, likelihood and speed. (ix) A footprint filter was applied to check for satellite AIS data consistency. All positions which were not compliant with the ship-satellite co-visibility were flagged as invalid.The AIS data were converted from their original format (NMEA) to CSV, and split into 12 files, each corresponding to a month of 2017. Overall the pre-processed dataset included about 1.9 billion records. Upon trying and importing the data into a database, it emerged that some messages still contained invalid characters. By running a series of commands from a Linux shell, all invalid characters were removed. The data were then imported into a PostgreSQL relational database. By querying the database it emerged that some MMSI numbers are associated to more than a ship type during the year. To cope with this issue, we thus created an unique MMSI/shyp type register where we attributed to an MMSI the most recurring ship type. The admissible ship types reported in the AIS messages were grouped into macro categories: 0 Other, 1 Fishing, 2 Service, 3 Dredging or underwater ops, 4 Sailing, 5 Pleasure Craft, 6 High speed craft, 7 Tug and towing, 8 Passenger, 9 Cargo, 10 Tanker, 11 Military and Law Enforcement, 12 Unknown and All ship types. The subsequent step consisted of creating points representing ship positions from the AIS messages. This was done through a custom-made script for ArcGIS developed by Lovell Johns. Another custom-made script reconstructed ship routes (lines) from the points, by using the MMSI number as a unique identifier of a ship. The script created a line for every two consecutive positions of a ship. In addition, for each line the script calculated its length (in km) and its duration (in hours) and appended them both as attributes to the line. If the distance between two consecutive positions of a ship was longer than 30 km or if the time interval was longer than 6 hours, no line was created. Both datasets (points and lines) were projected into the ETRS89/ETRS-LAEA coordinate reference system, used for statistical mapping at all scales, where true area representation is required (EPSG: 3035).The lines obtained through the ArcGIS script were then intersected with a custom-made 1km*1km grid polygon (21 million cells) based on the EEA's grid and covering the whole area of interest (all EU sea basins). Because each line had length and duration as attributes, it was possible to calculate how much time each ship spent in a given cell over a month by intersecting line records with grid cell records in another dedicated PostgreSQL database. Using the PostGIS Intersect tool, for each cell of the grid, we then summed the time value of each 'segment' in it, thus obtaining the density value associated to that cell, stored in calculated PostGIS raster tables. Density is thus expressed in hours per square kilometre per month. The final step consisted of creating raster files (TIFF file format) with QuantumGIS from the PostgreSQL vessel density tables. Annual average rasters by ship type were also created. The dataset was clipped according to the National Marine Planning Framework (NMPF) assessment area. None

This polygon was created using a combination of geospatial intersection tools and hand digitization methods. First, the 2023 Southeast Conservation Blueprint, the Western Highland Rim ecoregion, and 2021 NLCD Tree Canopy Cover layers where compared to one another to trace the eastern edge of the Nashville Highland Rim Forest, being sure to include some transition areas between developed and forested areas that provide ecosystem services and natural amenities to urban communities. Then, the Davidson county boundary/Metropolitan government of Nashville and Davidson County was used to intersect the western and northern side of the forest to complete the polygon. The Nashville Highland Rim Forest is relative to the county boundary because Nashville's Metro government covers the entire county. Interconnected Highland Rim forest blocks function outside of Davidson county, but focusing on the Davidson county portion makes it Nashville's Highland Rim Forest.The map below illustrates the how the Nashville Highland Rim Forest aligns with the 2023 Southeast Conservation Blueprint, the 2015 State Wildlife Action Plan's Western Highland Rim Conservation Opportunity Area, and some of the parks that fall within the forest.This polygon can be used to bring awareness of the significance of the forest to Nashville residents and visitors.

Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process.Digitizing is done as Geodatabase feature classes using ArcMap 10.X with NAIP or Google imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process.Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.2018 marked the first year a comparison could be made using the CDL methodology. The comparison between 2017 and 2018 showed a large change in agricultural land use to other land use. It was determined this shift was due to crop land being allowed to sit fallow for a season and did not represent a shift away from agricultural land. The following code amended the data:*************************************************************************************************************************************####On 02/07/2020 this dataset was amended with the following R script to better reflect agricultural land changes:require(arcgisbinding)arc.check_product()####Bring in layersLU17<-arc.open("Path to data")LU17<-arc.select(LU17)#####Amend dataLU17$Landuse[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Agricultural"LU17$CropGroup[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Fallow/Idle"LU17$IRR_Method[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Dry Crop"arc.write("Path to data", LU17)*************************************************************************************************************************************LUID -Unique ID number for each polygon in the final dataset, matches object.Landuse - Land use type, similar to land cover and represents our own categories of how the land is used.CropGroup - Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc.Description - Attribute that describes/indicates the various crop types and land use types determined by the GIS process.IRR_Method - Crop Irrigation Methods.Acres - Calculated acreage of the polygon.State - Spatial intersection identifying the State where the polygons are found.County - Spatial intersection identifying the County where the polygons are found.Basin - Spatial intersection identifying the Basin where the polygons are found. Basins, or Utah Hydrologic Basins are large watersheds created by DWRe.SubArea - Spatial intersection identifying the Subarea where the polygons are found. Subareas are subdivisions of the larger hydrologic basins created by DWRe.Label_Class - Combination of Label and Class_Name fields created during processing that indicates specific cover and use types.LABEL - Old shorthand descriptive label for each crop and irrigation type or land use type.Class_Name - Zonal Statistics majority value derived from the USDA CDL Cropscape raster layer, may differ from final crop determination.OldLanduse - This is the old short code found under landuse in past datasets and is kept to maintain connectivity with historical data.LU_Group - These codes represent some in-house groupings that are useful for symbology and other summarizing.SURV_YEAR - Indicates which year/growing season the data represents. Is useful when comparing to past layers.

The data illustrates the “Urbanized Area” for the Municipal Separate Storm Sewer System (MS4) program from the 2010 census. "Urbanized area" means a place and the adjacent densely populated territory that together have a minimum population of 50,000 people, as defined by the United States bureau of the census and as determined by the latest available decennial census. The data is provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the United States Environmental Protection Agency. The urbanized area is the regulated area for municipalities that are regulated under the MS4 program, including but not limited to cities, township, and villages."2020 Census Populations of 50K or more" and "Automatically Designated Areas" was provided by US EPA in July 2023 and combined with Michigan Open GIS Data (Minor Civil Divisions: Cities, Townships and Villages) using ESRI's ArcGIS Pro Software. Tools used include Pairwise Intersect, Merge, Pairwise Erase, and manual editing to combine the two layers.Please contact the individuals below with any questions.Christe Alwin: ALWINC@michigan.gov (point of contact)Patrick Klein: kleinp3@michigan.gov (creator)

FIELD NAME

DESCRIPTION

Name

Short name of the municipality (Lansing)

Label

The municipalities full name (City of Lansing)

Type

The type of municipality (city, township, or village)

SQMILEArea of the shape in Square Miles

ACRES

Area of the shape in Acres

Published in June 2024. Learn more about EGLE's Municipal Storm Water Program.Additional information describing Part 21 Wastewater Discharge Permits.

1m Contours derived from the 2023 1m DEM for the urban areas. The 1m DEM was generated from LiDAR data that was captured at the request of Porirua City Council by Landpro between 08/01/2023 and 16/04/2023 using the projection NZTM NZGD200 and vertical projection of NZVD16. The dataset was calibrated and classified by Landpro using automated and manual processes from which surface features were removed generating in a 1m bare earth DEM. The DEM provided by Landpro was smoothed by Porirua City's GIS team using a 3x3m grid with a neighborhood moving average prior to running the contour tool. Any contours less than 5m in length were removed. To reduce loading times, contours were clipped using the Pairwise Intersect tool and a 500m fishnet layer.

Natural conservation areas were created by clipping artificial pathways (generally, areas that correspond to major rivers) and intermittent and perennial stream features from the National Hydrography Dataset (NHD) flowline feature class to the Hanover County boundary. Intermittent NHD features that did not intersect the FEMA floodplain layer were deleted from the dataset. These final flowlines were then buffered by 100 feet. NHD water body features were also buffered by 100 feet. Features from the buffered water body layer were deleted if they did not intersect the buffered flowlines or the FEMA floodplain layer. Next, the buffered NHD flowlines, the FEMA floodplain layer, and the buffered water body polygons were all merged into one polygon feature class. The geoprocessing tool 'multipart to singlepart' was then run on the polygons to separate multi-part features into distinct regions. Next, the geoprocessing tool 'simplify by straight lines and circular arcs' was run on the polygon layer to reduce the number of feature vertices and improve performance. Finally, any polygons overlaying developed areas were removed from the dataset by erasing the portion of the region within the property boundary of the developed parcel.

The data illustrates the expanded “Urbanized Area” for the Municipal Separate Storm Sewer System (MS4) program from the 2020 census data. "Urbanized area" means a place and the adjacent densely populated territory that together have a minimum population of 50,000 people, as defined by the United States bureau of the census and as determined by the latest available decennial census. The data is provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the United States Environmental Protection Agency. The urbanized area is the regulated area for municipalities that are regulated under the MS4 program, including but not limited to cities, township, and villages."2020 Census Populations of 50K or more" and "Automatically Designated Areas" was provided by US EPA in July 2023 and combined with Michigan Open GIS Data (Minor Civil Divisions: Cities, Townships and Villages) using ESRI's ArcGIS Pro Software. Tools used include Pairwise Intersect, Merge, Pairwise Erase, and manual editing to combine the two layers.Please contact the individuals below with any questions.Christe Alwin: ALWINC@michigan.gov (point of contact)Patrick Klein: kleinp3@michigan.gov (creator)FIELD NAMEDESCRIPTIONNameShort name of the municipality (Lansing)LabelThe municipalities full name (City of Lansing)TypeThe type of municipality (city, township, or village)SQMILEArea of the shape in Square MilesACRESArea of the shape in AcresPublished in June 2024. Learn more about EGLE's Municipal Storm Water Program.Additional information describing Part 21 Wastewater Discharge Permits.

The CMP Objective Measures GIS layer includes scoring by road segment where congested road segment locations that meet CMP Objective Measure criteria than others contain higher score totals and are given stronger consideration for managing congestion. The final mapping is a composite of all the scores where a maximum score of 15 can be attained, and road segment locations with scores greater than nine are shown in brown.The CMP Objective Measures include: 1) increase mobility and reliability; 2) integrate modes and increase accessibility; 3) modernize infrastructure; 4) achieve Vision Zero; 5) make global connections; 6) strengthen security and enhance emergency preparedness; and 7) support Long-Range Plan principles.CMP Objective Measure Database FieldsSegID – INRIX Segment IDFRC – INRIX facility ID as applicableMiles – INRIX segment milesRoadNumber – INRIX Road Highway numberRoadName – INRIX Road nameState - INRIX StateCounty – INRIX CountyBearing – INRIXD BearingSEGID2X – INRIX Segment ID (string)TTIWKD0708 – INRIX Travel Time Index 7-8 amTTIWKD0809 – INRIX Travel Time Index 8-9 amTTIWKD1617 – INRIX Travel Time Index 4-5 pmTTIWKD1718 – INRIX Travel Time Index 5-6 pmPTIWKD0708 – INRIX Planning Time Index 7-8 amPTIWKD0809 – INRIX Planning Time Index 8-9 amPTIWKD1617 – INRIX Planning Time Index 4-5 pmPTIWKD1718 – INRIX Planning Time Index 5-6 pmREFSPDMEAN – Reference or Freeflow speedFFTIME – Freeflow TimeMAJTMC – Primary INRIX TMC associated with INRIX XDNHS – Roadway segment on NHS based on NPMRDS database Valid values: 1 – On NHS 2 – Not on NHSLOTTRMAX – Roadway segment LOTTR value TTTRMAX – Roadway segment TTTRMAX value PHEDVAL – Roadway segment PHEDVAL value from conflated TMC PHEDVALPMI – Roadway segment PHEDVAL value Per Mile of Roadway from conflated TMC FROMTONODE – Travel Demand Model From To Node LinkVC7to8am15 – Travel Demand Model V/C 7 am to 8 am 2015VC8to9am15 – Travel Demand Model V/C 8 am to 9 am 2015VC4to5pm15 – Travel Demand Model V/C 4 am to 5 pm 2015VC5to6pm15 – Travel Demand Model V/C 5 am to 6 pm 2015VCMaxPH15 – Highest of 2015 am and pm V/CsVC7to8am50 – Travel Demand Model V/C 7 am to 8 am 2050VC8to9am50 – Travel Demand Model V/C 8 am to 9 am 2050VC4to5pm50 – Travel Demand Model V/C 4 am to 5 pm 2050VC5to6pm50 – Travel Demand Model V/C 5 am to 6 pm 2050VCMaxPH50 – Highest of 2050 am and pm V/CsPCPH7to8AM – Percent Change in V/C from 2015 to 2050 during 7-8amPCPH8to9AM – Percent Change in V/C from 2015 to 2050 during 8-9amPCPH4to5PM – Percent Change in V/C from 2015 to 2050 during 4-5pmPCPH5to6PM – Percent Change in V/C from 2015 to 2050 during 5-6pmPCMaxPH – Highest of Percent Change V/CTRANSIT – Travel Demand Model road segments that include surface transit (bus or trolley) Valid values: 0 – No road segments with transit 1 – Road segments with any transit 2 – Road segments with substantial transit (2 or more in urban; 3 or more suburban)rmshash – PennDOT road segments county, route and segment identifiers from PennDOTs RMSSEG databasenewaadtv2 – Average Annual Daily Traffic (AADT)newadttv2 – Average Annual Daily Truck Traffic (AADTT)DVRPCCR_RT – Average crash rate analyzed separately for the PA and NJ portions of DVRPC region based on similar roadway characteristicsSEG_CR_RT – Actual crash rate by roadway segmentCR_INDEX - Crash Rate index comparing actual crash rate with average rate (analyzed separately for the PA and NJ portions of DVRPC region)FATALRATE – Fatal crash rate based on fatalities per 100,000,000 million vehicle miles traveledCR_INDEX_C – Crash Rate Valid values: 0 – Crash rate less than 4 times the state rate 1 – Crash rate 4 or more times the state rateCRASH_CT – Number of crashes by roadway segmentFATAL_CT – Number of fatal crashes by roadway segmentTOT_INJ_CT – Number of injury crashes by roadway segmentMAJ_INJ_CT – Number of major injury crashes by roadway segmentMOD_INJ_CT – Number of moderate injuries by roadway segmentMIN_INJ_CT – Number of minor injuries by roadway segmentUNK_INJ_CT - Unknown injuries by roadway segmentCRASHPERMI – Crashes per MileFATMAJINJC – Fatal and major injuries by roadway segment FATMIPMILE – Fatalities and Major Injuries Per MileFATMAPMICR – Fatalities and Major Injuries Per Mile Crash Index Valid values: 0 – Less Than 5 or more fatal and major injuries per roadway segment mile 1 – 5 or more fatal and major injuries per roadway segment mileTTTIMAXVAL – Highest Truck Travel Time Index value for 7-8am, 8-9am, 4-5pm and 5-6pmFACIDNEW – Focus Roadway Corridor Facility identifierFACIDDIR – Focus Roadway Corridor Facility direction, which is used to calculate AADTTTICR - Roadway segment congestion threshold – TTICRP Valid values: 0 – TTI value Less Than 1.20 1 – TTI value >= 1.20 and <= 1.50 (moderately congested) 2 – TTI > 1.50 (highly congested)TTIWKDMAX - Highest of INRIX Travel Time Index 7-8am, 8-9am, 4-5pm, 5-6pmPTIWKDMAX - Highest of INRIX Planning Time Index 7-8am, 8-9am, 4-5pm, 5-6pmPTICR - Roadway segment reliability threshold - PTICRP Valid values: 0 – PTI value Less Than 2.00 1 – PTI value >= 2.00 and =< 3.00 (moderately unreliable) 2 – PTI > 3.00 and <= 3.50 (highly unreliable) 3 – PTI > 3.50 (very highly unreliable)LOTTRCR – Roadway segment reliable threshold Valid values: 0 – LOTTR < 0 1.50; reliable 1 – LOTTR 1.50 to 2.00; moderately unreliable 2 – LOTTR > 2.00; highly unreliable TTTRCR – Roadway segment reliable threshold (trucks on interstates only) Valid values: 0 – TTTR value is 0 1 – TTTRMAX > 0 and Less Than 1.50; reliable 2 – TTTRMAX 1.50 to 2.49; moderately unreliable 3 – TTTRMAX >= 2.50; highly unreliablePHEDCR – Roadway segment that experiences excessive delay above the regional average (24,355 PHED/Mile) (based on NPMRDS database, not on conflated INRIX database). Valid values: 0 – Not above regional average 1 – At or above regional averageVC8550PCR – Travel Demand Model Segments with >=15% change from 2015 to 2050 Valid values: 0 – Segments < 15% Change in V/C 1 – Segments >= 15% Change in V/CVC8550CR – Travel Demand Model Road segments in 2050 with 0.85 or more V/C Valid values: 0 – Segments < 0.85 V/C 1 – Segments >= 0.85 V/CTTTICR – Truck Travel Time Index Roadway segment truck congestion threshold Valid values: 0 – Less Than 2.00 1 – 2.00 to 3.00 (moderately congested) 2 - > 3.00 (highly congested)SETRANSTCR – Transit Score Priority Valid values: 0 – Not Priority 1 – PriorityCRSSRIID – NJ Crash Identifier SRI popdenmnx2 – Road segments that intersect CBGs with population more than 2x the regional average Valid values: 0 –No 1 – Yesempdenmnx2 – Road segments that intersect CBGs with employment more than 2x the regional average Valid values: 0 –No 1 – Yesrailsta1mi – Road segments that intersect 1 mile buffer of rail stations Valid values: 0 –No 1 – Yestrnstqtrmi – Road segments that intersect 1/4 mile buffer of bus transit routes Valid values: 0 –No 1 – Yesraillin1mi – Road segments that intersect 1 mile buffer of passenger rail Valid values: 0 –No 1 – Yesfrtrail1mi – Road segments that intersect 1 mile buffer of freight rail Valid values: 0 –No 1 – Yesfrtcntr1mi - Road segments that intersect freight centers Valid values: 0 –No 1 – Yestrnstscor - Road segments that intersect CBG with very and high composite (pop, emp and 0 veh households) Valid values: 0 –No 1 – YesTPTIMAXVAL – Highest Truck Planning Time Index value for 7-8am, 8-9am, 4-5pm and 5-6pm.TPTICR – Truck Planning Time Index Roadway segment truck reliability threshold Valid values: 0 – Less Than 2.00 1 – 5.50 to 6.50 (moderately unreliable) 2 - > 6.50 (highly unreliable)HVTRST1mi – Heavily used transit stations: 3 in Philadelphia and 1 in each of the other PA counties; road segments that intersect within 1-mile buffer Valid values: 0 –No 1 – YesNUCPLT1MI – Road segments within 10 mile buffer of Limerick Nuclear Power Plant Valid values: 0 –No 1 – YesRDBRGR1MI – Road segments within 1 mile buffer of major road bridges carrying > 100,000 AADT Valid values: 0 –No 1 – YesFRBRGR1MI – Road segments within 1 mile buffer of major freight bridges Valid values: 0 –No 1 – YesRLBRGR1MI – Road segments within 1 mile buffer of major passenger rail bridges Valid values: 0 –No 1 – YesMIL1MI – Road segments within 1 mile buffer of major military sites Valid values: 0 –No 1 – YesSTDWTF1MI – Road segments within 1 mile buffer of major military sites Valid values: 0 –No 1 – Yeslucntr – Road segments that intersect 2050 land use centers Valid values: 0 –No 1 – YesIREGAreas – Road segments that intersect Infill, Redevelopment and Emergency Growth Areas Valid values: 0 –No 1 – YesENSCAreas – Road segments where 90% or more of segment score low in the Environmental Screening Tool (or not environmental sensitive areas) Valid values: 0 –No 1 – YesFLD100500 – Road segments that intersect 100-year and 500-year floodplain Valid values: 0 –No 1 – YesIPDEJLI – Road segments that intersect IPD CBG’s with well above average or above average low income populations Valid values: 0 –No 1 – YesIPDEJRM – Road segments that intersect IPD CBG’s with well above average or above average racial minority populations Valid values: 0 –No 1 – YesTTICRP – Travel Time Index criteria PointsPTICRP – Planning Time Index criteria PointsPHEDCRP – PHED criteria PointsVC8550CRP – high anticipated V/C criteria PointsVC8550PCRP – high anticipated growth in V/C criteria PointsLOTTRCRP – LOTTR criteria PointsIMRMAXP – increase mobility and reliability maximum PointstrnstscorP – transit score criteria Pointsrailsta1mP – near

IMPORTANT IN THE OPEN DATA PORTAL THERE IS ONE FEATURE CLASS FOR ALL POTENTIOMETRIC SURFACE MAPS. IF YOU WANT JUST ONE TIME PERIOD CLICK ON THE TABLE TAB, THEN CLICK ON THE DATE FIELD. IN THE FILTER BOX ON THE RIGHT ENTER THE MAP YOU WANT (MAY 2000, SEPTEMBER 2015, ETC.). WHEN YOU CLICK THE DOWNLOAD DATASET BUTTON SELECT SPREADSHEET OR KML OR SHAPEFILE UNDER THE FILTERED DATASET OPTION. YOU WILL ONLY GET THE FILTERED DATA FROM THIS DOWNLOAD.Contour lines are created for the potentiometric surface of the upper Floridan aquifer from water level data submitted by the water management districts. The points associated with the water level data are added to Geostatistical Analyst and ordinary kriging is used to interpolate water level elevation values between the points. The Geostatistical Analyst layer is then converted to a grid (using GA Layer to grid tool) and then contour lines (using the Contour tool). Post editing is done to smooth the lines and fix areas that are hydrologically incorrect. The rules established for post editing are: 1) rivers intersecting the UFA follow the rule of V’s; 2) potentiometric surface contour line values don’t exceed the topographic digital elevation model (DEM) in unconfined areas; and 3) potentiometric surface contour lines don’t violate valid measured water level data. Errors are usually located where potentiometric highs are adjacent to potentiometric lows (areas where the gradient is high). Expert knowledge or additional information is used to correct the contour lines in these areas. Some additional data may be river stage values in rivers that intersect the Floridan aquifer or land elevation in unconfined areas. Contour lines created prior to May 2012 may be calculated using a different method. The potentiometric surface is only meant to describe water level elevation based on existing data for the time period measured. The contour interval for the statewide map is 10 feet and is not meant to supersede regional (water management district) or local (city) scale potentiometric surface maps.

This hosted tile layer is included in our decision support map and application entitled, Deep Sea Coral Research & Restoration Scoping Tool.This data layer depicts the relative intensity of commercial bottom trawling off the U.S. West Coast from 1 Jan 2002 –11 Jun 2006, except areas where less than three vessels were operating. This was calculated as the total length of all towlines intersecting a standardized area. Each of the three coastal states administers a commercial logbook program, for which records are uploaded to the Pacific Fisheries Information Network (PacFIN) regional database. Database records were utilized for commercial trips using bottom trawl gear types (e.g., “small” footrope, “large” footrope, flatfish, selective flatfish, and roller trawl) regardless of fishery sector (e.g., limited entry, open access). Records from the majority of state-managed trawl fisheries (e.g., pink shrimp, ridgeback prawn, sea urchin) are not included in PacFIN and thus are not represented. Tows targeting one state-managed trawl fishery –California halibut –are submitted to PacFIN and thus are included in this data layer. The intensity layer is a raster layer with cell values representing the total length of all towlines intersecting a standardized area. To calculate this metric, a line density algorithm in ArcGIS™ geographical information system software (Environmental System Research Institute, Incorporated, Redlands, California) was used. The line density algorithm calculates density within a circular search area (radius = 3 km) centered at a grid cell (size 500 m x 500 m). The value (units: km/km2) for each raster cell is the quotient of total towline portions intersecting the circular area per cell area. Since density outputs are highly sensitive to the specified radius and cell size, the absolute values are less important than the relative nature of them. The benefit of this output over depicting towlines themselves is that the density output better identifies areas where fishing effort is concentrated, while still ensuring confidentiality of individual fishing locations. The initial density output was more spatially extensive than the one shown in the figures, because it included cells with density values calculated from tows made by less than three vessels. Those “confidential” cells were removed for the final published data layer. Density parameters were chosen in order to minimize data exclusion (due to confidentiality mandates) while still providing a fairly high spatial resolution (500 x 500 m). Within this time period, only 1.1% of all effort (i.e., length of towlines) was excluded from the published raster layer, although the proportion varies considerably in certain areas along the coast. This spatial summary of bottom trawl effort was developed from data represented only by start and end points of tows. It is recognized that tows rarely follow straight-line paths; however, this was the best information available on the spatial distribution of effort for vessels using bottom trawl gears. Because of this limitation and due to prohibitions of trawling within state waters, representatives of the states of Washington and California requested that any portions of the spatial summaries that intersect prohibited state waters be removed. In addition, Washington requested that effort occurring within both state and federal waters of the Salish Sea be removed since they felt that this information was incomplete and may not be representative of fishing effort within those areas. However, the National Marine Fisheries Service General Counsel has advised the Council EFHRC that there is not justification to limit access/display of these data from state waters so they are included in the map products.