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.

This dataset is a collection of regional boundary shapefiles, not elsewhere provided, used as spatial overlays to derive spatial summary statistics of various hazard, climate and other ecological raster data. The collection informed the assessment of freshwater and terrestrial natural environments as part of the National Climate Risk Assessment (NCRA). The shapefiles are derived from the intersection of publicly available 3rd party input data such as NCRA region boundaries, NCRA Hazard grids, Geofabric Level 2 Basins and Geofabric Network Streamlines.

The shapefiles (not elsewhere provided) in this collection include: NCRA Regions x Aggregate Ecosystem Groups 5km NCRA Hazard cells intersecting Geofabric Perennial Streams x Geofabric Level Basin 5km NCRA Hazard cells intersecting Geofabric Non- Perennial Streams x Geofabric Level Basin

All layers are ESRI shapefiles in the GCS WGS 1984 (EPSG 4326) Lineage: The input data used to derive the Regional Boundary shapefiles are:

1. NCRA Regions
2. A polygonised representation of NCRA Hazard raster data supplied by the Australian Climate Service (ACS)
3. Level 2 drainage basins from the Australian Hydrological Geospatial Fabric (AHGF) v3
4. Network Streams from the Australian Hydrological Geospatial Fabric (AHGF) v3
5. Aggregate Ecosystem Groups (AEGs) derived from National Vegetation Information System (NVIS) data

All processing was carried out using the Overlay Tools available in ArcGIS Desktop 10.8 with inputs reprojected to WGS84 before intersecting.

DERIVED Region Boundaries

1. NCRA Regions x Aggregate Ecosystem Groups: NCRA Regions were combined with the polygonised AEGs using ArcGIS Intersect tool output shapefile - NCRA_x_AEG_regions_WGS84.shp

2. 5km NCRA Hazard cells intersecting Geofabric Perennial Streams x Geofabric Level Basin: Hazard cell polygons were selected based on their spatial intersection with Geofabric Network Streams where the Perennial attribute is "Perennial". The selected cells were then combined with the Geofabric Level 2 Basins using ArcGIS Intersect tool. output shapefile - Perennial_x_GeofabricLevel2Basins_WGS84_singleparts2.shp

3. 5km NCRA Hazard cells intersecting Geofabric Non-Perennial Streams x Geofabric Level Basin: Hazard cell polygons were selected based on their spatial intersection with Geofabric Network Streams where the Perennial attribute is "Non Perennial". The selected cells were then combined with the Geofabric Level 2 Basins using ArcGIS Intersect tool. output shapefile - NonPerennial_x_GeofabricLevel2Basins_WGS84_singleparts2.shp

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.

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

According to the distribution of cultivated land in 18 districts and counties in the "One River and Two Tributaries" region of Tibet Autonomous Region, a 5km × 5km grid was adopted, covering all cultivated land and greenhouse land. A total of 1092 5km × 5km grids were set up, and each grid contains a number. Data processing method: the fishnet tool in ArcGIS 10.3 is used to generate the grid covering the administrative boundaries of 18 districts and counties in the "one river, two rivers" region of Tibet Autonomous Region, and then the intersect tool is used to generate the grid covering cultivated land. The data can be used to collect soil samples of cultivated land in "One River and Two Tributaries" area of Tibet Autonomous Region.

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.

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.

Descarga aquí el metadato:https://aplicaciones.siatac.co/geonetwork/srv/spa/catalog.search#/metadata/1742d666-50c8-4573-823e-5c5189ac0bbdDescarga aquí el shapefile:https://opendata.arcgis.com/datasets/31b4f21bfb6047659d5bc2b335d99eff_0.zipCorresponde a la capa de cicatrices por quemas en la Amazonía colombiana desde marzo del 2017 a escala 1:100.000. Para generar esta capa se seleccionan las imágenes satelitales, del programa LandSat; deben tener menos del 30% de nubes. Se hace una verificación de la cantidad puntos de calor detectados durante el mes de monitoreo, para corroborar cuales Path Row que cubren la región amazónica (4-57, 4-58, 4-59, 4-60, 4-61, 4-62, 4-63, 9-59, 9-60, 7-58, 7-59, 7-60, 7-61, 5-57, 5-58, 5-59, 5-60, 5-61, 5-62, 3-57, 3-58, 3-59, 8-58, 8-59, 8-60, 6-57, 6-58, 6-59, 6-60, 6-61, 6-62) deben priorizarse para la descarga.Para el procesamiento y clasificación de las imágenes, y los diferentes geoprocesos se usan herramientas del software ArcGis (Esri, 2022a). Con este programa se aplican los “Model Builder” que se han generado para este procesamiento, los cuales hacen parte de los flujos de trabajo (Workflow) construidos en la plataforma SIATAC. Con las imágenes se generan dos composiciones de color RGB , (1) una que integra el Índice de Vegetación de Diferencia Normalizada - NDVI (B5-B4/B5+B4), el Radio Normalizado de Quema-NBR (B5-B7/ (B5+B7) y la banda del infrarrojo cercano -IR (B5); (2) la otra composición se hace con las bandas B7-B5-B2; estas composiciones resaltan las áreas que han sufrido procesos de quema de la vegetación (Murcia & Otavo, 2018).Con la composición RGB (1) se hace una clasificación no supervisada tipo clúster (Clúster Iso) (Esri, 2022b) y se generan 11 clases. Sobre esta capa ráster se hace una verificación visual para determinar cuál de las 11 clases corresponde a las cicatrices, este proceso se hace con respaldo en el protocolo metodológico (Murcia et al., 2018) y las dos composiciones ya generadas. Una vez seleccionada la clase que se ha verificado como cicatrices, se hace una reclasificación binaria de las unidades, en la que uno (1) son cicatrices y cero (0) las otras clases. En el mismo proceso (Model Builder) se hace la vectorización y se genera la capa de polígonos de cicatrices.Luego se hace una verificación visual de los polígonos generados, para descartar aquellos que no son cicatrices, para esto se aplican los criterios previstos en el protocolo metodológico (Murcia et al., 2018) teniendo como referente las dos composiciones previamente generadas. Con la capa resultado se hace un proceso de análisis espacial de intersección (Esri, 2022c) para descartar las cicatrices que ya fueron clasificadas en el mes anterior.A la capa resultante se le hace control de calidad para verificar la exactitud temática, validando aspectos como delimitación, errores por omisión y errores por comisión. De igual modo, se verifica que la capa cumpla con todos los criterios de topología como la correcta adyacencia entre polígonos, y se aprueba la capa.En el siguiente paso, la capa aprobada se integra en un WorkFlow (Esri, 2022d) de la base de datos en la plataforma SIG de Esri, del SIATAC. Luego se aplica un proceso SIG de intersección mediante el cual se clasifican las cicatrices que se ubican en áreas que eran bosques, según la capa de bosques más reciente generada por el IDEAM (Ideam, 2022). Sobre los polígonos restantes, se aplica el mismo proceso SIG (intersección) con la capa de coberturas de la tierra, del periodo más reciente (Sinchi, 2022) y se clasifican las cicatrices que se ubican en donde había vegetación secundaria u otras coberturas, principalmente pastos.La capa resultante se somete a un proceso de análisis espacial de intersección para generar la información de las cicatrices con el tipo de cobertura vegetal afectada, por cada Unidad Espacial de Referencia (UER): Grandes paisajes, Jurisdicción de Corporaciones Autónomas Regionales o de Desarrollo sostenible, Estado legal del territorio, Departamentos y Municipios. Para finalizar, las estadísticas se publican en el portal del Sistema de Información Ambiental Territorial de la Amazonia colombiana -SIATAC (https://siatac.co/cicatrices-de-quema/).BIBLIOGRAFÍAMurcia, U. & Otavo, S. (2018). La amazonia se quema: Detección de áreas con mayor ocurrencia de incendios de vegetación como estrategia para la prevención y control. Revista Colombiana Amazónica No 11 de 2018, 59-72. https://sinchi.org.co/11-revista-colombia-amazonica.Cañon I., Gordillo G., León A., Murcia U., Romero H., Velásquez M. (2018). Protocolo para el monitoreo de cicatrices por quemas en la Amazonia colombiana. 46pp.Esri. (2022a). ArcGIS Desktop.https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview.Esri. (2022b). Clasificación no supervisada de clúster ISO.https://pro.arcgis.com/es/pro-app/2.8/tool-reference/spatial-analyst/iso-cluster-unsupervised-classification.htmEsri. (2022c). Intersección (Análisis).https://pro.arcgis.com/es/pro-app/latest/tool-reference/analysis/intersect.htmEsri. (2022d). ArcGIS Workflow Manager (Análisis).https://www.esri.com/en-us/arcgis/products/arcgis-workflow-manager/overviewIdeam. (2022). Sistema de Monitoreo de bosques y carbono SMBYC.https://smbyc.ideam.gov.co/MonitoreoBC-WEB/reg/indexLogOn.jspSinchi. (2022). Sistema de Monitoreo de las Coberturas de la tierra de la Amazonia colombiana SIMCOBA. Datos abiertos.https://datos.siatac.co/pages/coberturasDiccionario de datos:objectid: Corresponde al identificador propio de cada registro dentro de la capa de informaciónarea_ha: Corresponde al área en hectáreas de la unidad seleccionadaarea_km2: Corresponde al área en kilómetros cuadrados de la unidad seleccionadaano: Corresponde al año de publicación de la cicatriz de quemaorigen: Corresponde a la cobertura que fue afectada por la cicatriz de quemames: Corresponde al mes de publicación de la cicatriz de quemafecha_registro: Corresponde a la fecha de publicación de la cicatriz de quemashape: Corresponde a geometría del elementost_area(shape): Corresponde al área del elementost_length(shape): Corresponde al perímetro del elementoFuente:Modelos de Funcionamiento y Sostenibilidad del Laboratorio SIG y SRBogotá D.C., Colombia siatac.coCalle 20 # 5 - 44Código Postal: 110311 Teléfono: +57 (1) 4442060Horario de atención: 8:00 am - 5:00 pm de Lunes a Viernes Información de contacto:Establecer previo contacto telefónico o a través de correo electrónico, para realizar la solicitud o fijar una cita en el horario de atención.

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

The “DTPW Permit GIS Mapping Tool Web Map” serves as the foundational map for the DTPW Permit GIS Mapping Application—a public application for submitting, editing, and reviewing transportation permits within Miami-Dade County.Key Functions:

Provides editors with an intuitive map-based interface for adding and updating permit records. Incorporates Arcade expressions in the Editor forms to automatically populate address and intersection fields. Serves as the primary workspace for editing permit data before it's integrated into other DTPW workflows.Editable Feature Layers (For Permit Data Collection and Updates):

Add a Point Arcade Function: Automatically populates the address field by finding the nearest parcel from the Property Boundary layer within 60 meters.

Add a Segment Arcade Function: Populates start and end address fields by retrieving nearby intersection names from the GeoIntersection layer within a 25-meter buffer.

Add a Polygon Arcade Function: Populates address fields by identifying the nearest intersections for each of the polygon’s four corner vertices, using the GeoIntersection layer.

Reference Data Layers (For Spatial Validation and Data Extraction):

Property Boundary

Parcel boundaries containing address information. Used to support the automated address population for point permits.

GeoIntersection

A dataset of intersection points and names. Used to populate intersection-related fields in both segment and polygon permits.

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 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.

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 area within two (2) miles of the break walls at Rogers City. Restrictions: Harbor closure shall apply.Created a new point shapefile in ArcGIS 8.1. A point was located on the USGS Presque Isle county 1:24,000 DRG as outlined in the Consent Decree 2000 documentation. The point was generated on the nautical light located at the southeast end of the breakwall. A two mile buffer was then generated from the point shapefile using the buffer wizard tool in Arc Map. This buffered point location was then intersected with the National Oceanic and Atmoshperic Administration - Great Lakes Environmental Research Laboratory (NOAA - GLERL) shoreline layer using the intersecting tool from the geoprocessing wizard in Arc Map to create the final Rogers City Harbor Closure layer. The desired features were then reprojected from Michigan georef to Decimal Degrees to create the final Northern Lake Huron Inter- Tribal Fishing Zone layer. The NOAA - GLERL shoreline layer was cleaned by both Great Lakes Commission (GLC) and Lake Huron GIS (MDNR - LHGIS) after completion.The boundaries represented on consent decree maps are approximations based on the text contained in the 2000 Consent Decree. For legal descriptions of geographic extent or details pertaining to regulations for these representations refer to the original 2000 Consent Decree Document.

This map communicates the upcoming disruptions that will impact the transport network in the West Midlands Combined Authority conurbation.This map identifies the planned road and rail closures during the works with predicted timescales and severity of disruption.Planned roadworks on the West Midlands Strategic Road Network collected through open data RSS feed from Highways England.Highways England Area9 Roadspace Bookings received from Highways England contractor updates daily and aggregated to a weekly update digitised to OpenStreetMap basemapPlanned railway closures received as spreadsheet via Network Rail.Birmingham highways works foreward programme digitised using FME to map to Ordnance Survey Highways with Unique Street Reference Numbers (USRN) as identifier.Birmingham highways forward programme clashes created by running intersect tool between promoters on Birmingham highways works dataset.West Midlands Events captured as part of the Events Management collaboration project. Event data collated from Police and local authorities and stored in smartsheets. Spatial data created manually from venue location.

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.

A collaborative effort between DEP and the New Jersey State Office of Innovation, the Transportation Needs Index is a data-driven analysis tool that highlights areas where high demographic needs intersect with limited transit access, helping to identify areas for potential transportation investments. These locations include those where transportation may not be meeting the needs of all residents, especially for populations traditionally underserved by transportation services or left out of transportation decisions in the past.The data visualizations identify areas with potential transportation gaps by combining demographic factors (poverty levels, households with no vehicles, population density, senior and youth populations, and disability status) with transit accessibility metrics, particularly proximity to bus stops and rail stations.