Flood Hatch ShapefilesIn addition to the three sets of rasters (Maximum Wave Heights, Water Surface Elevations, and DFEs) provided, separate shapefiles were also created to overlap and highlight special areas within the raster datasets produced for calculating DFEs. A flood hatch shapefile is not provided for every ACFEP level or for every region, but when it is provided, it encompasses the special areas for that level and region. The same hatch shapefile is applicable for all datatypes for the particular level and region. Flood hatch shapefiles encompass all areas of special values within the data rasters (including areas of 9999, 9998, and 9997 values). All regions have a 0.1% ACFEP level flood hatch shapefile because all 0.1% ACFEP rasters contain 9999 values.The flood hatch shapefiles contain individual polygons that describe the type of special area underlying that polygon’s spatial extent. For 9999 and 9998 values in the value rasters (water surface elevations, waves, and DFEs), the special hatched polygons will have the same extent of those values within those rasters. For 9997 values in the value rasters, the hatch polygon will always encompass the 9997 values, but may be larger in extent than just the location of those value cells. For these areas, water surface elevation, wave heights, and DFEs values may be provided, but they still represent a special zone.The Hatch polygons have 5 fields (Column headers) that describe each polygon within the shapefile. These fields include FID, Shape, Hatch_Type, Zones_txt, Hatch, and Hatch_Txt. The FID field contains an ID number for each polygon within that shapefile, while the Shape fieldlists the type of shapefile contained (polygon in all cases). The Hatch_Type field contains the numerical value that can be found within the value rasters (wave height, water surface, and DFE) underlying that polygon. Zones_txt and Hatch_txt are string type fields that contain descriptors of the polygon type, while the Hatch Field contains a numerical value for the type of hatching (1 for 0.1% Edge Zone, 2 for Wave Overtopping Zones, 3 for Dynamic Zone). The following table is an example of what a flood hatch file’s attribute table might look like.FIDShapeHatch_TypeZones_TxtHatchHatch_Txt0Polygon9999Shallow water flooding during extreme storms10.1% Edge Zone1Polygon9997Influenced by wave overtopping (incl. 9997 areas)2Wave Overtopping Zone2Polygon9998Dynamic Landform Areas3Dynamic ZoneSpecifically, the various hatch shapefiles can be defined as follows:• FID 0 Hatch Type – These represent areas of shallow water flooding during extreme storms. These are locations where flooding can only be expected during the most extreme events (> 1000-year return period) or where there are only minor flood depths (shallow flooding) during 1000-year return period AEP. These values only appear in 0.1% ACFEP level since they only occur at the very upper extent of extreme flooding. Water surface elevation values in these regions can be set to 0.1 foot above the site-specific land elevation to provide an estimate of the water surface elevation. Site-specific survey information may be needed to determine the land elevation. These hatch areas directly match areas with 9999 values within the rasters.• FID 1 Hatch Type – These represent wave overtopping zones. These hatch layers encompass the 9997 areas, but also include areas that have known values. Hatched areas of this type covering 9997 values would be expected to experience flooding caused by intermittent wave spray and overtopping only. Hatched areas of this type covering locations with values indicate that the flooding is caused by both direct sheet flow and wave overtopping. These hatched zones are provided for informational purposes by identifying zones that may require special design considerations for wave overtopping. Site-specific coastal processes analysis may also be required in these areas.• FID 2 Hatch Type – These represent areas where geomorphology is extremely dynamic and as such expected flooding may vary drastically. These values can appear in any ACFEP level. There are minimal locations of this type. These hatch areas directly match areas with 9998 values within the rasters.

What do you need to do with your GIS data? Do you need to create earthquake hazard maps, find a location for your new business, or locate municipal utility lines? Perhaps you need to integrate your organization's data into a single system that will streamline resource management.At the core of all these projects lies the need to represent and store data in a way that supports meaningful, accurate analysis and organizational workflows. The geodatabase is the native data storage format for ArcGIS. It offers many advantages for modeling, analyzing, managing, and maintaining GIS data.With a geodatabase, you can create GIS features that mimic real-world feature behavior, apply sophisticated rules and relationships between features, and access all of your data from a centralized location. This course introduces the basic components of the geodatabase that will allow you to begin organizing your data to meet your GIS project needs.After completing this course, you will be able to:Describe the components of the geodatabase.Create geodatabase schema.Design and create a geodatabase.

Thinking Spatially Using GIS

Thinking Spatially Using GIS is a 1:1 set of instructional materials for students that use ArcGIS Online to teach basic geography concepts found in upper elementary school and above.
Each module has both a teacher and student file.

The zoo in your community is so popular and successful that it has decided to expand. After careful research, zookeepers have decided to add an exotic animal to the zoo population. They are holding a contest for visitors to guess what the new animal will be. You will use skills you have learned in classification and analysis to find what part of the world the new animal is from and then identify it.

To help you get started, the zoo has provided a list of possible animals. A list of clues will help you choose the correct answers. You will combine information you have in multiple layers of maps to find your answer.

The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG

All Esri GeoInquiries can be found at: http://www.esri.com/geoinquiries

Field Names – LEP20CD, LEP20NM, FID Field Types – Text, Text, Field Lengths – 9, 49 FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_April_2020_Names_and_Codes_in_England_2022/FeatureServer

Data file name: Beijing.rar Data deion: 1) after finishing public issued of Beijing city traffic figure, and Beijing map, and Beijing Tourism figure, by geometry corrected, and image distribution associate, work Hou, on the year road center line for vector quantitative, on vector quantitative of network data for edit, until network full, get has Beijing city five ring within, each 10 years around time interval of network GIS data, established has Beijing history network data set. 2) data file contains years of Beijing's road network data and route data is shapefile files and named for years (1969, 1978, 1990, 2000 and 2008). 3) shapefile file's property sheet for each year, the field "year_" section belongs to the year, the field "From_" indicates that this stretch of road network from previous vintages in the sections corresponding to the FID.

If you have any questions, please contact lianggao@bjtu.edu.CN.

This map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data.

Feature layer generated from running the Find Centroids solution for City_Owned_Parks_Diss (https://iowa.maps.arcgis.com/home/item.html?id=2ea12393440345db9792ed33913a2354)

Tidal Datum GIS outputsShapefiles are provided that present the approximate shore-parallel extent of tidal datums across coastal Massachusetts. These shapefiles are provided for 2030, 2050, and 2070 sea level rise scenarios. Individual shapefiles are provided for the north and south model domains for a total of 6 tidal datum shapefiles (2 model domains, 3 sea level rise scenarios). The results presented within these polygons are based upon tidal model simulations conducted using the MC-FRM, with north shapefiles created using the north model domain, and south using the south model domain. Separate polygons (zones) are provided for approximate location where MHW values vary to the nearest 0.1 ft interval. These zones are derived based on the variation in the MHW datum, and as such other datums (MHHW, MTL, MLW, and MLLW) may vary withineach segmented polygon, especially in areas of varied bathymetry. Data are presented in units of feet relative to the NAVD88 datum.These shapefiles contain the following fields: FID, Shape, Hatch, MHHW, MHW, MTL, MLW, and MLLW. The MHHW, MHW, MTL, MLW, and MLLW fields contain float type values representing the tidal datums calculated for each polygon rounded to the nearest tenth of a foot. The Hatch field contains a binary value (0 or 1), with 1 representing zones of uncertainty for tidal datums. These uncertain zones are either dynamic in terms of geomorphology or are restricted by smaller anthropogenic features (culverts, tide gates, etc.) that were not fully resolved in the MC-FRM. Zones with a 1 Hatch value may or may not contain tidal datum information. It is recommended that care be taken when utilizing the tidal benchmark information in these hatched zones and site-specific data observations (tide data) are recommended to verify the values in these areas. If datum information is not available 9999 values are located in the datum fields for that polygon. The FID and Shape fields contain an ID number and shape type contained in each polygon.The shapefiles provided are not intended to represent a spatial extent of the tidal benchmark (i.e., they do not present the geospatial location of water level). Rather, these shapefiles provide the tidal benchmark values that should be applied over each of the geospatial zones.

At NASA they use Geographic Information systems to provide:maps and powerful capabilities to visualise, analyse and interact with big dataFind out more about how they do this in this ArcGIS StoryMap created by NASA in 2020. This StoryMap includes a section on where you can find NASA data.

The Marine Environment Classification (MEC), a GIS-based environmental classification of the marine environment of the New Zealand region, is an ecosystem-based spatial framework designed for marine management purposes. Developed by NIWA with support from the Ministry for the Environment (MfE), Department of Conservation and Ministry of Fisheries, and with contributions from several other stakeholders, the MEC provides a spatial framework for inventories of marine resources, environmental effects assessments, policy development and design of protected area networks. Two levels of spatial resolution are available within the MEC. A broad scale classification covers the entire EEZ at a nominal spatial resolution of 1 km, whereas the finer scale classification of the Hauraki Gulf region has a nominal spatial resolution of 200 m. Several spatially-explicit data layers describing the physical environment define the MEC. A physically-based classification was chosen because data on these physical variables were available or could be modelled, and because the pattern of the physical environment is a reasonable surrogate for biological pattern, particularly at larger spatial scales. Classes within the classification were defined using multivariate clustering methods. These produce hierarchal classifications that enable the user to delineate environmental variation at different levels of detail and associated spatial scales. Large biological datasets were used to tune the classification, so that the physically-based classes maximised discrimination of variation in biological composition at various levels of classification detail. Thus, the MEC provides a general classification that is relevant to most groups of marine organisms (fishes, invertebrates and chlorophyll) and to ecologically important abiotic variables (e.g., temperature, nutrients).An overview report describing the MEC is available as a PDF file (External Link). The overview report covers the conceptual basis for the MEC and results of testing the classification: MEC Overview (PDF 2.7 MB)See here for a longer description: https://www.niwa.co.nz/coasts-and-oceans/our-services/marine-environment-classification_Item Page Created: 2018-11-12 22:47 Item Page Last Modified: 2025-04-05 20:20Owner: NIWA_OpenDataExclusive Economic Zone (EEZ)No data edit dates availableFields: FID,ENTITY,LAYER,ELEVATION,THICKNESS,COLORMEC EEZ 40 classNo data edit dates availableFields: FID,GRP_40,COUNT_MEC EEZ 20 classNo data edit dates availableFields: FID,GRP_20,COUNT_MEC EEZ 10 classNo data edit dates availableFields: FID,GRP_10,COUNT_MEC EEZ 05 classNo data edit dates availableFields: FID,GRP_5,COUNT_CoastlineNo data edit dates availableFields: FID,NZCOAST_ID,SHAPE_LENG

A file containing the names and codes of sustainability and transformation partnerships (STP) in England as at 1st April 2020. (File size - 16KB)Field Names - STP20CD, STP20CDH, STP20NM, FIDField Types - Text, Text, TextField Lengths - 9, 3, 67FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Sustainability_and_Transformation_Partnerships_April_2020_Names_and_Co_2022/FeatureServer

This dataset represents the centerline of all streets, roads and highways in Fulton County. Each record represents a street segment between at-grade intersections. Attributes include street name elements, odd and even address ranges, feature type, zip code (left and right) and highway number.The municipalities of Chattahoochee Hills, College Park, East Point, Fairburn, Hapeville, Palmetto, South Fulton, Union City, as well as the Fulton Industrial District (FID) are actively maintained by the Fulton County GIS Division. The data for Johns Creek, Milton, Alpharetta, Sandy Springs, and Roswell are obtained from the respective cities data portal or REST endpoint and incorporated into this countywide data. For questions or issues concerning these cities, please contact the owner of the respective data directly.

Field Names - TCITYCD, TCITYNM, FID Field Types - Text, Text, Number Field Lengths - 9, 20 FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Major_Towns_and_Cities_Dec_2015_Names_and_Codes_in_England_and_Wales_2022/FeatureServer

We present a computationally efficient methodology for screening microporous materials for adsorption-based gas separation. Specifically, we develop and employ artificial neural network (ANN)-based surrogate models that increase the speed of approximating transient adsorption behavior and breakthrough times by several orders of magnitude without compromising the predictive capability of a high-fidelity process model. We introduce the concept of breakthrough event times and develop ANN-based surrogate models for their accurate prediction. Our results for numerous hypothetical adsorbents indicate that the effects of different materials-centric metrics are well-captured by the column breakthrough times at the process scale, thus providing a scale-bridging measure toward a multiscale framework for materials screening with process insights. Using the framework, we also screen the list of existing pure-silica zeolite frameworks for postcombustion carbon capture and natural gas purification applications. For postcombustion carbon capture, the top materials include WEI, JBW, and GIS, and for natural gas purification, the top materials are GIS, SIV, and DFT. For any binary gas mixture, the developed ANN models can be leveraged for (i) fundamentally studying the materials properties that determine the dynamic breakthrough times and gas concentration profiles and (ii) high-throughput adsorbent screening and identification of novel materials with desired properties.

Inspired by the book, Dirkzwager’s Guide to the New-Waterway, Rotterdam, Dordrecht, Europoort and Botlek for 1978, this style re-creates its crisp modernist colors balanced with charming hand-drawn landcover features and incorporates the tangible variability of print ink and aged paper.I was shown a wonderful example, provided by Eelco Berghuis, which was a gift from his grandfather.So I sampled colors and created fill and line symbol features with a print-like texture and bleed. When applied (admittedly pretty haphazardly) to New York City (New Amsterdam), for example, the style looks like this... And here are the style elements that comprise it... Find this and other styles by browsing this gallery. Happy Harbor Mapping! John Nelson

Field Lengths - 9, 24, 22 FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portalREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Regions_Dec_2019_Names_and_Codes_in_England_2022/FeatureServer

This file contains names and codes for the Local Enterprise Partnerships (LEP) (non-overlapping parts) in England as at 1 April 2021. (File Size - 16 KB).

A lookup of wards to local authority districts to county to county electoral divisions in England as at 2nd May 2019. (File Size - 2 MB).

Field Names – WD19CD, WD19NM, LAD19CD, LAD19NM, CTY19CD, CTY19NM, CED19CD, CED19NM, FID
Field Types – Text, Text, Text, Text, Text, Text, Text, Text
Field Lengths – 9, 53, 9, 28, 9, 16, 9, 55

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

Field Names - REGD20CD, REGD20NM, FIDField Types - Text, TextField Lengths - 9, 35FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Registration_Districts_Dec_2020_Names_and_Codes_in_England_and_Wales_2022/FeatureServer