Historical FiresLast updated on 06/17/2022OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimeters in 2021.https://nifc.maps.arcgis.com/home/item.html?id=098ebc8e561143389ca3d42be3707caaFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send email

The main dataset is a 304 MB file of trajectory data (I90_94_stationary_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) vehicles and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for six distinct data collection “Runs” (I90_94_Stationary_Run_X_ref_image.png, where X equals 1, 2, 3, 4, 5, and 6). Associated centerline files are also provided for each “Run” (I-90-stationary-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94Stationary.csv” for more details). The dataset defines six northbound lanes using these centerline files. Twelve different numerical IDs are used to define the six northbound lanes (1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, and 15) depending on the run. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. Lane IDs are provided in the reference images in red text for each data collection run (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6). This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset. As part of this dataset, the following files were provided: I90_94_stationary_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I90_94_Stationary_Run_X_ref_image.png are the aerial reference images that define the geographic region for each run X. I-90-stationary-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and ve

About this dataset

Context

The dataset tabulates the data for the Pleasant View, UT population pyramid, which represents the Pleasant View population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Pleasant View, UT, is 37.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Pleasant View, UT, is 19.2.
• Total dependency ratio for Pleasant View, UT is 56.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Pleasant View, UT is 5.2.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Pleasant View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Pleasant View for the selected age group is shown in the following column.
• Population (Female): The female population in the Pleasant View for the selected age group is shown in the following column.
• Total Population: The total population of the Pleasant View for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pleasant View Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Prairie View, TX population pyramid, which represents the Prairie View population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Prairie View, TX, is 10.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Prairie View, TX, is 3.3.
• Total dependency ratio for Prairie View, TX is 13.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Prairie View, TX is 30.3.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Prairie View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Prairie View for the selected age group is shown in the following column.
• Population (Female): The female population in the Prairie View for the selected age group is shown in the following column.
• Total Population: The total population of the Prairie View for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Prairie View Population by Age. You can refer the same here

In recent decades antenatal screening has become one of the most routine procedure of pregnancy-follow up and the subject of hot debate in bioethics circles. In this paper the rationale behind doing antenatal screening and the actual and potential problems that it may cause will be discussed. The paper will examine the issue from the point of wiew of parents, health care professionals and, most importantly, the child-to-be. It will show how unthoughtfully antenatal screening is performed and how pregnancy is treated almost as a disease just since the emergence of antenatal screening. Genetic screening and ethical problems caused by the procedure will also be addressed and I will suggest that screening is more to do with the interests of others rather than those of the child-to be.

Interagency Wildland Fire Perimeter History (IFPH) Overview This national fire history perimeter data layer of conglomerated agency perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2024 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer, links are provided where possible below. In addition, many agencies are now using WFIGS as their authoritative source, beginning in mid-2020.Alaska fire history (WFIGS pull for updates began 2022)USDA FS Regional Fire History Data (WFIGS pull for updates began 2024)BLM Fire Planning and Fuels (WFIGS pull for updates began 2020)National Park Service - Includes Prescribed Burns (WFIGS pull for updates began 2020)Fish and Wildlife Service (WFIGS pull for updates began 2024)Bureau of Indian Affairs (Incomplete, 2017-2018 from BIA, WFIGS pull for updates began 2020)CalFire FRAS - Includes Prescribed Burns (CALFIRE only source, non-fed fires)WFIGS - updates included since mid-2020, unless otherwise noted Data LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoritative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.Attributes This dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer maintained by IrWIN. (This unique identifier may NOT replace the GeometryID core attribute) FORID - Unique identifier assigned to each incident record in the Fire Occurence Data Records system. (This unique identifier may NOT replace the GeometryID core attribute) INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name. FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT). AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin. SOURCE - System/agency source of record from which the perimeter came. DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy. MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Other GIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9 UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001 LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456. UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMP COMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or Unknown GEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID). Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4,781 Records thru 2021), other federal sources for AK data removed. No RX data included.CA: GEOID = OBJECT ID of downloaded file geodatabase (8,480 Records, federal fires removed, includes RX. Significant cleanup occurred between 2023 and 2024 data pulls resulting in fewer perimeters).FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2,959 Records), includes RX.BIA: GEOID = "FireID" 2017/2018 data (382 records). No RX data included.NPS: GEOID = EVENT ID 15,237 records, includes RX. In 2024/2023 dataset was reduced by combining singlepart to multpart based on valid Irwin, FORID or Unique Fire IDs. RX data included.BLM: GEOID = GUID from BLM FPER (23,730 features). No RX data included.USFS: GEOID=GLOBALID from EDW records (48,569 features), includes RXWFIGS: GEOID=polySourceGlobalID (9724 records added or replaced agency record since mid-2020)Attempts to repair Unique Fire ID not made. Attempts to repair dates not made. Verified all IrWIN IDs and FODRIDs present via joins and cross checks to the respective dataset. Stripped leading and trailing spaces, fixed empty values to

This page summarises how people understand their core values, how meaningful life feels, and how digital noise and AI shape decisions, focus and long-term direction.

This is a series-level metadata record. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. State Legislative Districts (SLDs) are the areas from which members are elected to state legislatures. The SLDs embody the upper (senate - SLDU) and lower (house - SLDL) chambers of the state legislature. Nebraska has a unicameral legislature, and the District of Columbia has a single council, both of which the Census Bureau treats as upper-chamber legislative areas for the purpose of data presentation. A unique three-character census code, identified by state participants, is assigned to each SLD within a state. States that had SLDU updates between the previous and current session include Georgia, Minnesota, Montana, North Carolina, North Dakota, Ohio, Washington, and Wisconsin. In Connecticut, Illinois, Louisiana, New Hampshire, Wisconsin, and Puerto Rico, the Redistricting Data Program (RDP) participant did not define the SLDUs to cover the entirety of the state or state equivalent area. In the areas with no SLDUs defined, the code ""ZZZ"" has been assigned, which is treated as a single SLDU for purposes of data presentation. There are no SLDU TIGER/Line shapefiles for the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). The state legislative district boundaries reflect information provided to the Census Bureau by the states by May 31, 2024. Note: Michigan is required by court order to redraw their state senate districts. However, these new SLDUs were not drawn by May 31, 2024, and will not be used until the next SLDU elections in 2026.

1) Data Introduction • The Netflix Users Dataset World Wide is a user-analyzed dataset that summarizes various attributes such as subscription types, countries, subscription dates, viewing patterns, and device information of Netflix users around the world.

2) Data Utilization (1) Netflix Users Dataset World Wide has characteristics that: • Each row contains a variety of user and behavior data, including User ID, Subscription Type (Basic/Standard/Premium), Country, Subscription Date, Latest Payment Date, Account Status (Active/Disactive), Key View Devices, Monthly View Time, Preferred Genre, Average Session Length, and Monthly Subscription Sales. • Data is designed to enable various analyses such as regional trends, usage behaviors, churn rates, and viewing preferences. (2) Netflix Users Dataset World Wide can be used to: • User Segmentation and Marketing Strategy: Data such as subscription type, country, viewing pattern, etc. can be used to define customer groups and to establish customized marketing and recommendation strategies. • Service improvement and departure prediction: Based on behavioral data such as device, viewing time, and account status, it can be applied to service improvement, departure risk prediction, and development of new features.

Filter is a configurable app template that displays a map with an interactive filtered view of one or more feature layers. The application displays prompts and hints for attribute filter values which are used to locate specific features.Use CasesFilter displays an interactive dialog box for exploring the distribution of a single attribute or the relationship between different attributes. This is a good choice when you want to understand the distribution of different types of features within a layer, or create an experience where you can gain deeper insight into how the interaction of different variables affect the resulting map content.Configurable OptionsFilter can present a web map and be configured with the following options:Choose the web map used in the application.Provide a title and color theme. The default title is the web map name.Configure the ability for feature and location search.Define the filter experince and provide text to encourage user exploration of data by displaying additional values to choose as the filter text.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsRequires at least one layer with an interactive filter. See Apply Filters help topic for more details.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

JPEG images of each seismic line were generated in order to incorporate images of the seismic data into Geographic Information System (GIS) projects and data archives utilizing HTML. The JPG format is universal and enables hassle-free transfer of data. These data yield a pictorial view of the seismic data acquired.In 1999, the USGS, in partnership with the South Carolina Sea Grant Consortium, began a study to investigate processes affecting shoreline change along the northern coast of South Carolina, focusing on the Grand Strand region. Previous work along the U.S. Atlantic coast shows that the structure and composition of older geologic strata located seaward of the coast heavily influences the coastal behavior of areas with limited sediment supply, such as the Grand Strand. By defining this geologic framework and identifying the transport pathways and sinks of sediment, geoscientists are developing conceptual models of the present-day physical processes shaping the South Carolina coast. The primary objectives of this research effort are: 1) to provide a regional synthesis of the shallow geologic framework underlying the coastal upland, shoreface and inner continental shelf, and define its role in coastal evolution and modern beach behavior; 2) to identify and model the physical processes affecting coastal ocean circulation and sediment transport, and to define their role in shaping the modern shoreline; and 3) to identify sediment sources and transport pathways; leading to construction of a regional sediment budget.

Overview

Purpose and Benefits A polygon feature service intended to serve as a repository to store daily wildfire perimeters for fires occurring within National Park Service parks. This service can be used to generate fire progressions.

Layer

Daily Wildfire Perimeter Attributes and their definitions can be found below.

Attributes:

        Fire Occurrence ID
        The Fire Occurence ID field is a unique identifier linking the daily wildfire perimeter to the wildland fire location feature class.


        Perimeter Date
        The Perimeter Date field is intended for users to capture the date the perimeter was collected.


        Feature Category
        The Feature Category field is intended for users to identify the type of event that occurred..


        GIS Acres
        The GIS Acres field is intended for users to capture the acres for the fire history or fuel treatment perimeter using GIS to calculate the acres.


        Public Display
        The Public Display field is intended for users to determine if the data can be used for public display – i.e any data representing sensitive information such as cultural resources should not be displayed on a public map.


        Data Access
        The Data Access field is intended for users to capture the accessibility of the data – i.e. most fire data is considered unrestricted, however, if cultural resources are included then the data would be restricted from sharing or use with others.


        Unit Code
        The UnitCode field is intended to allow users to identify the National Park that a particular resource may lie within. Some data collected and maintained by National Park Service may inventory resources outside NPS property or responsibility. To make data entry easier, the UnitCode field may select park unit names from a domain that contains all of the park unit 4-letter acronyms. All park units, associated monuments, memorials, seashores, etc., are represented in the domain values.



        Map Method 
        The Map Method field is intended for users to define how the geospatial feature was derived.


        Data Source 
        The Data Source field is intended for users to define the source of the data.


        Date of Source 
        The Source Date field is intended for users to define the date of the source data.


        XY Accuracy 
        The XY Accuracy field is intended to allow users to document the accuracy of the data.


        Notes 
        The Notes field is intended for users to add any additional information describing the feature.

About this dataset

Context

The dataset tabulates the data for the Mounds View, MN population pyramid, which represents the Mounds View population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Mounds View, MN, is 34.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Mounds View, MN, is 26.1.
• Total dependency ratio for Mounds View, MN is 60.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Mounds View, MN is 3.8.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Mounds View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Mounds View for the selected age group is shown in the following column.
• Population (Female): The female population in the Mounds View for the selected age group is shown in the following column.
• Total Population: The total population of the Mounds View for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mounds View Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Valley View, TX population pyramid, which represents the Valley View population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Valley View, TX, is 33.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Valley View, TX, is 25.9.
• Total dependency ratio for Valley View, TX is 58.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Valley View, TX is 3.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Valley View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Valley View for the selected age group is shown in the following column.
• Population (Female): The female population in the Valley View for the selected age group is shown in the following column.
• Total Population: The total population of the Valley View for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Valley View Population by Age. You can refer the same here

View Define p import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

This is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI).There are 5 related image services providing access to Digital Elevation Models:DEM Mosaic (Individual DEMs)DEM Global Mosaic (Elevation Values)DEM Global Mosaic (Color Shaded Relief)CUDEM Mosaic (Elevation Values)CUDEM Mosaic (Color Shaded Relief)NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. For more information about coastal DEMs at NCEI, please see: https://www.ncei.noaa.gov/products/coastal-elevation-models or learn more from our DEM Fact Sheet (1 MB PDF).This service provides data from many individual DEMs combined together as a mosaic (maximum of 80 rasters at once). By default, the rasters are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Alternatively, a single DEM or group of DEMs can be isolated using a filter/definition query or using the "Lock Raster" mosaic method in ArcMap.The DEMs can be viewed in NCEI's Bathymetric Data Viewer along with other bathymetric datasets stewarded at NCEI.Please see NCEI's corresponding DEM Footprints map service for polygon footprints and more information about the individual DEMs used to create this composite view. The newer 1/3 and 1/9 arcsecond "tiled" DEMs are hosted by NOAA's Office for Coastal Management; please see the Data Access Viewer for access to these data. View these services together in a single combined map.This service has a server-side function available. This can be selected in the ArcGIS Online layer using "Image Display", or in ArcMap under "Processing Templates".None: The default. Provides elevation/depth values in meters. Please refer to the vertical datum for each DEM.ColorHillshade: An elevation-tinted hillshade visualization. The depths are displayed using this color ramp:THREDDS catalog (for extracting/downloading DEMs)

This is a series-level metadata record. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. A tribal census tract is a relatively permanent statistical subdivision of a federally recognized American Indian reservation and/or off-reservation trust land, delineated by the American Indian tribal government and/or the Census Bureau for the purpose of presenting demographic data. For the 2020 Census, tribal census tracts are defined independently of the standard county-based census tract delineation. For federally recognized American Indian Tribes with reservations and/or off-reservation trust lands with a population less than 2,400, a single tribal census tract is defined. Qualifying areas with a population greater than 2,400 could define additional tribal census tracts within their area. The tribal census tract codes for the 2020 Census are six characters long with a leading ""T"" alphabetic character followed by a five-digit numeric code, for example, T01000, which translates as tribal census tract 10. Tribal block groups nest within tribal census tracts. Since individual tabulation blocks are defined within the standard State-county-census tract geographic hierarchy, a tribal census tract can contain seemingly duplicate block numbers, thus tribal census tracts cannot be used to uniquely identify census tabulation blocks for the 2020 Census. The boundaries of tribal census tracts are those delineated through the Participant Statistical Areas Program (PSAP) for the 2020 Census.

The submitted data relate to sections 2.3 and 2.4 of: H. Moisl (2022) Dynamical systems implementation of intrinsic sentence meaning, Minds and Machines 32 (2022), which describe the processing architecture of the model of intrinsic sentence meaning proposed there. Six separate programs are used to generate the results presented in the article, whose interrelationships are described in the above-cited sections. The paper with which the data are associated proposes a model for implementation of intrinsic natural language sentence meaning in a physical language understanding system, where 'intrinsic' is understood as 'independent of meaning ascription by system-external observers'. The proposal is that intrinsic meaning can be implemented as a point attractor in the state space of a nonlinear dynamical system with feedback which is generated by temporally sequenced inputs. It is motivated by John Searle's well known (1980) critique of the then-standard and currently still influential Computational Theory of Mind (CTM), the essence of which was that CTM representations lack intrinsic meaning because that meaning is dependent on ascription by an observer. The proposed dynamical model comprises a collection of interacting artificial neural networks, and constitutes a radical simplification of the principle of compositional phrase structure which is at the heart of the current standard view of sentence semantics because it is computationally interpretable as a finite state machine.

Reason for SelectionRiver networks with a variety of connected stream size classes are more likely to have a wide range of available habitat to support a greater number of species. This will help retain aquatic biodiversity in a changing climate by allowing species to access climate refugia and move between habitats. Input DataBase Blueprint 2022 extent Southeast Blueprint 2024 extentSoutheast Aquatic Resources Partnership’s Network Complexity metric The Southeast Aquatic Resources Partnership (SARP) developed metrics for their Southeast Aquatic Barrier Prioritization Tool. On June 7, 2023, Brendan Ward with Astute Spruce (software developer working on behalf of SARP) shared high resolution NHDPlus flowlines with attributes depicting the network complexity attribute for each functional network (see definition of “functional network” below). The network complexity attribute calculates the total number of different stream size classes within each functional network. SARP assigned stream and river reaches to size classes based on total drainage area:1a: Headwaters (<3.861 sq mi)1b: Creeks (≥3.861 and <8.61 sq mi)2: Small Rivers (≥38.61 and <200 sq mi)3a: Medium Tributary Rivers (≥200 and <1,000 sq mi)3b: Medium Mainstem Rivers (≥1,000 and <3,861 sq mi)4: Large Rivers (≥3,861 and <9,653 sq mi)5: Great Rivers (≥9,653 sq mi)Functional NetworkSARP compiles the Southeast Aquatic Barrier Inventory from national, regional, state, and local partner databases across the Southeast region. These include the National Inventory of Dams (2018), National Anthropogenic Barrier Dataset (2012), databases from state dam safety programs and other state agencies, information from local partners, and dam locations estimated by SARP. Waterfalls are compiled from national datasets and local partners. Dams and waterfalls are snapped to hydrologic networks extracted from the National Hydrography Dataset (NHD) - High Resolution Beta version. All dams and waterfalls are treated as “hard” barriers for network connectivity analysis. Aquatic networks are cut at the location of each barrier. All network “loops” (non-primary flowlines) are omitted from the analysis. An upstream functional network is constructed by traversing upstream from each barrier through all tributaries to the upstream-most origination point or upstream barrier, whichever comes first. Additional functional networks are defined from downstream-most non-barrier termination points, such as marine areas or other downstream termination points. The total length of all network segments within a functional network is summed to calculate the total network length of each functional network. Each flowline segment within the NHD is assigned to a size class based on total drainage area. This was used to calculate the number of unique size classes per functional network. Estimated Floodplain Map of the Conterminous U.S. from the Environmental Protection Agency’s (EPA) EnviroAtlas; see this factsheet for more information; download the data The EPA Estimated Floodplain Map of the Conterminous U.S. displays “...areas estimated to be inundated by a 100-year flood (also known as the 1% annual chance flood). These data are based on the Federal Emergency Management Agency (FEMA) 100-year flood inundation maps with the goal of creating a seamless floodplain map at 30-m resolution for the conterminous United States. This map identifies a given pixel’s membership in the 100-year floodplain and completes areas that FEMA has not yet mapped” (EPA 2018). National Hydrography Dataset Plus High Resolution (NHDPlus HR) National Release catchments, accessed 11-30-2022; download the data; view the user guideNHDPlus Version 2.1 medium resolution catchments (note: V2.1 is just the current sub-version of the dataset generally called NHDPlusV2); view the user guideCatchments A catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics. To learn more about catchments and how they’re defined, check out these resources:An article from USGS explaining the differences between various NHD productsThe glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key termsMapping StepsMerge the functional network lines from the 11 subregions delivered by SARP into one feature class. Convert the combined SARP network complexity values from the high resolution NHDPlus flowlines to a 30 m raster. Clip to the Base Blueprint 2022 extent.Apply the network complexity values to the NHDPlus HR catchments using the ArcPy Zonal Statistics “MAJORITY” function. This results in a raster where each catchment is assigned the majority network complexity value that intersects the catchment. Most catchments have only one intersecting line, but for catchments with interior dams, the analysis uses the majority network complexity value.To define the analysis extent of the indicator, make a copy of the NHDPlus HR catchments and convert it to raster, assigning it a value of 1.Clip the network complexity raster to the EPA floodplain layer. During this step, assign a value of 0 to areas outside the EPA floodplain. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Some areas of the floodplain are not scored in the resulting layer because they are missing SARP network complexity values. This is due to the fact that some small reaches, such as braids and loops in the stream network, are not assigned a network complexity value. SARP has to remove loops and braided streams in order to calculate network complexity because the analysis can only accommodate a one-way flow of water. Identify these holes in the floodplain and fill them in by looking at the network complexity value of the surrounding pixels and assigning the maximum value to the missing catchments in the floodplain. Note: This simplifies a complex series of analysis steps. For more specifics, please consult the code.Clip the network complexity raster to the NHDPlus V2.1 medium resolution catchments. This removes estuarine areas that are outside the intended scope of this indicator, particularly on the NC coast.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:7 = 7 connected stream classes6 = 6 connected stream classes5 = 5 connected stream classes4 = 4 connected stream classes3 = 3 connected stream classes2 = 2 connected stream classes1 = 1 connected stream class0 = Not identified as a floodplainKnown IssuesThis indicator does not include other smaller scale attributes of complexity (e.g., sinuosity, mixtures of riffles/pools/runs) that influence the habitat quality of the connections. The EPA Estimated Floodplain layer sometimes misses the small, linear connections made by artificial canals, especially when they go through areas that wouldn’t naturally be part of the floodplain. As a result, some areas (like lakes) that are connected via canals may appear to be disconnected, but still receive high scores.Small headwaters and creeks are not included in this indicator because the EPA estimated floodplain dataset does not include them.While this indicator generally includes the open water area of reservoirs, some open water portions of reservoirs (e.g., Kerr Lake in NC/VA) are missing from the estimated floodplain dataset.This indicator likely overestimates the number of connected stream classes in some areas due to missing barriers in the inventory, such as smaller dams or road-stream crossings. It could also underestimate the number of connected stream classes, given the extensive ongoing restoration work to improve aquatic connectivity across the SECAS geography. If you identify a missing barrier or a removed barrier, please let SARP know by emailing Kat Hoenke at kat@southeastaquatics.net. You can learn more about the current inventory of dams and road-stream crossings by visiting https://connectivity.sarpdata.com/.SARP did a lot of work to snap the dam locations to the line network, but there are likely still dams (including some large ones) that didn’t get snapped correctly due to the large distance between the centerpoint of the dam and the nearest flowline. If you see any of these cases when reviewing the data, please let SARP know (the giveaway is networks that look longer than they should on a map).In the area just south of Guadalupe Mountains National Park in West Texas, this indicator depicts the floodplain as a series of straight lines that poorly match the actual floodplain. This is due to an error in the EPA floodplain map used in this indicator.Due to issues with the national NHDPlus HR catchments layer, there are a handful of missing catchments (e.g., northwest TX, coastal LA, and eastern NC). These places receive a value of NoData in the indicator and are therefore underprioritized. We are investigating ways to resolve this in future updates.This indicator may slightly overvalue network complexity in WV compared to other Southeast states because the coverage of dams and barriers data is not as comprehensive. While the dams and barriers data coverage improved sufficiently for us to use the network complexity indicator across the entire state of WV in 2023 for the first time (whereas it was only used in the southern part of the state in 2022), there is still room for improvement and we anticipate significant

The VegaView extension for CKAN allows users to create interactive visualizations of data resources using VegaJS, a declarative visualization grammar. This extension primarily functions as a resource view plugin, enabling users to define and display custom graphs directly within the CKAN user interface. By leveraging Vega specifications, this enhancement to CKAN provides a flexible method for representing data in a visually engaging format. Key Features: VegaJS Integration: Enables the rendering of visualizations within CKAN resources using Vega's declarative JSON format specification which specifies visual representations of data. DataPusher Dependency: Requires that the data for the resource is available in DataPusher, ensuring that data is accessible and prepared for visualization. Specification Field: Introduces a Specification field in the resource view configuration where users can paste complete Vega specifications to define the graph. Data Accessibility: Provides access to the resource's data within the Vega specification through a standardized data object, simplifying the process of mapping data to visual elements. Debugging Support: Offers debugging guidance by suggesting inspection of the view page's HTML to troubleshoot specification formats. Technical Integration: The VegaView extension integrates with CKAN by adding itself as a plugin enabled via the ckan.plugins configuration setting. It adds a new resource view type that accepts a Vega specification, renders the visualization using VegaJS, and uses DataPusher as the data source, seamlessly connecting the data to the visual representation. Benefits & Impact: The VegaView extension offers users the ability to create custom data visualizations within CKAN without requiring advanced programming knowledge. By utilizing the power and flexibility of VegaJS, data consumers can quickly generate clear and insightful graphs directly integrated into the CKAN resource, facilitating data exploration, communication, and analysis.