Context

When I started exploring how to create interactive maps (using the leaflet() package in R) I come across this free data set (shapefile format) that contains the geographical coordinates (polygons) for all the countries in the world. I thought it would be nice to share this with the Kaggle community.

Content

The .zip folder contains all the necessary files needed for the shapefile data to work properly on your computer. If you are new to using the shapefile format, please see the information provided below:

https://en.wikipedia.org/wiki/Shapefile "The shapefile format stores the data as primitive geometric shapes like points, lines, and polygons. These shapes, together with data attributes that are linked to each shape, create the representation of the geographic data. The term "shapefile" is quite common, but the format consists of a collection of files with a common filename prefix, stored in the same directory. The three mandatory files have filename extensions .shp, .shx, and .dbf. The actual shapefile relates specifically to the .shp file, but alone is incomplete for distribution as the other supporting files are required. "

Acknowledgements

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com.

Files for Lab 4 Creating and Editing Shapefiles in UWSP's WATR 391/591 course

This shapefile contains tax parcel polygons for Eaton County, Michigan, USA. Because tax parcel information changes daily, this shapefile contains only geometry, the parcel identifier and a URL link to the current information for each parcel. Parcel geometries are not survey-grade and should not be used to make important decisions like where to build a structure or install a fence. In their current form, they are only useful in spatial terms for getting an inexact idea of where a parcel is located. If you need to know exactly where a property line falls, please consult a certified land surveyor. Parcel geometries will be updated either annually or bi-annually. New splits and combinations are typically not visible in the parcel geometry until changes become official via Board of Review in the following April.

This data publication is a compilation of six different multibeam surveys covering the previously unmapped Queen Charlotte Fault offshore southeast Alaska and Haida Gwaii, Canada. These data were collected between 2005 and 2018 under a cooperative agreement between the U.S. Geological Survey, Natural Resources Canada, and the National Oceanic and Atmospheric Administration. The six source surveys from different multibeam sonars are combined into one terrain model with a 30-meter resolution. A complementary polygon shapefile records the extent of each source survey in the output grid.

Data from various sources, including 2018 and 2019 multibeam bathymetry data collected by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS) were combined to create a composite 30-m resolution multibeam bathymetry surface of central Cascadia Margin offshore Oregon. These metadata describe the polygon shapefile that outlines and identifies each publicly available bathymetric dataset. The data are available as a polygon shapefile.

This resource is a member of a series. 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. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state but may extend across county and county subdivision boundaries. An incorporated place is usually a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs are often defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of most incorporated places in this shapefile are as of January 1, 2024, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census, but some CDPs were added or updated through the 2024 BAS as well.

Agricultural researchers are embracing remote sensing tools to phenotype and monitor agriculture crops. Specifically, large quantities of data are now being collected on small plot research studies using Unoccupied Aerial Systems (UAS, aka drones), ground systems, or other technologies but data processing and analysis lags behind. One major contributor to current data processing bottlenecks has been the lack of publicly available software tools tailored towards remote sensing of small plots and usability for researchers inexperienced in remote sensing. To address these needs we created plot shapefile maker (R/UAS::plotshpcreate): an open source R function which rapidly creates ESRI polygon shapefiles to the desired dimensions of individual agriculture research plots areas of interest and associates plot specific information. Plotshpcreate was developed to utilize inputs containing experimental design, field orientation, and plot dimensions for easily creating a multi-polygon shapefile of an entire small plot experiment. Output shapefiles are based on the user inputs geolocation of the research field ensuring accurate overlay of polygons often without manual user adjustment. The output shapefile is useful in GIS software to extract plot level data tracing back to the unique IDs of the experimental plots. Plotshpcreate is available on GitHub (https://github.com/andersst91/UAStools).

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.

This polygon shapefile describes the data sources used to create a composite 30-m resolution multibeam bathymetry surface of southern Cascadia Margin offshore Oregon and northern California.

Individual boundary polylines were created by first making a point shapefile of the line endpoints or a series of points, then converting the points to a polyline. The point/polyline conversion was done using XTools 'Make One Polyline from Points' tool. Point locations were based on latitude/longitude coordinates given in the technical report or geographic landmark (i.e. islands, points, state/international boundary lines, etc.). Points requiring an azimuth bearing were created in a projected view (UTM Zone 17 NAD27) using the Distance and Azimuth Tools v. 1.6 extension developed by Jenness Enterprises.The polyline shapefiles created in step 1 and an existing polyline shapefile of the international boundary were merged together using the ArcView GeoProcessing Wizard.The shapefile generated in step 2 was converted to a line coverage using the ArcToolbox Conversion Tools - Feature Class to Coverage.The line coverage topology was cleaned and updated using the ArcInfo Workstation CLEAN (dangle length and fuzzy tolerance both set to 0.001) and BUILD commands.The boundary line coverage and an existing Lake Erie shoreline shapefile (derived from ESRI 100k data) were merged together using the ArcView GeoProcessing Wizard.The shapefile generated in step 5 was converted to a line coverage using the ArcToolbox Conversion Tools - Feature Class to Coverage.Topology of the boundary/shoreline coverage was cleaned and updated using the ArcInfo Workstation CLEAN (dangle length and fuzzy tolerance both set to 0.00001) and BUILD commands. BUILD was done for both line and polygon topology.The polygon feature from the coverage generate in step 7 was converted to a shapefile using Theme\Convert to Shapefile in ArcView.

This dataset contains a comprehensive collection of geographic shapefiles representing the boundaries of countries and territories worldwide. The shapefiles define the outlines of each nation and are based on the most recent and accurate geographical data available. The dataset includes polygon geometries that accurately represent the territorial extent of each country, making it suitable for various geographical analyses, visualizations, and spatial applications.

Content: The dataset comprises shapefiles in the ESRI shapefile format (.shp) along with associated files (.shx, .dbf, etc.) that contain the attributes of each country, such as country names, ISO codes, and other relevant information. The polygons in the shapefiles correspond to the land boundaries of each nation, enabling precise mapping and spatial analysis.

Use Cases: This dataset can be utilized in a wide range of applications, including but not limited to:

• Creating choropleth maps to visualize and analyze various socio-economic indicators by country.
• Conducting spatial analysis to study population distribution, territorial areas, and geographic trends.
• Performing geopolitical research and country-level comparisons.
• Integrating with other datasets to enrich geographic analyses and insights.

Source: The shapefile data is sourced from reputable and authoritative geographic databases, ensuring its accuracy and reliability for diverse applications.

This data publication is a compilation of six different multibeam surveys covering the previously unmapped Queen Charlotte Fault offshore southeast Alaska and Haida Gwaii, Canada. These data were collected between 2005 and 2018 under a cooperative agreement between the U.S. Geological Survey, Natural Resources Canada, and the National Oceanic and Atmospheric Administration. The six source surveys from different multibeam sonars are combined into one terrain model with a 30-meter resolution. A complementary polygon shapefile records the extent of each source survey in the output grid.

This Python script (Shape2DJI_Pilot_KML.py) will scan a directory, find all the ESRI shapefiles (.shp), reproject to EPSG 4326 (geographic coordinate system WGS84 ellipsoid), create an output directory and make a new Keyhole Markup Language (.kml) file for every line or polygon found in the files. These new *.kml files are compatible with DJI Pilot 2 on the Smart Controller (e.g., for M300 RTK). The *.kml files created directly by ArcGIS or QGIS are not currently compatible with DJI Pilot.

This coverage displays the geographic range of select Pacific Ocean fish species.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

These shapefiles are used to create the maps in NAM2.6.2. They are mostly derived from the input files for the groundwater model. The shape files infclude:

ag_extraction: These are points that represent the location of groundwater bores used for agricultural extraction.

boundaries: These are line shape files used for defining the location and extent of lateral boundary conditions of different stratigraphic layers of the groundwater model

coal extraction: These are polygon shape files providing the areal extent of the baseline and ACRD coal mines in the Namoi subregion that are including in the groundwater model.

grid: Polygon shape file representing the mesh of the groundwater model. It also include points that represent the midpoints of each model cell and the suset that represents the model nodes that outcrop.

obs: Shape file of observation bores, the data from which is used for constraining the groundwater model.

River: Set of shape files containing the AWRA catchments, AWRA-R nodes, network of rivers and creeks classified into important reaches and non important reaches based on the distance form the CRDP areas, extent of flood and irrigation recharge

Purpose

The purpose of this dataset is to create pretty pictures. The actual model inputs files are archived separately.

These shapefiles are used along with the software ALGOMESH to generate inputs for the models including model initial and boundary conditions.

Thease are also used to generate maps in the product 2.6.2

Dataset History

Some of the components of this dataset are source data. These include the locations of groundwater and observation bores, river and creek network.

Other components are derived:

The groundwater model mesh and model cell centres are generated in the ALGOMESH software and exported as shape file.

The coal mine extents are derived from digitized mine footprints.

Dataset Citation

Bioregional Assessment Programme (2016) Namoi groundwater model input shapefiles. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/fb22671f-8b47-48e2-9fcd-232543fb8ad6.

Dataset Ancestors

• Derived From Murray-Darling Basin floodplain inundation 1 in 100 year extent

• Derived From Bioregional_Assessment_Programme_Catchment-scale Land Use Management (CLUM)

• Derived From NSW Office of Water - National Groundwater Information System 20141101v02

• Derived From Namoi groundwater observation bores

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

Abstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. This dataset contains shapefiles used mask features and layers in maps which are unwanted, or behind annotation layers, for clearer map presentation. This dataset is used for the report maps in the Bioregional Assessment Galilee Product 1.1 - Chapter 3. ## Purpose Cartographic masking to enhance label and feature legibility for maps in chapter 3 of the Bioregional Assessment Galilee subregion Product 1.1 ## Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. ## Dataset Citation Bioregional Assessment Programme (2014) Cartographic masks for map products GAL113. Bioregional Assessment Source Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/4b07aa28-d1a3-4311-9396-c30e8e00c9ce.

This layer is a high-resolution tree canopy change-detection layer for Baltimore City, MD. It contains three tree-canopy classes for the period 2007-2015: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2007 and 2015 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2007 and 2015 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction. 2006 LiDAR and 2014 LiDAR data was also used to assist in tree canopy change.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

Shapefile created using generalized single-line drainages including optional lake centerlines data from the 10 million rivers. The 50 million rivers primarily derive from World Data Bank 2. Double line rivers in WDB2 were digitized to created single line drainages. All rivers received manual smoothing and position adjustments to fit shaded relief generated from SRTM Plus elevation data, which is more recent and (presumably) more accurate.

Lake centerlines obtained by manually drawing connecting segments in reservoirs. When available, Admin 0 and 1 political boundaries in reservoirs serve as the lake centerlines.

Ranked by relative importance. Includes name and line width attributes for creating tapered drainages.

Abstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.1.7. The processes undertaken to produce this dataset are described in the History field in this metadata statement. ## Purpose Cartographic masks for map products GIP 117, used for clear annotation and masking unwanted features from report maps. ## Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. ## Dataset Citation Bioregional Assessment Programme (2015) Cartographic masks for map products GIP 117. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/1dd62e71-8324-4e9d-ad3b-61fe617ce1e6.