World War Polygon Data Download EXCLUSIVE

This is my first time doing any sort of spatial data visualization in R, and I'm stuck on a particular issue. I would like to clip a spatial polygon (specified by a series of lat/long coordinates) according to a world map, such that any part of the polygon which overlaps with a map polygon is removed. Using what I have in the below code as an example, I want to clip the rectangular spatial polygon so that only oceanic portions of the polygon remain.

I've found examples of how to retain the intersection between two spatial polygons, but I want to do the opposite. Perhaps there is a way to define the intersection, then "subtract" that from the polygon I wish to clip?

World War Polygon Data Download

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Here is a solution that stays in sf the entire time (I don't know sp), and illustrates constructing an sf object from scratch. st_difference create the geometry you want exactly, and then plotting can be done with the base plot method or the development version of ggplot which has geom_sf. I used map data from maps and rnaturalearth for this, you can adapt to your particular situation. Wrapping around the dateline is a little finicky regardless unfortunately.

The WDPA has two feature classes with associated spatialand tabular data on more than 200k protected areas. About 91%contain polygon boundaries, with the remaining only as points,representing the center of the protected area as much as possible.

Asset Naming Conventions. WCMC updates the WDPA on amonthly basis. The most recent version is always available asWCMC/WDPA/current/polygons and WCMC/WDPA/current/points. Historicalversions, starting with July 2017, are available in the formatWCMC/WDPA/YYYYMM/polygons and WCMC/WDPA/YYYYMM/points.

Protected area total extent, including both marine(if applicable) and terrestrial areas, in square kilometers calculatedby UNEP-WCMC projecting the protected area polygon in the standardMollweide projection and using GIS software tools.

I am looking for a free dataset of world cities. I checked all the datasets suggested in the question: Seeking Country/State/City Database?, but couldn't find one where the city location is available as a polygon (or similar), I found only latitude/longitude.

If you're really talking about cities down to 500-1000 inhabitants, you can't even get that data if you pay for it. Even the locations of cities with 1000 people are something that's hard to pay for -- there's a lot of cities of that size in the world.

Note that the original data from Natural Earth is public domain. While no credit isformally required a link back or credit to Natural Earth, Lexman and the Open Knowledge Foundation is much appreciated.

At Datahub, we provide various solutions to Publish and Deploy your Data with power and simplicity. Datahub is the fastest way for individuals, teams and organizations to publish, deploy and share their data.

Note that the original data from Natural Earth is public domain. While no credit is\\nformally required a link back or credit to Natural Earth, Lexman and the Open Knowledge Foundation is much appreciated.

The Population Estimation Service is a Web-based service for estimating population totals and related statistics within a user-defined region. It enables users of a wide variety of map clients and tools to quickly obtain estimates of the number of people residing in specific areas without having to download and analyze large amounts of spatial data. The service accepts polygons that define areas of interest, then returns population totals, land area, quality measures, and basic parametric statistics for the requested polygons based on data from SEDAC's Gridded Population of the World version 3 (GPWv3) dataset for the year 2005.

Note: A new version of the Population Estimation Service, based on data from SEDAC's Gridded Population of the World, Version 4 Revision 11 (GPWv4.11) data collection is available here: -v4/population-estimation-service.

The data has been derived from OpenStreetMap ways tagged withnatural=coastline. Ways are assembled into polygons and thensplit. Some errors in the OSM data are repaired in the process.Learn more about the coastline processing...

The coastline in OpenStreetMap is often broken. The update process will tryto repair it, but this does not always work. If the OSM data can't be repairedautomatically, the data here will not be updated.

Depth ranges included in the COMMENTS attribute field for some features represent the approximate depth in meters below sea level. These values were obtained by examining bathymetric maps and bathymetric raster data to determine the minimum and maximum depths that define the feature and its spatial extent. Depth ranges typically apply to features such as basins, plains, and seamounts, which are reasonably defined by closed isobaths, but do not apply to features such as canyons, fans, and escarpments. AREA_SQKM values were determined using ArcGIS Calculate Geometry, with datum WGS 84 and sinusoidal (equal area) projection. The sinusoidal projection preserves area but distorts perimeter values for high-latitude features.

To create the linked data, the vertices of the polygons were extracted from the shapefile in ArcMapTM along with the values of five selected attributes: UFI, DSG, UF_NAME, VAR_NAME, and COMMENTS. The coordinate pairs (vertices) were converted to WKT notation for polygons and three new attributes were added to the five extracted from the shapefile: GNS feature notes (from GNS advanced search), use constraints (condensed from the shapefile metadata), and a link to the source shapefile (as compressed in undersea_features.zip). The WKT and attribute values for each feature were assembled in a spreadsheet and then converted to linked data in Turtle format (Terse RDF Triple Language) following the recommendations of the World Wide Web Consortium (W3C, 2014, 2015) and Open Geospatial Consortium (OGC, 2012). This conversion required relabeling and merging some of the attributes to fit the classes and properties typically employed in linked data. In addition, the undersea feature designations, codes, and definitions employed in GNS (see United States Board on Geographic Names, 2005) were also converted to linked data.

The package rnaturalearth provides a map of countries of the entireworld. Use ne_countries to pull country data and choose the scale(rnaturalearthhires is necessary for scale = "large"). Thefunction can return sp classes (default) or directly sf classes,as defined in the argument returnclass:

A better, more flexible alternative is to use the power of sf:Converting the data frame to a sf object allows to rely on sf tohandle on the fly the coordinate system (both projection and extent),which can be very useful if the two objects (here world map, and sites)are not in the same projection. To achieve the same result, theprojection (here WGS84, which is the CRS code #4326) has to be a prioridefined in the sf object:

State names are part of this data, as the ID variable. A simple (butnot necessarily optimal) way to add state name is to compute thecentroid of each state polygon as the coordinates where to draw theirnames. Centroids are computed with the function st_centroid, theircoordinates extracted with st_coordinates, both from the package sf,and attached to the state object:

Note the warning, which basically says that centroid coordinates usinglongitude/latitude data (i.e. WGS84) are not exact, which is perfectlyfine for our drawing purposes. State names, which are not capitalized inthe data from maps, can be changed to title case using the functiontoTitleCase from the package tools:

To continue adding to the map, state data is directly plotted as anadditional sf layer using geom_sf. In addition, state names will beadded using geom_text, declaring coordinates on the X-axis and Y-axis,as well as the label (from ID), and a relatively big font size.

County data are also available from the package maps, and can beretrieved with the same approach as for state data. This time, onlycounties from Florida are retained, and we compute their area usingst_area from the package sf:

For the final map, we put everything together, having a generalbackground map based on the world map, with state and countydelineations, state labels, main city names and locations, as well as atheme adjusted with titles, subtitles, axis labels, and a scale bar:

This example fully demonstrates that adding layers on ggplot2 isrelatively straightforward, as long as the data is properly stored in ansf object. Adding additional layers would simply follow the samelogic, with additional calls to geom_sf at the right place in theggplot2 sequence.

To add location data from a spreadsheet into Google Earth, import the latitude and longitude info. You'll need a text file that is delimited, which means each line is a separate piece of coordinates info. Every location that you import from your text file is converted to a Google Earth placemark and listed in your Places.

If you want to use one color for all the points or lines from your imported data, select the 'Use single color' option. Then, click the colored square next to the option. From the color selector, choose a color or define your own color to apply to the data.

To use a variety of colors that are applied randomly by Google Earth Pro, select the 'Use random colors' option. If you are also supplying an icon for point data, the color is added to the existing color of the icon.

When using color, icon, or height mapping for specific fields in your data set, you can define a number of buckets to show different ranges of data. You can choose two basic types of fields from your data when mapping color, icon, or height values.

If a field type contains non-numeric data, Google Earth Pro maps the first 8 unique text fields to the style. If there are fewer than 8 values in your data, each unique value is paired to a different color, icon, or height. If there are more than 8 values, the first 8 unique values are mapped to a style, and the rest of the values are grouped together and mapped to a ninth style. For this reason, it typically is most useful to apply a style to text fields that contain small unique sets. 006ab0faaa