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There are numerous types of cartograms and they are typically categorized by their ability to preserve shape and maintain contiguous regions.
#Lat long visualizer 3d how to#
It doesn’t really matter what tool you use to obtain or create an sf object – once you have one, plot_ly() knows how to render it:Ĭartograms distort the size of geo-spatial polygons to encode a numeric variable other than the land size. The USAboundaries package is great for obtaining map data for the United States at any point in history (Mullen and Bratt 2018).
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The rnaturalearth package is better for obtaining any map data in the world via an API provided by (South 2017). A couple notable examples include rnaturalearth and USAboundaries. There are numerous packages for accessing geo-spatial data as simple features data structures. 14 The sf package itself does not really provide geo-spatial data – it provides the framework and utilities for storing and computing on geo-spatial data structures in an opinionated way. This allows each row to represent the real unit of observation/interest – whether it’s a polygon, multi-polygon, point, line, or even a collection of these features – and as a result, works seamlessly inside larger tidy workflows. The key idea behind sf is that it stores geo-spatial geometries in a list-column of a data frame. The sf R package is a modern approach to working with geo-spatial data structures based on tidy data principles (Pebesma 2018 Wickham 2014 b). It’s worth noting that the locationmode is currently limited to countries and US states, so if you need to a different geo-unit (e.g., counties, municipalities, etc), you should use the choroplethmapbox trace type and/or use a “custom” mapping approach as discussed in Section 4.2. By simply providing a z attribute, plotly_geo() objects will try to create a choropleth, but you’ll also need to provide locations and a locationmode. state data from the datasets package (R Core Team 2016). Comparatively speaking, choroplethmapbox is more powerful because you can fully specify the feature collection using GeoJSON, but the choropleth trace can be a bit easier to use if it fits your use case.įigure 4.6 shows the population density of the U.S. In addition to scatter traces, both of the integrated mapping solutions (i.e., plot_mapbox() and plot_geo()) have an optimized choropleth trace type (i.e., the choroplethmapbox and choropleth trace types). However, if you run into limitations with plotly’s mapping functionality, there is a very rich set of tools for interactive geospatial visualization in R, including but not limited to: leaflet, mapview, mapedit, tmap, and mapdeck (Robin Lovelace 2019).
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That said, there are benefits to using plotly-based maps since the mapping APIs are very similar to the rest of plotly, and you can leverage larger plotly ecosystem (e.g., linking views client side like Figure 16.23). It’s worth noting that plotly aims to be a general purpose visualization library, and thus, doesn’t aim to be the most fully featured geo-spatial visualization toolkit. Section 4.2 covers making sophisticated maps (e.g., cartograms) using the sf R package, but it’s also possible to make custom plotly maps via other tools for geo-computing (e.g., sp, ggmap, etc). On the other hand, the custom mapping approach offers complete control since you’re providing all the information necessary to render the geo-spatial object(s). The integrated approach is convenient if you need a quick map and don’t necessarily need sophisticated representations of geo-spatial objects. Currently there are two supported ways of making integrated maps: either via Mapbox or via an integrated d3.js powered basemap. Integrated maps leverage plotly.js’ built-in support for rendering a basemap layer. Generally speaking the approaches fall under two categories: integrated or custom.
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There are numerous ways to make a map with plotly – each with it’s own strengths and weaknesses.