R spatial presentation
Download as PPTX, PDF5 likes843 views
This document discusses using R for spatial and geospatial analysis. It provides an overview of key R packages for working with spatial data like ggplot2, rgdal, and maptools. It also demonstrates how to create basic maps from shapefiles, perform spatial joins to add data attributes, and generate choropleth and heat maps for visualization and analysis. The document serves as a tutorial for getting started with spatial analysis and visualization using open-source R tools and libraries.
![ggplot and shapefiles and choropleths
>merge.shp<-merge(combine, age_40, by=“id”, all.x=TRUE)
>final_join<-merge.shp[order(merge.shp$order),]](https://image.slidesharecdn.com/rspatialpresentation-171116195103/85/R-spatial-presentation-48-320.jpg)
R spatial presentation
- 2. Who are you? Todd Barr Spatial Statistician/GeoData Sherpa
- 10. Outline • Spatial Libraries • GeoData • Spatial Joins/Cholorpleth Map • Heat Map
- 11. BackgRound on R • Developed in the 90s base on a Proprietary Statistical Language called ‘S’ and S-Plus, by Statisticians for Statistical Models.
- 12. Advantages of R - Spatial • Command Line Interface • Customizable Graphics • Native Spatial Data • Easily Extensible through Libraries • Many Tools to Munge Data, both Spatial and Tabular • Native Modeling and Analysis in Code • Levels the Playing Field
- 13. Disadvantages of R - Spatial • Steepish Learning Curve (even with Rstudio) • Difficult to Visualize Data (lack of interactive canvas in Rstudio) • Difficult to Dynamically Select things from a map • Single Core Processing (can be augmented with Libraries) • Libraries with overlapping functionalities
- 14. R in “Traditional” Spatial/GIS • Spatial • ArcGIS • R interface supported by ESRI Open Project “R Bridge” • QGIS • Imbedded a Functionality • Use R in the Native Script Editor • GeoDA • Native R code, and functionality • gvSIG • R plugin to allow
- 15. R in “Traditional” Spatial/GIS •Kinda Spatial •Tableau •Spotfire
- 16. R in “Traditional” Spatial/GIS • Capable of GeoAnalytics • SAS • SPSS
- 20. 0 10 20 30 40 50 60 70 80 2009 2010 2011 2012 2013 2014 2015 2016 2017 R Spatial Libraries
- 21. Libraries we’re Going to Focus On • ggplot2 - Library for the declarative generation of graphics • ggmap - Library for draping data over a Google, or Statman Basemap • maps - displays data as maps, also contains Spatial Data • mapdata - More Map data, extends maps • rgdal - Access to the Geospatial Data Abstraction Library (GDAL). Creates the ability to read datatypes and change projections • maptools - library for building and manipulating spatial information • rgeos - geometry operations • Cairo - high quality graphic formats for export • scales - assists in determining best place for breaks
- 26. What is Spatial Data • Two Cataglories • Vector • Point / Multipoint • Line/Multiline • Polygon/Multipolygon • Raster • It’s a matrix of pixels and each pixel has numeric attributes • Imagery • Some generated graphics
- 27. Geospatial Data Formats •Data within R packages – map, mapdata or oz •Files or Endpoints with Latitude and Longitude attributes (just Points) •Shapefiles •Geopackage/geoJSON/GML •Google or other cloud-based mapping api's
- 28. Warming up – A Simple Map >library(maps) >library(mapdata) >map(“worldHires”, “Mexico”, xlim=c(-118.4, - 86.7),ylin=c(14.5321,32.71865), col=“purple”, fill=TRUE)
- 30. Cooling Off – Another Simple Map >library(maps) >library(mapdata) >map(“worldHires”,”Canada”, xilm=c(-141,-53),ylim=c(40,85), col=“red”, fill=TRUE)
- 32. Cooling Off – Fixed Zoom In >library(maps) >library(mapdata) >map(“worldHires”,”Canada”, xlim=c(-140,-110),ylim=c(48.64), col=“red”, fill=TRUE)
- 34. map meet ggplot2 >co <- map(“county”,”colorado”, plot=FALSE, fill=TRUE)
- 38. Maps and ggplot2
- 39. map and ggplot2 >ggplot(co,aes(long, lat)) + >geom_polygon(aes(group=group), color=“yellow”)
- 41. maps and ggplot2 >ggplot(co,aes(long, lat)) + >geom_polygon(aes(group=group), color=“yellow”, fill=“blue”)
- 43. ggplot and shapefiles and choropleths >library(ggplot2) >library(maptools) >library(rgeos) >library(ggmap) >library(scales) >library(RColorBrewer)
- 44. ggplot and shapefiles and choropleths >setwd(“$Folder_here”) >co.shp<-readShapeSpatial(“ACS1014_county.shp”) >class(co.shp) >names(co.shp)
- 45. ggplot and shapefiles and choropleths >print(co.shp$GEOID) >print(co.shp$NAME)
- 46. ggplot and shapefiles and choropleths >age_40<-data.frame(NAME_CO=co.shp$NAME, id=co.shp$COUNTYFP, prevalence=co.shp$age_40_49) >combine<-fortify(co.shp,region=“COUNTYFP”) >head(combine)
- 48. ggplot and shapefiles and choropleths >merge.shp<-merge(combine, age_40, by=“id”, all.x=TRUE) >final_join<-merge.shp[order(merge.shp$order),]
- 49. ggplot and shapefiles and choropleths >ggplot() + +geom_polygon(data=final_join, aes(x=long, y=lat,group=group, fill=prevalence),color=“black”, size=0.25) + +coord_map()
- 51. ggplot and shapefiles and choropleths >ggplot() + + geom_polygon(data=final_join, aes(x=long, y=lat, group=group, fill=prevalence), color=“black”, size=0.25) + + coord_map + + scale_fill_distiller(name=“Population”, palette=“YlGn”, breaks=pretty_breaks(n=5)) + + theme_nothing(legend=TRUE) + + labs(title=“40-49 year olds by County)
- 57. Heat Map >ggmap(co_map, extent=“device) + + geom_point(aes(x=long, y=lat), color=“red”, alpha=.1, size=2, data=co_fm)
- 59. Heat Map 2.0 >ggmap(co_map, extent = "device") +
- 60. Heat Map 2.0 + geom_density2d(data = co_fm,aes(x = long, y = lat), size = 0.3) +
- 61. Heat Map 2.0 + stat_density2d(data =co_fm, aes(x = lon, y = lat, fill = ..level.., alpha = ..level..), size = 0.01, bins = 16, geom = "polygon") +
- 62. Heat Map 2.0 + scale_fill_gradient(low = "green", high = "red") +
- 63. Heat Map 2.0 + scale_alpha(range = c(0, 0.3), guide = FALSE)