3 Maps and Spatial Visualization

Spatial data is central to many of our tasks as Data Scientists. Identifying patterns, correlations and relationships between those patterns delivers opportunities for delivering new services. Imagine predicting common routes for travellers this morning, and dynamically routing public transport to meet those needs. Fundamental to the Data Scientist is the ability to process, visualize and then model spatial data. Done right maps can be a very effective communications tool. Numerous R packages work together to bring us a sophisticated mapping and spatial analysis capability.

library(ggplot2)       # Plotting maps.
library(maps)          # Map data.
library(scales)        # For transperency Functions: alpha() comma()
library(maptools)      # For shapefiles
library(dismo)         # Obtaining gis data
library(broom)         # For conversion functions: tidy() 
library(rgdal)         # For shapefiles
library(dplyr)         # For sample function
library(OpenStreetMap) # OSM maps
library(ggmap)         # Google maps
library(leaflet)       # Interactive Maps
library(shiny)         # Interactive Viz
library(magrittr)      # XXXX.