13 Spatial Data and Maps

Focus on rosm throughout the chapter.

Spatial data is central to many of our tasks as Data Scientists. Identifying spatial patterns and spatial correlations and relationships between those patterns offers opportunities for identifying, for example, locations of new services for populations. 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.

The required packages for this chapter include:

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.

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