Goods Prices/Sales Data
In this chapter, we’ll combine spatial and temporal approaches to spatiotemporal data—data that includes both spatial components and time series components. To handle the time components, we’ll slice our datasets into overlapping time windows as we did with our stock data in Chapter 6. To handle the spatial components, we’ll calculate local Moran statistics based on correlations for each time slice and threshold the value to create a network for that time slice. Then, we’ll look at changes in Forman-Ricci curvature centrality and PageRank centrality across slices of time and space. Examples include the Burkina Faso millet dataset first seen in Chapter 2 and a new store sales dataset.
By the end of this chapter, you’ll understand how to set up spatiotemporal data analytics with networks to capture changes over time within the spatial and temporal relationships of the datasets using igraph and different types of plots...
