Delving into the specifics of spatial and temporal analytics, this chapter explores topics such as spatial neighborhood and temporal evolution of large amounts of network traffic data.

Focusing on the big data elements of cybersecurity, this chapter looks at the landscape of the big data technologies and the complexities of the different types of data, including spatial and graph data. It outlines examples in these complex data types and how they can be evaluated using data analytics.

This chapter describes the types of data that empirical community ecologists typically collect, and how these can be incorporated in the Hierarchical Modelling of Species Communities (HMSC) framework as input. While community ecologists apply theoretical, experimental and observational approaches to studying the processes that structure ecological communities, this chapter (and the entire book) focuses mainly on empirical research based on non-manipulative observational data. Understanding the basic features of the data and how they have been collected will be essential for appropriately setting up the HMSC model and interpreting the results. The chapter describes each type of input HMSC data, namely the community data (i.e. the occurrences or abundances of the species), environmental data, data describing the spatio-temporal context, species trait data and phylogenetic data. Finally, the chapter discusses how to best organise the data, as well as how to solve problems arising from missing data.

This chapter examines the links between Hierarchical Modelling of Species Communities (HMSC) outputs and the underlying community ecological processes. To do so, the chapter applies HMSC to simulated data generated from an agent-based model with known underlying assembly processes, and then assesses how those processes are captured from the patterns in the data. After simulating data with the spatial agent-based model, the chapter simulates two 'virtual ecologists' who sample data from the simulations, one applying a spatial study design and the other a temporal study design. While the main motivation of the chapter is to assess how community assembly processes translate into HMSC outputs, another motivation is to examine the robustness of HSMC to violations against structural model assumptions – namely, the data generated by the agent-based models violate some of the underlying assumptions of generalised linear mixed models and thus of HMSC. The chapter finishes by summarising what the virtual ecologists learned by applying HMSC to their data, particularly in light of the assembly processes that were used to simulate the data.