Landscape metrics are algorithms that quantify the spatial structure of patterns – primarily composition and configuration - within a geographic area. The term "landscape metrics" has historically referred to indices for categorical land cover maps, but with emerging datasets, tools, and software programs, the field is growing to include other types of landscape pattern analyses such as graph-based metrics, surface metrics, and three-dimensional metrics. The choice of which metrics to use requires careful consideration by the analyst, taking into account the data and application. Selecting the best metric for the problem at hand is not a trivial task given the large numbers of metrics that have been developed and software programs to implement them.

Landscape metrics are algorithms that quantify the spatial structure of patterns – primarily composition and configuration - within a geographic area. The term "landscape metrics" has historically referred to indices for categorical land cover maps, but with emerging datasets, tools, and software programs, the field is growing to include other types of landscape pattern analyses such as graph-based metrics, surface metrics, and three-dimensional metrics. The choice of which metrics to use requires careful consideration by the analyst, taking into account the data and application. Selecting the best metric for the problem at hand is not a trivial task given the large numbers of metrics that have been developed and software programs to implement them.

Landscape metrics are algorithms that quantify the spatial structure of patterns – primarily composition and configuration - within a geographic area. The term "landscape metrics" has historically referred to indices for categorical land cover maps, but with emerging datasets, tools, and software programs, the field is growing to include other types of landscape pattern analyses such as graph-based metrics, surface metrics, and three-dimensional metrics. The choice of which metrics to use requires careful consideration by the analyst, taking into account the data and application. Selecting the best metric for the problem at hand is not a trivial task given the large numbers of metrics that have been developed and software programs to implement them.

Agent-based models are dynamic simulation models that provide insight into complex geographic systems. Individuals are represented as agents that are encoded with goal-seeking objectives and decision-making behaviors to facilitate their movement through or changes to their surrounding environment. The collection of localized interactions amongst agents and their environment over time leads to emergent system-level spatial patterns. In this sense, agent-based models belong to a class of bottom-up simulation models that focus on how processes unfold over time in ways that produce interesting, and at times surprising, patterns that we observe in the real world.

Agent-based models are dynamic simulation models that provide insight into complex geographic systems. Individuals are represented as agents that are encoded with goal-seeking objectives and decision-making behaviors to facilitate their movement through or changes to their surrounding environment. The collection of localized interactions amongst agents and their environment over time leads to emergent system-level spatial patterns. In this sense, agent-based models belong to a class of bottom-up simulation models that focus on how processes unfold over time in ways that produce interesting, and at times surprising, patterns that we observe in the real world.