Choose and perform a core network analytical task for answering a given question.
Spatial network modeling has started with quantitative geography and its integration into GIS for analyzing transport and material flows. Concepts from graph theory were combined with spatio-temporal concepts to model spatial relationships. As GIS evolved, it established close links to transportation science and graph-based complexity models. Spatial networks thus provide a way to measure relationships between geo-located entities. Yet, though spatial networks are often modeled as spatially embedded graphs, the concepts involved are more specific: nodes can be locations or spatial objects with possibly changing locations, potentials or attractiveness scores, and edges can measure diverse quantified (extensive or intensive) relationships, such as distances, interactions, or flows. A primary example are transport networks. Here, key tasks include constructing distance networks based on road infrastructure, modeling potential interactions within defined distances, measuring accessibility by identifying proximity to services like schools, and measuring network centrality. Spatial networks also can be used to estimate flows between objects using gravity models, based on spatial interactions between the potential of origins and the attractiveness of destinations and their distance. Distances can thereby be modeled in various manners, including metric, topological or angular distances.