Graph analysis of geo-temporal information
Systems, methods, and non-transitory computer readable media may be configured to provide graph analysis of geo-temporal information. A location dataset, an entity dataset, and a movement dataset may be accessed. The location dataset may define locations. The entity dataset may define entities. The movement dataset may define movement of the entities among the locations. A graph may be generated based on the location dataset, the entity dataset, and the movement dataset. The graph may represent (1) the locations and the entities with nodes, and (2) the movement of the entities among the locations with edges.
1. A system comprising:
one or more processors; and
a memory storing instructions that, when executed by the one or more processors, cause the system to perform:
accessing a location dataset, the location dataset defining locations;
accessing an entity dataset, the entity dataset defining entities;
accessing a movement dataset, the movement dataset defining movement of the entities among the locations;
generating a graph based on the location dataset, the entity dataset, and the movement dataset, the graph representing:
the locations and the entities with nodes;
the movement of the entities among the locations with edges between the nodes;
respective rankings associated with the entities based on respective weights of the edges; and
respective rankings associated with the locations based on any of quantities or the weights of the edges connecting to the respective locations;
determining a membership of an entity of the entities in an entity group based on a characteristic associated with the edges;
predicting a change in the membership of the entity in the entity group based on patterns of changes among the edges over a time period; and
dynamically changing one of the respective rankings associated with the entities or the locations by changing one of the weights of the edges.
2. The system of claim 1 , wherein the locations include points of interest.
3. The system of claim 2 , wherein the points of interest include buildings.
4. The system of claim 3 , wherein the locations are arranged within a hierarchy of locations.
5. The system of claim 1 , wherein the entities include a person, a team, or an organization.
6. The system of claim 5 , wherein the entities are arranged within a hierarchy of entities.
7. The system of claim 1 , wherein the instructions, when executed by the one or more processors, further cause the system to perform:
analyzing the graph using a graph-based algorithm, the graph-based algorithm facilitating analysis of information within the location dataset, the entity dataset, and the movement dataset using a non-geospatial algorithm.
8. The system of claim 7 , wherein the graph-based algorithm includes a ranking algorithm, the ranking algorithm ranking the locations based on the nodes and the edges within the graph.
9. A method implemented by a computing system including one or more processors and storage media storing machine-readable instructions, wherein the method is performed using the one or more processors, the method comprising:
accessing a location dataset, the location dataset defining locations;
accessing an entity dataset, the entity dataset defining entities;
accessing a movement dataset, the movement dataset defining movement of the entities among the locations;
generating a graph based on the location dataset, the entity dataset, and the movement dataset, the graph representing:
the locations and the entities with nodes;
the movement of the entities among the locations with edges between the nodes;
respective rankings associated with the entities based on respective weights of the edges; and
respective rankings associated with the locations based on any of quantities or the weights of the edges connecting to the respective locations;
determining a membership of an entity of the entities in an entity group based on a characteristic associated with the edges;
predicting a change in the membership of the entity in the entity group based on patterns of changes among the edges over a time period; and
dynamically changing one of the respective rankings associated with the entities or the locations by changing one of the weights of the edges.
10. The method of claim 9 , wherein the locations include points of interest.
11. The method of claim 10 , wherein the points of interest include buildings.
12. The method of claim 9 , wherein the entities include a person, a team, or an organization.
13. The method of claim 12 , wherein the entities are arranged within a hierarchy of entities.
14. The method of claim 9 , wherein the instructions, when executed by the one or more processors, further cause the system to perform:
analyzing the graph using a graph-based algorithm, the graph-based algorithm facilitating analysis of information within the location dataset, the entity dataset, and the movement dataset using a non-geospatial algorithm.
15. The method of claim 14 , wherein the graph-based algorithm includes a ranking algorithm, the ranking algorithm ranking the locations based on the nodes and the edges within the graph.
16. A non-transitory computer readable medium comprising instructions that, when executed, cause one or more processors to perform:
accessing a location dataset, the location dataset defining locations;
accessing an entity dataset, the entity dataset defining entities;
accessing a movement dataset, the movement dataset defining movement of the entities among the locations;
generating a graph based on the location dataset, the entity dataset, and the movement dataset, the graph representing:
the locations and the entities with nodes;
the movement of the entities among the locations with edges between the nodes;
respective rankings associated with the entities based on respective weights of the edges; and
respective rankings associated with the locations based on any of quantities or the weights of the edges connecting to the respective locations;
determining a membership of an entity of the entities in an entity group based on a characteristic associated with the edges;
predicting a change in the membership of the entity in the entity group based on patterns of changes among the edges over a time period; and
dynamically changing one of the respective rankings associated with the entities or the locations by changing one of the weights of the edges.
17. The non-transitory computer readable medium of claim 16 , wherein the instructions, when executed, further cause the one or more processors to perform:
analyzing the graph using a graph-based algorithm, the graph-based algorithm facilitating analysis of information within the location dataset, the entity dataset, and the movement dataset using a non-geospatial algorithm.
18. The non-transitory computer readable medium of claim 17 , wherein the graph-based algorithm includes a ranking algorithm, the ranking algorithm ranking the locations based on the nodes and the edges within the graph.
19. The system of claim 1 , wherein the graph further represents lengths, distances, and modes associated with the respective movements based on types and thicknesses of the edges between the nodes.