/7.2/guide-tags/geospatial/Recent content in Geospatial on Kinetica DocsHugo -- gohugo.ioen/7.2/guides/quickstart-guide/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/quickstart-guide/Install Kinetica There are several different options for installing Kinetica Enterprise or Kinetica in AWS. However, to get started within minutes (and for free) we recommend either of the following two routes: Kinetica Cloud Free: This is a free managed version with 10 GB of storage, which is hosted in the cloud. Follow the instructions for Kinetica Cloud Free to create an account and launch Kinetica. Tip Kinetica Cloud takes only a couple of minutes to set up and is an excellent choice to get started./7.2/guides/solve_graph_seattle_backhaul/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/solve_graph_seattle_backhaul/The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a backhaul routing problem via the /solve/graph endpoint. For more information on Graphs & Solvers, see Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Backhaul Routing Download & Run Prerequisites The prerequisites for running the backhaul routing solve graph example are listed below:/7.2/guides/fitting_data/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/fitting_data/The following guide provides step-by-step instructions to get started with fitting existing road network data to a graph. Since edges in graphs can only be composed of two nodes, the weight (or cost to travel) for complex WKT LINESTRINGs (e.g., more than two points) that typically define road networks must be assigned consistently to the segments composing the LINESTRING, as noted under Weights. There are two methods for calculating and assigning weights to a graph with edges derived from large WKT LINESTRINGs:/7.2/guides/isochrones/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/isochrones/The following is a complete example, using the Python API, of generating images containing isochrones via the /visualize/isochrone and /wms endpoints using an existing graph. Prerequisites Python API Installation Python Virtual Environment PyPI Git Data File Script Detail Connections Constants Graph Creation Visualizing Isochrones Visualizing Isochrones Using WMS Download & Run Prerequisites The prerequisites for running the isochrones example are listed below: Graph server enabled Python API Isochrones example script DC Shape data CSV file Python API Installation Depending on the target operating system, a Python virtual environment may need to be installed first:/7.2/guides/graph_rest_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/graph_rest_guide/The following guide provides step-by-step instructions to get started with using the Graphs & Solvers in Kinetica. This guide demonstrates some key graph concepts as well as how to create and solve a graph using the Kinetica REST API. Prerequisites Data File Using the REST API Key Information and Concepts Data Graph Concepts Tutorial via REST Client Create Graph Solve the Graph (Shortest Path) Solve the Graph (Multiple Routing) Download & Run Prerequisites D./7.2/guides/graph_interface_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/graph_interface_guide/The following guide provides step-by-step instructions to get started with using the Graphs & Solvers in Kinetica. This guide demonstrates some key graph concepts as well as how to create and solve a graph using the GAdmin graph GUI. Prerequisites Data File Key Information and Concepts Data Graph Concepts Tutorial via GUI Create Schema Create Graph Solve the Graph (Shortest Path) Solve the Graph (Multiple Routing) Prerequisites D.C. Shape data file Access to GAdmin Data File The tutorial makes use of the dc_shape dataset, which can be ingested from the {{< download "/examples/data/dc_shape./7.2/guides/graph_video_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/graph_video_guide/In this video series, Hari Subhash walks through graphs in Kinetica, from high-level concepts to practical examples./7.2/guides/match_graph_seattle_markov/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/match_graph_seattle_markov/The following is a complete example, using the Python API, of matching GPS sample data to road network data via the /match/graph endpoint. For more information on Graphs & Solvers, see Graphs & Solvers Concepts. Prerequisites Python API Installation Data Files Script Detail Constants Graph Creation Matching the Graph without Fold-over Filtering Matching the Graph with Fold-over Filtering Download & Run Prerequisites The prerequisites for running the match graph example are listed below:/7.2/guides/solve_graph_seattle_multi_route/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/solve_graph_seattle_multi_route/The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a multiple routing problem via the /solve/graph endpoint. For more information on Graphs & Solvers, see Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Multiple Routing Download & Run Prerequisites The prerequisites for running the multiple routing solve graph example are listed below:/7.2/guides/match_graph_dc_multi_supply_demand/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/match_graph_dc_multi_supply_demand/The following is a complete example, using the Python API, of matching stores (demands) to depots/trucks (suppliers) via the /match/graph endpoint using an underlying graph created from a modified OpenStreetMap (OSM) dataset. For more information on Graphs & Solvers, see Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Table Setup Graph Creation Multiple Supply Demand with Priority Solution Analysis Multiple Supply Demand with Priority and Max Trip Cost Download & Run Prerequisites The prerequisites for running the match graph example are listed below:/7.2/guides/solve_graph_nyctaxi_page_rank/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/solve_graph_nyctaxi_page_rank/The following is a complete example, using the Python API, of solving a graph created with NYC Taxi data for a page rank problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Page Rank Download & Run Prerequisites The prerequisites for running the page rank solve graph example are listed below:/7.2/guides/solve_graph_seattle_shortest_path/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/solve_graph_seattle_shortest_path/The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a shortest path problem via the /solve/graph endpoint. For more information on Graphs & Solvers, see Graphs & Solvers Concepts. Prerequisites Python API Installation Python Virtual Environment PyPI Git Data File Script Detail Constants Graph Creation Shortest Path Single Source to Single Destination Single Source to Many Destinations Many Sources to Many Destinations Download & Run Prerequisites The prerequisites for running the shortest path solve graph example are listed below:/7.2/guides/solve_graph_dc_shortest_path_turn/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/solve_graph_dc_shortest_path_turn/The following is a complete example, using the Python API, of solving a graph created with a Washington, D.C. HERE dataset for a shortest path problem with turn penalties via the /solve/graph endpoint. For more information on Graphs & Solvers, see Graphs & Solvers Concepts. For more information on turn penalties and restrictions, see Using Turn-based Weights & Restrictions. Prerequisites Python API Installation Data File Script Detail Constants Table Setup Graph Creation Shortest Path Download & Run Prerequisites The prerequisites for running this solve graph example are listed below:/7.2/guides/wkt_data_geo_functions/Mon, 01 Jan 0001 00:00:00 +0000/7.2/guides/wkt_data_geo_functions/The following sections demonstrate how to ingest and work with WKT data as well as how to use geospatial functions via SQL and the Kinetica Python API. Details about the geospatial functions can be found under Geospatial/Geometry Functions. All geospatial functions are compatible in both native API and SQL. Prerequisites Loading Sample Data Examples via SQL Creating a Table and Inserting WKT Data Scalar Functions Aggregation Functions Joins Equality Examples via the Python API Creating a Table and Inserting WKT Data Scalar Functions Aggregation Functions Joins Equality Complete Samples Prerequisites KiSQL (if running the SQL examples) Python API (if running the Python examples) Sample CSV data files, which should be downloaded & copied to the host running the SQL client.