Create Maps from GTFS Data Using Python and Geopandas скачать в хорошем качестве

Create Maps from GTFS Data Using Python and Geopandas

The provided code demonstrates how to visualize public transport stops and route shapes using Python libraries such as Pandas, Geopandas, and Matplotlib. The data, sourced from [OpenMobilityData](https://transitfeeds.com/p/bkk/42), a project of MobilityData, a Canadian non-profit organization that improves global mobility by increasing access to public transportation information, is located in the GTFS format. First, necessary libraries are imported, and the GTFS data directory is specified. The stops.txt file is read into a Pandas DataFrame, and the stop coordinates are converted into geometric points using shapely.geometry.Point. These points are added to the DataFrame, which is then converted into a GeoDataFrame and plotted to visualize stop locations. Next, the shapes.txt file is read to obtain route shapes data. The route coordinates are similarly converted into geometric points and added to a new DataFrame, which is also converted into a GeoDataFrame. The data is grouped by shape_id, and the points for each route are aggregated and converted into shapely.geometry.LineString objects. This final GeoDataFrame, representing routes as lines, is plotted using Matplotlib, with different colors to distinguish between routes. This approach effectively visualizes public transport data, creating clear and informative maps. #gis #cartography #remotesensing #programming #coding #python #geopanda #pythonlibraries #shapefile #geospatial #geology #geography #geoinformatics #matplotlib #dataframe #shapely #publictransport #geodataframe #linestring #mapping #digitization #topography #lidar #mapping #geospatial #arcgispro #qgis #arcgis #arcmap