Geospatial Data Analysis Anita Graser Keynote Presentation Cca 2021 The “movement data in gis” series discusses all things related to working with movement data aka. trajectories in gis, covering concepts, libraries, desktop gis, databases, and big data technology. This workshop introduces movingpandas for analyzing movement data in python. movingpandas is based on the established pandas python data analysis library and its spatial extension geopandas.
Anita Graser Xyht The individual protocol steps are demonstrated using a dataset of vessel tracking data (ais) published by the danish maritime authority. the demo data covers two days (july, 1st 2017 and january, 1st 2018). It emphasizes different data models for trajectories, including spatial primitives, temporal primitives, and data manipulation functions, while comparing traditional gis with movingpandas' approach. Researchers and gis practitioners to efficiently interact with and analyse movement data. in this paper, we therefore introduce movingpandas, an extension to the python data analysis. This talk presents methods for the exploration of movement patterns in massive quasi continuous gps tracking datasets, with examples focusing on international maritime vessel movements.
Geospatial Data Analysis Anita Graser Keynote Presentation Cca 2021 Researchers and gis practitioners to efficiently interact with and analyse movement data. in this paper, we therefore introduce movingpandas, an extension to the python data analysis. This talk presents methods for the exploration of movement patterns in massive quasi continuous gps tracking datasets, with examples focusing on international maritime vessel movements. It presents novel methods enabling movement data exploration that scale to massive datasets, describes the development of a novel open source scientific python library for eda of movement data (movingpandas), and proposes the first structured eda protocol for movement data. This dissertation combines both novel methodological work as well as high quality scientific software development for mobility data science. Anita also serves on the qgis project steering committee and teaches python for qgis at unigis salzburg. she is the lead developer of movingpandas (a python library for analyzing movement data) and has developed tools such as the time manager plugin for qgis. To address this gap, we present three open source technology stacks for the exploratory analysis of movement data and discuss their capabilities and limitations.
Anita Graser Analyzing Movment Data With Movingpandas Pptx It presents novel methods enabling movement data exploration that scale to massive datasets, describes the development of a novel open source scientific python library for eda of movement data (movingpandas), and proposes the first structured eda protocol for movement data. This dissertation combines both novel methodological work as well as high quality scientific software development for mobility data science. Anita also serves on the qgis project steering committee and teaches python for qgis at unigis salzburg. she is the lead developer of movingpandas (a python library for analyzing movement data) and has developed tools such as the time manager plugin for qgis. To address this gap, we present three open source technology stacks for the exploratory analysis of movement data and discuss their capabilities and limitations.
Geospatial Data Analysis Anita Graser Keynote Presentation Cca 2021 Anita also serves on the qgis project steering committee and teaches python for qgis at unigis salzburg. she is the lead developer of movingpandas (a python library for analyzing movement data) and has developed tools such as the time manager plugin for qgis. To address this gap, we present three open source technology stacks for the exploratory analysis of movement data and discuss their capabilities and limitations.
Comments are closed.