This session will cover how analysts and data scientists can use the ArcGIS API in combination with data science libraries from Python for mapping, visualization and geospatial data analysis. This demo style talk will demonstrate how to perform sophisticated vector and raster analysis, geocoding, map making, routing and directions using a Pythonic API alongwith Jupyter notebooks and Pandas.
Python has positioned itself as a highly suitable programming language for data exploration and analysis with its rich ecosystem of libraries such as NumPy, SciPy, pandas, maptplolib, scikit-learn, etc. and interactive visualization environments such as Jupyter notebooks. The ArcGIS Python API follows suite in being your library for comprehensive analyses of geospatial data. With an intuitive design and easy to use syntax, the API opens up access to rich geoprocessing services and big data analysis capabilities of spatial data.
ArcGIS API for Python is a Python library for working with maps and geospatial data. It provides simple and efficient tools for sophisticated vector and raster analysis, geocoding, map making, routing and directions, as well as for organizing and managing a GIS with users, groups and information items. In addition to working with your own data, the library enables access to ready to use maps and curated geographic data from Esri and other autorotative sources. It also integrates well with the scientific Python ecosystem and includes rich support for Pandas and Jupyter notebook.
This course will cover how analysts and data scientists can use the ArcGIS platform in combination with data science libraries from Python for mapping, visualization and geospatial data analysis. A proposed outline of the talk is below: • Jupyter notebooks • Mapping o the map widget o web maps o web scenes • Exploratory data analysis o Feature and raster layers o pandas o spatial dataframe • Visualization o matplotlib and bokeh charting o smart mapping, heatmaps o hotspots, space time cubes • Analysis o Spatial analysis o GeoAnalytics (big data analysis) o Raster analysis • Integration with data science libraries o Opencv-python and imagery layers o Scikit-learn and feature data