Introduction to Geospatial Data

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This content introduces geospatial data and preparing the environment (install QGIS, Gdal, python, and some necessary libraries). Moreover, it shows how to start coding python and using Jupyter.

Education

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Introduction to Geospatial Data

Python started as a scripting language, but now it is the new trend everywhere and in particular for data science, the latest rage of computing. It didn’t get there by chance: tools and concepts built by nerdy scientists and geek sysadmins provide foundations for what is said to be the sexiest job: data scientist. In this talk I give a personal perspective on the progress of the scientific Python ecosystem, from numerical physics to data mining. What made Python suitable for science; Why the cultural gap between scientific Python and the broader Python community turned out to be a gold mine; And where this richness might lead us. The talk will discuss low-level and high-level technical aspects, such as how the Python world makes it easy to move large chunks of number across code. It will touch upon current technical details that make scikit-learn and joblib stand.

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Application of OpenStreetMap in Disaster Risk Management

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This content presents the four procedures were investigated in detail with an emphasis on simplicity for application to disaster management (download from OSM website, download using QGIS plugin, download a file converted to a universal file format (shapefile) and adding rendered map in the background). The use of these data for resilient urban planning are demonstrated including setting a hazard layer (flood Model), setting an exposure layer (population) and exposure analysis using InaSAFE plugin.

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