Pre-event Material: Reference

hackweeks combine interactive tutorials with open project time in a shared learning environment

everyone will need their own laptop to participate

we offer all tutorials in the Python programming language

we require everyone to work through these preliminary tutorials before arriving

Git is a version control system; GitHub is a web environment for sharing code

we use Git as a way to control who has access to our cloud resources

You must have a GitHub account to attend this hackweek

Your GitHub membership in our organization must be set to “Public” so you can access our shared cloud resources

JupyterHub provides a consistent environment within which we can work on tutorials and projects

accessing JupyterHub servers requires a GitHub userid

JupyterHub offers each participant their own separate Jupyter Notebook environment and disk space for storing temporary scripts and files

everyone is encouraged to arrive with Python installed on their laptop for the project work

there are several different versions of Python, but we will use Python 3.7 for this hackathon

Conda package manager will be used to install Python and other libraries

Conda can be installed in two ways (Anaconda and Miniconda)

Conda package manager works across systems

You are all set up with a HOTOSM account and ready for the Missing Maps turorial on day 1 at Geohackweek 2019!

Later during the hackweek, we will revisit accessing these vector data through the OSM API and explore ways to automate digitizing features of interest using raster, vector and machine learning methods.

We use the classification of buildings as a straight-forward example to teach data science skills that can be applied to any domain interested in classifying and analyzing features of interest (natural or man made) from imagery.

Python is a free, open source programming language widely used in data analysis and scientific computing. Introductory tutorials are freely available online.

With Numpy, Scipy and Matplotlib (along with a vast ecosystem of related and more specialized tools), the Python programming language offers a flexible and robust platform for many tasks in scientific research, from quick one-off analyses to large-scale projects.

With Pandas, Python provides an excellent environment for the analysis of tabular data.