Let's take the machines house hunting - FOSS4GNA 2019

One of the fundamental questions in real estate is the question of ‘where’. Numerous studies indicate the place you live can impact a multitude of wellness factors, including, your life expectancy. Home buyers try to weigh several factors such as cost, the distance to major facilities, noise, air quality, community, neighborhood, school district, risks due to natural calamities etc. while looking for a place to live. Such analysis is not limited to just house hunting, business analysts and entrepreneurs run a similar multi-criteria analysis for a multitude of problems such as finding a suitable spot for a new grocery store, dentist office, coffee shop, etc. In this talk, using house-hunting as an example spatial analysis problem, we will explore how to read spatial and non-spatial data in Python as Pandas DataFrame objects, perform exploratory and statistical analysis and visualize them on a map in a Jupyter notebook. We then score properties based on the criteria. We will finally teach a machine learning model (in scikit-learn) to understand our preferences and let it predict for us in the future. We will use the free ArcGIS API for Python to perform spatial analysis and learn how it can easily interoperate with popular data analysis libraries in the scientific Python and geospatial Python ecosystems.

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