Welcome to Methods 3, Lecture 7
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Methods 3
georeferencing?
Alie
Abby
modeling
models are simplified descriptions of reality
models attempt to answer questions we don't have perfect data for
where in the US do people lack access to food?
where in NYC are people most likely to be gentrified out of their neighborhood?
where in LA do people most need affordable housing?
where in LA do people need more parks?
seek out a model that suits your needs
it's okay to critique a model and change it
you can also make up your own models
when you make your own, try to test the model
where in Queens are renters most likely to be negatively affected by Airbnb listings?
we'll model this using:
- median income
- percentage of renters
- number of Airbnb listings
for our convenience, we'll scale each field down to a 0 → 1 range
for example, median income goes from 15474 to 151964
for example, median income goes from 15474 to 151964
tracts closer to 15474 will be closer to 0
for example, median income goes from 15474 to 151964
tracts closer to 151964 will be closer to 1
scale_linear( "med_income",
15474, 151964,
0, 1)
then we can add each of these scaled fields together and know 0 is the lowest and 3 is the highest
if you have s_income, s_renters and s_airbnb, you can add them together to get your model value (index)
a dataset is like a file on your computer
shp, geojson, csv, ...
shapefiles must be zipped
like project in QGIS