1. GETTING READY
1.1. input
1.1.1. geoDF
1.1.2. variables (social, etc.)
1.2. merge?
1.2.1. DF into geoDF
1.2.1.1. you are ready
1.3. to the Cloud!!
1.3.1. all these data to GitHub
1.3.1.1. use the links to read the data
1.4. Planing
1.4.1. which variable(s) to use?
1.4.2. why those variables? what do I want to inform?
1.4.2.1. the right plot?
1.4.2.1.1. Would I need trial and error?
1.4.3. what preprocessing would be needed?
1.4.4. would my output be ready for R (or other tools)?
2. The Options
2.1. Dot Density Maps
2.1.1. encodes
2.1.1.1. variable values by means of DENSITY of plots
2.1.2. preprocessing
2.1.2.1. input
2.1.2.1.1. one GeoDF with DATA!
2.1.2.1.2. one variable
2.1.2.2. new unit
2.1.2.2.1. the dot represents...
2.1.2.2.2. decide the UNIT per DOT
2.1.2.2.3. A DOT represents a **constant** amount.
2.1.2.3. new layer (map)
2.1.2.3.1. the dots are a new map
2.1.2.3.2. random location of dots
2.1.2.3.3. set CRS of **GeoDF** to an EqualArea projection
2.1.3. Ready to be exported?
2.1.3.1. if you have multipoints...
2.1.3.1.1. a point per row is what other programs expect!
2.2. Proportional Symbols Maps
2.2.1. encodes
2.2.1.1. variable values by means of SIZE of ONE symbol
2.2.1.1.1. located generally in the center of polygon (centroid?)
2.2.2. preprocessing
2.2.2.1. Input
2.2.2.1.1. one GeoDF with DATA!
2.2.2.1.2. one variable
2.2.2.2. new scale
2.2.2.2.1. the variable itself vs its scaled version!!
2.2.2.3. new layer
2.2.2.3.1. the symbols are a new map
2.2.2.3.2. computing location for the symbol (centroid?)
2.2.2.3.3. set CRS of **GeoDF** to an EqualArea projection
2.2.3. Ready to be exported?
2.2.3.1. the scaled size needed for python
2.2.3.1.1. keep the original column
2.3. Choropleth Maps
2.3.1. represented by COLOR maps
2.3.2. encodes
2.3.2.1. variable values by a shade of color map
2.3.3. preprocessing
2.3.3.1. normalization
2.3.3.1.1. density
2.3.3.1.2. proportion
2.3.3.1.3. average
2.3.3.1.4. change
2.3.3.2. discretization
2.3.3.2.1. cut the variable
2.3.3.3. set CRS to an EqualArea projection