Studio 05 Choropleth Mapping

Author

Serge Rey

Instructions

Teams
DUE: Wednesday, October 2, 2024 3:30pm

The team leader will submit a pdf version of the notebook showing all the work to answer the questions.

The first cell of the notebook should have a list of the team members (first and last names) with the team leader in bold. (Hint: Markdown cell).

Data

You will use the south data set introduced in the previous lecture for this studio.

Reading the relevant file to create a GeoDataFrame

import libpysal
import geopandas
south = libpysal.examples.load_example('South')

Reading the shapefile

south_gdf = geopandas.read_file(south.get_path('south.shp'))

Explore the documentation of the example

You will be mapping and analyzing family income inequality in the South. Determine the name of the relevant variable to use

Develop a choropleth map for inequality in 1960.

Use Quintiles
Explore different color maps
Develop and document several versions of the map for 1960
Justify your final choice
Interpret the spatial distribution as it is shown in your final map

Develop diverging maps of changes in inequality from 1960-1990

Justify your color map
Justify your classification scheme
Interpret the spatial distribution as it is shown in your final map

---
title: Studio 05 Choropleth Mapping
author: Serge Rey
format:
  html:
    code-fold: false
jupyter: python3
---

## Instructions

[Teams](https://sdsu.instructure.com/courses/157862/assignments/1402797)  
**DUE: Wednesday, October 2, 2024 3:30pm** 

The team leader will submit a pdf version of the notebook showing all the work
to answer the questions.

The first cell of the notebook should have a list of the team members (first and
last names) with the team leader in bold. (Hint: Markdown cell).

## Data
You will use the `south` data set introduced in the previous lecture for this studio.

## Reading the relevant file to create a GeoDataFrame

```{python}
#| tags: []
import libpysal
import geopandas
south = libpysal.examples.load_example('South')
```
## Reading the shapefile
```{python}
#| tags: []
south_gdf = geopandas.read_file(south.get_path('south.shp'))
```
## Explore the documentation of the example
You will be mapping and analyzing family income inequality in the South.
Determine the name of the relevant variable to use

## Develop a choropleth map for inequality in 1960.

- Use Quintiles
- Explore different color maps
- Develop and document several versions of the map for 1960
- Justify your final choice
- Interpret the spatial distribution as it is shown in your final map

## Develop diverging maps of changes in inequality from 1960-1990
- Justify your color map
- Justify your classification scheme
- Interpret the spatial distribution as it is shown in your final map