introduction_spatial_analysis_geoda.qmd

# Spatial Analysis with GeoDa

```{r, echo = FALSE}
source("glossary_funcs.R")
glossary_setup()
```

```{r, echo = FALSE, results='asis'}
glossary_setup_style()
```

Now, you've got lots of fantastic maps that show some very interesting things - but how can you dig deeper into what these maps are telling you, and confirm that the variation you are seeing is statistically significant?

This is where the GeoDa toolkit comes in.

GeoDa isn't meant to replace QGIS or creating maps in Python - but it can enhance your outputs from either by allowing you to do more advanced analysis.

You can watch the video below to get an overview of the capabilities of GeoDa.

{{< video https://www.youtube.com/watch?v=--8vhhmpgdM >}}


GeoDa is available both as a standalone programme and as a library for both [Python](https://github.com/GeoDaCenter/pygeoda) and [R](https://github.com/GeoDaCenter/rgeoda).

What is special about GeoDa?

>"\[Geoda\] helps researchers and analysts translate data into insights by adding spatial statistical tests to simple map visualization...
>
> ... Since we are prone to 'patternicity' or overdetection of patterns, statistical tests help differentiate clusters that are real from those that are not."
> - [The University of Chicago Center for Spatial Data Science](https://spatial.uchicago.edu/geoda)^[The University of Chicago Center for Spatial Data Science](https://spatial.uchicago.edu/geoda)


The HSMA course does not cover GeoDa at present (though HSMA 7 may be updated to include it!), but you can work through the two GeoDa workbooks created by the GeoDa team if you want to learn how to use it.

[Book 1](https://lanselin.github.io/introbook_vol1/)
[Book 2](https://lanselin.github.io/introbook_vol2/)

There is also a [great cheatsheet available on the geoda website](http://geodacenter.github.io/cheatsheet.html) and the GeoDa [Youtube channel](https://www.youtube.com/@GeoDaCenter) provides lots of prerecorded lectures.

# References