> For the complete documentation index, see [llms.txt](https://navigatinggis.gitbook.io/home/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://navigatinggis.gitbook.io/home/spatial-analysis/normalization.md).

# Normalization

<figure><img src="/files/ojeFDOCrvC9IonSvov82" alt="" width="375"><figcaption><p>2 empanadas per 2 people is different from 2 empanadas per 10 people</p></figcaption></figure>

[Normalization](https://support.esri.com/en-us/gis-dictionary/normalization) in the geospatial context involves adjusting and standardizing geographic data to ensure consistency and comparability. This process is crucial when combining data from different sources, scales, or formats to create a cohesive dataset. For example, normalization can adjust various measurement units (like converting miles to kilometers) or transform data onto a common scale.

### **Why It Matters for Environmental Justice**

Normalization is significant for environmental justice because it ensures that data from various sources can be fairly compared and analyzed. It helps in accurately assessing environmental impacts across different regions and communities, particularly those that are marginalized or vulnerable. Proper normalization ensures that decisions and policies are based on reliable and equitable data, [avoiding biases that could worsen disparities](https://www.tandfonline.com/doi/full/10.1080/17445647.2023.2235385).

### **Guidance**&#x20;

For organizations looking to use normalization in their GIS projects, here are a few guidelines:

* **Assess the Need for Normalization**: Before beginning, understand what disparities might exist in your data—be it in scale, units, or coverage.
* **Validate and Test**: After normalizing your data, review the results to ensure accuracy. Testing with sample analyses and reviewing trends with the community can help confirm that the normalization process hasn't introduced any new biases.
* **Document the Process**: Keep detailed records of how data was normalized, including the methods and parameters used. This transparency is crucial for replicability and for stakeholders to trust the results.

{% hint style="info" %}
**Acknowledgment**: [Aaron Adams](https://www.linkedin.com/in/aaron-maxwell-adams/)\
**Art:** [Fanesha Fabre](https://www.faneshafabreart.com/)
{% endhint %}


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