> For the complete documentation index, see [llms.txt](https://docs.sov.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sov.ai/realtime-datasets/economic-datasets/government-traffic.md).

# Government Traffic

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Dataset contains 2600+ government domains, available from 2017-04-25 onwards.
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`Tutorials` are the best documentation — [<mark style="color:blue;">`Government Traffic Analysis Tutorial`</mark>](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/datasets/Government%20Internet.ipynb)

## Description

This dataset provides web traffic data for U.S. government agencies and domains, offering insights into public engagement with government websites.

It enables analysis of traffic trends, inter-agency comparisons, and patterns of citizen interaction with government online resources.

## Data Access

```python
import sovai as sov
sov.token_auth(token="your_token_here")

# Agency-level traffic data
df_agencies = sov.data("government/traffic/agencies")

# Domain-level traffic data
df_domains = sov.data("government/traffic/domains")
```

<figure><img src="/files/X4GKiQNCMSMYtsFLkT2Z" alt=""><figcaption></figcaption></figure>

### Dataset Contents

1. **Agency Traffic (df\_agencies)**
   * Provides traffic data aggregated at the agency level.
   * Allows for high-level analysis of government agency website usage.
2. **Domain Traffic (df\_domains)**
   * Offers more granular data on traffic to specific government domains.
   * Enables analysis of individual website performance within agencies.

### Analysis Capabilities

* Time series analysis of traffic patterns
* Correlation analysis between different domains or agencies
* Calculation of statistical measures like coefficient of variation
* Filtering for specific types of domains (e.g., embassies)

### Example Analyses

1. Plotting agency-level traffic:

   ```python
   df_agencies.plot()
   ```
2. Analyzing embassy website traffic:

   ```python
   df_embassy = df_domains.loc[:, df_domains.columns.str.contains('embassy', case=False)]
   df_embassy.plot()
   ```
3. Correlation analysis:

   ```python
   df_embassy.corr()
   ```
4. Advanced statistics (e.g., coefficient of variation):

   ```python
   cv = df_embassy.std().div(df_embassy.mean()).sort_values()
   ```

This dataset is valuable for understanding government web presence, analyzing public engagement with government resources, and identifying trends in how citizens interact with government websites.