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--- |
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icon: traffic-light-slow |
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description: >- |
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This dataset provides insights into web traffic patterns for various U.S. |
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government agencies and domains. |
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--- |
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# Government Traffic |
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> **Data Notice**: This dataset provides academic research access with a 6-month data lag. |
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> For real-time data access, please visit [sov.ai](https://sov.ai) to subscribe. |
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> For market insights and additional subscription options, check out our newsletter at [blog.sov.ai](https://blog.sov.ai). |
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```python |
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from datasets import load_dataset |
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df_agencies = load_dataset("sovai/government/traffic/agencies", split="train").to_pandas().set_index(["date"]) |
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``` |
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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) |
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## Description |
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This dataset provides web traffic data for U.S. government agencies and domains, offering insights into public engagement with government websites. |
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It enables analysis of traffic trends, inter-agency comparisons, and patterns of citizen interaction with government online resources. |
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## Data Access |
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```python |
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import sovai as sov |
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sov.token_auth(token="your_token_here") |
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# Agency-level traffic data |
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df_agencies = sov.data("government/traffic/agencies") |
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# Domain-level traffic data |
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df_domains = sov.data("government/traffic/domains") |
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``` |
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<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/government_traffic_1 (2).png" alt=""><figcaption></figcaption></figure> |
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### Dataset Contents |
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1. **Agency Traffic (df\_agencies)** |
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* Provides traffic data aggregated at the agency level. |
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* Allows for high-level analysis of government agency website usage. |
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2. **Domain Traffic (df\_domains)** |
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* Offers more granular data on traffic to specific government domains. |
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* Enables analysis of individual website performance within agencies. |
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### Analysis Capabilities |
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* Time series analysis of traffic patterns |
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* Correlation analysis between different domains or agencies |
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* Calculation of statistical measures like coefficient of variation |
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* Filtering for specific types of domains (e.g., embassies) |
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### Example Analyses |
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1. Plotting agency-level traffic: |
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```python |
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df_agencies.plot() |
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``` |
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2. Analyzing embassy website traffic: |
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```python |
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df_embassy = df_domains.loc[:, df_domains.columns.str.contains('embassy', case=False)] |
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df_embassy.plot() |
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``` |
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3. Correlation analysis: |
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```python |
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df_embassy.corr() |
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``` |
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4. Advanced statistics (e.g., coefficient of variation): |
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```python |
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cv = df_embassy.std().div(df_embassy.mean()).sort_values() |
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``` |
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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. |
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