# Visualize Atomic Files

Here, we present how to visualize [atomic files](../user_guide/data/atomic_files.md) with the help of a `VisHelper`  in `LibCity`.

## Prepare Atomic Files

To begin with, simply place the atomic file in the `./raw_data` folder, which is the same as the preparation of running a model. If you don't know where to get a dataset, click [here](./run_model.md) for more information.

## Run the Visualization Script

To simplify user operation to the most extent, there are only two parameters to be specified:

* `dataset`: the name of the dataset
* `save_path`: the path to save visualization files, `default="./visualized_data/"`

The script will automatically detect the `geo` file and `dyna` files in the `dataset` folder and convert them to [GeoJSON](https://geojson.org/) files. (**For all datasets, make sure that (1) there's only one `geo` file and (2) there's at least one file with `coordiantes` column**.)

For example, if you are running a map matching task on **Seattle** dataset, you can use the following command

```shell
python visualize.py --dataset Seattle --save_path "./visualized_data"
```

to start a data type conversion.

The grid dataset and the state dataset can be visualized in the same way. **It is worth noticing that the `properties` (`inflow` and `outflow` for example) are averaged over the full time period in grid dataset and state dataset.**

## Visualize GeoJSON

[GeoJSON](https://geojson.org/) is a format for encoding a variety of geographic data structures and is supported by most GIS tools. Here, we present our visualization of **Seattle** dataset using [QGIS](https://www.qgis.org/en/site/).

The red lines represents the road network of **Seattle**. The yellow lines represents GPS trajectory of the **Seattle** dataset.

![](/_static/data_visualization1.png)

![](/_static/data_visualization2.png)

We also present our heat map visualization of **METR_LA** which is a traffic speed dataset.

![](/_static/data_visualization3.png)