https://github.com/chrieke/prettymapp

🖼️ Create beautiful maps from OpenStreetMap data in a streamlit webapp
https://github.com/chrieke/prettymapp

app art cartography geography map osm prettymaps streamlit

Last synced: 2 days ago
JSON representation

🖼️ Create beautiful maps from OpenStreetMap data in a streamlit webapp

• Host: GitHub
• URL: https://github.com/chrieke/prettymapp
• Owner: chrieke
• License: mit
• Created: 2021-09-27T15:15:02.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-12-29T21:29:48.000Z (27 days ago)
• Last Synced: 2025-01-16T15:02:11.609Z (9 days ago)
• Topics: app, art, cartography, geography, map, osm, prettymaps, streamlit
• Language: Python
• Homepage:
• Size: 37.4 MB
• Stars: 2,418
• Watchers: 10
• Forks: 363
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

Awesome Lists containing this project

• awesome - chrieke/prettymapp - 🖼️ Create beautiful maps from OpenStreetMap data in a streamlit webapp (Python)
• awesome-github-repos - chrieke/prettymapp - 🖼️ Create beautiful maps from OpenStreetMap data in a streamlit webapp (Jupyter Notebook)
• stars - chrieke/prettymapp - 🖼️ Create beautiful maps from OpenStreetMap data in a streamlit webapp (Python)

README

        
# prettymapp 🖼️

**Prettymapp is a webapp and Python package to create beautiful maps from OpenStreetMap data**

---



    🎈 Try it out here: prettymapp on streamlit 🎈 



---



    






    

## Based on the prettymaps project

Prettymapp is based on a rewrite of the fantastic [prettymaps](https://github.com/marceloprates/prettymaps) project by

[@marceloprates](https://github.com/marceloprates). All credit for the original idea, designs and implementation go to him.

The prettymapp rewrite focuses on speed and adapted configuration to interface with the webapp.

It drops more complex configuration options in favour of improved speed, reduced code complexity and 

simplified configuration interfaces. It is partially tested and adds a [streamlit](https://streamlit.io/) webapp component.

## Running the app locally

You can use the webapp directly under [prettymapp.streamlit.app](https://prettymapp.streamlit.app/) or run it locally via:

```bash

git clone https://github.com/chrieke/prettymapp.git

cd prettymapp

pip install -r streamlit-prettymapp/requirements.txt

streamlit run streamlit-prettymapp/app.py

```

## Python package

You can also use prettymapp without the webapp, directly in Python. This lets you customize the functionality or 

build your own application.

**Installation:**

```bash

pip install prettymapp

```

**Define the area, download and plot the OSM data:**

You can select from 4 [predefined styles](prettymapp/settings.py#L35): `Peach`, `Auburn`, `Citrus` and `Flannel`.

```python

from prettymapp.geo import get_aoi

from prettymapp.osm import get_osm_geometries

from prettymapp.plotting import Plot

from prettymapp.settings import STYLES

aoi = get_aoi(address="Praça Ferreira do Amaral, Macau", radius=1100, rectangular=False)

df = get_osm_geometries(aoi=aoi)

fig = Plot(

    df=df,

    aoi_bounds=aoi.bounds,

    draw_settings=STYLES["Peach"],

).plot_all()

fig.savefig("map.jpg")

```

You can also plot exported OSM XML files e.g. from openstreetmap.org:

```python

from prettymapp.osm import get_osm_geometries_from_xml

df = get_osm_geometries_from_xml(filepath="Berlin.osm")

aoi_bounds = df.total_bounds

...

```

**Customize styles & layers**

Edit the `draw_settings` input to create your own custom styles! The map layout can be further customized with the additional arguments of the [`Plot`](prettymapp/plotting.py#L24) class (e.g. `shape`, `contour_width` etc.). Check the webapp [examples](streamlit-prettymapp/examples.json) for inspiration.

```python

from prettymapp.settings import STYLES

custom_style = STYLES["Peach"].copy()

custom_style["urban"] = {

    "cmap": ["#3452eb"],

    "ec": "#E9724C",

    "lw": 0.2,

    "zorder": 4,

}

fig = Plot(

    df=df,

    aoi_bounds=aoi.bounds,

    draw_settings=custom_style,

    shape="circle",

    contour_width=0,

).plot_all()

```

You can also customize the selection of OSM landcover classes that should be displayed! Customize the 

default settings or create your own dictionary! See [settings.py](prettymapp/settings.py#L3) for the defaults.

```python

from prettymapp.settings import LANDCOVER_CLASSES

custom_lc_classes = LANDCOVER_CLASSES.copy()

custom_lc_classes["urban"]["building"] = False # drops all building subclasses

custom_lc_classes["grassland"]["leisure"] = True # Include all leisure subclasses

custom_lc_classes["grassland"]["natural"] = ["island"] # Selects only specific natural subclasses

df = get_osm_geometries(aoi=aoi, landcover_classes=custom_lc_classes)

```