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https://github.com/guidorice/mogeo

Mojo geographic and geometric vector features
https://github.com/guidorice/mogeo

geoarrow geojson geometry-processing geoparquet geospatial gis mojo python simple-features wkt

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Mojo geographic and geometric vector features

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# MoGeo: Mojo geographic and geometric vector features



[![Run Tests](https://github.com/guidorice/mogeo/actions/workflows/tests.yaml/badge.svg)](https://github.com/guidorice/mogeo/actions/workflows/tests.yaml)



[Mojo🔥](https://github.com/modularml/mojo) package for geographic and geometric

vector features and analytics, such as location data or earth observation data.



## status 2024-01-05



| :construction: pre-alpha, not yet usable! |

|-------------------------------------------|



In 2023, this package (formerly `geo-features`) served for learning Mojo 0.x

and to experiment with the memory layout advocated by

[GeoArrow](https://geoarrow.org). GeoArrow seems quite promising, but is

already well served by it's C, Rust and Python implementations. In Mojo, there

is not yet support for zero-copy shared memory buffers, so I created a [feature

request for the python buffer

protocol](https://github.com/modularml/mojo/issues/1515). Until that is

supported, I not see any practical use for a GeoArrow implementation in pure

Mojo.



In 2024 and beyond I'm exploring an alternative backend and memory layout using

dual quaternions. Dual quaternions can represent rotations and translations,

and should be useful in solving the [antimeridian crossing

problem](https://macwright.com/2016/09/26/the-180th-meridian.html). Dual

quaternions have been successfully used in robotics, physics simulations, game

dev, and graphics. To this end I added "Be useful for many application

domains..." to the [project goals](#project-goals).



## project goals



- Apply Mojo's systems programming features to create a native geo package with strong

type safety and high performance.

- Be useful for many application domains, not only Geographic Information

Systems (GIS). Additionally: planetary information systems, oceanography,

robotics, gamedev, graphics, embedded systems.

- Promote [cloud native geospatial](https://cloudnativegeo.org/) computing and

open geospatial standards.

- Leverage the vast Python ecosystem, wherever possible, to enable rapid

development and development and interoperability.



## requirements



- [Mojo](https://github.com/modularml/mojo) >= 0.6

- [Python](https://www.python.org/) >= 3.9

- [Conda](https://docs.conda.io/en/latest/)



## roadmap



### core structs



- [ ] Envelope

- [ ] Feature

- [ ] FeatureCollection

- [ ] GeometryCollection

- [ ] LinearRing

- [ ] LineString

- [ ] Memory Layout

- [ ] MultiLineString

- [ ] MultiPoint

- [ ] MultiPolygon

- [ ] Point

- [ ] Polygon



### serialization and interchange formats



- [ ] GeoArrow

- [ ] GeoJSON

- [ ] GeoParquet

- [ ] TopoJSON

- [ ] WKT



### methods and algorithms



- [ ] area

- [ ] perimeter

- [ ] centroid

- [ ] intersection

- [ ] union

- [ ] difference

- [ ] parallelized+vectorized spatial join

- [ ] rasterize from vector

- [ ] vectorize from raster

- [ ] re-projection and CRS support

- [ ] simplify or decimate

- [ ] stratified sampling?

- [ ] zonal stats?

- [ ] smart antimeridian crossing mode (quaternions?)



## architectural decisions



- ~~Implement a columnar memory layout similar to [GeoArrow](https://geoarrow.org/), for

efficient representation of coordinates, features and attributes.~~ See [status 2024-01-05](#status-2024-01-05).



## related software



- [GEOS](https://libgeos.org/) - Geometry Engine, Open Source.

- [GDAL/OGR](https://gdal.org) - Geospatial Data Abstraction Library.

- [Shapely](https://shapely.readthedocs.io) - Python package for computational geometry.

- [JTS Topology Suite](https://github.com/locationtech/jts) - Java library for creating and manipulating vector geometry.

- [TG](https://github.com/tidwall/tg) - Geometry library for C that is small, fast, and easy to use.

- [Turf.js](https://turfjs.org) - Advanced geospatial analysis for browsers and Node.js.

- [TurfPy](https://turfpy.readthedocs.io/en/latest/) - Python library for performing geospatial data analysis which reimplements turf.js.



## specs



- [ISO/OGC Simple Features](https://en.wikipedia.org/wiki/Simple_Features) - Set of standards that specify a common

  storage and access model of geographic features.

- [GeoJSON](https://geojson.org) - Geospatial data interchange format based on JavaScript Object Notation (JSON).

- [GeoArrow](https://geoarrow.org) - Specification for storing geospatial data in Apache Arrow and Arrow-compatible data

  structures and formats.

- [GeoParquet](https://geoparquet.org) - Specification for storing geospatial vector data (point, line, polygon) in

  Parquet.



## setup dev environment



1. Clone this repo, including submodules:



    ```shell

    git clone --recurse-submodules https://github.com/guidorice/mogeo

    ```



2. Create a Python environment using [environment.yml](./environment.yml). This is required for supporting packages used

by `mogeo`, for example for interchange, serialization, and unit testing.

[Conda](https://docs.conda.io/projects/miniconda/en/latest/) is recommended because it puts a copy of libpython into

each conda env.



    ```text

    conda env create -n mogeo --file environment.yml

    ```



3. Set `MOJO_PYTHON_LIBRARY` environment variable to your libpython. An example of doing

this on MacOS is the [scripts](./scripts/setup-mojo-conda-env-macos.sh)

directory. Help: [Using Mojo with Python](https://www.modular.com/blog/using-mojo-with-python) .



4. Run targets in [Makefile](./Makefile), ex: `make test`, `make package`, `make format`.