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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`.

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