SIGPAC-Tools

SIGPAC-Tools is a Python library that provides the user a set of tools to work with the SIGPAC data. The library is designed to be easy to use and to provide the user the ability to work with the data in a simple way.

This library is designed to be used in conjunction with the SIGPAC data provided by the Spanish government. The data is available in the SIGPAC website.

Installation

To install the library, you can use the following command:

git clone https://github.com/KhaosResearch/sigpac-tools
python -m pip install ./sigpac-tools

or you can install it directly from the repository:

python -m pip install git+https://github.com/KhaosResearch/sigpac-tools

Usage

Once the library is installed, you can use it in your Python code. The library provides a set of tools to work with the SIGPAC data, these are the main features:

Search for an specific plot

You can search for an specific plot using the search function in the search module. This function will return a list of plots that match the search criteria.

from sigpac_tools.search import search

data = {
    "community": 1,
    "province": 1,
    "municipality": 8,
    "polygon": 8,
    "parcel": 572
}

geojson = search(data)

It is important to note that the search function will return a GeoJSON object with the information of the plots that match the search criteria. The data dictionary must contain at least the 'province' or the 'community' key to return a valid result. Note that all the keys must be in lower case.

Get the information of a plot

You can get the information of a plot using the get_metadata function in the anotate module. This function will return a dictionary with the information of the plot, given the same data as the search function.

from sigpac_tools.anotate import get_metadata

layer = "parcela"
data = {
    "community": 1,
    "province": 1,
    "municipality": 8,
    "polygon": 8,
    "parcel": 572,
    # "enclosure": 1,
    # "aggregate": 0,
    # "zone": 0
}

metadata = get_metadata(
    layer,
    data
)

enclosure is only necesary if the layer is "recinto". aggregate and zone are optional and depend on the location of the parcel or enclosure searched for.

Get the geometry of a plot

Given the coordinates of the plot, that may be gathered from the function search, you can get the geometry of the plot using the geometry_from_coords function in the locate module. This function will return a GeoJSON object with the geometry of the plot.

The user may specify a reference parcel or enclosure to get the geometry of and so on the function will return the geometry of that specific parcel or enclosure if it exists. If the reference parcel or enclosure does not exist, the function will return None.

If the reference parcel or enclosure is not provided, the function will return a Feature Collection with all the parcels that match the search criteria.

from sigpac_tools.locate import geometry_from_coords

layer = "parcela"
lat = 37.384 
lng = -4.98
reference = None

geometry = geometry_from_coords(
    layer,
    lat,
    lng,
    reference
)

Get information from a specific cadastral registry

Known a cadastral registry, you can get the polygon and metadata from it using the find_from_cadastral_registry from the module find. This function will return a tuple with a GeoJSON object of the polygon and a dictionary with the metadata of the plot. It will only return the metadata of the "parcela" layer.

Note that urban cadastral registries are not supported yet, due to the lack of information about them in the SIGPAC database.

If an invalid cadastral registry is provided, the function will raise a ValueError. If it detects that the cadastral registry is urban, it will raise a NotImplementedError.

An example of how to use this function is shown below:

from sigpac_tools.find import find_from_cadastral_registry

cadastral_registry = "06001A028000380000LH"
geom, metadata = find_from_cadastral_registry(cadastral_registry)

Acknowledgements

This project was inspired by the JavaScript SIGPAC client made by Daniel Cebrián.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright 2024 Khaos Research, all rights reserved.