semvec/pypi-packages huggingface dataset:
geobootstrap 0.12
Python tools for geobootstrapping
License: MIT
Keywords: bootstrap,spatial

# Geobootstrap
`geobootstrap` is a novel computer simulation method that extends the classic bootstrap method (Efron 1979) in statistical analysis, to generate representative statistical measures e.g. standard deviations, confidence intervals and probability based values).
Instead of passing the default None weights to pandas.sample, which results in equal probability weighting, kernel-based weights are used.
The weights are determined by a distance decay function, that determines how quickly weights decrease as distances increase.
This means that key statistical measures are computed by pooling or borrowing strength from neighbouring units.
Thus, geobootstrap takes advantage of the spatial structure of GeoDataFrames.

Geobootstrap is based on `pandas.sample` and operates on `geopandas.GeoDataFrames`.
The resampling takes place for each spatial entity and the fraction of samples returned is based on the fraction of the original dataset.
So a GeoDataFrame with 10 spatial entities and a fraction of 1.0 (default) will return a list of 10 entries, each containing 10 different spatial entities.
If the fraction was set to 0.8, then a list of 10 entries, each containing 8 different spatial entities would be returned.

Further descriptions about the methodology will be later provided but the contents of the package should provide enough detail for now.
The potential use cases of this methodology include areal interpolation, which are illustrated in the [coss package](https://github.com/tastatham/coss).
coss 0.12.4
Python tools for spatial interpolation
License: MIT
Keywords: spatial,interpolation,support,areal

# Change of Spatial Support (CoSS)

Python tools for spatial interpolation

## Introduction

Change of Spatial Support (CoSS) is a package that adds support for `spatial interpolation`.
Spatial interpolation is often referred in the literature as the process of using points with known values to estimate values of the same variable at other points.
However, spatial vector data (points, lines and polygons) is not limited to just points and several methods exist for making interpolations between these different vector types.

Several packages exist that allow different areal interpolation methods to be applied but they are often written in multiple languages and are not standardised - making them inaccessible and comparisons between methods challenging.
This package aims to make "spatial interpolation" more accessible by using a standardised classes.

## Areal interpolation
The `areal_interpolation` class supports several areal interpolation, which includes;
1. Areal weighting
2. Dasymetric mapping
	- Binary
3. Pycnophylactic interpolation (from [tobler](https://github.com/pysal/tobler))
4. Model (regression)
	- General Linear Model
	- Ordinary Least Square
5. Areal geobootstrap - 

Areal [geobootstrap](https://github.com/tastatham/geobootstrap) is a novel simulation based method, which pools neighbouring observations based on a distance decay funcion.

Further spatial interpolation methods will be supported, including those between different vector types, with raster support.

This repository is an alpha release and **will** contain errors - if you spot anything, file an issue or submit a PR.