# `tobler`: Areal Interpolation, Dasymetric Mapping & Change of Support
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[](https://doi.org/10.5281/zenodo.3386576)
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`tobler` is a python package for areal interpolation, dasymetric mapping, change of
support, and small area estimation. It provides a suite of tools with a simple interface
for transferring data from one polygonal representation to another. Common examples
include standardizing census data from different time periods to a single representation
(i.e. to overcome boundary changes in successive years), or the conversion of data
collected at different spatial scales into shared units of analysis (e.g. converting zip
code and neighborhood data into a regular grid). `tobler` is part of the
[PySAL](https://pysal.org) family of packages for spatial data science and provides
highly performant implementations of basic and advanced interpolation methods,
leveraging [`shapely`](https://shapely.readthedocs.io/en/latest/) to optimize for
multicore architecture. The package name is an homage to the legendary quantitative
geographer [Waldo Tobler](https://en.wikipedia.org/wiki/Waldo_R._Tobler), a pioneer in
geographic interpolation methods, spatial analysis, and computational social science.
## Interpolation Methods
`tobler` provides functionality for three families of spatial interpolation methods. The
utility of each technique depends on the context of the problem and varies according to
e.g. data availability, the properties of the interpolated variable, and the resolution
of source and target geometries. For a further explanation of different interpolation
techniques, please explore some of the field's [background
literature](https://pysal.org/tobler/references.html)
### Area Weighted
Areal interpolation uses the area of overlapping geometries to apportion variables. This
is the simplest method with no additional data requirements, aside from input and output
geometries, however this method is also most susceptible to the [modifiable areal unit
problem](https://en.wikipedia.org/wiki/Modifiable_areal_unit_problem).
### Dasymetric
Dasymetric interpolation uses auxiliary data to improve estimation, for example by
constraining the areal interpolation to areas that are known to be inhabited. Formally,
`tobler` adopts a binary dasymetric approach, using auxiliary data to define which land
is available or unavailable for interpolation. The package can incorporate additional
sources such as
* raster data such as satellite imagery that define land types
* vector features such as roads or water bodies that define habitable or inhabitable land
either (or both) of which may be used to help ensure that variables from the source
geometries are not allocated to inappropriate areas of the target geometries. Naturally,
dasymetric approaches are sensitive to the quality of ancillary data and underlying
assumptions used to guide the estimation.
### Model-based
Model-based interpolation uses [spatial] statistical models to estimate a relationship
between the target variable and a set of covariates such as physical features,
administrative designations, or demographic and architectural characteristics.
Model-based approaches offer the ability to incorporate the richest set of additional
data, but they can also difficult to wield in practice because the true relationship
between variables is never known. By definition, some formal assumptions of regression
models are violated because the target variable is always predicted using data from a
different spatial scale than it was estimated.
### Extensions
`tobler` is under active development and will continue to incorporate emerging
interpolation methods as they are introduced to the field. We welcome any and all
contributions and if you would like to propose an additional method for adoption please
raise an issue for discussion or open a new pull request!
## Installation
```bash
$ conda env create -f environment.yml
$ conda activate tobler
$ pip install -e . --no-deps
```
## Contribute
PySAL-tobler is under active development and contributors are welcome.
If you have any suggestion, feature request, or bug report, please open a new
[issue](https://github.com/pysal/tobler/issues) on GitHub. To submit patches, please
follow the PySAL development
[guidelines](http://pysal.readthedocs.io/en/latest/developers/index.html) and open a
[pull request](https://github.com/pysal/tobler). Once your changes get merged, you’ll
automatically be added to the [Contributors
List](https://github.com/pysal/tobler/graphs/contributors).
## License
The project is licensed under the [BSD license](https://github.com/pysal/tobler/blob/main/LICENSE.txt).
## Funding
Award #1733705 [Neighborhoods in Space-Time Contexts](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733705&HistoricalAwards=false)
Award #1831615 [Scalable Geospatial Analytics for Social Science Research](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831615)