dsGeospatialClient/README.Rmd at main · FederatedMethods/dsGeospatialClient · GitHub

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---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# dsGeospatial

**Privacy-preserving geospatial visualisation and analysis for health and environmental data in DataSHIELD**


The goal of dsGeospatial is to ...

## Installation

You can install the development version of dsGeospatial from [GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("FederatedMethods/dsGeospatial")
```

## Overview

**dsGeospatial** is a DataSHIELD package that provides federated methods for the
visualisation and spatial analysis of health and environmental data while preserving
the privacy of individual-level records.

The package has been developed as part of the **GROVE** project within the **DARE UK** programme,
with the aim of enabling secure, collaborative geospatial analyses across distributed
datasets without transferring sensitive household-level data.

Unlike conventional geospatial workflows, **dsGeospatial** performs all
computations within the secure DataSHIELD environment.
Only non-disclosive summary statistics and visualisations are returned to the
analyst, allowing organisations to collaboratively investigate spatial patterns
and environmental-health relationships while maintaining data governance and confidentiality.

## Motivation

Many important public health questions require linking health outcomes with environmental exposures.
The package can be used to investigate questions such as:

* How does asthma prevalence vary with proximity to greenspace?
* Which neighbourhoods have the highest average air pollution exposure?
* Are environmental exposures distributed equally across socio-economic groups?
* Can spatial trends be reproduced across multiple organisations without sharing household-level data?


The datasets for such research are often held by different organisations and cannot be combined
because of privacy, governance or legal constraints.

**dsGeospatial** provides a federated analytical framework that enables these
data to be explored safely without exposing household-level information.

## Features

Current and planned functionality includes:

### Geospatial visualisation

* Heatmaps of region-level summaries
* Choropleth maps - Geographic hotspot identification
* Spatial correlation analysis

### Federated analytics

* Distributed computation across multiple DataSHIELD servers
* Automatic aggregation of study-level results
* Privacy-preserving disclosure controls
* Compatible with Opal, Armadillo and DSLite environments

## Example

This is a basic example which shows you how to solve a common problem:

```{r example, eval = FALSE}
library(dsGeospatial)
## basic example code
```

## Privacy and disclosure protection

All analyses are performed within the DataSHIELD framework.

No individual-level health or environmental records leave the secure data repositories.
Results returned to the analyst consist only of disclosure-controlled aggregated outputs that comply with DataSHIELD disclosure rules.

## Development status

**dsGeospatial** is currently under active development.

Planned future capabilities include:
*
*
*

## Contributing

Contributions, feature requests and bug reports are welcome.

Please see .github/:

* `CONTRIBUTING.md`
* `ISSUE_template.md`
* `DISCLOSUREissue_template.md`

for information on contributing to the project.