territoria

https://github.com/inbo/territoria

Keywords

Repository

README.Rmd

---
output: github_document
---



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

# territoria


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The goal of `territoria` is to cluster observations from different breeding bird surveys into territoria.

## Installation

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

``` r
# install.packages("remotes")
remotes::install_github("inbo/territoria")
```

## Example

We start by simulating some observations.
We need for  every observation their `x` and `y` coordinates in a projected coordinate system.
`survey` is an integer id for every survey.
A survey is a unique combination of an area and date.
`status` is an integer indication the breeding status.
A higher value assume more certainty about breeding.
Set this to a constant value if you don't distinct between different certainties.
In this example we use three classes: `1`, `2` and `3`.

```{r sim-data}
library(territoria)
set.seed(20210806)
obs <- simulate_observations()
names(obs)
summary(obs$centroids)
summary(obs$observations)
obs <- obs$observations[obs$observations$observed, ]
```

Once we have a data.frame with the observations, we connect to a SQLite database and import the observations.
This assigns every observation to its own cluster.

```{r import-data}
conn <- connect_db()
import_observations(observations = obs, conn = conn, max_dist = 336)
result <- get_cluster(conn = conn)
nrow(result$observations) == nrow(result$cluster)
```

Next, we need to calculate the distance matrix.
This is not the full distance matrix.
We omit all irrelevant distances, e.g. between observations from the same survey or with a distance larger than twice the maximum cluster distance.

```{r distance}
distance_matrix(conn = conn, max_dist = 366)
```

Now we can start the clustering.
The clustering takes into account all observations with a `status` greater than or equal to the set status.

```{r cluster-3}
cluster_observation(conn = conn, status = 3, max_dist = 336)
result3 <- get_cluster(conn = conn)
nrow(result3$observations) > nrow(result3$cluster)
```

Repeat the clustering for every status level.
Note that skipping levels implies that we combine them with the lower level.

```{r cluster-1}
cluster_observation(conn = conn, status = 1, max_dist = 336)
result1 <- get_cluster(conn = conn)
nrow(result1$observations) > nrow(result1$cluster)
nrow(result3$cluster) > nrow(result1$cluster)
summary(result1$observations)
summary(result1$cluster)
```

Owner

Citation (CITATION.cff)

cff-version: 1.2.0
message: If you use this software, please cite it using these metadata.
title: "territoria: Clustering Observations from Breeding Birds into Territoria"
authors:
- given-names: Thierry
  family-names: Onkelinx
  affiliation: Research Institute for Nature and Forest (INBO)
  orcid: 0000-0001-8804-4216
keywords:
- breeding bird
- cluster
contact:
- given-names: Thierry
  family-names: Onkelinx
  affiliation: Research Institute for Nature and Forest (INBO)
  orcid: 0000-0001-8804-4216
doi: ~
license: GPL-3.0
repository-code: https://github.com/inbo/territoria/
type: software
abstract: "Clusters individual observations based on breeding indication and distance
  between observations."
version: 0.0.3

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