# Accessing Spatial and Population Data from Statistics Finland OGC api

#### Markus Kainu

#### 2025-04-28

## Introduction

The `geofi` package provides tools to access spatial data from Statistics Finland’s OGC API, including administrative boundaries, population data by administrative units, and population data by statistical grid cells. This vignette demonstrates how to use the package’s core functions to:

- Retrieve Finnish administrative area polygons (e.g., municipalities, regions).
- Fetch population data linked to administrative units.
- Access population data for statistical grid cells.

Unlike some other spatial data APIs, no API key is required to access Statistics Finland’s OGC API, making it straightforward to get started. The package handles pagination, spatial filtering, and coordinate reference system (CRS) transformations, delivering data as `sf` objects compatible with the `sf` package for spatial analysis and visualization.

## Package Overview

The `geofi` package includes the following key functions for accessing Statistics Finland data:

- `ogc_get_statfi_area()`: Retrieves administrative area polygons (e.g., municipalities, wellbeing areas) for specified years, scales, and tessellation types.
- `ogc_get_statfi_area_pop()`: Fetches administrative area polygons with associated population data, pivoted into a wide format.
- `ogc_get_statfi_statistical_grid()`: Retrieves population data for statistical grid cells at different resolutions (1km or 5km).
- `fetch_ogc_api_statfi()`: An internal function that handles low-level API requests and pagination (not typically called directly by users).

All functions return spatial data as `sf` objects, making it easy to integrate with spatial analysis workflows in R.

## Step 1: Retrieving Administrative Area Polygons

The `ogc_get_statfi_area()` function retrieves polygons for Finnish administrative units, such as municipalities (`kunta`), wellbeing areas (`hyvinvointialue`), or regions (`maakunta`). You can customize the output with parameters like:

- `year`: The year of the boundaries (2020–2022).
- `scale`: Map resolution (1:1,000,000 or 1:4,500,000).
- `tessellation`: Type of administrative unit (e.g., kunta, hyvinvointialue).
- `crs`: Coordinate reference system (EPSG:3067 or EPSG:4326).
- `limit`: Maximum number of features (or NULL for all).
- `bbox`: Bounding box for spatial filtering.

### Example: Downloading Municipalities

Fetch all municipalities for 2022 at the 1:4,500,000 scale:

    munis <- ogc_get_statfi_area(year = 2022, scale = 4500, tessellation = "kunta")
    print(munis)

Visualize the municipalities using `ggplot2`:

    ggplot(munis) +
      geom_sf() +
      theme_minimal() +
      labs(title = "Finnish Municipalities (2022)")

### Example: Spatial Filtering with a Bounding Box

To retrieve municipalities within a specific area (e.g., southern Finland), use the bbox parameter. Coordinates should match the specified crs.

    bbox <- "200000,6600000,500000,6900000"  # In EPSG:3067
    munis_south <- ogc_get_statfi_area(
      year = 2022,
      scale = 4500,
      tessellation = "kunta",
      bbox = bbox,
      crs = 3067
    )

Visualize the filtered results:

    ggplot(munis_south) +
      geom_sf() +
      theme_minimal() +
      labs(title = "Municipalities in Southern Finland (2022)")

### Example: Fetching Wellbeing Areas

Retrieve wellbeing areas (hyvinvointialue) for 2022:

    wellbeing <- ogc_get_statfi_area(
      year = 2022,
      tessellation = "hyvinvointialue",
      scale = 4500
    )

## Step 2: Retrieving Population Data by Administrative Area

The `ogc_get_statfi_area_pop()` function fetches administrative area polygons with associated population data, pivoted into a wide format where each population variable is a column. Parameters include:

- `year`: The year of the data (2019–2021).
- `crs`: Coordinate reference system (EPSG:3067 or EPSG:4326).
- `limit`: Maximum number of features (or `NULL` for all).
- `bbox`: Bounding box for spatial filtering.

### Example: Fetching Population Data

Retrieve population data for 2021:

    pop_data <- ogc_get_statfi_area_pop(year = 2021, crs = 3067)
    print(pop_data)

Visualize population density (assuming a variable like `population_total` exists):

    ggplot(pop_data) +
      geom_sf(aes(fill = population_total)) +
      scale_fill_viridis_c(option = "plasma") +
      theme_minimal() +
      labs(title = "Population by Administrative Area (2021)", fill = "Population")

## Example: Population Data with Bounding Box

Fetch population data within a bounding box:

    bbox <- "200000,6600000,500000,6900000"
    pop_south <- ogc_get_statfi_area_pop(year = 2021, bbox = bbox, crs = 3067)

## Step 3: Retrieving Population Data by Statistical Grid

The `ogc_get_statfi_statistical_grid()` function retrieves population data for statistical grid cells at 1km or 5km resolution. Data is returned in EPSG:3067 (ETRS89 / TM35FIN). Parameters include:

- `year`: The year of the data (2019–2021).
- `resolution`: Grid cell size (1000m or 5000m).
- `limit`: Maximum number of features (or `NULL` for all).
- `bbox`: Bounding box for spatial filtering.

### Example: Fetching 5km Grid Data

Retrieve population data for a 5km grid in 2021:

    grid_data <- ogc_get_statfi_statistical_grid(year = 2021, resolution = 5000)
    print(grid_data)

Visualize the grid data:

    ggplot(grid_data) +
      geom_sf(aes(fill = population_total), color = NA) +
      scale_fill_viridis_c(option = "magma") +
      theme_minimal() +
      labs(title = "Population by 5km Grid Cells (2021)", fill = "Population")

### Example: 1km Grid with Bounding Box

Fetch 1km grid data within a bounding box:

    bbox <- "200000,6600000,500000,6900000"
    grid_south <- ogc_get_statfi_statistical_grid(
      year = 2021,
      resolution = 1000,
      bbox = bbox
    )

## Advanced Features

### Pagination

When `limit = NULL`, the `fetch_ogc_api_statfi()` function automatically paginates through large datasets, fetching up to 10,000 features per request. This ensures all available data is retrieved, even for large administrative or grid datasets.

### Error Handling

The package includes robust error handling:

- Validates inputs (e.g., year, scale, tessellation, CRS, bounding box format).
- Provides informative error messages for API failures or invalid responses.
- Returns `NULL` with a warning if no data is retrieved, helping users diagnose issues.

### Coordinate Reference Systems

The functions support two CRS options:

- EPSG:3067 (ETRS89 / TM35FIN): The default for Finnish spatial data, suitable for local analyses.
- EPSG:4326 (WGS84): Useful for global compatibility or web mapping.

Note that `ogc_get_statfi_statistical_grid()` is fixed to EPSG:3067, as per the API’s design.

### Bounding Box Filtering

The `bbox` parameter allows spatial filtering to focus on specific regions. Ensure coordinates match the specified `crs` (e.g., EPSG:3067 for grid data). Example format: “`200000`,`6600000`,`500000`,`6900000`”.

## Best Practices

- Test with Limits: For large datasets (e.g., 1km grids), start with a small `limit` or `bbox` to estimate runtime before fetching all features.
- CRS Selection: Use `EPSG:3067` for Finnish data unless you need `EPSG:4326` for compatibility with other systems.
- Check Tessellation Types: Verify valid `tessellation` options (`kunta`, `hyvinvointialue`, etc.) when using `ogc_get_statfi_area()`.
- Inspect Output: Population data from `ogc_get_statfi_area_pop()` and `ogc_get_statfi_statistical_grid()` is pivoted into wide format. Check column names to identify available variables.

## Additional Resources

- Statistics Finland Geoserver: Documentation for the OGC API.
- geofi GitHub Repository: Source code and issue tracker.
- sf Package Documentation: For working with sf objects.
- ggplot2 Documentation: For visualizing spatial data.

## Conclusion

The `geofi` package simplifies access to Statistics Finland’s spatial and population data, enabling analyses of administrative boundaries, population distributions, and grid-based statistics. With no API key required, users can quickly retrieve and visualize data using `sf` and `ggplot2`. Try the examples above to explore Finland’s spatial and demographic datasets!