Using the Data Source Manager with WFS and WMS

Duration: ~20 minutes

Learning Goals

By the end of this exercise, you will be able to:

Navigate and use the QGIS Data Source Manager
Connect to and load WFS (Web Feature Service) layers from Dutch data portals
Connect to and load WMS (Web Map Service) layers from Dutch data portals
Understand the difference between WFS and WMS services
Save connections for future use
Perform basic geoprocessing operations (selection, clipping, dissolve, buffer)

Software and Plugins Required

QGIS (version 3.x or higher)
PDOK Services Plugin for QGIS
Active internet connection

Introduction

In this exercise, you will learn how to connect to external geospatial data sources using QGIS’s Data Source Manager. You’ll work with real Dutch open data, combining different data layers to create a focused analysis area around Rotterdam and Schiedam neighbourhoods.

You will retrieve neighbourhood boundaries via WFS, perform spatial operations to create a study area, and then use this area to clip climate adaptation data. Finally, you’ll add aerial imagery via WMS to visualize your results.

This workflow demonstrates a common GIS task: defining a study area, retrieving relevant data, and preparing it for analysis.

What is WFS?

WFS (Web Feature Service) provides access to vector data (points, lines, polygons) that you can query, style, analyze, and edit in QGIS. Think of it as downloading the actual geographic features with all their attributes. WFS is ideal when you need to perform spatial analysis or select specific features.

What is WMS?

WMS (Web Map Service) provides access to pre-rendered map images - essentially picture tiles of maps. You cannot query individual features or access attributes, but WMS layers load quickly and are perfect for background/reference maps. Think of it as viewing a map image rather than working with the data itself.

Key Differences Between WFS and WMS

Feature	WFS	WMS
Data type	Vector (features)	Raster (images)
File size	Can be large	Smaller
Queryable	Yes	No
Stylable in QGIS	Yes	Limited
Editable	Yes	No
Best for	Analysis, editing, selection	Background maps, visualization

Steps

Part 1: Setting Up Your Project and Retrieving Neighbourhood Data

Open QGIS and create a new project
Set the project CRS (Coordinate Reference System) to EPSG:28992 (Amersfoort / RD New - the Dutch standard projection)
- Go to Project > Properties > CRS
- Search for “28992” and select “Amersfoort / RD New”
- Click OK
Using the PDOK Services plugin, retrieve data via WFS of this dataset:
- Open the PDOK Services Plugin (look for the PDOK icon in the toolbar or go to Web > PDOK Services Plugin)
- In the plugin window, search for “CBS Wijken en Buurten 2024”
- Select the WFS option (not WMS!)
- Choose the layer: cbs_buurten_2024 (neighbourhoods)
- Click to add it to your map
Important

Please pay attention to retrieve the correct data, as there are many buurten layers, both for WFS and WMS! What you need is a vector layer containing all 14,668 buurten (neighbourhoods) in the Netherlands. After loading, check the feature count in the layer properties to confirm you have the complete dataset.

Figure 1 - PDOK Services Plugin showing CBS Buurten 2024 WFS layer ready to add
You must now select only the 20 buurten (neighbourhoods) that meet these criteria:
- They belong to the municipalities of Rotterdam OR Schiedam (gemeentenaam = “Rotterdam” OR gemeentenaam = “Schiedam”) AND
- Their name (buurtnaam) is one of the following: “Stationsbuurt”, “Singelkwartier”, “Natuurkundigenbuurt”, “Marconibuurt”, “Glasbuurt”, “Nieuw-Mathenesse”, “Wetenschappersbuurt”, “Newtonbuurt”, “Boylebuurt”, “Oud Mathenesse”, “Nieuw Mathenesse”, “Witte Dorp”, “Spangen”, “Tussendijken”, “Bospolder”, “Schiemond”, “Nieuwe Westen”, “Delfshaven”, “Middelland”, “Dijkzigt”
To select these neighbourhoods:
- Open the attribute table (right-click on layer > Open Attribute Table)
- Click on Select features using an expression (the yellow icon with ε)
- Enter this expression:
```
"gemeentenaam" IN ('Schiedam', 'Rotterdam') AND 
"buurtnaam" IN ('Stationsbuurt', 'Singelkwartier', 'Natuurkundigenbuurt', 
'Marconibuurt', 'Glasbuurt', 'Nieuw-Mathenesse', 'Wetenschappersbuurt', 
'Newtonbuurt', 'Boylebuurt', 'Oud Mathenesse', 'Nieuw Mathenesse', 
'Witte Dorp', 'Spangen', 'Tussendijken', 'Bospolder', 'Schiemond', 
'Nieuwe Westen', 'Delfshaven', 'Middelland', 'Dijkzigt')
```
- Click Select Features
- Verify that 20 features are selected (shown at the top of the attribute table)
Now save the selected features:
- Right-click on the layer > Export > Save Selected Features As...
- Format: GeoPackage
- File name: Navigate to folder part1_data and name it buurten.gpkg
- Layer name: buurten
- CRS: EPSG:28992
- Select fields to keep:
  - Click on the fields list and uncheck all columns except:
    - buurtnaam (name of the neighbourhood)
    - buurtcode (code of the neighbourhood)
    - gemeentenaam (name of the municipality)
  Tip
  
  You can use the Deselect All button and then manually check only the 3 fields you want to keep.
- Click OK to save
Add the area column:
- Open the attribute table of the newly saved buurten layer
- Enable editing mode (click the pencil icon)
- Open the Field Calculator (Ctrl+I or click the abacus icon)
- Check Create a new field
- Output field name: buurt_area
- Output field type: Decimal number (real)
- Precision: 2
- In the expression box, enter: round($area, 2)
- Click OK
- Save edits (click the floppy disk icon) and disable editing mode (click the pencil icon again)
Figure 2 - Field Calculator showing the area calculation expression that rounds the area to 2 decimal places
Dissolve the geometries of the 20 buurten into a single polygon:
- Go to Vector > Geoprocessing Tools > Dissolve
- Input layer: buurten
- Leave “Dissolve field(s)” empty (this will dissolve all features into one polygon representing the entire study area)
- Click Run
- After the temporary layer is created, right-click on it > Export > Save Features As...
- Format: GeoPackage
- File name: Select the existing buurten.gpkg file
- Layer name: buurten_dissolved
- In the fields section, uncheck all attributes (we only need the geometry)
- Click OK
Note

When unchecking all attributes, the resulting layer will have an empty attribute table containing only the feature ID (fid). This is expected - we only need the dissolved boundary geometry for further processing.

Figure 3 - Dissolved neighbourhood boundary showing all 20 neighbourhoods merged into a single polygon
Compute the bounding box (rectangular extent) of buurten_dissolved:
- Go to Vector > Research Tools > Extract layer extent
- Input layer: buurten_dissolved
- Click Run
- A temporary layer will be created showing a rectangle that encompasses your dissolved neighbourhoods
- Export this layer to the GeoPackage:
  - Right-click on the extent layer > Export > Save Features As...
  - File name: Select the existing buurten.gpkg
  - Layer name: bbox
  - Keep only columns: minx, miny, maxx, maxy (these represent the bounding box coordinates)
  - Click OK
Figure 4 - Bounding box (rectangular extent) of the dissolved study area shown as a red rectangle
Finally, compute a 200 meter buffer around the bbox layer:
- Go to Vector > Geoprocessing Tools > Buffer
- Input layer: bbox
- Distance: 200 (meters)
- Segments: 5 (default is fine)
- End cap style: Flat (the end cap style parameter controls how line endings are handled in the buffer)
- Join style: Miter (creates sharp corners at angles)
- Miter limit: 2.0 (default)
- Click Run
- Export the result to the GeoPackage:
  - Right-click on the buffered layer > Export > Save Features As...
  - File name: Select the existing buurten.gpkg
  - Layer name: bbox_buffered
  - Uncheck all attributes in the fields section (we only need the geometry)
  - Click OK
Why create a buffer?

The 200m buffer extends our study area slightly beyond the neighbourhood boundaries. This ensures we capture climate data for buildings that are close to but just outside the neighbourhoods, providing better context for our analysis.

Figure 5 - 200m buffer around the bounding box, creating an expanded study area shown in green

Part 2: Connecting to WFS for Climate Adaptation Data

Open the Data Source Manager
- Click the “Open Data Source Manager” button in the toolbar, OR
- Go to Layer > Data Source Manager, OR
- Press Ctrl+L
Select the WFS / OGC API - Features tab on the left side of the dialog
Create a new WFS connection to Zuid-Holland climate data:
- Click New to create a new connection
- Name: Zuid-Holland Climate Adaptation
- URL: https://geodata.zuid-holland.nl/geoserver/klimaat/wfs?service=WFS&version=2.0.0&request=GetCapabilities
- Leave other settings at default
- Click OK
Load a WFS layer:
- Select the “Zuid-Holland Climate Adaptation” connection from the dropdown and click Connect
- Wait for the available layers to load (this may take a few seconds)
- Find and select klimaat:PANDEN_WATEROVERLAST_HITTE_GROEN (this layer contains climate adaptation data for buildings: water overlay risk, heat stress, and biodiversity indicators from 2022)
- Click Add at the bottom
- Click Close to close the Data Source Manager
Layer name explanation

PANDEN_WATEROVERLAST_HITTE_GROEN translates to “Buildings - Water Overlay, Heat, Green” and contains climate vulnerability scores for individual buildings.

Figure 6 - Data Source Manager showing the Zuid-Holland WFS connection with available climate layers
Clip the layer to your study area:
- Go to Vector > Geoprocessing Tools > Clip
- Input layer: klimaat:PANDEN_WATEROVERLAST_HITTE_GROEN (the climate buildings layer)
- Overlay layer: bbox_buffered
- Click Run
- This will create a new temporary layer containing only the climate data within your buffered study area
- Save the clipped layer:
  - Right-click on the clipped temporary layer > Make Permanent...
  - Format: GeoPackage
  - File name: Select or create climate_data.gpkg in your part1_data folder
  - Layer name: climate_buildings_clipped
  - Click OK
- You can now remove the original (unclipped) WFS layer to improve performance
Why clip the data?

The WFS layer contains data for all of Zuid-Holland province (thousands of buildings). Clipping reduces the dataset to only the buildings within our study area, making the data easier to work with and improving QGIS performance.
Explore the layer:
- Zoom to your study area: right-click on bbox_buffered layer > Zoom to Layer
- Notice that buildings are vector features (polygons) you can select and query
- Open the attribute table (right-click layer > Open Attribute Table)
- Explore the climate adaptation attributes, such as:
  - wateroverlast_score - Water overlay/flood risk score
  - hittestress_score - Heat stress vulnerability score
  - groen_score - Green/biodiversity score
- Each building has multiple climate vulnerability indicators
Figure 7 - Attribute table of clipped climate buildings layer showing various climate risk scores for individual buildings

Part 3: Connecting to WMS for Background Imagery

What is WMS? WMS provides access to pre-rendered map images - like a background map. You cannot query individual features, but it loads faster for large areas.

Important: Optimize WMS Loading

Before retrieving the WMS layer, you should first zoom to the study area by right-clicking the bbox_buffered layer and selecting Zoom to Layer(s). This ensures QGIS only requests tiles for your area of interest, reducing download time and file size.

Open the Data Source Manager again (Ctrl+L)
Select the WMS / WMTS tab on the left
Create a new WMS connection:
- Click New to create a new connection
- Name: PDOK Aerial Photos
- URL: https://service.pdok.nl/hwh/luchtfotorgb/wmts/v1_0?request=GetCapabilities&service=wmts
- Leave other settings at default
- Click OK
Load a WMTS layer:
- Select the “PDOK Aerial Photos” connection and click Connect
- Wait for available layers to load
- Find and select Luchtfoto 2025 Ortho 25cm RGB (current high-resolution 25cm aerial photos)
- Important: In the “Coordinate Reference System” section, select EPSG:28992 from the dropdown
- Click Add
- Click Close
WMTS vs WMS

WMTS (Web Map Tile Service) is a variant of WMS that serves pre-generated map tiles, making it faster for viewing at standard zoom levels.
Save a local copy of the orthophoto:

This step saves the aerial imagery as a permanent file that you can use offline and in future projects.
- Select the orthophoto layer in the Layers panel
- Right-click on it and choose Export > Save As...
In the resulting dialog, set the following parameters:
- Output mode: Rendered image (this saves what you see on screen)
- Format: GeoTIFF
- Important: Uncheck “Create VRT” (we want a standard GeoTIFF file, not a virtual raster)
- File name: Click ... and navigate to the \part1_data\raster folder (create it if it doesn’t exist)
  - Name the file: ortho_25cm.tif
- CRS: EPSG:28992 (should already be set)
- Extent: Click the dropdown and select Calculate from Layer > bbox_buffered
  - This ensures you only save imagery for your study area
- Resolution:
  - Horizontal: 0.25 (meters per pixel)
  - Vertical: 0.25 (meters per pixel)
- Click OK to start the export
- Wait for the export to complete (this may take 1-2 minutes depending on the area size)
- The saved GeoTIFF will be automatically added to your map
Understanding Resolution

The 0.25m resolution means each pixel in the saved image represents 25cm × 25cm on the ground. This matches the native resolution of the PDOK aerial photos, ensuring no quality loss.

Figure 8 - Export Raster dialog showing proper settings for saving the aerial photo with correct extent and resolution
Arrange your layers for optimal visualization:
- In the Layers panel, drag layers to arrange them in this order from top to bottom:
  1. buurten (neighbourhood boundaries - should be visible as outlines)
  2. climate_buildings_clipped (climate data polygons)
  3. bbox_buffered (buffer outline - optional, can be hidden)
  4. ortho_25cm.tif or the WMTS aerial photo layer (at the bottom as background)
Style adjustments:
- Neighbourhood boundaries (buurten):
  - Right-click > Properties > Symbology
  - Set fill to “Simple fill”
  - Fill style: No Brush (transparent)
  - Stroke color: Red or dark blue
  - Stroke width: 0.5 - 1.0 mm
- Climate buildings:
  - Adjust transparency: Right-click > Properties > Symbology > Layer Rendering tab
  - Set opacity to 70-80% so you can see the aerial photo underneath
  - Optionally, style by climate scores using categorized or graduated symbology
Figure 9 - Final map view showing neighbourhood boundaries, climate data, and aerial imagery properly layered with appropriate transparency

Recap

What you’ve learned:

The Data Source Manager is your central hub for connecting to external data sources in QGIS
WFS provides vector data that you can style, query, and analyze - great for operational data and spatial analysis
WMS/WMTS provides raster map images - great for background maps and visualization
How to use the PDOK Services Plugin to easily access Dutch open geospatial data
Basic geoprocessing workflows: selection, dissolve, buffer, clip, and extract extent operations
How to organize and save multiple layers in a GeoPackage for efficient data management
How to export and save WMS/WMTS imagery as permanent GeoTIFF files
Dutch PDOK (Publieke Dienstverlening Op de Kaart) offers free, reliable geospatial web services
Provincial and municipal data portals (like Zuid-Holland) provide specialized datasets
Saved connections can be reused in future projects

Key differences between WFS and WMS:

Feature	WFS	WMS/WMTS
Data type	Vector (features)	Raster (images)
File size	Can be large	Smaller
Queryable	Yes	No
Stylable in QGIS	Yes	Limited
Best for	Analysis, editing	Background maps

Useful Dutch Data Portals:

PDOK: https://www.pdok.nl/ (national geospatial infrastructure)
Data.overheid.nl: Open data from Dutch government
Nationaal Georegister: https://www.nationaalgeoregister.nl/ (search for geospatial services)
Most Dutch provinces and municipalities also offer their own WFS/WMS services

Extension Activities (Optional)

Explore other PDOK services at https://www.pdok.nl/datasets
Try filtering WFS layers using the Data Source Manager’s “SQL” or “Filter” options before loading
Analyze the climate adaptation scores in your selected Rotterdam and Schiedam neighbourhoods
Compare different neighbourhoods’ vulnerability to heat, water, or biodiversity loss using statistics
Add the RIVM Energy Labels WMS (https://data.rivm.nl/geo/nl/wms?request=GetCapabilities, layer rvo_energielabels) to see building energy performance ratings
Create a styled thematic map showing the relationship between neighbourhood characteristics and climate risks
Calculate summary statistics (mean, max, min) of climate scores by neighbourhood

--- title: "Using the Data Source Manager with WFS and WMS" format: html --- **Duration:** \~20 minutes ------------------------------------------------------------------------ ::: callout-note ## Learning Goals By the end of this exercise, you will be able to: - Navigate and use the QGIS Data Source Manager - Connect to and load WFS (Web Feature Service) layers from Dutch data portals - Connect to and load WMS (Web Map Service) layers from Dutch data portals - Understand the difference between WFS and WMS services - Save connections for future use - Perform basic geoprocessing operations (selection, clipping, dissolve, buffer) ::: ::: callout-note ## Software and Plugins Required - **QGIS** (version 3.x or higher) - **PDOK Services Plugin** for QGIS - Active internet connection ::: ## Introduction In this exercise, you will learn how to connect to external geospatial data sources using QGIS's Data Source Manager. You'll work with real Dutch open data, combining different data layers to create a focused analysis area around Rotterdam and Schiedam neighbourhoods. You will retrieve neighbourhood boundaries via WFS, perform spatial operations to create a study area, and then use this area to clip climate adaptation data. Finally, you'll add aerial imagery via WMS to visualize your results. This workflow demonstrates a common GIS task: defining a study area, retrieving relevant data, and preparing it for analysis. ::: callout-tip ## What is WFS? **WFS (Web Feature Service)** provides access to **vector data** (points, lines, polygons) that you can query, style, analyze, and edit in QGIS. Think of it as downloading the actual geographic features with all their attributes. WFS is ideal when you need to perform spatial analysis or select specific features. ::: ::: callout-tip ## What is WMS? **WMS (Web Map Service)** provides access to **pre-rendered map images** - essentially picture tiles of maps. You cannot query individual features or access attributes, but WMS layers load quickly and are perfect for background/reference maps. Think of it as viewing a map image rather than working with the data itself. ::: ::: callout-important ## Key Differences Between WFS and WMS | Feature | WFS | WMS | |----|----|----| | Data type | Vector (features) | Raster (images) | | File size | Can be large | Smaller | | Queryable | Yes | No | | Stylable in QGIS | Yes | Limited | | Editable | Yes | No | | Best for | Analysis, editing, selection | Background maps, visualization | ::: ## Steps ### Part 1: Setting Up Your Project and Retrieving Neighbourhood Data 1. Open QGIS and create a new project 2. Set the project CRS (Coordinate Reference System) to **EPSG:28992** (Amersfoort / RD New - the Dutch standard projection) - Go to `Project` \> `Properties` \> `CRS` - Search for "28992" and select "Amersfoort / RD New" - Click `OK` 3. Using the PDOK Services plugin, retrieve data via WFS of this dataset: - Open the PDOK Services Plugin (look for the PDOK icon in the toolbar or go to `Web` \> `PDOK Services Plugin`) - In the plugin window, search for "CBS Wijken en Buurten 2024" - Select the **WFS** option (not WMS!) - Choose the layer: `cbs_buurten_2024` (neighbourhoods) - Click to add it to your map ::: callout-important Please pay attention to retrieve the correct data, as there are many buurten layers, both for WFS and WMS! What you need is a **vector layer** containing all 14,668 buurten (neighbourhoods) in the Netherlands. After loading, check the feature count in the layer properties to confirm you have the complete dataset. ::: ![Figure 1 - PDOK Services Plugin showing CBS Buurten 2024 WFS layer ready to add](using_data_source_manager/fig%201.png) 4. You must now select only the 20 buurten (neighbourhoods) that meet these criteria: - They belong to the municipalities of **Rotterdam** OR **Schiedam** (`gemeentenaam` = "Rotterdam" OR `gemeentenaam` = "Schiedam") **AND** - Their name (`buurtnaam`) is one of the following: "Stationsbuurt", "Singelkwartier", "Natuurkundigenbuurt", "Marconibuurt", "Glasbuurt", "Nieuw-Mathenesse", "Wetenschappersbuurt", "Newtonbuurt", "Boylebuurt", "Oud Mathenesse", "Nieuw Mathenesse", "Witte Dorp", "Spangen", "Tussendijken", "Bospolder", "Schiemond", "Nieuwe Westen", "Delfshaven", "Middelland", "Dijkzigt" **To select these neighbourhoods:** - Open the attribute table (right-click on layer \> `Open Attribute Table`) - Click on `Select features using an expression` (the yellow icon with ε) - Enter this expression: ``` "gemeentenaam" IN ('Schiedam', 'Rotterdam') AND "buurtnaam" IN ('Stationsbuurt', 'Singelkwartier', 'Natuurkundigenbuurt', 'Marconibuurt', 'Glasbuurt', 'Nieuw-Mathenesse', 'Wetenschappersbuurt', 'Newtonbuurt', 'Boylebuurt', 'Oud Mathenesse', 'Nieuw Mathenesse', 'Witte Dorp', 'Spangen', 'Tussendijken', 'Bospolder', 'Schiemond', 'Nieuwe Westen', 'Delfshaven', 'Middelland', 'Dijkzigt') ``` - Click `Select Features` - Verify that **20 features are selected** (shown at the top of the attribute table) **Now save the selected features:** - Right-click on the layer \> `Export` \> `Save Selected Features As...` - Format: `GeoPackage` - File name: Navigate to folder `part1_data` and name it `buurten.gpkg` - Layer name: `buurten` - CRS: `EPSG:28992` - **Select fields to keep:** - Click on the fields list and uncheck all columns except: - `buurtnaam` (name of the neighbourhood) - `buurtcode` (code of the neighbourhood) - `gemeentenaam` (name of the municipality) ::: callout-tip You can use the `Deselect All` button and then manually check only the 3 fields you want to keep. ::: - Click `OK` to save **Add the area column:** - Open the attribute table of the newly saved `buurten` layer - Enable editing mode (click the pencil icon) - Open the Field Calculator (Ctrl+I or click the abacus icon) - Check `Create a new field` - Output field name: `buurt_area` - Output field type: `Decimal number (real)` - Precision: 2 - In the expression box, enter: `round($area, 2)` - Click `OK` - Save edits (click the floppy disk icon) and disable editing mode (click the pencil icon again) ![Figure 2 - Field Calculator showing the area calculation expression that rounds the area to 2 decimal places](using_data_source_manager/fig%202.png) 5. Dissolve the geometries of the 20 buurten into a single polygon: - Go to `Vector` \> `Geoprocessing Tools` \> `Dissolve` - Input layer: `buurten` - Leave "Dissolve field(s)" empty (this will dissolve all features into one polygon representing the entire study area) - Click `Run` - After the temporary layer is created, right-click on it \> `Export` \> `Save Features As...` - Format: `GeoPackage` - File name: Select the existing `buurten.gpkg` file - Layer name: `buurten_dissolved` - In the fields section, uncheck all attributes (we only need the geometry) - Click `OK` ::: callout-note ## Note When unchecking all attributes, the resulting layer will have an empty attribute table containing only the feature ID (fid). This is expected - we only need the dissolved boundary geometry for further processing. ::: ![Figure 3 - Dissolved neighbourhood boundary showing all 20 neighbourhoods merged into a single polygon](using_data_source_manager/fig%203.png) 6. Compute the bounding box (rectangular extent) of `buurten_dissolved`: - Go to `Vector` \> `Research Tools` \> `Extract layer extent` - Input layer: `buurten_dissolved` - Click `Run` - A temporary layer will be created showing a rectangle that encompasses your dissolved neighbourhoods - Export this layer to the GeoPackage: - Right-click on the extent layer \> `Export` \> `Save Features As...` - File name: Select the existing `buurten.gpkg` - Layer name: `bbox` - Keep only columns: `minx`, `miny`, `maxx`, `maxy` (these represent the bounding box coordinates) - Click `OK` ![Figure 4 - Bounding box (rectangular extent) of the dissolved study area shown as a red rectangle](using_data_source_manager/fig%204.png) 7. Finally, compute a 200 meter buffer around the bbox layer: - Go to `Vector` \> `Geoprocessing Tools` \> `Buffer` - Input layer: `bbox` - Distance: `200` (meters) - Segments: 5 (default is fine) - End cap style: `Flat` (the end cap style parameter controls how line endings are handled in the buffer) - Join style: `Miter` (creates sharp corners at angles) - Miter limit: 2.0 (default) - Click `Run` - Export the result to the GeoPackage: - Right-click on the buffered layer \> `Export` \> `Save Features As...` - File name: Select the existing `buurten.gpkg` - Layer name: `bbox_buffered` - Uncheck all attributes in the fields section (we only need the geometry) - Click `OK` ::: callout-tip ## Why create a buffer? The 200m buffer extends our study area slightly beyond the neighbourhood boundaries. This ensures we capture climate data for buildings that are close to but just outside the neighbourhoods, providing better context for our analysis. ::: ![Figure 5 - 200m buffer around the bounding box, creating an expanded study area shown in green](using_data_source_manager/fig%205.png) ### Part 2: Connecting to WFS for Climate Adaptation Data 8. Open the **Data Source Manager** - Click the "Open Data Source Manager" button in the toolbar, OR - Go to `Layer` \> `Data Source Manager`, OR - Press `Ctrl+L` 9. Select the **WFS / OGC API - Features** tab on the left side of the dialog 10. Create a new WFS connection to Zuid-Holland climate data: - Click `New` to create a new connection - **Name:** Zuid-Holland Climate Adaptation - **URL:** `https://geodata.zuid-holland.nl/geoserver/klimaat/wfs?service=WFS&version=2.0.0&request=GetCapabilities` - Leave other settings at default - Click `OK` 11. Load a WFS layer: - Select the "Zuid-Holland Climate Adaptation" connection from the dropdown and click `Connect` - Wait for the available layers to load (this may take a few seconds) - Find and select `klimaat:PANDEN_WATEROVERLAST_HITTE_GROEN` (this layer contains climate adaptation data for buildings: water overlay risk, heat stress, and biodiversity indicators from 2022) - Click `Add` at the bottom - Click `Close` to close the Data Source Manager ::: callout-note ## Layer name explanation **PANDEN_WATEROVERLAST_HITTE_GROEN** translates to "Buildings - Water Overlay, Heat, Green" and contains climate vulnerability scores for individual buildings. ::: ![Figure 6 - Data Source Manager showing the Zuid-Holland WFS connection with available climate layers](using_data_source_manager/fig%206.png) 12. Clip the layer to your study area: - Go to `Vector` \> `Geoprocessing Tools` \> `Clip` - Input layer: `klimaat:PANDEN_WATEROVERLAST_HITTE_GROEN` (the climate buildings layer) - Overlay layer: `bbox_buffered` - Click `Run` - This will create a new temporary layer containing only the climate data within your buffered study area - **Save the clipped layer:** - Right-click on the clipped temporary layer \> `Make Permanent...` - Format: `GeoPackage` - File name: Select or create `climate_data.gpkg` in your `part1_data` folder - Layer name: `climate_buildings_clipped` - Click `OK` - You can now remove the original (unclipped) WFS layer to improve performance ::: callout-tip ## Why clip the data? The WFS layer contains data for all of Zuid-Holland province (thousands of buildings). Clipping reduces the dataset to only the buildings within our study area, making the data easier to work with and improving QGIS performance. ::: 13. Explore the layer: - Zoom to your study area: right-click on `bbox_buffered` layer \> `Zoom to Layer` - Notice that buildings are vector features (polygons) you can select and query - Open the attribute table (right-click layer \> `Open Attribute Table`) - Explore the climate adaptation attributes, such as: - `wateroverlast_score` - Water overlay/flood risk score - `hittestress_score` - Heat stress vulnerability score\ - `groen_score` - Green/biodiversity score - Each building has multiple climate vulnerability indicators ![Figure 7 - Attribute table of clipped climate buildings layer showing various climate risk scores for individual buildings](using_data_source_manager/fig%207.png) ### Part 3: Connecting to WMS for Background Imagery **What is WMS?** WMS provides access to pre-rendered map images - like a background map. You cannot query individual features, but it loads faster for large areas. ::: callout-important ## Important: Optimize WMS Loading Before retrieving the WMS layer, you should first **zoom to the study area** by right-clicking the `bbox_buffered` layer and selecting `Zoom to Layer(s)`. This ensures QGIS only requests tiles for your area of interest, reducing download time and file size. ::: 14. Open the **Data Source Manager** again (`Ctrl+L`) 15. Select the **WMS / WMTS** tab on the left 16. Create a new WMS connection: - Click `New` to create a new connection - **Name:** PDOK Aerial Photos - **URL:** `https://service.pdok.nl/hwh/luchtfotorgb/wmts/v1_0?request=GetCapabilities&service=wmts` - Leave other settings at default - Click `OK` 17. Load a WMTS layer: - Select the "PDOK Aerial Photos" connection and click `Connect` - Wait for available layers to load - Find and select `Luchtfoto 2025 Ortho 25cm RGB` (current high-resolution 25cm aerial photos) - **Important:** In the "Coordinate Reference System" section, select `EPSG:28992` from the dropdown - Click `Add` - Click `Close` ::: callout-note ## WMTS vs WMS WMTS (Web Map Tile Service) is a variant of WMS that serves pre-generated map tiles, making it faster for viewing at standard zoom levels. ::: 18. Save a local copy of the orthophoto: This step saves the aerial imagery as a permanent file that you can use offline and in future projects. - Select the orthophoto layer in the Layers panel - Right-click on it and choose `Export` \> `Save As...` In the resulting dialog, set the following parameters: - **Output mode:** `Rendered image` (this saves what you see on screen) - **Format:** `GeoTIFF` - **Important:** Uncheck "Create VRT" (we want a standard GeoTIFF file, not a virtual raster) - **File name:** Click `...` and navigate to the `\part1_data\raster` folder (create it if it doesn't exist) - Name the file: `ortho_25cm.tif` - **CRS:** `EPSG:28992` (should already be set) - **Extent:** Click the dropdown and select `Calculate from Layer` \> `bbox_buffered` - This ensures you only save imagery for your study area - **Resolution:** - Horizontal: `0.25` (meters per pixel) - Vertical: `0.25` (meters per pixel) - Click `OK` to start the export - Wait for the export to complete (this may take 1-2 minutes depending on the area size) - The saved GeoTIFF will be automatically added to your map ::: callout-tip ## Understanding Resolution The 0.25m resolution means each pixel in the saved image represents 25cm × 25cm on the ground. This matches the native resolution of the PDOK aerial photos, ensuring no quality loss. ::: ![Figure 8 - Export Raster dialog showing proper settings for saving the aerial photo with correct extent and resolution](using_data_source_manager/fig%208.png) 19. Arrange your layers for optimal visualization: - In the Layers panel, drag layers to arrange them in this order from **top to bottom**: 1. `buurten` (neighbourhood boundaries - should be visible as outlines) 2. `climate_buildings_clipped` (climate data polygons) 3. `bbox_buffered` (buffer outline - optional, can be hidden) 4. `ortho_25cm.tif` or the WMTS aerial photo layer (**at the bottom** as background) **Style adjustments:** - **Neighbourhood boundaries (`buurten`):** - Right-click \> `Properties` \> `Symbology` - Set fill to "Simple fill" - Fill style: `No Brush` (transparent) - Stroke color: Red or dark blue - Stroke width: 0.5 - 1.0 mm - **Climate buildings:** - Adjust transparency: Right-click \> `Properties` \> `Symbology` \> `Layer Rendering` tab - Set opacity to 70-80% so you can see the aerial photo underneath - Optionally, style by climate scores using categorized or graduated symbology ![Figure 9 - Final map view showing neighbourhood boundaries, climate data, and aerial imagery properly layered with appropriate transparency](using_data_source_manager/fig%209.png) ------------------------------------------------------------------------ ## Recap **What you've learned:** - The **Data Source Manager** is your central hub for connecting to external data sources in QGIS - **WFS** provides vector data that you can style, query, and analyze - great for operational data and spatial analysis - **WMS/WMTS** provides raster map images - great for background maps and visualization - How to use the **PDOK Services Plugin** to easily access Dutch open geospatial data - Basic **geoprocessing workflows**: selection, dissolve, buffer, clip, and extract extent operations - How to organize and save multiple layers in a **GeoPackage** for efficient data management - How to **export and save WMS/WMTS imagery** as permanent GeoTIFF files - Dutch **PDOK** (Publieke Dienstverlening Op de Kaart) offers free, reliable geospatial web services - Provincial and municipal data portals (like Zuid-Holland) provide specialized datasets - Saved connections can be reused in future projects **Key differences between WFS and WMS:** | Feature | WFS | WMS/WMTS | |------------------|-------------------|-----------------| | Data type | Vector (features) | Raster (images) | | File size | Can be large | Smaller | | Queryable | Yes | No | | Stylable in QGIS | Yes | Limited | | Best for | Analysis, editing | Background maps | **Useful Dutch Data Portals:** - **PDOK:** https://www.pdok.nl/ (national geospatial infrastructure) - **Data.overheid.nl:** Open data from Dutch government - **Nationaal Georegister:** https://www.nationaalgeoregister.nl/ (search for geospatial services) - Most Dutch provinces and municipalities also offer their own WFS/WMS services ------------------------------------------------------------------------ ## Extension Activities (Optional) - Explore other PDOK services at <https://www.pdok.nl/datasets> - Try filtering WFS layers using the Data Source Manager's "SQL" or "Filter" options before loading - Analyze the climate adaptation scores in your selected Rotterdam and Schiedam neighbourhoods - Compare different neighbourhoods' vulnerability to heat, water, or biodiversity loss using statistics - Add the RIVM Energy Labels WMS (`https://data.rivm.nl/geo/nl/wms?request=GetCapabilities`, layer `rvo_energielabels`) to see building energy performance ratings - Create a styled thematic map showing the relationship between neighbourhood characteristics and climate risks - Calculate summary statistics (mean, max, min) of climate scores by neighbourhood