Mastering Attribute Tables in QGIS: Smart Spatial Queries for Real-World GIS Analysis

Introduction

In the world of Geographic Information Systems (GIS), the map is only half the story. The real intelligence lies beneath—the rows and columns that describe every feature you see, the Attribute tables.

Whether you're identifying climate-vulnerable zones, planning infrastructure, or analyzing biodiversity loss, smart spatial queries through the attribute table are how you ask—and answer—the right questions.

This post is about asking smarter questions—and finding them inside QGIS’s attribute tables.

What Is an Attribute Table?

Every vector layer in QGIS- points, lines, or polygons- has an associated attribute table. Think of it like an Excel sheet tied directly to your map.

Each row represents a feature (like a tree, building, or river), and each column is an attribute (like height, type, or flow rate).

Figure 1.  An example of an attribute table in QGIS showing rows (features) and columns (attributes) linked to a spatial layer.

The magic of GIS lies in being able to query this data spatially and logically asking things like “Show me all wetlands near roads that are degraded” or “Select rivers in erosion-prone zones with sediment levels above normal.”

Why Smart Queries Matter

Attribute tables let you filter, calculate, and analyze features using conditions just like using a search engine, but for spatial data.

Using QGIS, you can:

• Combine spatial and non-spatial conditions

• Calculate new attributes

• Clean and format raw data

• Run SQL-like logic

• Visualize results dynamically on the map

And the best part, It’s all no code and user friendly.

Let’s apply these capabilities in a real-world environmental analysis.

Case Study: Identifying Risk Farmlands Near Rivers

The goal is to identify farmlands located close to rivers so we could prioritize those areas for buffer planting and soil erosion control. We can make use of the following workflow:

1. Load the data

-To execute this, we are using a few shapefiles: one for rivers, one for land use, another for administrative boundaries.

-A rivers layer, which contains the line features representing water bodies like rivers and streams. A land use layer, showing different land types such as cropland, forest, or urban areas. An administrative boundary layer, which helps define the spatial extent and provides regional context, such as districts or zones.

-You can use publicly available datasets for this. It’s also important to ensure all your layers are using the same coordinate system so that your analysis results are accurate and aligned.

2. Filter the croplands

According to the objective, using the attribute table and a simple query, We can only filter out the croplands.

Once the selection is made, you’ll see only the cropland features highlighted on your map. This gives you a cleaner view and helps narrow down your analysis. After selecting them, export the selected features and create a new layer with just the cropland data.

Figure 2. Filtered cropland class from the LULC layer using the attribute table in QGIS.

3. Build River Buffer

Rivers often influence the land around them, especially when it comes to parameters like erosion, runoff, or changes in soil quality. To analyze areas that might be affected, we can create a buffer zone around the river features.

QGIS makes this easy:

• Go to Vector > Geoprocessing > Buffer

• Create a buffer of 500 meters

This creates a spatial buffer, an area that helps highlight where water-related impacts like erosion or nutrient runoff may be more significant.

Figure 3.A 500-meter buffer generated around river features to identify potential impact zones.

4. Select Cropland Within Buffer

This is where the attribute table and spatial logic team up, combining attribute-based selections with spatial relationships. After creating the 500-meter river buffer, the next step is to find which cropland areas fall within that zone. In other words, we want to know: Which croplands are close enough to rivers to possibly be at environmental risk?

Use Select by Location:

• Source layer: river buffer

• Condition: intersects

• Target layer: cropland

Now this has highlighted only those croplands that sit close enough to rivers to matter for conservation planning.

Figure 4. "Using the Select by Location tool in QGIS to identify cropland intersecting the river buffer.

Figure 5. Highlighted cropland areas located within 500 meters of rivers—ideal for conservation planning.

Turning Data Into Decisions: The Role of the Attribute Table

Once the croplands are selected, the attribute table can be used to explore the data further. You can add new fields like priority level, land area in hectares, or zone names to help turn the selection into useful information for planning or decision-making.

This is just one way QGIS helps turn data into useful decisions. Whether you’re checking flood-prone areas, studying land use changes, or planning green spaces, the attribute table is a powerful tool. Try it with your own data and see what you can find!

Conclusion

Attribute tables in QGIS are more than just spreadsheets linked to spatial features—they are the foundation of intelligent spatial analysis. By learning how to apply filters, perform spatial joins, and run queries through intuitive tools like Select by Attribute and Select by Location, users can transform raw geospatial data into actionable insights.

Whether you are identifying croplands near rivers for conservation planning, analyzing urban growth patterns, or assessing flood-prone areas, the attribute table empowers you to ask the right questions—and find accurate answers without writing a single line of code.

The case study presented in this blog highlights how simple tools in QGIS can support complex environmental decision-making. From loading and preparing data to building spatial buffers and querying relationships, each step plays a critical role in solving real-world problems with geospatial intelligence.

As you continue your journey with QGIS, mastering the attribute table will open up powerful new possibilities for spatial thinking, data exploration, and impactful GIS applications.