Checking the Attribute Table

What Are Attributes in Spatial Data?

Each spatial data entry is called a feature. A feature consists of geometry information and attribute information. Geometry defines the shape and location of a feature—represented as a point, line, or polygon—based on a coordinate system. 

Assume that, as in “Adding Layer,” you have added the sample “Starbucks layer” provided by Spatial Analyzer to the canvas. Starbucks stores are represented as point geometries. Attributes refer to non-spatial information stored in database fields (columns), such as store name, address, and opening year.

With the sbucks layer selected in the Layers panel:

[Method 1] Click the Open Attribute Table icon from the top menu.

[Method 2] Press the F6 shortcut key.

[Method 3] Right-click the layer on the Layer Panel and select Open Attribute Table.

Result: A window displaying the attribute information of the layer appears. 

Adding Basemaps

[1] In the Browser panel on the left side of the screen, find XYZ Tiles and double-click.

[2] In the list, double-click OpenStreetMap.

[3] Result:

Adding Layers

Adding Layers the Usual Way

The quickest way to add data for analysis in QGIS is to drag files from Windows Explorer onto the canvas. You can also use the top menu as described below.

Add a Vector Layer

For vector data such as Shapefile, GeoJSON, or CSV, go to Layer → Add Layer → Add Vector Layer. In the dialog, set Source type to File. Then, next to Vector dataset(s), click the … button to choose the file, and click Add. The layer will appear in the Layers panel.

Add a Raster Layer

For raster data such as GeoTIFF or IMG, go to Layer → Add Layer → Add Raster Layer. In the dialog, set Source type to File. Then, next to Raster dataset(s), click the … button to choose the file, and click Add. The layer will appear in the Layers panel.

Adding Spatial Analyzer Sample Data

For convenience, SpatialAnalyzer bundles datasets required for the exercises inside the plugin. For example, to load the Starbucks dataset used in Exploratory Spatial Data Analysis, navigate to the path below and double-click the file to add it as a layer:

Quick Start SpatialAnalyzer → Sample Data → sbucks.gpkg

Tutorial: Accumulative Mean Center (Plug-in Method)

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Q: How has the mean center of Starbucks stores in Gangnam, Seoul changed over the years?

Before you begin this tutorial, do the following first:

• Install the Spatial Analyzer Plugin
• Start Spatial Analyzer
• Add Starbucks Layer
• Add a Basemap

Running the Spatial Analyzer

[1] Click the Processing menu at the top, then click Toolbox.
The Processing Toolbox panel will appear on the right side of the window.

If the Spatial Analyzer plugin is not installed, refer to "Installing Urban Analyzer".

[2] In the Processing Toolbox, expand the SpatialAnalyzer submenu and double-click Mean Center Tracker.

[3] In the “Mean Center Tracker” window, set Start Field to open_year, then click Run.

[4] Execution Result:

Tutorial: Accumulative Mean Center (Manual Method)

Q: How has the mean center of Starbucks stores in Gangnam, Seoul changed over the years?

Before you begin this tutorial, do the following first:

• Add Starbucks Layer
• Add a Basemap

The method presented in this practice relies on basic QGIS functionality but can be time-consuming for practical use. This practice is designed primarily for learning QGIS operations. For practical cumulative mean center calculation, use the plug-in method

Checking the Layer Attribute Table

With the sbucks layer selected in the Layers panel, press the F6 shortcut key.

Result: The attribute table for sbucks opens. We will use the open_year field to compute cumulative mean centers.

A. Cumulative Mean Center up to 2002

Tutorial: Mean Center, Median Center and Central Feature(Plug-in Method)

Q: Find the Median Center and Central Feature of Starbucks Stores in Gangnam, Seoul.

Before you begin this tutorial, do the following first:

• Install the Spatial Analyzer Plugin
• Start Spatial Analyzer
• Add Starbucks Layer
• Add a Basemap

Finding the Median Center and Central Feature

[1] At the top of the screen, click the Processing menu, then click Toolbox. The Processing Toolbox panel will appear on the right side of the window.

[2] In the Processing Toolbox, expand SpatialAnalyzer at the bottom. Under Spatial Central Tendency, double-click Centers (Mean Center, Median Center, Central Feature)

[3] In the Spatial Central Tendency window, ensure Median Center and Central Feature are checked, then click Run. (By default, Mean Center, Median Center, and Central Feature are selected.)

Weight Field lets you compute weighted centers using an attribute instead of pure distance. Examples include seating capacity, sales revenue, or number of employees. If you choose “sales revenue,” the resulting mean or median center will shift toward higher-revenue areas.

Group Field computes centers separately for each category when your data is classified. For example, if there is an administrative-district attribute, selecting that field will calculate centers per district. This method is discussed further in cluster analysis.

Result

Three point layers representing the Mean Center, Median Center, and Central Feature are created.

Tutorial: Compute the Mean Center(Built-in Method)

Adding the Starbucks Layer

1. Launch QGIS.
2. Add Starbucks Layer(sbucks.gpkg) from the Sample Data.
3. Result: 

The locations of Starbucks stores in the Gangnam area of Seoul are added as point features on the QGIS canvas.

For attribute information of this file, see Checking the Attribute Table.

Adding the Mean Center

[1] With the Starbucks layer selected, go to Vector → Analysis Tools → Mean Coordinate(s)...

[2] Confirm the input layer shows sbucks [EPSG:5179], then click Run.

[3] Result: The mean center point is added to the canvas.

In the Mean Coordinate(s) tool:
• Weight field lets you compute a weighted mean center using an attribute (e.g., number of seats, sales volume, or employees). If you choose sales volume as the weight, the mean center reflects where sales are geographically concentrated.
• Unique ID field enables separate mean centers for each group. For example, if the administrative district is stored in an attribute, selecting that field will compute a mean center for each district.