Learn GIS Layers, Features, and Attributes | Foundations of Geospatial Data

Geospatial Data Science with Python

Geographic Information Systems (GIS) organize spatial data using a structure of layers. Each layer contains a collection of features — such as points, lines, or polygons — that represent real-world objects like cities, roads, or lakes. Every feature in a layer is linked with attribute data, which provides descriptive information about that feature. For example, a city feature might store its name, population, and elevation as attributes. This organization allows you to analyze, visualize, and manage spatial data efficiently by grouping related features and their properties together.


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import geopandas as gpd

# Load a sample GeoPandas dataset of world countries
gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Display the first five rows to show geometry and attribute columns
print(gdf.head())

Cities (Point features)

City name;
Population;
Elevation above sea level;
Administrative region;
Year founded.

Roads (Line features)

Road name;
Road type (highway, street, etc.);
Number of lanes;
Speed limit;
Surface material.

Lakes (Polygon features)

Lake name;
Area (sq km);
Average depth;
Water quality index;
Protected status.

1. In a GIS, what is an attribute?

2. What does a GIS 'layer' typically contain?

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Section 1. Chapter 3

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              1234567
            
import geopandas as gpd

# Load a sample GeoPandas dataset of world countries
gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Display the first five rows to show geometry and attribute columns
print(gdf.head())