Making maps with geographic data

Making maps from geographic data is a powerful way to analyze patterns, understand locations, and visualize relationships between data and place.

This guide will explain everything step-by-step in simple terms, and we’ll use Python (a beginner-friendly programming language) with easy libraries.

1️⃣ What Is Geographic Data?

Geographic data (also called spatial data) is any data that contains location information.

There are two main types:

A) Vector Data (Most Common)

Represents features as:

• Points → Cities, schools, restaurants
• Lines → Roads, rivers
• Polygons → Countries, states, lakes

Common file types:

• .shp (Shapefile)
• .geojson
• .gpkg

B) Raster Data

Represents data as pixels (like images):

• Satellite images
• Elevation maps
• Weather maps

File types:

• .tif
• .jpg
• .png

2️⃣ Important Geographic Concepts (Simple Explanation)

🌍 Latitude and Longitude

The Earth uses coordinates:

• Latitude → North/South (−90 to 90)
• Longitude → East/West (−180 to 180)

Example:

CityLatitudeLongitudeNew York40.7128-74.0060London51.5074-0.1278

Markdown version (copy-paste ready):

| City      | Latitude | Longitude |
|-----------|----------|-----------|
| New York  | 40.7128  | -74.0060  |
| London    | 51.5074  | -0.1278   |

🗺️ Coordinate Reference System (CRS)

CRS tells the computer how the Earth is projected onto a flat map.

The most common CRS:

• WGS84 → EPSG:4326

It uses latitude and longitude.

3️⃣ Tools We’ll Use (Python)

We’ll use:

• pandas → handles data tables
• geopandas → handles geographic data
• matplotlib → plots maps
• folium → interactive web maps

Install them:

pip install pandas geopandas matplotlib folium

4️⃣ Example 1: Making a Simple Map from Latitude & Longitude

Step 1: Create Sample Data

import pandas as pd

data = {
    "City": ["New York", "London", "Tokyo"],
    "Latitude": [40.7128, 51.5074, 35.6895],
    "Longitude": [-74.0060, -0.1278, 139.6917]
}

df = pd.DataFrame(data)
print(df)

Step 2: Convert to Geographic Data

import geopandas as gpd
from shapely.geometry import Point

geometry = [Point(xy) for xy in zip(df["Longitude"], df["Latitude"])]

gdf = gpd.GeoDataFrame(df, geometry=geometry)

gdf.set_crs(epsg=4326, inplace=True)

print(gdf)

Step 3: Plot the Map

import matplotlib.pyplot as plt

gdf.plot(color="red", markersize=100)

plt.title("City Locations")
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.show()

You now created your first geographic map 🎉

5️⃣ Example 2: Making an Interactive Map (Beginner Friendly)

Interactive maps are better for beginners because you can zoom and click.

import folium

# Create a base map
m = folium.Map(location=[20, 0], zoom_start=2)

# Add markers
for index, row in df.iterrows():
    folium.Marker(
        location=[row["Latitude"], row["Longitude"]],
        popup=row["City"]
    ).add_to(m)

m

This creates a zoomable interactive map.

6️⃣ Working with Real Geographic Files (Shapefiles)

Example: Load a country boundary file.

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

print(world.head())

Plot it:

world.plot(figsize=(10, 6))
plt.title("World Map")
plt.show()

7️⃣ Choropleth Maps (Color Based on Data)

A choropleth map colors regions based on values.

Example: Color countries by population.

world.plot(column="pop_est", cmap="OrRd", legend=True)
plt.title("Population by Country")
plt.show()

What happens:

• column="pop_est" → use population data
• cmap="OrRd" → color style
• legend=True → show color scale

8️⃣ Spatial Analysis (Basic Ideas)

Once data is on a map, we can:

• Measure distance
• Find nearby locations
• Count points inside areas
• Detect patterns

Example: Calculate distance between two cities

from geopy.distance import geodesic

ny = (40.7128, -74.0060)
london = (51.5074, -0.1278)

distance = geodesic(ny, london).km
print("Distance:", distance, "km")

9️⃣ Common Workflow in Geographic Data Analysis

Here’s how professionals usually work:

| Step | Description |
|------|------------|
| 1 | Collect geographic data |
| 2 | Clean and format data |
| 3 | Convert to GeoDataFrame |
| 4 | Check CRS |
| 5 | Visualize data |
| 6 | Perform spatial analysis |
| 7 | Export results |

🔟 Exporting Your Map

Save to file:

gdf.to_file("cities.shp")

Save interactive map:

m.save("map.html")

FULL COMPILED CODE (All Code Together)

Below is all the code combined into one script: