Aprende Challenge: Map Local News Events | Data Analysis and Visualization for Media

Mapping local news events is a powerful way to tell stories visually, allowing audiences to quickly grasp the scope, distribution, and types of incidents occurring in their communities. By placing events on a map according to their geographic coordinates, you can reveal patterns and clusters that might otherwise go unnoticed in raw data tables. This approach is especially valuable for journalists aiming to contextualize news, highlight hotspots, or draw attention to trends across neighborhoods or regions.


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import pandas as pd
import matplotlib.pyplot as plt

# Sample DataFrame of local news events
data = {
    "event": ["Burglary", "Town Hall", "Charity Run", "Robbery", "Election Rally", "Food Drive"],
    "type": ["crime", "politics", "community", "crime", "politics", "community"],
    "latitude": [40.7128, 40.7135, 40.7150, 40.7142, 40.7130, 40.7160],
    "longitude": [-74.0060, -74.0050, -74.0070, -74.0045, -74.0080, -74.0030]
}
df = pd.DataFrame(data)

# Assign a color to each event type
color_map = {"crime": "red", "politics": "blue", "community": "green"}
df["color"] = df["type"].map(color_map)

# Plotting the events
plt.figure(figsize=(8, 6))
for event_type in df["type"].unique():
    subset = df[df["type"] == event_type]
    plt.scatter(subset["longitude"], subset["latitude"], 
                c=subset["color"], label=event_type.capitalize(), s=100, edgecolor="black")

plt.title("Local News Events Map")
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.legend(title="Event Type")
plt.show()

Using color-coding for different event types makes the map much easier to interpret at a glance. When each category—such as crime, politics, or community—is assigned a distinct color, you can quickly identify clusters, outliers, or locations where certain types of events are more common. This not only improves the visual appeal of your map but also helps your audience draw meaningful conclusions from the data.

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Write a function that takes a DataFrame of local news events and produces a scatter plot map.

Plot each event as a point using its longitude and latitude.
Assign a unique color to each event type category.
Include a legend that explains the color for each event type.
Add a title to the map.

Solución

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Sección 2. Capítulo 7

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Suggested prompts:

Can you explain how to add more event types and colors to the map?

How can I use real-world data instead of the sample data provided?

What are some best practices for interpreting patterns on these maps?

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import pandas as pd
import matplotlib.pyplot as plt

# Sample DataFrame of local news events
data = {
    "event": ["Burglary", "Town Hall", "Charity Run", "Robbery", "Election Rally", "Food Drive"],
    "type": ["crime", "politics", "community", "crime", "politics", "community"],
    "latitude": [40.7128, 40.7135, 40.7150, 40.7142, 40.7130, 40.7160],
    "longitude": [-74.0060, -74.0050, -74.0070, -74.0045, -74.0080, -74.0030]
}
df = pd.DataFrame(data)

# Assign a color to each event type
color_map = {"crime": "red", "politics": "blue", "community": "green"}
df["color"] = df["type"].map(color_map)

# Plotting the events
plt.figure(figsize=(8, 6))
for event_type in df["type"].unique():
    subset = df[df["type"] == event_type]
    plt.scatter(subset["longitude"], subset["latitude"], 
                c=subset["color"], label=event_type.capitalize(), s=100, edgecolor="black")

plt.title("Local News Events Map")
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.legend(title="Event Type")
plt.show()