Why spatial algorithms solve geometry problems in SciPy - See It in Action

Why spatial algorithms solve geometry problems

📖 Scenario: Imagine you are organizing a small park with several benches. You want to find out which benches are closest to each other to plan walking paths efficiently.

🎯 Goal: You will create a list of bench coordinates, set a distance threshold, use a spatial algorithm to find pairs of benches closer than this threshold, and print those pairs.

📋 What You'll Learn

Create a list of bench coordinates as tuples with exact values

Create a distance threshold variable

Use scipy.spatial.distance.pdist and scipy.spatial.distance.squareform to find distances

Find pairs of benches closer than the threshold

Print the pairs of bench indices that are close

💡 Why This Matters

🌍 Real World

Spatial algorithms help in mapping, navigation, and planning by quickly finding nearby points or objects in space.

💼 Career

Data scientists and GIS analysts use spatial algorithms to analyze geographic data, optimize routes, and solve location-based problems.

1

Create bench coordinates list

Create a list called benches with these exact coordinates as tuples: (1, 2), (3, 4), (5, 1), (2, 2), (6, 5).

SciPy

# Create the list benches with the given coordinates
# Your code here

Need a hint?

Use a Python list with tuples for each bench coordinate.

2

Set distance threshold

Create a variable called distance_threshold and set it to 3.0.

SciPy

benches = [(1, 2), (3, 4), (5, 1), (2, 2), (6, 5)]
# Set the distance_threshold to 3.0
# Your code here

Need a hint?

Just assign 3.0 to the variable distance_threshold.

3

Find close bench pairs using spatial algorithm

Import pdist and squareform from scipy.spatial.distance. Use pdist on benches to compute pairwise distances, then convert to a square matrix with squareform. Create a list called close_pairs containing tuples of bench indices (i, j) where the distance is less than distance_threshold and i < j.

SciPy

benches = [(1, 2), (3, 4), (5, 1), (2, 2), (6, 5)]
distance_threshold = 3.0
# Import pdist and squareform
# Calculate pairwise distances
# Find pairs with distance less than distance_threshold
# Your code here

Need a hint?

Use nested loops to check pairs and add those closer than threshold to close_pairs.

4

Print close bench pairs

Print the variable close_pairs to display the list of bench index pairs that are closer than the distance threshold.

SciPy

from scipy.spatial.distance import pdist, squareform

benches = [(1, 2), (3, 4), (5, 1), (2, 2), (6, 5)]
distance_threshold = 3.0

distances = squareform(pdist(benches))
close_pairs = []
for i in range(len(benches)):
    for j in range(i + 1, len(benches)):
        if distances[i][j] < distance_threshold:
            close_pairs.append((i, j))

# Print the close_pairs list
# Your code here

Need a hint?

Use print(close_pairs) to show the result.