Hierarchical Clustering of ecotourism zones in national park ecotourism areas Based on trail length and environmental cleanliness

Project info

• Field of studyTourism and Hospitality
• LevelMaster's thesis (S2)
• Method typeClustering
• Estimated build time± 5 working days
• Toolsscipy.cluster.hierarchy linkage, dendrogram, fcluster, cophenet
• PriceUS$22 - US$147

Research goal

Build a dendrogram of ecotourism zones in national park ecotourism areas from trail length and environmental cleanliness and determine a suitable number of clusters.

Variables

Example result

• 4Clusters k
• 0.68Silhouette
• 0.85Cophenetic corr.
• 0.451Davies-Bouldin
• 686.835Calinski-Harabasz
• 0.778ARI vs. true groups

Ward hierarchical clustering forms 4 clusters on ecotourism zones, trail length and environmental cleanliness with a silhouette of 0.680.

Computed from illustrative synthetic data, not from a real study.

About Hierarchical Clustering

Hierarchical clustering builds a merge tree (dendrogram) of data points based on similarity. It suits small to medium data when the number of clusters is unknown and a nested structure is of interest.

Steps

1. 1Standardise features and choose a distance measure
2. 2Compute the linkage (for example Ward)
3. 3Draw the dendrogram
4. 4Cut the dendrogram at the chosen number of clusters
5. 5Compute the cophenetic coefficient and silhouette
6. 6Profile the clusters

Assumptions to check

• Distance measure and linkage method fit the data
• Features are standardised
• Merges that have already happened cannot be undone

Reading and reporting the results

Show the dendrogram and explain the cut height chosen with its rationale. Report the cophenetic coefficient and silhouette as quality measures.

Example Python code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import linkage, dendrogram, fcluster, cophenet
from scipy.spatial.distance import pdist
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import silhouette_score

X, _ = make_blobs(n_samples=80, centers=3, cluster_std=1.2, random_state=16)
X = StandardScaler().fit_transform(X)
trail_length = X[:, 0]
environmental_cleanliness = X[:, 1]

Z = linkage(X, method="ward")
coph, _ = cophenet(Z, pdist(X))
print("Cophenetic correlation:", round(coph, 3))

ecotourism_zones = fcluster(Z, t=3, criterion="maxclust")
print("Cluster sizes:", np.bincount(ecotourism_zones)[1:])
print("Silhouette:", round(silhouette_score(X, ecotourism_zones), 3))

fig, ax = plt.subplots(1, 2, figsize=(11, 4))
dendrogram(Z, ax=ax[0], no_labels=True)
ax[0].set_title("Dendrogram (Ward)")
ax[1].scatter(trail_length, environmental_cleanliness, c=ecotourism_zones, s=20)
ax[1].set_title("3 clusters")
plt.tight_layout()
plt.show()

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

• Payment: 50% down payment before work starts, balance after the deliverables are handed over
• Tax: Prices exclude 11% VAT
• Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
• Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions