Utah League of Cities and Towns

Making Life BETTER

Cluster Analysis

Cluster profiling will be used as an analytic tool for policy analysis and review. We continue to advocate ‘one size does not fit all’, but also recognize splintering policy into 246 pieces is unrealistic. Clustering, in a more scientific manner, will enable us to identify policy implications for a group of cities that share similar characteristics. Cities will not be perfectly matched in every category – cities are simply too diverse.  Individual cities will have characteristics that do and do not fit into general groups.  However, the analysis gives policymakers a way to understand how each individual community fits into the landscape of the overall state of Utah’s cities.

What are the twelve Utah clusters?

  1. Major Population Centers (10 Cities)
  2. Commercial Centers (22 Cities)
  3. High Growth Communities (9 Cities)
  4. Residential Transitioning Communities (31 Cities)
  5. High Income Residential (18 Cities)
  6. Urban Edge Cities (15 Cities)
  7. Resort Communities (7 Cities)
  8. Natural Resource/Mining Based Communities (23 Cities)
  9. Old Established Communities (19 Cities)
  10. Traditional Agricultural (23 Cities)
  11. Small Towns (66 Cities)
  12. Capital City (1 City)
Cluster Name Description No. of Cities Example City
A Major Population Cities Largest population base, minimal growth, established communities, large commercial centers 10 Provo, St. George
B Commercial Centers Larger population, significant commercial and industrial regional centers, growing communities, 22 Cedar City, Taylorsville
C High Growth Communities Communities with highest growth rates, high household income, low commercial 9 Saratoga Springs, Bluffdale
D Residential Transitioning Modest commercial property, increasing growth, many transitioning communities 31 Nibley, Santaquin
E High Income Residential Highest median household income, moderate growth, low commercial 18 Highland, Fruit Heights
F Edge Cities High per capita commercial and industrial property, moderate population size and population growth 15 North Logan, South Ogden
G Resort Communities Low population, high commercial property, high per capita revenue 7 Park City, Alta
H NR/Mining Based Older, low growth rural communities, small commercial property 23 Duchesne, Price
I Old Established Communities Older communities, low or declining population, some commercial component 19 Lewiston, Manti
J Traditional Agricultural Traditional agricultural communities, primarily residential with increasing population, some growing commercial element 23 Ephraim, Nephi
K Small Towns Smallest population, older established communities with low or declining growth, low commercial property 66 Hatch, Scofield
L Capital City Economic center of the state 1 Salt Lake City

What factors were used to determine the cluster analysis?

How will this benefit my city?

Many cities frequently seek to compare either their services or their revenue to other municipal governments. Often this process of comparison seeks out the neighboring cities or cities sharing a similar population. We contend however, that this cluster grouping gives cities a better idea of who might share the similar challenges or framework as their own municipality. In addition the cluster analysis is an effective tool for evaluating the potential impact of proposed pieces of legislation.

Where has this been done before?

Utah League of Cities and Towns:

For our 2016 Cluster analysis we used the same variables used in our 2007 analysis which is available here.  This report goes into more detail describing the clusters and the methodology used for both the 2007 and 2016 analyses.

Washington Association of Cities:

As part of the 2005 State of the Cities research and analysis, AWC sorted Washington’s 281 cities and towns into 14 groups, or “clusters.”
The cities in each cluster have similar economic and other characteristics including:

The League of Minnesota Cities:

In 2003, analyzed trends in demographics and municipal finances, and outlined policy implications for Minnesota cities. But without a classification scheme, it is hard to draw any “meaningful” conclusions for different cities. Grouping cities by size or location alone cannot provide satisfactory results for the analysis because of the diversity of cities in the same region or of the same scale. The hierarchical cluster method, which is based on multiple demographic and financial characteristics, reorganized the 853 cities into relatively homogeneous groups. Four used criteria: