Use of cluster analysis in exploring economic indicator

differences among municipalities in Latvia

Ieva BraukšaUniversity of Latvia

11.11.2011

This work has been supported by the European Social Fund within the project «Support for Doctoral Studies at University of Latvia»

Description of situation

• 2009 Administrative territorial reform 119 municipalities (110 districts + 9 cities)

• Wide debates about borders and differences of these municipalities.

• Cluster analysis – possiblity to look at municipality differences another perspective.

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DO MUNICIPALITIES GROUP BY PLANING REGIONS OR BY OTHER ASPECTS OF

SIMILARITY?

Data usedData from State Regional Development Agency (VRAA)

Variables used during clustering:– Changes in number of permanet residents (2006-2011)– Share of residents at working age (1.1.2011)– Level of unemployment (1.1.2011)

These variables include basic information about inhabitant structure and economic conditions.

Data are standartized (because variables have different measurement units, so standartized to avoid influence of different variable variance) – mean 0, std 1.

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Disjoint clustering

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Results of disjoint clustering*

Cluster Inhabitant change Working age Unemployed

1-0.59 -0.48 1.74

(0.25) (0.791) (0.954)

2-0.08 0.77 -0.27

(0.474) (0.554) (0.397)

33.00 0.90 -1.17

(1.158) (0.872) (0.164)

4-0.24 -0.80 -0.31

(0.42) (0.682) (0.497)

7*using FASTCLUS clustering method in SAS® software

Table showing cluster means and standard deviations

Cluster group diferences

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Cluster 1• Less than average

share of inhabitants at workig age

• Highest unemployment

• Fastest decrease of permanent residents

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Latgale 16Vidzeme 1Zemgale 1Kurzeme 1

• Aglonas novads• Aluksnes novads• Auces novads• Baltinavas novads• Balvu novads• Ciblas novads• Dagdas novads• Karsavas novads• Kraslavas novads• Livanu novads

• Ludzas novads• Priekules novads• Rezeknes novads• Riebinu novads• Rugaju novads• Varkavas novads• Vilakas novads• Vilanu novads• Zilupes novads

Municipalities in cluster:

Cluster 2• Relatively

larger share of inhabitants at working age

• “Mainstream”

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Kurzeme 4 Latgale 2 Pierīga 12 Vidzeme 12 Zemgale 13

• Aizkraukles novads• Aknistes novads• Alsungas novads• Bauskas novads• Beverinas novads• Burtnieku novads• Cesu novads• Daugavpils novads• Dobeles novads• Gulbenes novads• Iecavas novads• Incukalna novads• Jaunjelgavas novads

• Jaunpils novads• Jelgavas novads• Keguma novads• Kocenu novads• Kokneses novads• Krimuldas novads• Lielvardes novads• Madonas novads• Malpils novads• Nauksenu novads• Neretas novads• Ogres novads• Olaines novads

Municipalities in cluster:

• Pargaujas novads• Preilu novads• Priekulu novads• Raunas novads• Ropazu novads• Rundales novads• Salas novads• Salaspils novads• Saldus novads• Sejas novads• Siguldas novads• Smiltenes novads• Talsu novads

• Tervetes novads• Vecpiebalgas novads• Vecumnieku novads• Ventspils novads

Cluster 3• The only cluster with

significant increase of number of permanent residents

• Largest share of inhabitants at working age

• Smallest levels of unemployment

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Pierīga 8

• Adazu novads• Babites novads• Carnikavas novads• Garkalnes novads• Ikskiles novads• Kekavas novads• Marupes novads• Stopinu novads

Municipalities in cluster:

Cluster 4• The smallest

number of customers at working age

• Not high unemployment, decrease of permanet residets – moderate

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Kurzeme 12 Latgale 1 Pierīga 8 Vidzeme 12 Zemgale 6

• Aizputes novads• Alojas novads• Amatas novads• Apes novads• Baldones novads• Brocenu novads• Cesvaines novads• Dundagas novads• Durbes novads• Engures novads• Erglu novads• Grobinas novads• Ilukstes novads

• Jaunpiebalgas novads• Jekabpils novads• Kandavas novads• Krustpils novads• Kuldigas novads• Ligatnes novads• Limbazu novads• Lubanas novads• Mazsalacas novads• Nicas novads• Ozolnieku novads• Pavilostas novads• Plavinu novads

Municipalities in cluster:

• Rojas novads• Rucavas novads• Rujienas novads• Salacgrivas novads• Saulkrastu novads• Skriveru novads• Skrundas novads• Strencu novads• Tukuma novads• Vainodes novads• Valkas novads• Varaklanu novads• Viesites novads

Cluster summaryGroup 1

UnemploymentDecrease of number of inhabitants

Group 2

More inhabitants at working age

Group 3

More employedIncrease number of ihabitantsLarger share of working age population

Group 4

Less inhabitants at working age

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Hierarchial clustering

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Hierarchial clustering methods

Several used to test if results are similar:– average linkage (group average, unweighted pair-

group method using arithmetic averages)– centroid method (unweighted pair-group method

using centroids, centroid sorting, weighted-group method)

– complete linkage (furthest neighbor, maximum method)

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Average linkage method

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Average linkage method – dividing dendrogram in

groups

Group 1 – municipalities from Latgale

22Note: P – Pierīga, L – Latgale, V – Vidzeme, Z – Zemgale, K - Kurzeme

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Group 2 – mainly Vidzeme

Note: P – Pierīga, L – Latgale, V – Vidzeme, Z – Zemgale, K - Kurzeme

24Note: P – Pierīga, L – Latgale, V – Vidzeme, Z – Zemgale, K - Kurzeme

Group 3

Group 4

25Note: P – Pierīga, L – Latgale, V – Vidzeme, Z – Zemgale, K - Kurzeme

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Group 5

Note: P – Pierīga, L – Latgale, V – Vidzeme, Z – Zemgale, K - Kurzeme

Group 6 - Pierīga

27Note: P – Pierīga, L – Latgale, V – Vidzeme, Z – Zemgale, K - Kurzeme

Conclusions

Cluster analysis based on inhabitant structure and basic economic indicator analysis shows that:– There are some regional similarities – dendrogram shows

two distinct groups for Pierīga and some Latgale municipalities;

– Other planing regions don’t create separate groups. There are similar municipalities across all of them.

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Thank you!

Ieva.Brauksa@inbox.lv