Лесоторговое районирование России: разработка методики кластеризации

Известия РАН. Серия географическая. 2024. Т. 88. № 2. С. 214–230.

Rusakov D., Елютин М. С., Синицын Н. А., Коротков А. С., Меняльщиков Р. Р., Кушлевич А. О.

Network analysis of the Russian timber industry was based on the information about timber transactions between
Russian companies in 2020. The data were collected from the Unified State Automated Information System
Accounting Timber and Transactions with It. The method of graph clustering was developed. The first step is clus-
tering of the hole graph by Leiden algorithm. The second step is drawing of each Leiden created cluster in abstract
space using Fruchterman–Reingold layout and extraction of clusters from layout using meanshift algorithm.
Such approach helped us to divide many Leiden-created clusters into components. This algorithm has a problem
of resolution limit. It tends to combine some medium and small clusters into large ones. Also, a special method
was developed to limit the uncertainty of clustering – both Leiden algorithm and Fruchterman–Reingold layout
are non-deterministic algorithms. Wood trade clusters vary in size and content. They are rather autonomous.
The average share of intra-cluster trade is 89%. The modularity score of our partition is 0.863. Each cluster has
been described using open sources from the Internet. The content of the clusters does not contradict the logic
of the wood industry processes. The clusters create rather compact areas on the map – wood trade regions. Their
borders often overlap with existing administrative boundaries. Five types of network structure of clusters were de-
fined: vertical monocentric, vertical polycentric, horizontal, dendritic, simple. There are three factors of cluster-
ing in wood industry of Russia: production chains (4 types – pulp, plywood, lumber, chipboard chains), demand
chains (redistribution of raw material between manufacturers within the cluster, accumulation of wood volumes for some enterprises outside the cluster, wood trade between traders within the cluster), common holder. We have
shown that existing approaches to cluster detection based on location quotients often misrepresent economic
networks.

Keywords: сетевой анализ, лесная промышленность, районирование, graph clustering, экономические кластеры, economic clusters, wood industry, trade regions, кластеризация графа