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Si-ning: Doprava, Kultura & Geografie
Si-ning (čínsky pchin-jinem Xīníng, znaky 西宁; tibetsky: ཟི་ལིང་།, Siling) je městská prefektura a hlavní město provincie Čching-chaj na severozápadě Čínské lidové republiky. V minulosti bylo město důležitým obchodním uzlem, skrz vedla jedna z cest Hedvábné stezky. Ve městě je silné zastoupení národnostních menšin, především Chuejů a Tibeťanů.
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Si-ning topic overview
The analysis highlights Doprava, Kultura and Geografie as prominent areas in the source structure around Si-ning.
Related topics
34
Source areas
7
Connected nodes
41
Extracted relationships
69
Concept neighborhoods
16
Bridge connections
41
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Doprava · 10 topics
Overview · 10 topics
Geografie · 4 topics
Partnerská města · 4 topics
Administrativní dělení · 3 topics
Demografie · 2 topics
Kultura · 1 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Etnické složení
Chanové, Tibeťané, Chuejové
Hustota zalidnění
324,4 obyv./km²
Nadmořská výška
2 275 m n. m.
Oficiální web
www.xining.gov.cn
Označení vozidel
青A
Počet obyvatel
2 467 965 (2020)
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Si-ning connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Si-ning shows recurring relationship patterns in the source. For example, Si-ning → Cchao-ťia-pao, Dnes, G109, G214, G227, G315, Golmud, Hedvábné, Jün-nan, Kašgar, Lan-čou, Lhasa, Lhasy, Meng-chaj, Od, Peking, Sining, Siningu, Ve Another extracted example is Si-ning → Bydlí, Každoročně, Lhori, Ma, Ma Pu-fang, Ma Pu-fanga, Na, Nedaleko, Největší, Pej-šan, Si-ningu, Siningu, Tibetu Kumbum, Tibeťanů, Tung-kuan, Vstup. Use these groups to spot repeated connection types before inspecting the individual relationships.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 69 structured relationships around Si-ning. Examples in this analysis include Si-ning → Etnické složení → Chanové, Tibeťané, Chuejové and Si-ning → Hustota zalidnění → 324,4 obyv./km². The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Si-ning
Etnické složení
Chanové, Tibeťané, Chuejové
1.00
infobox
Si-ning
Hustota zalidnění
324,4 obyv./km²
1.00
infobox
Si-ning
Nadmořská výška
2 275 m n. m.
1.00
infobox
Si-ning
Oficiální web
www.xining.gov.cn
1.00
infobox
Si-ning
Označení vozidel
青A
1.00
infobox
Si-ning
Počet obyvatel
2 467 965 (2020)
1.00
infobox
Si-ning
PSČ
810000
1.00
infobox
Si-ning
Rozloha
7 606,78 km²
1.00
infobox
Si-ning
Souřadnice
36°37′2″ s. š., 101°46′43″ v. d.
1.00
infobox
Si-ning
Starosta
Čchang Siao-rung
1.00
infobox
Si-ning
Status
hlavní město provincie
1.00
infobox
Si-ning
Stát
Čína Čína
1.00
infobox
Si-ning
Telefonní předvolba
(+86)0971
1.00
infobox
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Si-ning bring nearby vocabulary together. In this analysis, examples include Provincie, Commons and Hlavní. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Si-ning
Provincie
Commons
Hlavní
Městská
Prefektura
Wikimedia
Čínské
Město
Datové
Pchin-jinem
Položky
Tibetsky
si-ning
Provincie
Commons
Hlavní
Městská
Prefektura
Wikimedia
Čínské
Město
Datové
Pchin-jinem
Položky
Tibetsky
čínské lidové republiky
Provincie
Pchin-jinem
Tibetsky
Znaky
Čínsky
Chuejové
Hlavní
Městská
Peking
Prefektura
Si-ning
Tibeťané
pchin-jinem
Tibetsky
Znaky
Čínsky
Hlavní
Městská
Prefektura
Čching-chaj
Čínské
Provincie
Město
Si-ning
městská prefektura
Prefektura
Pchin-jinem
Tibetsky
Znaky
Čínsky
Hlavní
Čching-chaj
Si-ning
Čínské
Provincie
Město
hedvábné stezky
Hedvábné
Stezky
Skrz
Kilometrů
Leží
Město
Města
partnerská města
Roce
Golmud
Kumbum
Skrz
Stezky
Tibeťanů
Čínské
znaky
Tibetsky
Čínsky
Čching-chaj
Čínské
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Si-ning, one of the stronger structural bridges in this analysis connects Si-ning with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Si-ning
Nodes42
Edges41
Triples69
Avg. degree1.95
Density0.047619
Components1
Source & methodology
TTTA analyzes the structure around Si-ning to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Doprava, Kultura & Geografie, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
