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Pingliang (simplified Chinese: 平凉; traditional Chinese: 平涼; pinyin: Píngliàng; lit. 'Pacify Liang') is a prefecture-level city in eastern Gansu province, China, bordering Shaanxi province to the south and east and the Ningxia Hui Autonomous Region to the north. The city was established in 376 AD. It has a residential population of 2,125,300 in 2019. The…
The analysis highlights Geography and Regions as prominent areas in the source structure around Pingliang.
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.
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.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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.
The extracted context around Pingliang shows recurring relationship patterns in the source. For example, Pingliang → Baiyin, Baoji, Bordering, December, Dingxi, Due, Guyuan, It, January, July, June, Köppen Dwb, Loess Plateau, Much, Ningxia, Qingyang, September, Shaanxi, The, Tianshui Another extracted example is Pingliang → Huangfu Mi, Niu Sengru, Southern Song, Wu Jie, Wu Lin. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
city gansu ningxia prefecture-level china south population known shaanxi elevation ft province kongtong mountains location bordering east north book first
TTTA extracted 45 structured relationships around Pingliang. Examples in this analysis include Pingliang → Area code → 0933 and Pingliang → Country → People's Republic of China. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Pingliang | Area code | 0933 | 1.00 | infobox |
| Pingliang | Country | People's Republic of China | 1.00 | infobox |
| Pingliang | Elevation | 1,398 m (4,587 ft) | 1.00 | infobox |
| Pingliang | Highest elevation | 2,857 m (9,373 ft) | 1.00 | infobox |
| Pingliang | ISO 3166 code | CN-GS-08 | 1.00 | infobox |
| Pingliang | Licence plate prefixes | 甘L | 1.00 | infobox |
| Pingliang | Lowest elevation | 890 m (2,920 ft) | 1.00 | infobox |
| Pingliang | Municipal seat | Kongtong District | 1.00 | infobox |
| Pingliang | Postal code | 744000 | 1.00 | infobox |
| Pingliang | Province | Gansu | 1.00 | infobox |
| Pingliang | Time zone | UTC+8 (China Standard) | 1.00 | infobox |
| Pingliang | Website | www.pingliang.gov.cn | 1.00 | infobox |
| Pingliang | • Density | 165.1027/km2 (427.6140/sq mi) | 1.00 | infobox |
| Pingliang | • Metro | 871,600 | 1.00 | infobox |
| Pingliang | • Per capita | CN¥ 16,595 US$ 2,664 | 1.00 | infobox |
| Pingliang | • Prefecture-level city | 11,196.71 km2 (4,323.07 sq mi) | 1.00 | infobox |
| Pingliang | • Prefecture-level city | 1,848,607 | 1.00 | infobox |
| Pingliang | • Prefecture-level city | CN¥ 34.8 billion US$ 5.6 billion | 1.00 | infobox |
The concept neighborhoods around Pingliang bring nearby vocabulary together. In this analysis, examples include Book, Climate and First. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Pingliang, one of the stronger structural bridges in this analysis connects Pingliang 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.
TTTA analyzes the structure around Pingliang to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Geography & Regions, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Pingliang · EN edition · Analysis: TopicsToTalkAbout