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Ústí nad Labem

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Related Images

Squares in Ústí nad Labem
Vysočina
Karlovy Vary
Olomouc
South Bohemian
Gardens and parks in Ústí nad Labem
Prague
Moravian-Silesian
Zlín
Hradec Králové
Pardubice
Housing estates in Ústí nad Labem
Plzeň
Liberec
South Moravian
Postcards of Ústí nad Labem
Central Bohemian
Structures in Ústí nad Labem
Micromobility in Czech Republic
Sports in Ústí nad Labem

Analyzing Network Connections...

Loading Map...

Nearest Locations

  • 📍
    Pohraniční 118.2 km away
  • 📍
    Belvedere Weesenstein32.5 km away
  • 📍
    Kirche Graupa37.8 km away
  • 📍
    Lohengrinhaus, Graupa38.7 km away
  • 📍
    Lohengrinhaus38.7 km away

Network Profile

Overall Strength
i
0.40% of network
(28.8K)
Strength Breakdown
  • This Post (0.40%)
  • Karlovy Vary (1.61%)
  • Prague (1.61%)
  • Liberec (0.80%)
  • Plzeň (0.80%)
  • South Bohemian (0.80%)
  • Central Bohemian (0.40%)
  • Hradec Králové (0.40%)
  • Moravian-Silesian (0.40%)
  • Olomouc (0.40%)
  • Pardubice (0.40%)
  • South Moravian (0.40%)
  • Vysočina (0.40%)
  • Zlín (0.40%)
  • Česko (0.40%)
Dominant nodes (excluded from chart)
Czech Republic 90.36%
Influence Score
i
5.26% of network
(8)
Influence Breakdown
  • This Post (5.26%)
  • Liberec (10.53%)
  • Plzeň (10.53%)
  • South Bohemian (10.53%)
  • Czech Republic (5.26%)
  • Central Bohemian (5.26%)
  • Hradec Králové (5.26%)
  • Karlovy Vary (5.26%)
  • Moravian-Silesian (5.26%)
  • Olomouc (5.26%)
  • Pardubice (5.26%)
  • Prague (5.26%)
  • South Moravian (5.26%)
  • Vysočina (5.26%)
  • Zlín (5.26%)
  • Česko (5.26%)
Direct Connections 2

Node & Network Details

How is this calculated?

The math continuously tracks how strongly this post is connected to the rest of the network. Every tag forms a 2-way link. The base stats determine personal node strength, and the pie charts below show this node's share against its direct neighbours.

// 1. Base variables (floored at 1 to prevent zero-multiplication math errors)
$inbound = max(1, 1) = 1
$outbound = max(1, 1) = 1

// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 1 * 1 = 1
Base_Influence (IV) = $inbound / $outbound = 1 / 1 = 1

// 3. Exponential Network Values (accumulating 15 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 1 *
                           ( 2 [Liberec] *
                            2 [Plzeň] *
                            2 [South Bohemian] *
                            225 [Czech Republic] *
                            1 [Central Bohemian] *
                            1 [Hradec Králové] *
                            4 [Karlovy Vary] *
                            1 [Moravian-Silesian] *
                            1 [Olomouc] *
                            1 [Pardubice] *
                            4 [Prague] *
                            1 [South Moravian] *
                            1 [Vysočina] *
                            1 [Zlín] *
                            1 [Česko]
                           )

                         = 28.8K

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 2 [Liberec] *
                            2 [Plzeň] *
                            2 [South Bohemian] *
                            1 [Czech Republic] *
                            1 [Central Bohemian] *
                            1 [Hradec Králové] *
                            1 [Karlovy Vary] *
                            1 [Moravian-Silesian] *
                            1 [Olomouc] *
                            1 [Pardubice] *
                            1 [Prague] *
                            1 [South Moravian] *
                            1 [Vysočina] *
                            1 [Zlín] *
                            1 [Česko]
                           )

                         = 8
Outbound 1 Tags on post
Inbound 1 Posts tagging this
Connections 15 Total nodes
Base Node Strength 1
Base Node Influence 1
Strength Share (vs Direct Neighbours)
0.40% (28.8K overall)
  • This Post (0.40%)
  • Karlovy Vary (1.61%)
  • Prague (1.61%)
  • Liberec (0.80%)
  • Plzeň (0.80%)
  • South Bohemian (0.80%)
  • Central Bohemian (0.40%)
  • Hradec Králové (0.40%)
  • Moravian-Silesian (0.40%)
  • Olomouc (0.40%)
  • Pardubice (0.40%)
  • South Moravian (0.40%)
  • Vysočina (0.40%)
  • Zlín (0.40%)
  • Česko (0.40%)
Dominant nodes (excluded from chart)
Czech Republic 90.36%
Influence Share (vs Direct Neighbours)
5.26% (8 overall)
  • This Post (5.26%)
  • Liberec (10.53%)
  • Plzeň (10.53%)
  • South Bohemian (10.53%)
  • Czech Republic (5.26%)
  • Central Bohemian (5.26%)
  • Hradec Králové (5.26%)
  • Karlovy Vary (5.26%)
  • Moravian-Silesian (5.26%)
  • Olomouc (5.26%)
  • Pardubice (5.26%)
  • Prague (5.26%)
  • South Moravian (5.26%)
  • Vysočina (5.26%)
  • Zlín (5.26%)
  • Česko (5.26%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
Liberec ↗
Str: 2Inf: 2
Plzeň ↗
Str: 2Inf: 2
South Bohemian ↗
Str: 2Inf: 2
Czech Republic ↗
Str: 225Inf: 1
Central Bohemian ↗
Str: 1Inf: 1
Hradec Králové ↗
Str: 1Inf: 1
Karlovy Vary ↗
Str: 4Inf: 1
Moravian-Silesian ↗
Str: 1Inf: 1
Olomouc ↗
Str: 1Inf: 1
Pardubice ↗
Str: 1Inf: 1
Prague ↗
Str: 4Inf: 1
South Moravian ↗
Str: 1Inf: 1
Vysočina ↗
Str: 1Inf: 1
Zlín ↗
Str: 1Inf: 1
Česko ↗
Str: 1Inf: 1
Weakest Connections (Lowest Multipliers)
Česko ↗
Str: 1Inf: 1
Zlín ↗
Str: 1Inf: 1
Vysočina ↗
Str: 1Inf: 1
South Moravian ↗
Str: 1Inf: 1
Prague ↗
Str: 4Inf: 1
Pardubice ↗
Str: 1Inf: 1
Olomouc ↗
Str: 1Inf: 1
Moravian-Silesian ↗
Str: 1Inf: 1
Karlovy Vary ↗
Str: 4Inf: 1
Hradec Králové ↗
Str: 1Inf: 1
Central Bohemian ↗
Str: 1Inf: 1
Czech Republic ↗
Str: 225Inf: 1
South Bohemian ↗
Str: 2Inf: 2
Plzeň ↗
Str: 2Inf: 2
Liberec ↗
Str: 2Inf: 2

Connection Health Audit (Red = broken 1-way link)

Outbound Tags (1)
Czech Republic
Inbound Posts (1)
Czech Republic
Last calculated: Sep 29, 8:03 AM
Ústí nad Labem, Ústí nad Labem Region, Czechia45

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