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Olomouc

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Related Images

South Moravian
Liberec
Ústí nad Labem
Hradec Králové
Central Bohemian
South Bohemian
Zlín
Karlovy Vary
Plzeň
Prague
Functionalism in Olomouc
Structures in Olomouc
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Vysočina
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Analyzing Network Connections...

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Nearest Locations

  • Thumbnail for Zlín
    Zlín50.7 km away
  • 📍
    Arboretum Nový Dvůr53.2 km away
  • Thumbnail for Lubomír Nohavica
    Lubomír Nohavica54.7 km away
  • Thumbnail for Czech Silesia
    Czech Silesia55.1 km away
  • Thumbnail for Archeologický skanzen Modrá
    Archeologický skanzen Modrá55.8 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%)
  • Pardubice (0.40%)
  • South Moravian (0.40%)
  • Ústí nad Labem (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%)
  • Pardubice (5.26%)
  • Prague (5.26%)
  • South Moravian (5.26%)
  • Ústí nad Labem (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 [Pardubice] *
                            4 [Prague] *
                            1 [South Moravian] *
                            1 [Ústí nad Labem] *
                            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 [Pardubice] *
                            1 [Prague] *
                            1 [South Moravian] *
                            1 [Ústí nad Labem] *
                            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%)
  • Pardubice (0.40%)
  • South Moravian (0.40%)
  • Ústí nad Labem (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%)
  • Pardubice (5.26%)
  • Prague (5.26%)
  • South Moravian (5.26%)
  • Ústí nad Labem (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
Pardubice ↗
Str: 1Inf: 1
Prague ↗
Str: 4Inf: 1
South Moravian ↗
Str: 1Inf: 1
Ústí nad Labem ↗
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
Ústí nad Labem ↗
Str: 1Inf: 1
South Moravian ↗
Str: 1Inf: 1
Prague ↗
Str: 4Inf: 1
Pardubice ↗
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, 7:06 PM
Olomouc, Olomouc Region, Czechia43

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