Exploring correlated data in any context is quite interesting. Neo4j is an amazing tool, which is useful for such exploration and insights.
By relating key words mapped with correlations, we can share knowledge articles or research papers spread across various locations.
Let us dive into an interesting example, which validates this:
You will find that there are various locations around the world, which share the same name. By finding the link between cities or states/prefectures across world, we can explore history with geographical data.
Below schema was created to visualize how we could correlate cities and states/prefectures across
Fig1. Schema visualization
Below few visualizations and data insights from Neo4j Graph database.
As seen in below table, names of 2 cities that were commonly present in at least three different states across world.
Table1. City names commonly present in atleast 3 states
By exploring path pattern between four countries: India, Australia, Japan & United States, we are able to find an interesting connection with other countries as well.
Fig2. Path patterns between multiple countries.
As can be seen in below example, when trying to identify shortest path between Japan and Australia, we inadvertently found connection through Columbia based on source data.
Fig3. Shortest path between Japan and Australia with Neo4j Bloom based on data
Spread of city names in India also shared with locations around world.
Fig 4. Spread of City names in India shared across
Spread of city/Prefecture names in Japan also shared with locations around world.
Fig5. Spread of city/Prefecture names in Japan shared across
Data used as a source for above visualizations might vary when compared to other sources.
This example is to highlight, how we can explore connected data in detail to provide deeper insights.
Thanks and credits to Wikipedia for data.