Understanding Interconnectivities in Ration Card Transaction Data with Knowledge Graphs | Esri India

 Unveiling Insights with ArcGIS Knowledge

Organizations today face significant challenges in data fusion and discovery, specifically in quickly finding the right information and analyzing it in the right context. This blog introduces ArcGIS Knowledge, the knowledge graph and graph analysis capability for ArcGIS Enterprise and ArcGIS Pro, designed to simplify the creation, discovery, and exploration of data connections.

Example: Ration Card Transactions

To illustrate the capabilities of ArcGIS Knowledge, consider the example of ration card transactions. To ensure food security for vulnerable populations, particularly migrant workers, the Government of India launched the “One Nation One Ration Card” initiative. This digital transformation model allows ration cardholders to collect food grains from any fair price shop across India. The transactions, recorded in relational databases, amount to over 23,000 ration cards in just one month, resulting in approximately 140,000 transactions.

Hidden within this dataset lies valuable spatial and non-spatial information. How can we uncover the relationships that reveal the full story? This is where Knowledge Graphs come into play. ArcGIS Knowledge, an optional server capability, allows organizations to connect, explore, and analyze their data within knowledge graphs, thereby discovering new insights and making more informed decisions.

Transforming Data into Entities and Relationships

The process begins by transforming your data into entities and relationships. For instance, state names and ration card numbers are assigned as "Entities," while relationships, such as a ration card holder’s home state, are defined as “Relationship Types.” This transformation can be done using the “Load Table” functionality.

Let's take a subset of six ration cardholders' data and add it to the knowledge graph. ArcGIS Pro provides tools to explore this knowledge graph and answer various questions, such as identifying the home state and sale state of each cardholder. We can delve deeper into transaction details, determining when and what items were purchased. Using the “Select by Attributes” tool within Link Charts, we can query the graph to filter beneficiaries of the PMGKY scheme.

Integrating Geospatial Data

With ArcGIS Knowledge, we are not limited to the data within the knowledge graph. We can seamlessly switch back to the map to incorporate geospatial perspectives. By using the load data functionality, we can enrich the knowledge graph with additional data to analyze migration patterns within the country.

For example, lines extending from home states to sale states, labeled with the number of transactions, reveal high inflows to Haryana from Bihar and Uttar Pradesh, while Assam shows outflows and minimal inflow. Notably, there is minimal movement of migrant laborers within the southern states. This analysis helps policymakers ensure there is no gap between supply and demand.

Broader Applications

This example demonstrates just one application of ArcGIS Knowledge. The technology can be applied in various fields, such as managing a network of assets, investigating crimes, or examining spending patterns. This hybrid approach to understanding data enables more informed, data-driven decisions. 

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