Data bias in Indian geography refers to the systematic exclusion of certain groups or regions from data collection, leading to an incomplete or distorted representation of geographical information. This can result in policies and decisions that are biased against the excluded groups or regions.

Data bias can lead to discrimination in Indian geography by perpetuating stereotypes and prejudices against certain groups or regions, justifying unequal distribution of resources and opportunities, and making it difficult for marginalized groups to access essential services. This can result in systemic discrimination and social injustice.

The study in question exhibits data bias by excluding rural populations, which can lead to a distorted understanding of the geographical distribution and characteristics of the population in India. This bias can have implications for policymaking and resource allocation, potentially marginalizing rural communities.

Addressing data bias and discrimination in Indian geography faces several challenges, including lack of awareness about the issue, resistance from vested interests, and limited resources for data collection and analysis. These challenges can hinder efforts to promote fair and accurate representation of geographical information.

Mitigating data bias and discrimination in Indian geography requires a multi-pronged approach, including raising awareness, promoting inclusivity, and developing methodologies to identify and correct biases. These strategies can help ensure that geographical data is fair, accurate, and representative of the diverse realities of India.