In the context of Nepal, the data sources heavily rely on traditional data sources such as surveys, census, and administrative records Mobile phone and Satellite data can significantly contribute to filling data gaps in monitoring SDG target 1.1.1, which focuses on the proportion of the population living below the international poverty line by various demographic factors and geographic locations. It can provide insights into human mobility patterns, including migration trends, commuting behaviors, and travel patterns. Advances in non-traditional data sources are revolutionizing how we collect, analyze, and utilize information. Non-traditional data sources refer to information not typically available through economic authorities or statistical offices, such as national accounts, labor force surveys, or trade data. Non-traditional data sources come from (satellite images); mobile telecommunications (call records), social networks (sentiment analysis), and citizen-generated data (civil society data), which are cost-effective methods and address the data gap. Additionally, non-traditional data also helps to monitor and track the Sustainable Development Goals (SDGs).
Nepal’s data landscape is characterized by a reliance on traditional data sources like surveys, censuses, and administrative records. However, these sources are infrequent and often delayed, leading to a significant data gap. **Detailed Text:**
Nepal’s journey towards a robust data ecosystem is marked by a reliance on traditional data sources.
This study highlights the new possibilities of non-traditional data in policy analysis. The study’s findings suggest that GDP figures alone can be misleading, as they often fail to capture the true economic activity of autocratic regimes. The researchers found that night-time light data, which measures the amount of light emitted by cities at night, provides a more accurate picture of economic activity.
They are also time-consuming to collect and analyze, and often lack the granularity needed for nuanced understanding of complex social issues. Non-traditional data, on the other hand, offers a more accessible and cost-effective alternative. It leverages diverse sources like social media, mobile phone data, and satellite imagery. These sources provide rich, real-time information that can be analyzed using readily available tools and techniques.
Nepal has a unique context with its diverse geography, cultural diversity, and unique challenges. This requires a focus on innovative data collection methods and leveraging existing data sources to monitor progress towards the SDGs. The summary provided is a good starting point, but it needs to be expanded upon to create a comprehensive and insightful text.
Geo-spatial data for SDG target 6.3.1.1, on the proportion of untreated industrial wastewater, allows for precise mapping and monitoring. We can integrate satellite imagery and geographic information systems (GIS) to gain real-time insights into the extent of untreated industrial wastewater discharge. Satellite data is instrumental in monitoring SDG target 11.1.1.1, focused on the population living in slum areas. Satellite imagery can provide valuable information on infrastructure, housing conditions, land use, basic amenities and informal settlements. This also allows policymakers to target interventions effectively in urban areas. The non-traditional data sources complement the national statistics. The Government of Nepal needs strategic policy interventions to address the data gap. Investment in technical capacity building, including training programs for data scientists and statisticians, is essential. Recruitment and retention strategies for data experts within government agencies should be implemented, fostering interagency collaboration and partnerships with civil society to facilitate data flow. Additionally, promoting data literacy among government officials is crucial for effective data utilization in policymaking.
This is crucial for ensuring the quality and reliability of data used for decision-making. The summary provided highlights the importance of institutional mandates and coordination in Nepal for data management. Let’s delve deeper into this aspect.
