Using Integrated Data Tools to Understand Urban Poverty and Vulnerability in the Philippines

The Asian Development Bank and its partners piloted the Poverty Impact and Vulnerability Evaluation (PIVE) tool in the Philippines to integrate surveys, census data, mobile GPS, satellite imagery, and community inputs into a single platform for understanding poverty, vulnerability, and disaster risk. The pilot shows that combining traditional statistics with modern data sources can deliver more timely, local, and actionable insights for urban planning and disaster response, while also highlighting the need for strong governance and capacity to scale such tools effectively.

The Asian Development Bank, together with the Philippine Statistics Authority and private research and technology partners such as LocationMind Inc. and GLODAL Inc., set out to address a growing problem in development policymaking: the gap between rapidly changing realities on the ground and slow, fragmented data systems. Their collaboration led to the development of the Poverty Impact and Vulnerability Evaluation (PIVE) tool, a pilot data integration platform tested in selected areas of the Philippines. The project responds to a broader global shift, where traditional surveys and censuses alone can no longer meet the demand for timely, local, and detailed data needed to reduce poverty, manage disasters, and build inclusive cities.

From Traditional Statistics to Integrated Data

For decades, governments have relied on household surveys, population censuses, and administrative records to understand poverty and social conditions. While these sources remain essential, they are costly, infrequent, and often unable to capture rapid changes, especially at the city or neighborhood level. The COVID-19 pandemic highlighted these weaknesses, as policymakers struggled to access real-time information on mobility, exposure, and vulnerability. Integrating official statistics with new data sources, such as mobile phone data and satellite imagery, proved crucial for faster and more targeted responses. The PIVE initiative builds on this lesson, arguing that combining existing datasets is a practical way to improve decision-making without constantly launching expensive new surveys.

Why the Philippines Was Chosen

The Philippines offers a clear example of why integrated data is urgently needed. The country is among the most disaster-prone in the world, facing frequent typhoons, floods, earthquakes, and landslides. Over the past two decades, disasters have affected millions of people and caused major economic losses. At the same time, rapid urbanization has led to the growth of informal settlements, many located in high-risk areas such as floodplains or unstable hillsides. Poverty has declined in recent years, but official poverty data are still produced only every few years and are not reliable at the level of cities or barangays. This makes it difficult for governments to design targeted poverty reduction and disaster risk management programs.

How the PIVE Tool Works

The PIVE tool brings together different types of data into a single, easy-to-use dashboard designed for nontechnical users. It integrates mobile GPS data to understand daily movement and transport patterns, satellite imagery to detect informal settlements and land-use changes, government census and survey data to provide demographic context, and information from local governments. It also uses field surveys and a chatbot called PIVEBOT, which allows communities to report conditions, request assistance, and share information during emergencies.

In the pilot areas, Barangay Payatas in Quezon City, Kasiglahan Village in Rizal Province, and Baguio City, the tool showed how these data sources can complement one another. For example, GPS data revealed commuting patterns between relocation sites and job centers, while satellite images highlighted settlement growth in hazard-prone zones. By layering hazard maps with population and mobility data, PIVE supports disaster preparedness, evacuation planning, and relief distribution, while also helping planners understand long-term poverty and vulnerability trends.

What Worked, What Needs Improvement

Feedback from government agencies, local governments, NGOs, and community representatives was largely positive. Users appreciated the level of detail, the visual dashboard, and the way different datasets were brought together in one place. Transport and mobility data were seen as especially useful for understanding how people cope with daily challenges and shocks.

At the same time, the pilot revealed important challenges. Stakeholders asked for more detailed indicators, especially related to gender, disability, and social inclusion, and warned against duplication with existing systems. Issues around data privacy, system maintenance, user training, and long-term funding were also raised. These concerns highlight that while technology is powerful, successful data integration also depends on governance, institutional capacity, and sustained investment.

Overall, the PIVE pilot shows how integrated data can make poverty and vulnerability more visible, especially in disaster-prone urban areas. It demonstrates that combining traditional statistics with modern data sources can lead to better, faster, and more inclusive decisions if the right systems and institutions are in place to support it.