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#VizRisk Challenge – Locations & Datasets

The Labs team at GFDRR and partner country programs have prepared four suggested locations and recommended datasets to help inspire you and get you started.

You are also welcome to select another geography and search for other datasets (we encourage you to use open data as much as possible).

Based on the UR Field Lab in Chiang Mai, Thailand, we have added in another location full of new data!

Urban Flooding in Chiang Mai, Thailand [city scale]

Chiang Mai is a flood-prone urban area in northern Thailand with around 1 million residents. It’s a cultural center of the region, with hundreds of Buddhist temples and numerous important holidays and festivals. It is also a technology hub, and a destination for Thai and international software developers who want to participate in the rapidly growing digital economy. In 2017 the national government announced major investments aimed at helping Chiang Mai become a “smart city.” In Chiang Mai along with many other cities around the world, flood risk is increasing due to growing population, changing land-use and climate change. Floods are becoming increasingly frequent, limiting the ability of cities to achieve sustainable and equitable development. How can the risk of floods, especially to the most vulnerable populations, be communicated effectively to different user groups?  

The month-long UR Field Lab: Chiang Mai Urban Flooding un-conference has spent the last few weeks collecting various datasets:

Flooding in Monrovia, Liberia [city scale]

The capital city of Liberia is growing rapidly, including the expansion of informal settlements and residential and commercial infrastructure. At the same time, flooding – both from storm events and sea-level rise – has been a repeated and mounting threat. How can local government officials better understand, predict, prevent, and recover from flooding? What infrastructure is most vulnerable? Where might flood impacts be the most severe, or the most preventable?

Landslides in Nepal [national scale]

Rapid urban expansion in Nepal is one factor in exposure to natural hazards. Source: Making a Riskier Future

In the many mountainous areas of Nepal, landslides – both during monsoon season and from earthquakes – are a recurring threat. Landslides can have severe and lasting damage on infrastructure and livelihoods. How can national officials and organizations reduce the negative impact of landslides? Which communities are most vulnerable? How might the loss of a road impact people’s travel times? Where might impacts be the most severe, or the most preventable?

Hurricane hazards in the Caribbean [regional scale]

NOAA’s GOES-16 satellite captured this geocolor image of Hurricane Maria on the evening of September 18, 2017, as it made landfall over Dominica. Credit: CIRA

In 2017, Dominica saw over 90 percent of all buildings—including government facilities— damaged by Hurricane Maria. The majority of the damage was from the hurricane force winds that battered the island. Many other Caribbean islands are regularly impacted by hurricanes, and for small island nations, the scale of threat and potential damage can be very challenging to translate into actionable plans for governments or citizens. How can national officials and organizations better plan for hurricanes? What infrastructure is most vulnerable? Where might impacts be the most severe, or the most preventable?

Or choose another location and hazard!

You may also choose your own area and data of interest (we highly encourage you to use only open data.)

For more on working with risk and hazard data, and links to other recommended databases, see understandrisk.org/vizrisk/data.