|Student:||Sergio Carneiro Freire|
|Timeline:||January 2016 - 1 January 2020|
Geospatial information on population distribution (e.g. mapping inhabited vs uninhabited areas) and densities (people/km2) is one of the most fundamental and critical datasets to study human presence, interactions, impacts, and vulnerabilities. As human life is the most important asset to protect from disasters, assessing population exposure to actual or potential disasters is a key to Disaster Risk Management (DRM), and can benefit all phases of disaster management cycle, e.g. risk and impact assessment, mitigation, preparedness (including early warning and evacuation), and response. Assessing population exposure requires geo-information on population distribution at a range of spatial and temporal scales, as disasters can strike at any time, and affect from local to global areas.
For effective support to DRM, population data should be up-to-date, have sufficient resolution (spatial, temporal, thematic), and be readily available (i.e. produced beforehand or rapidly computable on-demand). Population data are still lacking for many areas and regions, both rich and poor, and conducting DRM at global scale would benefit from complete, consistent, and integrated datasets. If developed adequately, such population datasets are multipurpose and can serve a wide range of application domains: spatial planning (urban, regional, infrastructure, facilities), environmental assessment, epidemiology, GeoMarketing, etc.
The mapping of human distribution and population exposure has lagged behind hazard modeling and mapping, in terms of accuracy, detail, and currency. This research will address contributions of population distribution modeling to advancing Disaster Risk Management by (i) developing geospatial models that improve population distribution datasets at a range of spatio-temporal scales, (ii) applying those data to real disaster risk scenarios for different hazards, and (iii) discussing their impacts and contributions. The research will consider aspects related to population definition and practical concepts, geospatial data and technology, spatio-temporal scales, hazard types and their characteristics, and the specific population related information requirements throughout the Disaster Risk Management Cycle.