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|Title:||Population Density Estimations for Disasters Management - Case Study Rural Zimbabwe|
|Authors:||SCHNEIDERBAUER Stefan; EHRLICH DANIELE|
|Citation:||Proceedings of Geo-Information for Disaster Management Gi4DM p. 901-921|
|Type:||Contributions to Conferences|
|Abstract:||The number of people living in an area affected or endangered by a hazardous event is the basic parameter for impact estimations and assessments of disasters on human life. Population data of high accuracy is in particular lacking in less developed countries where disasters cause most damage and fatalities. Available data for those countries are mostly out of date, of coarse resolution or allocated to large administrative units and / or of questionable or not verifiable quality. This paper addresses population data estimations in developing countries. The overall objective of the work is to develop a methodology for population density and distribution estimations at a fine resolution (grid cell size <500 m) based on existing topographic data and information extracted from remotely sensed images. The work is conducted within the ISFEREA (Information Support for Effective and Rapid External Action) project of the JRC (Joint Research Centre) of the European Commission, which aims to support the European Commission’s aid allocation and humanitarian assistance operations. Case study area is Zimbabwe where people are currently suffering from famine and AIDS epidemic. A model for the estimation of population distribution and density in Zimbabwe is under development . Reference data is the last official Zimbabwean census from 2002. The modelling procedure takes into account (1) populated places [source: countrywide 1:250,000 topographic maps, in urban areas: 1:50,000 topographic maps and IKONOS satellite images of 1 m resolution], (2) information on land tenure, land use and vegetation cover [source: countrywide: geo-datasets on farming systems, LANDSAT TM satellite images of 30 m resolution and GLC 2000 satellite image classification of 1 km resolution] (3) additional spatial information layers such as road networks, bore holes, river lines and elevation [all countrywide] (4) expert and local knowledge about land use systems and recent developments in the land reform process and its impact on the population distribution. Based on these datasets and information, we will allocate one or several specific social group(s) to each grid cell within a populated area. For example, the urban poor (in densely populated urban areas with a specific housing structure and lack of infrastructure) or the subsistence farmers (in relatively densely populated rural area (communal land ), with small-sized field structure). First data analysis reveal a strong correlation between population density, land tenure type, land cover class (in the GLC 2000) and reflection values in the Landsat TM scenes. For each social group an average value for population density will be estimated. The final result will provide for each grid cell the estimated number of people living. The model will also take into account regional specifications such as the quality of the natural environment regarding agricultural activities (climate, soil, relief etc.).|
|JRC Institute:||Institute for the Protection and Security of the Citizen|
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