Data application of the month: Population and settlement data

How are population and settlement data used in disaster risk reduction and response efforts

Population data helps disaster risk managers to assess what proportion of the population is potentially at risk due to its exposure to a hazard.  In addition, it helps emergency managers to assess how many people reside within an affected area. Exposure maps are generated overlaying population data on top of hazard-related layers. These maps are extremely useful for disaster risk and emergency managers as they provides an estimate of the number of people currently residing inside high hazard areas.  Such information on the number of people exposed is beneficial for preparedness efforts in anticipation of a major event such as a storm, as high exposure areas can be prioritized for evacuation efforts in order to potentially save a significant number of lives. Furthermore, exposure maps can also be used to investigate the likely impact caused by a natural hazard, as estimations can be made regarding the extent of the damage and the number of possible casualties knowing the number of people that reside within an affected location and the magnitude of the event.

While census data shows how many people are living in an administrative unit, satellite data shows where the people live and provides information about population dynamics. Satellite-based population and settlement data provides useful information for exposure, and risk mapping. It can also contribute to monitor some of the indicators of the Sendai Framework for Disaster Risk Reduction, as well as those related to the Sustainable Development Goals.

A key advantage regarding satellite imagery to keep in mind is the fact that satellite imagery is available on a daily basis while national census data is only updated every decade, or even less frequently depending on the availability of funds. Satellite imagery is capable or monitoring changes in population and settlement dynamics on a very frequent basis. For example, the Global Human Settlement Layer or the Global Urban Footprint tracks the dynamics of growing settlement areas using satellite imagery.

Satellite-based population data can contribute to monitor target 2 of the Sendai Framework for Disaster Risk Reduction 2015-2030, i.e. "a substantial reduction in numbers of affected people". In addition, population and settlement data can contribute to monitor some of the Sustainable Development Goals and targets, for example target 11.3 "By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countries". The indicator "ratio of land consumption rate to population growth rate" is currently proposed for this target, and settlement data such as the Global Human Settlement Layer (cf. below links to the data) is foreseen as a possible data source to monitor this indicator.

 

How are urban settlements mapped from space and how does satellite imagery contribute to the estimation of population data?

Population data is commonly collected through a census. Originally collected as detailed data at the household level, census data is then spatially aggregated to the level of administrative units such as a municipal district (cf. top image of figure 1). This means that each pixel within an administrative boundary is assigned the same population value, no matter whether it is an urban area or a forest. It is obvious that natural hazards do not follow administrative borders, and humans settle in specific patterns that are not represented by aggregated population data either. Therefore, to create meaningful exposure maps within an administrative boundary, it is necessary to disaggregate the census data. The disaggreagation of census data is possible using satellite imagery as it is done for example with the Worldpop and Landscan databases (cf. bottom image of figure 1).

WorldPop and LandScan are two population datasets that have been developed using remote sensing technology. The WorldPop dataset is generated using satellite imagery to map settlements, specifically the 30 meter spatial resolution Landsat Enhanced Thematic Mapper (ETM) satellite imagery. A combination of widely available, remotely-sensed and geospatial datasets (e.g. settlement locations, settlement extents, land cover, roads, building maps, health facility locations, satellite nightlights, vegetation, topography, refugee camps) contribute to the modeled dasymetric weights, and then a gridded estimage of population density is generated at a 100 meter spatial resolution. Gridded population maps using a 100 meter x100 meter grid represent a more realistical representation of population distributions across a landscape than administrative units. 


Figure 1: Census data that are aggregated by administrative unit (top image) can be spatially disaggregated with the help of satellite-based land cover map and satellite-based lights at night map. The bottom image shows the result of the random forest modelling approach by WorldPop. The maps are located in Northern Vietnam. (http://www.worldpop.org.uk/about_our_work/case_studies).

LandScan is the finest resolution global population distribution data available, at approximately 1 kilometer resolution. The LandScan algorithm uses spatial data and a multi-variable dasymetric modeling approach to disaggregate census data within an administrative boundary. Based upon the spatial data and the socioeconomic and cultural understanding of an area, cells are preferentially weighted for the possible occurrence of population during a day. Within each country, the population distribution model calculates a “likelihood” coefficient for each cell and applies the coefficients to the corresponding census data. The total population for any given area is then allocated proportionally to each cell.  Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region.   


Figure 2: LandScan population distribution dataset (http://web.ornl.gov/sci/landscan/index.shtml).

The above examples provide estimates on how many people are residing where. Using archived satellite imagery it is also possible to provide estimates regarding when people have resided where. Understanding and measuring settlement dynamics is important to update exposure and risk maps, especially when urban fringes with vulnerable people expand into hazard-exposed areas. The video presented below shows how human settlements are mapped from space.

Video 1: Mapping human settlements from space. (cf. also presentation held by Thomas Kemper of JRC, which can be downloaded here)

An innovative approach to map population dynamics even during day and night can be carried out through the use of mobile phone data as shown in video 2 below. For this aproach, a dense network of cell towers is necessary, i.e. the approach is not applicable in all regions of the world. Even though this is not a space-based application, we include this approach here since it is showing what is possible to estimate this extremely important data.

Video 2: Dynamic population mapping using mobile phone data. For more information read the paper by Deville et al. (2014).

 

How can I access population and settlement data?

There are several datasets which can be used to assess population which are based on remote sensing technology. In addition to population estimations, there are also additional products related to population data which map global urban extent, urban land use, human settlement layers, socioeconomic data and urban hazards.  The products where this data can be accessed are presented below.

Population data

Additional related urban data

 

How are the datasets used for disaster risk management and emergency response?

An operational example of the use of satellite-based population data for emergency response can be found in the Copernicus Emergency Mapping Service (EMS). The Copernicus EMS portfolio for rapid mapping includes reference maps and delineation maps. Reference maps provide a quick updated information on the territory and existing assets using data collected before the disaster. By combining the reference information with Landscan population data, Copernicus provides an estimate of people exposed within the area of interest (AOI). Delineation maps provide an assessment of the geographical extent of the event. Combining the geographical extent polygon with the Landscan population data, Copernicus estimates the number of people affected.

Take, for example, the floods in Malawi in January 2015. Heavy rains during several weeks led to severe flooding across the country. The floods caused the displacement of thousands of people, extensive damage to crops, livestock and infrastructure. Some areas were inaccessible, impeding the conduction of assessments regarding damages and losses on the ground. Copernicus EMS was activated by the European Commission's Humanitarian and Civil Protection Department (DG ECHO) on 25 January 2015 (cf. activation website). The first reference map was published on the following day (cf. figure 3) giving a first rough estimate on the exposed population within the Area of Interest or AOI (cf. table 1). On 28 January 2015 the first delineation maps were published (cf. figure 4), giving a more precise estimate of the number of people affected within the flooded area (cf. table 2).


Figure 3: Reference map. (Source: Copernicus)


Table 1: Exposure table of reference map (zoom). The estimated number of exposed population within the area of interest (AOI) is estimated using Landscan data.  (Source: Copernicus)


Figure 4: Delineation map. (Source: Copernicus)


Table 2: Affected population, settlements, and road infrastructure. The estimate of the number of affected population within the flooded area is based on Landscan data. (Source: Copernicus)

Zircon - This is a contributing Drupal Theme
Design by WeebPal.