Exposure Database: Background and Work Packages

Background
Global datasets about built-up areas and buildings do not directly characterize earthquake vulnerability. Statistis offices do not have an adequate framework to produce timely housing data (durability and secure tenure); such data have rarely been part of the established routine data collection mechanism of national statistics offices. Information collected through censuses and household surveys are usually on tenure type but not on the type of location or the building codes. Also there is a gap in proper urban planning practice, where either location (spatial information) or census information is omitted or not taken into a consideration. In many parts of the world, data measuring the internal spatial structure of the city are not collected. Instead, data about urban areas commonly delineate socio-economic information about the population, geophysical characteristics of the territories, water bodies and human-induced features, such as the classes of land use (with very different legends). Most of this information, including more precise land-use data, does not distinguish between the structural classes required by damage functions.

The lack of structural information required to evaluate exposure is typically addressed through methods exploiting parameters coming from above mentioned data at a somewhat coarse scale, to be then refined as much as possible using building level information from cadastral maps or using remotely sensed imagery, which may also be useful to extract building density, size, a regional profile of building heights and other information. The UN's Global Urban Observatory for example has developed and introduced a methodology to estimate durability of housing. 
     For exposure, mentioned methods use very different mapping schemes to extract the exposure information for each building or group of buildings by inference from mostly terrestrial and partly remotely sensed data, with a high level of flexibility with respect to geographical scales and locations. In general it can be said that it is feasible to provide a very coarse statistical basis at the regional level, but with the need to obtain building age, size, shape, materials and structural characteristics on sub-regional level, where drastically different approaches are used. That is why, short of an exhaustive worldwide inventory of structures, a statistical approach to characterize structural class from development patterns will be required to develop a reasonably accurate Global Exposure Database.

Work Packages

WP1 Project Management
The project is managed by the University of Pavia. The Project Coordinator is in charge of day-by-day management of the consortium and the links with other GEM entities, including the “Ontology and Taxonomy”, the “Inventory Data Capture Tools” and the “Global Vulnerability Estimation Methods” consortia. He will also have the right to dispose a redistribution of the funds for the 2nd and 3rd years of the projects according to the level of effort and involvement of the partners with respect to the schedule and tasks in this proposal.
The Project Coordinator is supported by a Scientific Steering Committee (SSC), composed by the lead scientists of each team. The SSC will constantly discuss issues and outcomes related with the schedule and tasks of the project via teleconference. Relevant decision about changes and recover actions during the project will be taken by majority voting in the SSC on items proposed by the Project Coordinator. Finally, All Hand Meetings (AHM), involving all the partners, will be held in different premises every 6 months, starting with the Kick-Off of the project in Pavia.

Task 1.1 Management of day-by-day interactions by the Project Coordinator with the components of the working team, GEM entities and other global component consortia.
Task 1.2 Management by the Scientific Steering Committee of decisions about discrepancies between the work plan and the advancement level of the project.
Task 1.3 Design and manage a quality assurance plan, which will ensure that the consortium maintains a high and steady quality in its execution of the contractual obligations, but also that data processing and ingestion is performed with a constant quality.

WP2 Identification of existing Exposure Data
This WP is aimed at the collection of existing databases that are useful to populate the Global Exposure Database. According to the best understanding of this consortium, they should comprise:

  • Vector - Open street maps, Digital Chart of the World, GeoNames, USGS Global Landcover, GADM political boundaries
  • Demographic - UN Population, CIA World factbook, World Gazette
  • Data for inference algorithms - EERI World Housing Encyclopedia, USGS PAGER mapping schemes
  • Remote sensing based - Landscan, GlobCover, GLC 2000, DMSP OLS, GRUMP, IMPSA, NOAA Nightlights, MODIS databases.

Task 2.1 Identify, evaluate and homogenize various existing databases that provide economic, social and building stock distributions for countries, regions and cities throughout the world for at least the first level of sub-national boundaries for each country.
Task 2.2 Prepare a summary of these databases elaborating their strength and weakness, including comparison across the countries/regions to help identify gaps.

WP3 GED Design
This WP concerns the design and development of the infrastructure of the whole database. It is therefore connected to the outputs of the “Ontology and Taxonomy” and the “Global Vulnerability Estimation Methods” consortia, the general requirements set by GEM, the results of the previous WPs and the possible outcomes and procedure developed by the Inventory Data Capture Tools consortium. It is of vital importance that this design is carried out by the whole consortium under the guidance of a partner well experienced in global databases development, both at the scientific and implementation level, which is the reason why CIESIN is in charge of it.

Task 3.1 Develop an inventory matrix for the global building stock, in a resolution that suits GEM's needs. Each grid cell is to be geo-referenced with administrative divisions of the respective country/region and include as much as possible the following attributes, together with – whenever possible – an estimate of their uncertainty: a) total number of buildings; b) total population; c) floor area of buildings; d) relative distribution of building types (building types include basic structural/constructive features (e.g. timber, masonry, R/C, steel) and performance-influencing features (e.g. number of storey classes, construction quality and year of construction classes depending on countries); e) relative distribution of occupancy types (e.g. residential, industrial, public, commercial); f) relative temporal distribution of the population (e.g. day- and night-time and transient) between building types and occupancy types; g) critical facilities with large loss potential and infrastructure needed for emergency relief.
Task 3.2 Select and collect the best suitable databases among those identified in Task 2.1 and discussed in Task 2.2 according to the requirements set in Task 3.1.
Task 3.3 Design a multi-scale GED platform/software together with an interface allowing the user to move between multiple scales of detail and with the capability to link up with the existing databases as well as the data provided with the inventory data capture tools.

WP4 GED Population
The population of the GED will happen in waves, with selected pilot areas first and then with a schedule connected to the completeness and precision of the database collected and analyzed in the previous WPs. It is expected that the population will be done mostly using (semi)automatic procedure based on the previous knowledge that the team members have of the collected databases and their structure and the wise design of the GED in WP3. However, improved approaches will be continuously developed during the population phase to ease the task even for future optimization of the GED.

Task 4.1 Select some pilot areas of the world (one country per continent), possibly considering those with the highest seismic hazard or with the largest availability of data sets.
Task 4.2 Populate the inventory step-by-step using as much as possible semi-automatic procedures, starting from the pilot areas and then include all areas according to GEM requirements.

WP5 Regional optimization of GED
During and after the first population of the GED, many lacks in the databases are likely to be discovered. Moreover, for some areas no information may be available at the level of detail required. On the contrary, there may be areas where more detailed than required information is available and could be used. As a result, the project will implement some specialized regional approaches able to exploit the data that might be available and to infer the missing ones as much as possible. Contacts will made with national and international agencies to proceed with these further data, not strictly needed for the first population or not forecasted in the original collection phase. Eventually, the data fusion algorithm suitable to the regional/national databases to be considered additionally will be implemented in an iterative way in order to improve the information included into the GED.

Task 5.1 Collect further sources of data available at higher levels than the starting grid of the database (f.e. population data are available from a number of sources on a 1km grid, and building-by-building data will be collected by the Inventory Data Capture Tools global component).
Task 5.2 Develop, implement and optimize regional data fusion algorithms (taking into account regional characteristics) and mapping schemes to infer missing data at various levels from the original and additional sources. 

WP6 Guidelines for more detailed population of GED
The GED will need to be constantly and continuously updated and improved after its first establishment, including new data sets coming from public or private bodies, but also obtained by ground surveys, local socio-economic data sets and possibly extracted from remotely sensed data. The consortium will work actively with national agencies, the open forum on the GED portal, together with the Inventory Data Capture Tools consortium and with other interested parties in development of procedures and identification of best practices. These will be based on own experience, the implemented GED structure and the level of accuracy and completeness achieved in different parts of the world at the end of this project.

Task 6.1 Identify, develop and/or recommend proxy procedures and mapping schemes based on existing (mostly terrestrial) data sets and able to incorporate new ones, such as built-up area recognition tools for the identification of useful characteristics human settlements and their characteristics from satellite imagery, in accordance with the Inventory Data Capture Tools consortium.
Task 6.2 Identify best practices and develop recommendations for countries and/or regions such that concerned organizations can efficiently improve and update their existing inventories.

WP7 Dissemination
The consortium will work with GEM Secretariat to promote the GED and to ensure that proper strategies are put in place to make it generally known and available. The project will set up its own web site, including an open forum for interaction with users and interested parties, designed in order to be well connected and eventually to be moved as a part of the OpenGEM risk assessment platform, and also to the GED portal, hosted by the GEM Model Facility, with whom close connections will be kept.

Task 7.1 Develop a programme on training and capacity building for the GEM user community.
Task 7.2 Present the work in available and useful workshops, conferences and discussions involving the consortium and the GED, in order to improve the awareness of the earthquake mitigation community. Prepare a final workshop for the presentation of GED in accordance with the GEM Secretariat: the workshop will be also the final meeting of the project and the time when the GED will be eventually released to the public.
Task 7.3 Participate in GEM initiatives, develop presentations and possibly brochures
Task 7.4 Providing free, but controlled access to the database and the procedure for populating it via a specialized web interface.
Task 7.5 Produce a final report, listing all the characteristics of the GED database, as well as the input data and the algorithms and techniques used for populating it.
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