User guide: Field sampling strategies for estimating building inventories
Creators: name: Porter, Keith
name: Hu, ZhengHui
name: Huyck, Charlie
name: Bevington, John
Publisher: name: GEM Foundation, Pavia, Italy
Year: 2014
Subjects: Inventory
Sampling
Survey
Building type
Distribution
Risk assessment
GEM

description: GEM Technical Report 2014-02
description: Data capture tools
description: The Global Earthquake Model (GEM) aims to provide a set of tools and models for hazard and risk analysis. The fundamental aim of the GEM Inventory Data Capture Tools (IDCT) risk global component is to provide a set of software and accompanying user protocols to enable users to collect and modify building exposure information, which can be input into the Global Exposure Database and the Global Earthquake Consequences Database. This report offers procedures to estimate the area distribution of building types in a community through a combination of remote sensing, local expertise, and field observations. The resulting inventory can be used for probabilistic seismic risk analysis. The community is first divided into geographic regions (referred to here as zones) of relatively homogeneous use. These procedures show how to estimate the area distribution of building types for one zone. One estimates the total building area in each zone by remote-sensing procedures that are not discussed here, and then multiplies by the distribution estimated using the present procedures. The result is an estimate of the building area of each building type in each zone. One repeats the process for each zone in the community to create an estimate of the building area of each building type in the community. Four sampling strategies are offered for sampling a zone depending on whether (a) the zone has homogeneous building heights, (b) all important building features are visible, (c) expert advice is available on recognizing hidden features, and (d) a prior estimate from experts on building-type distribution is available. The strategies are: 1) Simple sampling without prior expert judgment. This procedure applies where the survey team can identify building type from visible features. It is useful for zones with homogeneous (fairly uniform) heights. That is, one cannot easily pick out clusters of buildings that are on average shorter, typical, or taller in terms of number of stories. It uses simple weighted averages to extrapolate field observations to the entire zone. 2) Stratified sampling without prior expert judgment. Like 1, except that the zone has a heterogeneous (not so uniform) mix of heights, e.g., a central business district with some generally low-, mid- and high-rise blocks. It uses a procedure called moment matching to select the sample and extrapolate to the zone. 3) Use local expertise to infer types from visible features. Like 1 or 2, but a least one attribute used to define building type is not visible. Prior expert advice is needed to infer building type from visible features. Field data collectors then employ either simple or stratified sampling depending on height homogeneity. If the expert is willing to estimate the distribution of building types, this procedure shows how to do that. 4) Field survey to enhance prior expert opinion of type distribution. Like 3, except that it requires the expert to provide a prior distribution of building type by area, which is then combined with data from the field observations using Bayes’ theorem. It is estimated that one team of two people can survey enough buildings to represent the distribution of one zone in approximately one day. A separate procedure is offered for special buildings, such as buildings that serve some important function or are known to be particularly seismically vulnerable or particularly seismically resilient. Examples include hospitals, tall masonry towers, or base-isolated buildings. An appendix provides the mathematical basis of these procedures, including stratified sampling, Bayesian updating, and a discussion of the trade-off between sample size and sensitivity to rare building types.