Using volunteered geographic information (VGI) in design-based statistical inference for area estimation and accuracy assessment of land cover

Stehman SV, Fonte CC, Foody GM, & See L (2018). Using volunteered geographic information (VGI) in design-based statistical inference for area estimation and accuracy assessment of land cover. Remote Sensing of Environment 212: 47-59. DOI:10.1016/j.rse.2018.04.014.

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Project: Harnessing the power of crowdsourcing to improve land cover and land-use information (CROWDLAND, FP7 617754), A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring (LANDSENSE, H2020 689812)

Abstract

Volunteered Geographic Information (VGI) offers a potentially inexpensive source of reference data for estimating area and assessing map accuracy in the context of remote-sensing based land-cover monitoring. The quality of observations from VGI and the typical lack of an underlying probability sampling design raise concerns regarding use of VGI in widely-applied design-based statistical inference. This article focuses on the fundamental issue of sampling design used to acquire VGI. Design-based inference requires the sample data to be obtained via a probability sampling design. Options for incorporating VGI within design-based inference include: 1) directing volunteers to obtain data for locations selected by a probability sampling design; 2) treating VGI data as a “certainty stratum” and augmenting the VGI with data obtained from a probability sample; and 3) using VGI to create an auxiliary variable that is then used in a model-assisted estimator to reduce the standard error of an estimate produced from a probability sample. The latter two options can be implemented using VGI data that were obtained from a non-probability sampling design, but require additional sample data to be acquired via a probability sampling design. If the only data available are VGI obtained from a non-probability sample, properties of design-based inference that are ensured by probability sampling must be replaced by assumptions that may be difficult to verify. For example, pseudo-estimation weights can be constructed that mimic weights used in stratified sampling estimators. However, accuracy and area estimates produced using these pseudo-weights still require the VGI data to be representative of the full population, a property known as “external validity”. Because design-based inference requires a probability sampling design, directing volunteers to locations specified by a probability sampling design is the most straightforward option for use of VGI in design-based inference. Combining VGI from a non-probability sample with data from a probability sample using the certainty stratum approach or the model-assisted approach are viable alternatives that meet the conditions required for design-based inference and use the VGI data to advantage to reduce standard errors.

Item Type: Article
Uncontrolled Keywords: Probability sampling; External validity; Pseudo-weights; Data quality; Model-based inference; Volunteered geographic information (VGI); Crowdsourcing
Research Programs: Ecosystems Services and Management (ESM)
Depositing User: Luke Kirwan
Date Deposited: 27 Apr 2018 08:52
Last Modified: 27 Apr 2018 11:34
URI: http://pure.iiasa.ac.at/15247

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