A Comparison of Two Methods for Measuring Land Use in Public Health Research
Author(s) -
Katherine E. King
Publication year - 2015
Publication title -
sage open
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.357
H-Index - 32
ISSN - 2158-2440
DOI - 10.1177/2158244015589438
Subject(s) - aerial photography , data collection , categorization , construct (python library) , flexibility (engineering) , photography , computer science , range (aeronautics) , public health , data science , data set , cohen's kappa , statistics , data mining , geography , remote sensing , machine learning , artificial intelligence , mathematics , medicine , art , materials science , nursing , composite material , visual arts , programming language
Public health researchers have identified numerous healthimplications associated with land use. However, it is unclear which of multiple methodsof data collection most accurately captures land use, and “gold standard” methods varyby discipline. Five desirable features of environmental data sources are presented anddiscussed (cost, coverage, availability, construct validity, and accuracy). Potentialaccuracy issues are discussed by using Kappa statistics to evaluate the level ofagreement between data sets collected by two methods (systematic social observation[SSO] by trained raters and publicly available data from aerial photography coded usingadministrative records) from the same blocks in Chicago, Illinois. Significant Kappastatistics range from 0.19 to 0.60, indicating varying levels of intersource agreement.Most land uses are more likely to be reported by researcher-designed direct observationthan in the publicly available data derived from aerial photography. However, when cost,coverage, and availability outweigh a marginal improvement in accuracy and flexibilityin land-use categorization, coded aerial photography data may be a useful data sourcefor health researchers. Greater interdisciplinary and interorganization collaboration inthe production of ecological data is recommended to improve cost, coverage,availability, and accuracy, with implications for construct validity
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