Chanam Lee | Search PubMed

PubMed2005

Built environment and behavior: spatial sampling using parcel data.

PURPOSE: The quality and economy of inferential research rely heavily on the sampling method. This paper addresses a methodological challenge in environment-behavior research: sampling respondents in relation to their built environmental characteristics. METHODS: A discussion of issues related to traditional neighborhood-based sampling serves to introduce a new spatial sampling strategy. Spatial sampling consists of defining conceptual population of interest, constructing spatial sample frame using parcel-level environmental data in GIS, examining the sample frame, determining the sampling design and size, and drawing the samples. An application of this method is illustrated using a recent study examining environmental correlates of walking and biking. RESULTS: Spatial sampling with parcel-level data ensures sufficient variations in and proper distributions of the environmental variables of interest, while controlling for the conditions of no interest. The use of the individual as unit of analysis offers an economic, generalizable, and easily interpretable approach to environment-behavior research, and discourages the potentially erroneous a priori definition of neighborhoods and aggregation problems. CONCLUSIONS: With its capacity to consider a broad range of detailed environmental variables, spatial sampling contributes to finding new or stronger environment-behavior associations and to the growing number of studies using the social ecologic model.

Bicycling↗

PubMedDate not supplied

Walking and bicycling: an evaluation of environmental audit instruments.

PURPOSE: This paper reviews existing environmental audit instruments used to capture the walkability and bikability of environments. The review inventories and evaluates individual measures of environmental factors used in these instruments. It synthesizes the current state of knowledge in quantifying the built environment. The paper provides health promotion professionals an understanding of the essential aspects of environments influencing walking and bicycling for both recreational and transportation purposes. It serves as a basis to develop valid and efficient tools to create activity-friendly communities. DATA SOURCES: Keyword searches identified journal articles from the computer-based Academic Citation Databases, including the National Transportation Library, the Web of Science Citation Database, and MEDLINE. Governmental publications and conference proceedings were also searched. STUDY INCLUSION AND EXCLUSION CRITERIA: All instruments to audit physical environments have been included in this review, considering both recreation- and transportation related walking and bicycling. Excluded are general methods devised to estimate walking and cycling trips, those used in empirical studies on land use and transportation, and research on walking inside buildings. DATA EXTRACTION METHODS: Data have been extracted from each instrument using a template of key items developed for this review. The data were examined for quality assurance among three experienced researchers. DATA SYNTHESIS: A behavioral model of the built environment guides the synthesis according to three components: the origin and destination of the walk or bike trip, the characteristics of the road traveled, and the characteristics of the areas surrounding the trip's origin and destination. These components, combined with the characteristics of the instruments themselves, lead to a classification of the instruments into the four categories of inventory, route quality assessment, area quality assessment, and approaches to estimating latent demand for walking and bicycling. Furthermore, individual variables used in each instrument to measure the environment are grouped into four classes: spatiophysical, spatiobehavioral, spatiopsychosocial, and policy-based. MAJOR CONCLUSIONS: Individually, existing instruments rely on selective classes of variables and therefore assess only parts of built environments that affect walking and bicycling. Most of the instruments and individual measures have not been rigorously tested because of a lack of available data on walking and bicycling and because of limited research budgets. Future instrument development will depend on the acquisition of empirical data on walking and bicycling, on inclusion of all three components of the behavioral model, and on consideration of all classes of variables identified.

Bicycling↗