เปรียบเทียบวิธี

	Behavioral Mapping	Behavioral Observation Coding
สาขาวิชา	Urban Studies	Anthropology
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1980	1974
ผู้ริเริ่ม	William H. Whyte; Jan Gehl	Behavioral sampling tradition (Altmann), adapted to anthropology (Bernard)
ประเภท	Systematic spatial observation of how people occupy and move through public space	Systematic procedure for sampling and coding observed behavior
แหล่งต้นตำรับ	Whyte, W. H. (1980). The Social Life of Small Urban Spaces. Conservation Foundation. ISBN: 9780891640578	Altmann, J. (1974). Observational study of behavior: sampling methods. Behaviour, 49(3–4), 227–267.
ชื่อเรียกอื่น	Place-Centered Mapping, Individual-Centered Mapping, Public Life Observation, Spatial Behavior Mapping	Behavior Coding, Systematic Observation Coding, Behavioral Sampling and Coding, Observational Coding
ที่เกี่ยวข้อง	4	4
สรุป	Behavioral mapping is the systematic, time-sampled observation of how people actually occupy and move through a public space, recorded directly onto a plan of the site. Developed in the urban context by William H. Whyte's filmed studies of New York plazas and formalized into a public-life methodology by Jan Gehl, it produces an empirical picture of where people sit, stand, walk, and gather, rather than where designers assumed they would. The two core modes — place-centered mapping of who is doing what at fixed moments, and individual-centered tracking of how single people move — together turn the invisible life of a space into mappable data.	Behavioral observation coding is the systematic practice of recording who does what, when, by applying an explicit sampling rule and a predefined coding scheme to observed behaviour. Rather than jotting impressions, the observer commits in advance to a rule — focal-individual, scan, ad libitum, or continuous sampling — and to a list of mutually defined behaviour categories, so that records are reproducible and comparable. Because two trained observers should code the same scene the same way, the method also requires measuring inter-observer reliability before the data are trusted.