เปรียบเทียบวิธี

	Mixed-Use Index	Urban Sprawl Measurement
สาขาวิชา	Urban Studies	Urban Studies
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1997	2014
ผู้ริเริ่ม	Cervero & Kockelman (land-use diversity / 3Ds); Frank et al. (entropy walkability term)	Reid Ewing & Shima Hamidi (building on Galster et al.)
ประเภท	Index of how evenly land uses are mixed within an area	Composite index combining multiple dimensions of urban form into a sprawl/compactness score
แหล่งต้นตำรับ	Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219.	Ewing, R., & Hamidi, S. (2015). Compactness versus sprawl: A review of recent evidence from the United States. Journal of Planning Literature, 30(4), 413–432.
ชื่อเรียกอื่น	Land-Use Mix Entropy, Land-Use Diversity Index, Herfindahl Land-Use Index, Entropy Land-Use Mix	Sprawl Index, Compactness Index of Sprawl, Ewing Sprawl Index, Composite Sprawl Measure
ที่เกี่ยวข้อง	4	4
สรุป	A mixed-use index measures how evenly different land uses — residential, retail, office, civic, industrial — are blended within an area, turning the planning ideal of vibrant, walkable mixed-use districts into a number. The dominant formulation borrows the entropy measure from information theory: a value near zero when one use dominates and near one when uses are perfectly balanced. Popularised through the 'density, diversity, design' framework of Cervero and Kockelman and embedded in walkability indices by Frank and colleagues, these indices quantify land-use diversity for studies of travel behaviour, walkability and urban vitality.	Urban sprawl measurement quantifies how compact or sprawling a metropolitan region is by combining several distinct dimensions of urban form into a single composite index. The dominant approach, developed by Reid Ewing, Shima Hamidi and colleagues, captures four factors — development density, land-use mix, activity centering, and street-network connectivity — and folds standardized indicators of each into one score, calibrated so the average region equals 100 and higher values mean greater compactness. Because sprawl is multidimensional, no single variable such as density adequately describes it, which is why the composite-index strategy has become the standard for comparing regions and linking form to outcomes.