Supervised Machine Learning Approaches to Modeling Residential Infill Development in the City of Los Angeles-Reference-Cited by-同舟云学术

Supervised Machine Learning Approaches to Modeling Residential Infill Development in the City of Los Angeles

Published:2022-03 Issue:1 Volume:148 Page:
ISSN:0733-9488
Container-title:Journal of Urban Planning and Development
language:en
Short-container-title:J. Urban Plann. Dev.

Author:

Kim Dohyung¹^ORCID,Shim Jongmin²^ORCID,Park Jiyoung³^ORCID,Cho John⁴,Kumar Shathesh⁵

Affiliation:

1. Professor, Dept. of Urban and Regional Planning, California State Polytechnic Univ., Pomona, CA 91768. ORCID: .

2. Associate Professor, Dept. of Civil, Structural, and Environmental Engineering, State Univ. of New York, Buffalo, NY 14260 (corresponding author). ORCID: .

3. Associate Professor, Dept. of Urban and Regional Planning, State Univ. of New York, Buffalo, NY 14260; Adjunct Associate Professor, Regional Information Major, Seoul National Univ, Seoul 00826, South Korea. ORCID: .

4. Senior Regional Planner, Southern California Association of Governments, Los Angeles, CA 90017.

5. Graduate Student, Dept. of Civil, Structural, and Environmental Engineering, State Univ. of New York, Buffalo, NY 14260.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Urban Studies,Development,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000787

Reference40 articles.

1. Determining the capacity infill development in growing metropolitans: A case study of Urmia city

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3. Charles, S. L. 2011. Suburban gentrification: Understanding the determinants of single-family residential redevelopment: A case study of the inner-ring suburbs of Chicago, IL, 2000–2010. Cambridge, MA: Joint Center for Housing, Harvard Univ.

4. Support-vector networks

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