GSTDTAP  > 资源环境科学
DOI10.1016/j.landurbplan.2017.03.006
Recipes for neighborhood development: A machine learning approach toward understanding the impact of mixing in neighborhoods
Hipp, John R.2,3; Kane, Kevin1; Kim, Jae Hong1
2017-08-01
发表期刊LANDSCAPE AND URBAN PLANNING
ISSN0169-2046
EISSN1872-6062
出版年2017
卷号164页码:43477
文章类型Article
语种英语
国家USA
英文摘要

Scholars of New Urbanism have suggested that mixing along various dimensions in neighborhoods (e.g., income, race/ethnicity, land use) may have positive consequences for neighborhoods, particularly for economic dynamism. A challenge for empirically assessing this hypothesis is that the impact of mixing may depend on various socio-demographic characteristics of the neighborhood and takes place in a complex fashion that cannot be appropriately handled by traditional statistical analytical approaches. We utilize a rarely used, innovative estimation technique kernel regularized least squares that allows for non-parametric estimation of the relationship between various neighborhood characteristics in 2000 and the change in average household income in the neighborhood from 2000 to 2010. The results demonstrate that the relationships between average income growth and both income mixing and racial/ethnic mixing are contingent upon several neighborhood socio-demographic "ingredients". For example, racial mixing is positively associated with average income over time when it occurs in neighborhoods with a high percentage of Latinos or immigrants, high population density, or high housing age mixing. Income mixing is associated with worsening average household income in neighborhoods with more poverty, unemployment, immigrants, or population density. It appears that considering the broader characteristics of the neighborhood is important for understanding economic dynamism. (C) 2017 Elsevier B.V. All rights reserved.


英文关键词Neighborhoods Household incomes Machine learning Social mix
领域资源环境
收录类别SCI-E ; SSCI
WOS记录号WOS:000403516800001
WOS关键词SOCIAL-DISORGANIZATION THEORY ; URBAN NEIGHBORHOODS ; MIXED-INCOME ; CRIME ; COMMUNITY ; ROBUST ; CITY ; TIES ; SEGREGATION ; DIMENSIONS
WOS类目Ecology ; Environmental Studies ; Geography ; Geography, Physical ; Regional & Urban Planning ; Urban Studies
WOS研究方向Environmental Sciences & Ecology ; Geography ; Physical Geography ; Public Administration ; Urban Studies
引用统计
被引频次:25[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/25158
专题资源环境科学
作者单位1.Univ Calif Irvine, Dept Planning Policy & Design, Irvine, CA USA;
2.Univ Calif Irvine, Dept Criminol Law & Soc, 3311 Social Ecol 11, Irvine, CA 92697 USA;
3.Univ Calif Irvine, Dept Sociol, Irvine, CA USA
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GB/T 7714
Hipp, John R.,Kane, Kevin,Kim, Jae Hong. Recipes for neighborhood development: A machine learning approach toward understanding the impact of mixing in neighborhoods[J]. LANDSCAPE AND URBAN PLANNING,2017,164:43477.
APA Hipp, John R.,Kane, Kevin,&Kim, Jae Hong.(2017).Recipes for neighborhood development: A machine learning approach toward understanding the impact of mixing in neighborhoods.LANDSCAPE AND URBAN PLANNING,164,43477.
MLA Hipp, John R.,et al."Recipes for neighborhood development: A machine learning approach toward understanding the impact of mixing in neighborhoods".LANDSCAPE AND URBAN PLANNING 164(2017):43477.
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