TY - JOUR
T1 - Spatial patterns and determinants of ethnic minority cultural integration in megacities
T2 - The case of Shanghai
AU - Li, Mengya
AU - Yi, Leying
AU - Ta, Na
AU - Weng, Shihong
AU - Gao, Xiangdong
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/12
Y1 - 2025/12
N2 - This study investigates the spatial patterns and determinants of ethnic minority cultural integration in Shanghai using multi-source spatial data. Six cultural systems were identified based on ethnic composition, with cultural facility data extracted through text mining of multidimensional cultural lexicons. To quantify cultural spatial integration, five metrics (Cultural Density, Typological Diversity, Cultural Dominance, Proximity Mixture, and Cultural Coexistence) were formulated at the subdistrict level. Gaussian Mixture Model (GMM) clustering was then applied to these metrics to identify four patterns of spatial integration: Deeply-Integrated, Single-Dominant, Scarce-Discrete, and Sparse-Mixed. Modeling results of SAC reveal that cross-regional spatial association is the primary driver, showing significant positive spillover effects. Demographic factors (with migrant proportion showing inhibitory effect and youth proportion demonstrating promotional effect) and economic conditions (commercial density and average housing price) also contribute. MGWR reveals pronounced spatial heterogeneity and shows that cultural infrastructure density (non-significant in OLS) exerts significant local effects, while other public service resources remain largely non-significant. Findings suggest that cross-regional collaboration, rather than relying solely on local intervention, is essential for promoting ethnic minority cultural integration. This study contributes to theoretical insights into the differentiated nature of cultural integration and can inform cultural space governance in megacities.
AB - This study investigates the spatial patterns and determinants of ethnic minority cultural integration in Shanghai using multi-source spatial data. Six cultural systems were identified based on ethnic composition, with cultural facility data extracted through text mining of multidimensional cultural lexicons. To quantify cultural spatial integration, five metrics (Cultural Density, Typological Diversity, Cultural Dominance, Proximity Mixture, and Cultural Coexistence) were formulated at the subdistrict level. Gaussian Mixture Model (GMM) clustering was then applied to these metrics to identify four patterns of spatial integration: Deeply-Integrated, Single-Dominant, Scarce-Discrete, and Sparse-Mixed. Modeling results of SAC reveal that cross-regional spatial association is the primary driver, showing significant positive spillover effects. Demographic factors (with migrant proportion showing inhibitory effect and youth proportion demonstrating promotional effect) and economic conditions (commercial density and average housing price) also contribute. MGWR reveals pronounced spatial heterogeneity and shows that cultural infrastructure density (non-significant in OLS) exerts significant local effects, while other public service resources remain largely non-significant. Findings suggest that cross-regional collaboration, rather than relying solely on local intervention, is essential for promoting ethnic minority cultural integration. This study contributes to theoretical insights into the differentiated nature of cultural integration and can inform cultural space governance in megacities.
KW - Cultural spatial integration
KW - Ethnic minority
KW - Shanghai
KW - Spatial associations
KW - Spatial patterns
UR - https://www.scopus.com/pages/publications/105016649828
U2 - 10.1016/j.habitatint.2025.103595
DO - 10.1016/j.habitatint.2025.103595
M3 - 文章
AN - SCOPUS:105016649828
SN - 0197-3975
VL - 166
JO - Habitat International
JF - Habitat International
M1 - 103595
ER -