In this paper, we model the locations of five major banks in mainland France, two lucrative and three cooperative institutions based on socio-economic considerations. Locations of banks are collected using web scrapping and constitute a bivariate spatial point process for which we estimate nonparametrically summary functions (intensity, Ripley and cross-Ripley's K functions). This shows that the pattern is highly inhomogenenous and exhibits a clustering effect especially at small scales, and thus a significant departure to the bivariate (inhomogeneous) Poisson point process is pointed out. We also collect socio-economic datasets (at the living area level) from INSEE and propose a parametric modelling of the intensity function using these covariates. We propose a group-penalized bivariate composite likelihood method to estimate the model parameters, and we establish its asymptotic properties. The application of the methodology to the banking dataset provides new insights into the specificity of the cooperative model within the sector, particularly in relation to the theories of institutional isomorphism.

翻译：本文基于社会经济因素，对法国大陆五家主要银行（两家盈利性机构与三家合作性机构）的网点分布进行建模。通过网页抓取技术收集银行网点数据，构建二元空间点过程，并采用非参数方法估计其摘要函数（强度函数、Ripley K函数与交叉Ripley K函数）。分析表明该空间格局呈现高度非均匀性，尤其在较小尺度上表现出显著聚类效应，由此指出其与二元（非均匀）泊松点过程存在显著偏离。同时从法国国家统计与经济研究所（INSEE）获取居住区层面的社会经济数据集，并利用这些协变量建立强度函数的参数化模型。我们提出一种群组惩罚二元复合似然估计方法用于模型参数估计，并论证其渐近性质。将该方法应用于银行数据集，为揭示合作性银行模式在行业内的特殊性提供了新见解，尤其深化了对制度同构理论的理解。