In Andalusia, Community Social Service Centres (CSSCs) are funded by regulations following demographic, geographic, economic, and social disadvantage criteria. This study aims to analyse the geographical distribution of funding per inhabitant of CSSC by catchment area in Andalusia in 2019, and to study the statistical associations between funding and a range of demographic and socioeconomic indicators. The study spatial units (n = 184) included the catchment areas of CSSCs and, in the case of intramunicipal areas in large municipalities, they were grouped at the municipal level. Spatial autocorrelation measures were used to identify spatial clusters of high/low funding rates per inhabitant. Later, nonspatial and spatial regressions were applied to search for associations with different indicators (sex ratio, ageing index, dependency index, emigration rate, immigration rate, unemployment rate, population density, and employment rate in the primary sector). The geographical distribution of the funding of social services in Andalusia was not random since the analyses identified several spatial clusters with significantly high (hot spots) and low (cold spots) funding per inhabitant (p < 0.05). The funding rates were significantly (p < 0.05) and directly associated with the ageing index and the percentage of primary sector employees, and indirectly with the proportion of foreigners in the population and the population density. The hot spots were mainly located in rural and deprived areas, while the cold spots were in urban areas. The variables related to the regulated funding distribution criteria were not fully associated with higher financing. Instead, other additional variables showed significant associations (p < 0.05), such as primary-sector workers and foreign populations. The results showed that spatial analyses may support service assessment and decision-making in social policy.

在安达卢西亚，社区社会服务中心 (CSSC) 的资金来源遵循人口、地理、经济和社会弱势标准的法规。本研究旨在分析 2019 年安达卢西亚流域每个 CSSC 居民的资金地理分布，并研究资金与一系列人口和社会经济指标之间的统计关联。研究空间单元（n  = 184）包括CSSC的服务区域，对于大城市的市内区域，它们被分组在市一级。空间自相关测量用于识别每个居民高/低资助率的空间集群。随后，应用非空间和空间回归来寻找与不同指标（性别比、老龄化指数、抚养指数、迁出率、迁入率、失业率、人口密度和第一部门就业率）的关联。安达卢西亚社会服务资金的地理分布不是随机的，因为分析确定了几个空间集群，每个居民的资金显着高（热点）和低（冷点）（p  < 0.05）。资助率与老龄化指数和第一产业雇员比例显着（p  <0.05）直接相关，与人口中外国人比例和人口密度间接相关。热点地区主要分布在农村和贫困地区，冷点则集中在城市地区。与受监管的资金分配标准相关的变量与更高的融资并不完全相关。相反，其他附加变量显示出显着关联（p  < 0.05），例如初级部门工人和外国人口。结果表明，空间分析可以支持社会政策中的服务评估和决策。