Abstract
The transition to a service-based economy represents a key global macroeconomic trend, with productive services playing a critical role in driving economic growth. For China, the development of productive services is a strategic priority in its pursuit of high-quality development. Most existing research primarily relies on traditional data to examine the spatial agglomeration and influencing factors of productive services in economically advanced regions, often overlooking the integration of multi-source data and spatial analyses in less developed areas. This study focuses on Kunming as the case study, employing methods such as Standard Deviation Ellipses (SDE), Kernel Density Estimation (KDE), and Local Spatial Autocorrelation (Moran's I) to investigate its spatial differentiation and agglomeration patterns. Additionally, Geodetector is applied to analyze influencing factors, utilizing multi-source data including Point of Interest(POI), LandScan, the annual China Land Cover Dataset (CLCD), OpenStreetMap (OSM), and socio-economic data to examine the evolutionary patterns of productive services. The findings suggest that Kunming's productive service sectors currently exhibit a predominant southward diffusion, influenced primarily by transportation infrastructure and economic conditions. Moreover, different categories of productive services exhibit unique spatial differentiation and influencing factors. Moving forward, it is essential to prioritize upgrading the internal structure of productive services to foster sustainable and high-quality sectoral development.