Spatial analysis of household-scale solar PV systems in Hungary (2024)

The dataset comprises all raw and aggregated data used in the spatial analysis. The analysis was conducted to examine the spatial patterns of household-scale solar photovoltaic (PV) systems and to identify the main drivers of technology adoption across Hungarian districts in 2024. To assess spatial heterogeneity, the Theil T index was applied, while spatial dependence was tested using Moran’s I statistic. Spatial autocorrelation was further explored through mapping techniques based on Local Moran’s I and Local Geary statistics. In addition, a spatial error model was employed to identify the factors influencing the share of household-scale solar PV systems per 100 households. The results indicate that the spatial error term has the strongest effect, complemented by variables such as household education levels, the age and size of the building stock, population growth, and the extent of built-up areas.本数据集涵盖了本次空间分析所使用的全部原始数据与聚合数据。本次分析旨在探究2024年匈牙利各行政区内户用太阳能光伏（PV）系统的空间分布格局，并识别该技术采纳的主要驱动因素。为评估空间异质性，本研究采用了泰尔T指数（Theil T index），并通过莫兰I统计量（Moran’s I statistic）检验空间相关性；此外，本研究还基于局部莫兰I与局部吉尔里统计量（Local Geary statistics），借助制图技术进一步探索了空间自相关特征。同时，本研究采用了空间误差模型（spatial error model），以识别影响每百户家庭户用太阳能光伏系统占比的相关因素。研究结果表明，空间误差项的影响最为显著，家庭受教育水平、建筑存量的房龄与规模、人口增长以及建成区面积等变量也起到了补充性作用。