以唐山市为研究区域，将夜间灯光数据、OSM路网数据与5余万条城市热点（POI）数据相结合，通过3种数据综合识别城市主要职能中心，并利用平均最邻近分析、标准差椭圆分析和核密度分析等方法，探究不同类型POI数据的空间分布特征与聚集程度，明确各职能中心的空间分布，对唐山市内部进行精细的识别。结果表明：唐山市内部发展不均衡，路南、路北区的平均灯光和路网聚集程度显著高于其他县区，且为POI主要聚集区域，是城市的主要中心区；唐山市城市热点在空间上存在不均衡分布，存在较强的方向性，在整体上呈现出明显的“多中心—组团式”空间结构，且具有明显的聚集性；各职能中心在空间结构上呈现出明显的聚集式分布，主要集中分布于路南区、路北区两个中心城区，其中生活中心的热点数据聚集度最高，金融中心的热点数据聚集度最低。

Taking Tangshan as the research area, combing the night light data, OSM road network data and more than 50 000 urban hot spot(POI)data, we comprehensively identified the main centers of city, explored the spatial distribution characteristics and aggregation degree of different types of POI data by using the methods of average nearest neighbor analysis, standard deviation ellipse and kernel density analysis, made clear the spatial distribution of each functional center, and made a fine identification of the interior of Tangshan City. The results show that the internal development of Tangshan is uneven. The average lighting and road network aggregation degree of Lunan District and Lubei District are significantly higher than that of other counties and districts, and they are the main gathering area of POI and the main central area of city. Tangshan urban hot spots are unevenly distributed in space and have strong directionality. On the whole, they show an obvious “multi center-cluster” spatial structure and obvious aggregation. The spatial structure of each functional center shows an obvious aggregation distribution, which is mainly concentrated in the two central urban areas of Lunan District and Lubei District. The hot spot data aggregation degree of life center is the highest and that of financial center is the lowest.