Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context  

作　　者：Junchuan Fan Gautam Thakur 

机构地区：[1]Geospatial Science and Human Security Division,Oak Ridge National Laboratory,Oak Ridge,TN,USA

出　　处：《International Journal of Digital Earth》2023年第1期430-445,共16页国际数字地球学报（英文）

摘　　要：The combination of spatial distribution,semantic characteristics,and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics.Most previous studies on POI-based land use modeling research focused on one geographic region and select one spatial scale and semantic granularity for land use characterization.There is a lack of understanding on the impact of spatial scale,semantic granularity,and geographic context on POI-based land use modeling,particularly large-scale land use modeling.In this study,we developed a scalable POI-based land use modeling framework and examined the impact of these three factors on POI-based land use characterization using data from three geographic regions.We developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data sources.Then,by combining POIs with a neural network language model,we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs.We trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities.The classification performance of different land use classes was analyzed and compared across three geographic regions to identify the semantic representativeness of POI-based AOI embedding and the impact of geographic context.

关 键 词：Land use POI geospatial semantic deep learning semantic granularity spatial scale 

分 类 号：F301.2[经济管理—产业经济]