位置服务大数据下广州市土地利用类型模拟探讨

Inferring Land Use of Guangzhou from Big Data of Location Service

以广州市为例,利用百度地图平台APP联盟用户的位置服务大数据,以150 m×150 m格网为精度单元,应用监督分类方法中的随机森林方法模拟识别土地利用类型.研究结果表明:随机森林方法能有效地将高密度地理行为大数据应用于土地利用类型推断模拟中,印证了其在土地利用类型模拟推断的潜在价值及居民活动强度周期性变化与土地利用类型之间的显著相关性;预测总准确率达72.40%,其中,村镇建设用地、工业用地、公园绿地、居住用地和商业金融用地的预测准确率分别为50.39%、82.65%、49.53%、82.66%和49.56%.

The big data of the APP alliance users of Baidu location service is used to infer the land use in Guangzhou. The random forest method in the supervisory classification algorithm is used to simulate the land use types ta-king the 150 m×150 m grid as the precision unit. The validation result confirms that,on the basis of the big data of high-density geographic behavior,the random forest method can be used to infer land use types effectively,demonstrating the potential values of Baidu location service big data in urban land use inference and revealing significant linkage between the spatial pattern of the residents’ activity and the underlying urban land use. The overall accuracy reaches 72.40%,and,specifically,the simulation accuracy rates of villages and towns construction land,industrial land,park green land,residential land,commercial and financial land are 50.39%,82.65%,49.53%,82.66% and 49.56% respectively.