【目的】当前在微地图的内容检索领域尚缺乏系统性的研究。为了填补这一研究空白,本文提出了一种YOLOv8l-FMSC-Spatial (You Only Look Once v8l-Fewer Multi-Scale Convolution-Spatial, YOLOv8l-FMSC-Spatial)模型,实现在手绘地图场...【目的】当前在微地图的内容检索领域尚缺乏系统性的研究。为了填补这一研究空白,本文提出了一种YOLOv8l-FMSC-Spatial (You Only Look Once v8l-Fewer Multi-Scale Convolution-Spatial, YOLOv8l-FMSC-Spatial)模型,实现在手绘地图场景下地理要素的提取及检索。【方法】首先通过对比YOLO系列模型,选取最优的YOLOv8l模型,引入C2f-FMSC模块改进最优模型,建立应用于微地图的YOLOv8l-FMSC训练模型,利用该模型实现栅格地图的地理要素提取;其次针对地理要素的检索需要,建立地理要素的空间关系数据库,设计空间计算检索模块Spatial,通过Spatial模块实现地理要素信息的传递与筛选,进一步地计算用户检索信息与数据库地理要素信息的空间关系关联程度;最后根据空间关系关联程度,从微地图数据库中索引包含相关地理要素信息的地图,实现基于空间关系的地理要素检索模型构建。依据上述方法,在手绘校园地图检索场景中进行验证。实验数据源自各个学校发布内容以及学生自由制作,共计493幅手绘校园地图,在全国范围内研究学校代表性地理要素检索,此类要素包括水体、操场、特色建筑,确保准确识别和检索这些特征元素,验证所提模型的实际适用性。【结果】实验结果表明:训练后的YOLOv8l模型可有效识别手绘地图中的地理要素,并在收集的数据集上验证了模型的有效性和鲁棒性;引入FMSC模块后的YOLOv8l-FMSC模型精确率可达0.8、召回率可达0.764,为实际对比中的最优模型;引入Spatial模块计算模型度量空间关系,可有效捕捉到相关地理要素的空间信息,减少与正射地图检索的差距。【结论】综上,提出的YOLOv8l-FMSC-Spatial模型可根据顾及空间关系的地理要素条件,快速准确地检索到内容相关的手绘地图,从而填补微地图在内容检索方面的研究空缺。展开更多
The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination ...The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.展开更多
文摘【目的】当前在微地图的内容检索领域尚缺乏系统性的研究。为了填补这一研究空白,本文提出了一种YOLOv8l-FMSC-Spatial (You Only Look Once v8l-Fewer Multi-Scale Convolution-Spatial, YOLOv8l-FMSC-Spatial)模型,实现在手绘地图场景下地理要素的提取及检索。【方法】首先通过对比YOLO系列模型,选取最优的YOLOv8l模型,引入C2f-FMSC模块改进最优模型,建立应用于微地图的YOLOv8l-FMSC训练模型,利用该模型实现栅格地图的地理要素提取;其次针对地理要素的检索需要,建立地理要素的空间关系数据库,设计空间计算检索模块Spatial,通过Spatial模块实现地理要素信息的传递与筛选,进一步地计算用户检索信息与数据库地理要素信息的空间关系关联程度;最后根据空间关系关联程度,从微地图数据库中索引包含相关地理要素信息的地图,实现基于空间关系的地理要素检索模型构建。依据上述方法,在手绘校园地图检索场景中进行验证。实验数据源自各个学校发布内容以及学生自由制作,共计493幅手绘校园地图,在全国范围内研究学校代表性地理要素检索,此类要素包括水体、操场、特色建筑,确保准确识别和检索这些特征元素,验证所提模型的实际适用性。【结果】实验结果表明:训练后的YOLOv8l模型可有效识别手绘地图中的地理要素,并在收集的数据集上验证了模型的有效性和鲁棒性;引入FMSC模块后的YOLOv8l-FMSC模型精确率可达0.8、召回率可达0.764,为实际对比中的最优模型;引入Spatial模块计算模型度量空间关系,可有效捕捉到相关地理要素的空间信息,减少与正射地图检索的差距。【结论】综上,提出的YOLOv8l-FMSC-Spatial模型可根据顾及空间关系的地理要素条件,快速准确地检索到内容相关的手绘地图,从而填补微地图在内容检索方面的研究空缺。
文摘The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.
