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Assessing the impacts of human activities and climate variations on grassland productivity by partial least squares structural equation modeling(PLS-SEM) 被引量:8
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作者 SHA Zongyao XIE Yichun +3 位作者 TAN Xicheng BAI Yongfei LI Jonathan LIU Xuefeng 《Journal of Arid Land》 SCIE CSCD 2017年第4期473-488,共16页
The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling(SEM) demonstrated the potential for analyzing such associati... The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling(SEM) demonstrated the potential for analyzing such associations. We applied the variance-based partial least squares SEM(PLS-SEM) and geographically-weighted regression(GWR) modeling to assess the human-climate impact on grassland productivity represented by above-ground biomass(AGB). The human and climate factors and their interaction were taken to explain the AGB variance by a PLS-SEM developed for the grassland ecosystem in Inner Mongolia, China. Results indicated that 65.5% of the AGB variance could be explained by the human and climate factors and their interaction. The case study showed that the human and climate factors imposed a significant and negative impact on the AGB and that their interaction alleviated to some extent the threat from the intensified human-climate pressure. The alleviation may be attributable to vegetation adaptation to high human-climate stresses, to human adaptation to climate conditions or/and to recent vegetation restoration programs in the highly degraded areas. Furthermore, the AGB response to the human and climate factors modeled by GWR exhibited significant spatial variations. This study demonstrated that the combination of PLS-SEM and GWR model is feasible to investigate the cause-effect relation in socio-ecological systems. 展开更多
关键词 spatial modeling human-natural interaction grazing urbanization road network
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融合多源地理大数据的城市街区综合活力评价 被引量:17
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作者 唐璐 许捍卫 丁彦文 《地球信息科学学报》 CSCD 北大核心 2022年第8期1575-1588,共14页
随着城市人口、物资、信息流动的日益频繁,城市居民活动特征和生产生活方式更加复杂多变,同时,城市空间无序扩张,发展规划不足,引发了交通堵塞、人口流失、公共空间缺乏等一系列问题,最终引发了城市活力消解难题。因此,如何科学高效地... 随着城市人口、物资、信息流动的日益频繁,城市居民活动特征和生产生活方式更加复杂多变,同时,城市空间无序扩张,发展规划不足,引发了交通堵塞、人口流失、公共空间缺乏等一系列问题,最终引发了城市活力消解难题。因此,如何科学高效地进行城市活力定量分析成为了重点研究问题。本文基于OpenStreetMap、百度地图兴趣点(Point of Interest,POI)、微信宜出行、美团、高德建筑物轮廓等多源地理大数据,从人与空间双重角度,分别对人群活力、活力多样性、活动满意度和空间交互潜能进行量化研究;引入空间权重矩阵,构建了改进的空间优劣解距离法(Technique for Order Preference by Similarity to Ideal Solution,TOPSIS)综合活力评价模型,实现对南京市中心城区综合活力的评价,最后分析了工作日、周末的街区活力空间分布特征及活力极的异同,并比较了传统的熵值TOPSIS综合活力评价结果,以此探究空间关系对城市街区活力的影响,以求帮助城市规划者系统的认识当前城市活力现状,为城市规划研究提供一种可行性方案。 展开更多
关键词 地理大数据 OSM路网 POI 南京市 城市街区活力 引力模型 优劣解距离法 空间相互作用
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