[目的]以构建广东省东莞市生态网络格局为目标,评估生态廊道重要性以及识别生态廊道夹点、障碍点,确定生态保护修复的关键区域,提出相应生态修复策略,为后续相关国土空间规划与相关专项规划的编制和调整提供科学依据。[方法]基于传统的...[目的]以构建广东省东莞市生态网络格局为目标,评估生态廊道重要性以及识别生态廊道夹点、障碍点,确定生态保护修复的关键区域,提出相应生态修复策略,为后续相关国土空间规划与相关专项规划的编制和调整提供科学依据。[方法]基于传统的“生态源识别—建立阻力面—提取生态廊道”研究思路,加入城市大数据兴趣点(points of interest,POIs),弥补生态源地识别与阻力面构建精度不足的问题,再通过电路理论识别区域生态廊道与生态夹点、障碍点,从而构建东莞市整体生态网络格局。[结果](1)共识别生态源29处,占研究区面积20.45%,主要位于东莞市南部片区;(2)共生成生态廊道74条,其中潜在生态廊道12条,水乡片区以及市域边缘区生态廊道需要重点关注与保护。[结论]东莞市南部生态条件较好,生态源地较为集中,但城市边缘区以及北部水乡片区生态夹点与障碍点较多,需要进行重点生态修复与维护。展开更多
Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,usin...Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,using Machine Learning(ML)algorithms to predict passengers’trip purpose from Smart Card(SC)data and Points-of-Interest(POIs)data.The feasibility of the framework is demonstrated in two phases.Phase I focuses on extracting activities from individuals’daily travel patterns from smart card data and combining them with POIs using the proposed“activity-POIs consolidation algorithm”.Phase II feeds the extracted features into an Artificial Neural Network(ANN)with multiple scenarios and predicts trip purpose under primary activities(home and work)and secondary activities(entertainment,eating,shopping,child drop-offs/pick-ups and part-time work)with high accuracy.As a case study,the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose.The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.展开更多
The use of rodenticides in Gaza in the past years becomes easy due to rodent outbreak. The municipalities allow the use of rodenticides without quality control or technical supports. This situation leads to an easy pu...The use of rodenticides in Gaza in the past years becomes easy due to rodent outbreak. The municipalities allow the use of rodenticides without quality control or technical supports. This situation leads to an easy purchasing of zinc phosphide or its analog. This resulted in acute poising among adults and children. Herein we reported an accidental zinc phosphide poisoning to an adult man and discussed the associated medical complications with this case. Gastric lavage followed by atropine and vitamin K gavage on the 1st day of poisoning (hospital admission) resulted in a slight improvement of the case. Clinical symptoms included severe abdominal colic without losing conscious. Complete blood count (CBC) showed elevated levels of white blood cells (WBCs) and granulocytes (GRAN) levels indicating medical complication of the case. Biochemical analysis showed elevated levels of liver and cardiac biomarkers indicating potential liver and heart injuries. Continuous gavage of vitamin K, resulted in a gradual improvement of the case. On the 4th day of medical period, abdominal colic disappeared and CBC parameters became near the reference range. Consequently, the patient was set free from the hospital.展开更多
【目的】城市功能区是城市规划和人类活动共同作用、相互影响的结果,其准确识别对于优化配置公共资源和高效组织商业活动具有重要意义。目前,许多研究利用新兴的社会感知大数据进行城市功能区识别,但往往未能挖掘这些数据中蕴含的深层...【目的】城市功能区是城市规划和人类活动共同作用、相互影响的结果,其准确识别对于优化配置公共资源和高效组织商业活动具有重要意义。目前,许多研究利用新兴的社会感知大数据进行城市功能区识别,但往往未能挖掘这些数据中蕴含的深层次特征,或者未能充分捕捉和利用不同特征之间的相互关系和关联性,导致识别精度较低。【方法】针对这些问题,本研究提出了一种融合区域嵌入表示的城市功能区识别框架。该方法基于手机定位数据和兴趣点数据(Point of Interest,POI),采用Node2vec算法提取工作日与周末6个时段的区域间空间交互特征,并利用GloVe模型提取区域的语义特征。随后,通过多头注意力机制进行特征融合,并结合部分人工标注的功能区进行分类识别,在福州市三环以内地区进行了实证研究。【结果】实验结果表明,本方法生成的区域表示特征具有较高区分度,能够有效识别6类功能区,总体精度(OA)为81%,Kappa系数为0.77。【结论】与DTW_KNN和Word2Vec方法相比,精度分别提高了30%和20%,能够充分挖掘具有全局性质的空间交互特征和语义特征。此外,消融实验进一步表明,与单一数据源或简单融合方法相比,本方法在捕捉区域内部和区域间复杂关系的同时,对重要特征赋予更高的权重,使得模型的整体OA值相较于单源数据提高了约18%和6%,相较于简单融合方法提高了约13%,尤其在住宅区和混合区的识别方面表现出了显著优势。展开更多
文摘[目的]以构建广东省东莞市生态网络格局为目标,评估生态廊道重要性以及识别生态廊道夹点、障碍点,确定生态保护修复的关键区域,提出相应生态修复策略,为后续相关国土空间规划与相关专项规划的编制和调整提供科学依据。[方法]基于传统的“生态源识别—建立阻力面—提取生态廊道”研究思路,加入城市大数据兴趣点(points of interest,POIs),弥补生态源地识别与阻力面构建精度不足的问题,再通过电路理论识别区域生态廊道与生态夹点、障碍点,从而构建东莞市整体生态网络格局。[结果](1)共识别生态源29处,占研究区面积20.45%,主要位于东莞市南部片区;(2)共生成生态廊道74条,其中潜在生态廊道12条,水乡片区以及市域边缘区生态廊道需要重点关注与保护。[结论]东莞市南部生态条件较好,生态源地较为集中,但城市边缘区以及北部水乡片区生态夹点与障碍点较多,需要进行重点生态修复与维护。
基金This work is part of the Consumer Data Research Centre project(ES/L011840/1)funded by the UK Economic and Social Research Council(grant number 1477365).
文摘Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,using Machine Learning(ML)algorithms to predict passengers’trip purpose from Smart Card(SC)data and Points-of-Interest(POIs)data.The feasibility of the framework is demonstrated in two phases.Phase I focuses on extracting activities from individuals’daily travel patterns from smart card data and combining them with POIs using the proposed“activity-POIs consolidation algorithm”.Phase II feeds the extracted features into an Artificial Neural Network(ANN)with multiple scenarios and predicts trip purpose under primary activities(home and work)and secondary activities(entertainment,eating,shopping,child drop-offs/pick-ups and part-time work)with high accuracy.As a case study,the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose.The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.
文摘The use of rodenticides in Gaza in the past years becomes easy due to rodent outbreak. The municipalities allow the use of rodenticides without quality control or technical supports. This situation leads to an easy purchasing of zinc phosphide or its analog. This resulted in acute poising among adults and children. Herein we reported an accidental zinc phosphide poisoning to an adult man and discussed the associated medical complications with this case. Gastric lavage followed by atropine and vitamin K gavage on the 1st day of poisoning (hospital admission) resulted in a slight improvement of the case. Clinical symptoms included severe abdominal colic without losing conscious. Complete blood count (CBC) showed elevated levels of white blood cells (WBCs) and granulocytes (GRAN) levels indicating medical complication of the case. Biochemical analysis showed elevated levels of liver and cardiac biomarkers indicating potential liver and heart injuries. Continuous gavage of vitamin K, resulted in a gradual improvement of the case. On the 4th day of medical period, abdominal colic disappeared and CBC parameters became near the reference range. Consequently, the patient was set free from the hospital.
文摘【目的】城市功能区是城市规划和人类活动共同作用、相互影响的结果,其准确识别对于优化配置公共资源和高效组织商业活动具有重要意义。目前,许多研究利用新兴的社会感知大数据进行城市功能区识别,但往往未能挖掘这些数据中蕴含的深层次特征,或者未能充分捕捉和利用不同特征之间的相互关系和关联性,导致识别精度较低。【方法】针对这些问题,本研究提出了一种融合区域嵌入表示的城市功能区识别框架。该方法基于手机定位数据和兴趣点数据(Point of Interest,POI),采用Node2vec算法提取工作日与周末6个时段的区域间空间交互特征,并利用GloVe模型提取区域的语义特征。随后,通过多头注意力机制进行特征融合,并结合部分人工标注的功能区进行分类识别,在福州市三环以内地区进行了实证研究。【结果】实验结果表明,本方法生成的区域表示特征具有较高区分度,能够有效识别6类功能区,总体精度(OA)为81%,Kappa系数为0.77。【结论】与DTW_KNN和Word2Vec方法相比,精度分别提高了30%和20%,能够充分挖掘具有全局性质的空间交互特征和语义特征。此外,消融实验进一步表明,与单一数据源或简单融合方法相比,本方法在捕捉区域内部和区域间复杂关系的同时,对重要特征赋予更高的权重,使得模型的整体OA值相较于单源数据提高了约18%和6%,相较于简单融合方法提高了约13%,尤其在住宅区和混合区的识别方面表现出了显著优势。
