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An Analysis Of The Network Spatial Pattern Of Ecological Environmental Public Attention

Posted on:2021-01-29Degree:MasterType:Thesis
Country:ChinaCandidate:H S GuoFull Text:PDF
GTID:2381330611462677Subject:Cartography and Geographic Information System
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Since 2013,large-scale,high-intensity haze weather has affected many cities,seriously affecting public health,life and travel,and economic development.Posi-tive and effective environmental protection can not do without public attention and participation,In the information age,the public ecological environmental attention and participation can be quantified by online search data.The study first put forward the concept of the network spatial pattern of public ecological environmental atten-tion between regions,based on Baidu index and environmental monitoring data,us-ing Pearson coefficient,Granger causality test,spatial autocorrelation and K-means clustering methods to Taking Shanghai,Guangzhou,Hangzhou,and Hangzhou as an example,the spatial and temporal distribution characteristics of smog pollution in China from April 2014 to October 2017,the spatial and temporal changes of the spatial pattern of public ecological environmental concern network,and influencing factors were studied.The main research contents and conclusions are as follows:?using descriptive statistics and K-means clustering method,the main pollu-tant of provinces and cities,the space and time distribution characteristics were an-alyzed,and the results show that the main pollutants exist obvious seasonal charac-teristic,the CO has no obvious change,PM2.5,SO2,NO2 and PM10 are relatively low,high in winter and spring,and low in summer and autumn.At present,the treatment is effective,and the indicators are generally declining.O3 index is high in summer and autumn,low in winter and spring,and shows an overall rising trend,which has gradually become the main pollutant in various provinces and cities.Serious pollu-tion areas in China mainly include Beijing-Tianjin-Hebei,Xinjiang,Chengdu-Chongqing,three eastern provinces,Guanzhong,Yangtze river delta and pearl river delta.?Descriptive statistics,k-means clustering and spatial autocorrelation methods were used to analyze the spatial and temporal distribution characteristics of public attention on ecological environment.The results showed that public attention on ecological environment was high in winter and spring,but low in summer and au-tumn.The public pays the most attention to the ecological environment in Beijing and Shanghai,but less attention to the ecological environment in Guangzhou and Hangzhou.The public environment awareness is spatial correlation,gathering effect exists in the space,spatial spillover effect is stronger,the ecological environment awareness high value zones including Beijing-Tianjin-Hebei,Yangtze river delta,the pearl river delta and the Chengdu-Chongqing expressway,the Guanzhong region,a majority of the rest of the central provinces and cities are not significant random distribution area,together with high value low gathered area roughly equal Numbers,and the public environment awareness high value area,no significant regional and low-value geographically from east to west,from the coast to inland distribution.?Pearson coefficient and granger causality test were used to analyze the cor-relation and causality between public ecological environment attention and various influencing factors.The results show that the public attention to the ecological envi-ronment is only positively correlated with O3 indicators of Beijing,Shanghai,Guangzhou and Hangzhou,However,the public's awareness of the atmospheric pol-lutants O3,which is currently erupting in summer,is biased,and there is a time lag?1 month?.In addition,the attention paid by provinces and cities to the ecological environment in Beijing,Shanghai,Guangzhou and Hangzhou is also related to the local GDP,local AQI and the distance between provinces and cities and Beijing,Shanghai,Guangzhou and Hangzhou.
Keywords/Search Tags:Smog and haze, public ecological environment attention, Baidu index, network spatial pattern, correlation
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