RETRACTED ARTICLE: Evaluation of human activity intensity in geological environment problems of Ji’nan City

Statement of Retraction We, the Editor and Publisher of the journal European Journal of Remote Sensing, have retracted the following articles that were published in the Special Issue titled “Remote Sensing in Water Management and Hydrology”: Marimuthu Karuppiah, Xiong Li & Shehzad Ashraf Chaudhry (2021) Guest editorial of the special issue “remote sensing in water management and hydrology”, European Journal of Remote Sensing, 54:sup2, 1-5, DOI: 10.1080/22797254.2021.1892335 Jian Sheng, Shiyi Jiang, Cunzhu Li, Quanfeng Liu & Hongyan Zhang (2021) Fluid-induced high seismicity in Songliao Basin of China, European Journal of Remote Sensing, 54:sup2, 6-10, DOI: 10.1080/22797254.2020.1720525 Guohua Wang, Jun Tan & Lingui Wang (2021) Numerical simulation of temperature field and temperature stress of thermal jet for water measurement, European Journal of Remote Sensing, 54:sup2, 11-20, DOI: 10.1080/22797254.2020.1743956 Le Wang, Guancheng Jiang & Xianmin Zhang (2021) Modeling and molecular simulation of natural gas hydrate stabilizers, European Journal of Remote Sensing, 54:sup2, 21-32, DOI: 10.1080/22797254.2020.1738901 Tianyi Chen, Lu Bao, Liu Bao Zhu, Yu Tian, Qing Xu & Yuandong Hu (2021) The diversity of birds in typical urban lake-wetlands and its response to the landscape heterogeneity in the buffer zone based on GIS and field investigation in Daqing, China, European Journal of Remote Sensing, 54:sup2, 33-41, DOI: 10.1080/22797254.2020.1738902 Zhiyong Wang (2021) Research on desert water management and desert control, European Journal of Remote Sensing, 54:sup2, 42-54, DOI: 10.1080/22797254.2020.1736953 Ji-Tao Li & Yong-Quan Liang (2021) Research on mesoscale eddy-tracking algorithm of Kalman filtering under density clustering on time scale, European Journal of Remote Sensing, 54:sup2, 55-64, DOI: 10.1080/22797254.2020.1740894 Wei Wang, R. 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Dinesh Jackson Samuel (2021) Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling, European Journal of Remote Sensing, 54:sup2, 284-295, DOI: 10.1080/22797254.2020.1771774 Yishu Qiu, Zhenmin Zhu, Heping Huang & Zhenhua Bing (2021) Study on the evolution of B&Bs spatial distribution based on exploratory spatial data analysis (ESDA) and its influencing factors—with Yangtze River Delta as an example, European Journal of Remote Sensing, 54:sup2, 296-308, DOI: 10.1080/22797254.2020.1785950 Liang Li & Kangning Xiong (2021) Study on peak cluster-depression rocky desertification landscape evolution and human activity-influence in South of China, European Journal of Remote Sensing, 54:sup2, 309-317, DOI: 10.1080/22797254.2020.1777588 Juan Xu, Mengsheng Yang, Chaoping Hou, Ziliang Lu & Dan Liu (2021) Distribution of rural tourism development in geographical space: a case study of 323 traditional villages in Shaanxi, China, European Journal of Remote Sensing, 54:sup2, 318-333, DOI: 10.1080/22797254.2020.1788993 Lin Guo, Xiaojing Guo, Binghua Wu, Po Yang, Yafei Kou, Na Li & Hui Tang (2021) Geo-environmental suitability assessment for tunnel in sub-deep layer in Zhengzhou, European Journal of Remote Sensing, 54:sup2, 334-340, DOI: 10.1080/22797254.2020.1788994 Hui Zhou, Cheng Zhu, Li Wu, Chaogui Zheng, Xiaoling Sun, Qingchun Guo & Shuguang Lu (2021) Organic carbon isotope record since the Late Glacial period from peat in the North Bank of the Yangtze River, China, European Journal of Remote Sensing, 54:sup2, 341-347, DOI: 10.1080/22797254.2020.1795728 Chengyuan Hao, Linlin Song & Wei Zhao (2021) HYSPLIT-based demarcation of regions affected by water vapors from the South China Sea and the Bay of Bengal, European Journal of Remote Sensing, 54:sup2, 348-355, DOI: 10.1080/22797254.2020.1795730 Wei Chong, Zhang Lin-Jing, Wu Qing, Cao Lian-Hai, Zhang Lu, Yao Lun-Guang, Zhu Yun-Xian & Yang Feng (2021) Estimation of landscape pattern change on stream flow using SWAT-VRR, European Journal of Remote Sensing, 54:sup2, 356-362, DOI: 10.1080/22797254.2020.1790994 Kepeng Feng & Juncang Tian (2021) Forecasting reference evapotranspiration using data mining and limited climatic data, European Journal of Remote Sensing, 54:sup2, 363-371, DOI: 10.1080/22797254.2020.1801355 Kepeng Feng, Yang Hong, Juncang Tian, Xiangyu Luo, Guoqiang Tang & Guangyuan Kan (2021) Evaluating applicability of multi-source precipitation datasets for runoff simulation of small watersheds: a case study in the United States, European Journal of Remote Sensing, 54:sup2, 372-382, DOI: 10.1080/22797254.2020.1819169 Xiaowei Xu, Yinrong Chen, Junfeng Zhang, Yu Chen, Prathik Anandhan & Adhiyaman Manickam (2021) A novel approach for scene classification from remote sensing images using deep learning methods, European Journal of Remote Sensing, 54:sup2, 383-395, DOI: 10.1080/22797254.2020.1790995 Shanshan Hu, Zhaogang Fu, R. Dinesh Jackson Samuel & Prathik Anandhan (2021) Application of active remote sensing in confirmation rights and identification of mortgage supply-demand subjects of rural land in Guangdong Province, European Journal of Remote Sensing, 54:sup2, 396-404, DOI: 10.1080/22797254.2020.1790996 Chen Qiwei, Xiong Kangning & Zhao Rong (2021) Assessment on erosion risk based on GIS in typical Karst region of Southwest China, European Journal of Remote Sensing, 54:sup2, 405-416, DOI: 10.1080/22797254.2020.1793688 Zhengping Zhu, Bole Gao, Renfang Pan, Rong Li, Yang Li & Tianjun Huang (2021) A research on seismic forward modeling of hydrothermal dolomite:An example from Maokou formation in Wolonghe structure, eastern Sichuan Basin, SW China, European Journal of Remote Sensing, 54:sup2, 417-428, DOI: 10.1080/22797254.2020.1811160 Shaofeng Guo, Jianmin Zheng, Guohua Qiao & Xudong Wang (2021) A preliminary study on the Earth’s evolution and condensation, European Journal of Remote Sensing, 54:sup2, 429-437, DOI: 10.1080/22797254.2020.1830309 Yu Gao, Ying Zhang & Hedjar Alsulaiman (2021) Spatial structure system of land use along urban rail transit based on GIS spatial clustering, European Journal of Remote Sensing, 54:sup2, 438-445, DOI: 10.1080/22797254.2020.1801356 Xia Mu, Sihai Li, Haiyang Zhan & Zhuoran Yao (2021) On-orbit calibration of sun sensor’s central point error for triad, European Journal of Remote Sensing, 54:sup2, 446-457, DOI: 10.1080/22797254.2020.1814164 Following publication, the publisher identified concerns regarding the editorial handling of the special issue and the peer review process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.


Introduction
Human activity intensity is a composite indicator for representing the influences of human activity on land surface (Xu et al., 2016).The human activity intensity is the main cause of geological environment problems (Zang et al., 2019).With the population growth and social progress, the scale and quantity of human activity are getting larger and larger, and the influence on geological environment is becoming more serious (Li, 2011).As the factors of geological environment, human activity keeps pace with natural process, and even surpasses the role of natural process in some aspects, so it is necessary to carry out special research on the evaluation of human activity intensity, and make further analysis on the relationship between human activity intensity and geological environment at the same time (Wang et al., 2009).
The sharp increase of human activity intensity changes the local topography and geomorphology, leading to geological environment deterioration (Shen et al., 2018).Human activity has a major impact on the geological environment, in addition to, is also related to the geological environment carrying capacity (Zou, 2012).Geological environment carrying capacity is one of the criteria for judging the compatibility of human activity and geological environment.The evaluation of geological environment carrying capacity and human activity intensity is the key to achieving sustainable development of regional economy (Li & Wang, 2019).The interference of human activity intensity and the changes in the geological environment system have a complicated relationship, and often nonlinear (Verstraeten et al., 2017).When the human activity intensity exceeds the carrying capacity, the geological environment will lose its security.The impact of human activity on the surrounding environment needs to be quantified (Yang et al., 2019).A multi-index superposition system was established based on weights to evaluate the human activity intensity (Liu et al., 2018).Trend analysis and residual analysis were used to calculate the human activity intensity based on MODIS-NDVI data, combining with remote sensing data and regional socioeconomic data (Wen & Yao, 2018).The human activity intensity at the regional scale can be monitored by remote sensing techniques (Gao et al., 2019).Although there are many methods for assessing human activity intensity from the geological environment, there is no uniform system or index for the quantitative model.In view of this, this paper establishes a model to quantitatively analyze the evaluation of human activity intensity in geological environment.Quantitative evaluation is the value judgment of the quantitative results for the evaluation objects by using mathematical methods with collecting and processing data.
As the capital of Shandong Province, Ji'nan City has a dense population and complex geological environment.The evaluation of human activity intensity is particularly difficult in geological environmental problems.The establishment of index system is the key of regional human activity intensity evaluation.The evaluation of human activity intensity involves three aspects of society, economy and culture, and each aspect is affected by many factors.If various factors are collected, a large indicator system will be obtained, which will increase the workload.Based on the development characteristics of Jinan city and the correlation with environmental engineering geological problems, population density, GDP per capita, mining points, construction land ratio and land utilization ratio are selected as quantitative indexes.The weights of indexes are obtained by using objective valuation method.Further work, the quantitative evaluation model and systematic analysis of human activity intensity for Ji'nan city are established by multifactor method.

Evaluation index
Due to the difference in dimensions and units between the five indexes of population density, per capita GDP, mining points, construction land ratio and land utilization ratio, it is impossible to directly carry out analysis and comparison.Therefore, in order to unify the indexes, these five indexes should be treated nondimensionally by adopting the normalized dimensionless method.After normalized calculation, the values of each index data are 0 and 1.The normalized dimensionless formula can be described as: where x ij is the j-th original data of the i-th index, n is the number of original data of the i-th index, and m is the number of indexes; x max and x min are the maximum and minimum values of the i-th index, respectively.
After dimensionless, all indexes are converted to specific values between 0 and 1, which can be compared and analyzed.

Index weights
Subjective weighting method and objective weighting method are commonly used to determine the index weights.Subjective weighting method refers to the

R E T R A C T E D
method that researchers determine index weight by subjective judgment based on their own professional knowledge and experience.The idea of subjective weighting method is by inviting a group of experts who have a deep understanding of the research problem, and let them assign weight to each evaluation indexes independently.Then, the expert opinions are gathered, and the average and variance of each indexes weight are calculated.The subjective weighting method often depends on the experience of researchers and their familiarity with the research area, which is prone to bias.According to the relationship between the original data, the objective weighting method uses mathematical model to determine the weight.Objective weighting method relies on sufficient sample data, which can effectively avoid the influence of researchers' subjective factors on the results, so the objective weighting method is used for quantitative analysis in this paper.The coefficient of variation method is one of the objective weighting methods, which determines the weight of indexes according to the degree of difference between the values of each index.The coefficient of variation method is used to objectively assign values to each index, and the steps for determining the weight by the coefficient of variation method are as follows: Determination of coefficient of variation V i can be described as: where δ is the standard deviation of each values of the i-th index, and x i is the average of each values of the i-th index.
The weight ω i for each indexes can be obtained by simplifying the coefficient of variation, which is shown as follows: The weights of each indexes for the quantitative evaluation of human activity intensity are obtained

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accord the above steps, and the results are shown in Table 1.

Calculation of human activity intensity
According to the above data results, the index F j representing the human activity intensity is calculated by weighting method, the mathematical model can be described as: where F j is the intensity index of human activity, ω i is the weight for each indexes, y ij is normalized dimensionless value.
After calculation, the index of human activity intensity under the jurisdiction of Ji'nan city is shown in Table 2.

Results
According to quantitative evaluation model, the index of human activity intensity in Ji'nan area is obtained, and all township-level street offices in Ji'nan are divided into four levels, highest, higher, middle, lower, according to the values of 0.6, 0.4-0.6,0.2-0.4,and 0.2, respectively.The research area is divided into many grids, and all partitioned grids are attached the index of human activity intensity, as shown in Figure 1.After the vectorization in the comprehensive partition map, the comprehensive partition of different grades of human activity intensity is shown in Figure 2.
As can be seen from Figure 2, the areas with the highest intensity of human activity are mainly distributed in the urban areas of Lixia District, Zhongcheng District, Huaiyin District, Tianqiao District and Licheng District.

R E T R A C
T E D

Discussion and conclusions
The areas with highest intensity of human activities are mainly distributed in the urban areas of Lixia District, Zhongcheng District, Huaiyin District, Tianqiao district and Licheng District.

Disclosure statement
No potential conflict of interest was reported by the authors.

Figure 1 .
Figure 1.Raster map of human activity intensity in Ji'nan city.
The higher areas are mainly distributed in some villages and towns of Licheng District, Zhangqiu District and Bucun Sub-district Office, Changqing District, Jiyang County District, Shanghe County and Pingyin County.The middle areas are mainly distributed in the areas close to urban areas of Ji'nan City and each counties.The lower areas are mainly distributed in the areas far away from the county towns of Shanghe County and Pingyin County and in a few areas of Changqing District and Zhangqiu

Figure 2 .
Figure 2. Distribution map of human activity intensity in Ji'nan city.

Table 1 .
Ji'nan human activity intensity analysis index weight.

Table 2 .
Intensity index of human activity in Ji'nan city.
District.The highest index is Lixia District of Jinan City, with a value of 0.87.The lowest index is Cuizhai Town in Jiyang County and Yuhuangmiao Town in Shanghe County, only with a value of 0.11.The results are in good agreement with the current economic and social development in various places, and also reflect the great difference of human activity intensity.
The higher areas are mainly distributed in some villages and towns of Licheng District, Zhangqiu District, Bucun Sub-district Office, Changqing District, Jiyang County, Shanghe County and Pingyin County.The middle areas are mainly distributed in the areas close to Jinan City and the districts and counties.The lower areas are mainly distributed in the areas far away from the county seat of Shanghe County and Pingyin County and in a few areas of Changqing District and Zhangqiu District.The results are in good agreement with the current economic and social development in various places.