Sharp tools make good work

--The Analects of Confucius 

 

Geodetector

Software for testing spatial stratified heterogeneity (SSH) and making spatial attribution

 

Contents

1.       Introduction

2.       How to Use This Software

3.       Output of Geodetector

4.       Download of Geodetector Software and Example Datasets

5.       Citations

6.       Bibliography of Geodetector

7.       Developers and Contact Information

Acknowledgement

 

1.      Introduction

Spatial stratified heterogeneity (SSH) refers to the phenomena that within strata are more similar than between strata, such as well known landuse types and climate zones and that yet to be known, is ubiquitous in spatial data. As a set of information, SSH has been a window for humans to understand the nature and society since Aristotle time. In another aspect, SSH may cause confounding in modelling and bias of a sample, which can be solved if SSH is identified then modeling in strata.

Geographical detector is a novel tool to identify SSH and to attribute spatial patterns: (1) measure and find SSH of a variable Y; (2) test the association between two variables Y and X, according to the coupling between their spatial distributions, without assumption of linearity; and (3) investigate general interaction between two explanatory variables X1 and X2 to a dependent variable Y, without any assigned interaction such as product in econometrics. All of the tasks are implementable by the geographical detector q-statistic (Fig. 1):

 

Image 1

Fig. 1. Principle of Geodetector

 

where N and  stand for the number of units and the variance of Y in study area, respectively;  the population Y is composed of L strata (h = 1, 2, …, L), Nh and  stand for the number of units and the variance of Y in stratum h, respectively. The strata of Y (red polygons in Fig.1) are a partition of Y, either by itself (h(Y) in Fig. 1) or by an explanatory variable X which is a categorical variable (h(X) in Fig. 1). X should be stratified if it is a numerical variable, the number of strata L might be 2-10 or more, according to prior knowledge or a classification algorithm. [(N-L)q]/[(L-1)(1-q)] ~ F(L-1, N-L, g), where g is a non central parameter (Wang et al 2016).

In Geodetector, the terms “spatial stratified heterogeneity (SSH)”, “stratification”, “classification” and “partition” are equivalent.

Interpretation of q value (please refer to Fig.1). The value of q  [0, 1].

If Y is stratified by itself h(Y), then q = 0 indicates that Y is not SSH; q = 1 indicates that Y is SSH perfectly; the value of q indicates that the degree of SSH of Y is q.

If Y is stratified by an explanatory variable h(X), then q = 0 indicates that there is no association between Y and X; q = 1 indicates that Y is completely determined by X; the value of q-statistic indicates that X explains 100q% of Y. Please notice that the q-statistic measures the association between X and Y, both linearly and nonlinearly.

Besides identification of SSH and attribution, Geodetector q statistic helps to avoid confounding in global models, overfitting in local models, and sampling bias in data collection. (1) SSH in population causes confounding in global models, appeared as insignificant statistical results; (2) Local models sometimes result in overfitting or their parameters are too many to be interpretable; (3) A sample would be biased to population if the latter is SSH and not all strata are sampled. All the three problems can be avoided if SSH is identified (by Geodetector q statistic) so simple models or stratified sampling is applied to each of the strata, separately. Please note that the “spatial” in SSH can be either geospatial or the spatial in mathematics such as time, attributes.

Functions of Geodetector. Geographical detector consists of four functions:

(1)    The risk detector indicates potential risk areas Y(X);

(2)    The factor detector q-statistic measures the SSH of a variable Y, or the determinant power of a covariate X of Y;

(3)    The ecological detector identifies the impact differences between two risk factors X1 ~ X2;

(4)    The interaction detector reveals whether the risk factors X1 and X2 (and more X) have an interactive influence on a disease Y. (Fig.2)

 

Interaction

Fig.2. Interaction between X1 and X2 upon Y

 

2.      How to Use This Software

The GeoDetector software was developed using Excel and R, respectively. The tools are free of charge, freely downloadable, and easy to use, and were designed without any GIS plug-in components and with “one click” execution. Users can run the following demo, then simply replace the demo data in the software using your own data, click Run and you get results ! We henceforth describe Excel Geodetector software. R users can download the R Geodetector software in the following section “Download of Geodetector Software and Example Datasets”.

 

As a demo, neural-tube birth defects (NTD) Y and suspected risk factors or their proxies Xs in villages are provided, including data for the health effect layers “NTD prevalence” and environmental factor layers, “elevation”, “soil type”, and “watershed”. Their field names are defined as Y and X1, X2, X3 respectively.

Step 1. Download the software and input your data in Excel

(1) Download the Excel Geodetector software (In the following section “Software and Examples Data Download”), one click to download any one of the three Examples, unzip the downloaded file, you will find an Excel file (this is Geodetector software with an Example dataset!) and double click the Excel file, Fig. 3 and Fig. 5 appear. Fig. 3 gives the format of the input data for the GeoDetector: each row denotes a sample unit (e.g. a village); the 1st column record the disease prevalence (Y); the 2nd and following columns denote partitions of Y or factors X, the latter were partitioned according to the similarity within strata.

(2) Input your data into the Excel Geodetector software in the format of Fig. 3. Then go to Step 2.

 

说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: image004

Fig. 3. Input data in Excel and the execution interface

(Note: Y is numerical; X MUST be categorical, e.g. landuse types. If X is numerical it should be transformed to be categorical, e.g. GDP per capita is stratified into 5 strata)

 

(3) If your data is in GIS format, as Fig. 4, please transform the GIS data into Excel data as Fig. 3.

 

Fig. 4. Data in GIS format

 

Step 2. Run GeoDetector software

Only one operation interface was designed (Fig. 5). The function of the “Read Data” button is to load data; thus, when the button is clicked, all variables are listed in the “variables” list box. Then, disease and partition of Y or environmental factor variables are selected into their corresponding list boxes Y and X on the right of the interface. Finally, GeoDetector is executed by clicking the “Run” button.

 

说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: UI

Fig. 5. User interface for GeoDetector

 

3.      Output of Geodetector

GeoDetector outputs results from the risk detector, factor detector, ecological detector, and interaction detector in four Excel spreadsheets (Fig. 6).

 

说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: image014

Fig. 6. Interface for GeoDetector results

 

In the “Risk detector” sheet (Fig. 7), result information for each environmental risk factor is presented in two tables. The first table gives the average disease incidence in each stratum of a risk factor, the name of which is written at the top left of the table. The second table gives the statistically significant difference in the average disease incidence between two strata; if there is a significant difference, the corresponding value is “Y”, else it is “N”.

 

risk-detector

Fig. 7. Results of risk detector

 

The Fig. 8 shows the output format of the q values for each environmental risk factor, as given in the “Factor detector” sheet. The table header gives the names of the environmental risk factors, while the associated q values (q1, q2, …, qn) and their corresponding p values are presented in the row below.

 

 

factor-detector

Fig. 8. Results of factor detector

 

In the “Ecological detector” sheet (Fig. 9), results of the statistically significant differences between two environmental risk factors are presented. If Y(X1) (risk factor names in row) was significantly bigger than Y(X2) (risk factor names in column), the associated value is “Y”, while “N” expresses the opposite meaning.

ecological-detector

Fig. 9. Results of ecological detector

 

The format of the results for the interaction detector is shown in Fig. 10.Interaction relationships” below the table represent the interaction relationship for the two factors. The relationship is defined in a coordinate axis. It has 5 intervals, including “(-min(q(x), q(y)))”,“(min(q(x), q(y)), max(q(x), q(y)))”, “(max(q(x), q(y)), q(x) + q(y))”,“q(x) + q(y)”,“( q(x) + q(y),+∞)”, and the interaction relationship is determined by the location of q(xÇy) in the 5 intervals (see Table 1).

 

interaction-detector

Fig. 10. Results of interaction detector

 

Table 1 Redefined interaction relationships

Graphical representation

Description

Interaction

 

q(X1ÇX2) < Min(q(X1), q(X2))

 

Weaken, nonlinear

Min(q(X1),q(X 2))<q(X1Ç X2)<Max(q(X1)), q(X2))

 

Weaken, uni-

 

q(X1Ç X2) > Max(q(X1), q(X2))

 

Enhance, bi-

 

q(X1Ç X2) = q(X1)+ q(X2)

 

Independent

 

q(X1Ç X2) > q(X1)+ q(X2)

 

Enhance, nonlinear

Legend

说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: image029

 

4.      Download of Geodetector Software and Example Datasets

The software was developed using Excel 2007 and R, respectively. It is completely free. You can click any one of the following links to download the Geodetector software. The first three are Geodetector software in Excel: (1) click one and unzip the file, an Excel file appears; (2) click the Excel file to start the Geodetector, you may exercise the demo data; then (3) input your own data to get your own results.

1: GeoDetector Software in Excel, enclosed an Example of a Disease Dataset

2: GeoDetector Software in Excel, enclosed an Example of a Toy Dataset

3: GeoDetector Software in Excel, enclosed an Example of a NDVI Dataset

4: GeoDetector Software in R

 

5.  Citations. The Geodetector can be cited as:

[1] Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X & Zheng XY. 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science 24(1): 107-127.

[2] Wang JF, Zhang TL, Fu BJ. 2016. A measure of spatial stratified heterogeneity. Ecological Indicators 67(2016): 250-256.

[3] http://www.geodetector.org/

 

6.  Geodetector Bibliography

[1] Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X & Zheng XY. 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science 24(1): 107-127.

[2] Luo W, Jasiewicz J, Stepinski T, Wang JF, Xu CD, Cang XZ. 2016. Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophysical Research Letters 43(2): 692-700.

[3] 刘彦随,     . 2012. 中国县域城镇化的空间特征与形成机理. 地理学报 67(8):1011-1020.

   Liu YS, Yang R.2012.Spatial characteristics and formation mechanism of the county urbanization in China. Acta Geographica Sinica 67(8): 1011-1020.

[4] 王劲峰,徐成东. 2017. 地理探测器:原理与展望. 地理学报 72(1): 116-134.

   Wang JF, Xu CD. 2017. Geodetector:Principle and prospective. Acta Geographica Sinica 72(1): 116-134

[5] Lecture ppt in 181203: Geodetector & its Applications in Natural and Social Sciences

2010

1.         Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X & Zheng XY. 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science 24(1): 107-127.

2.         Liao YL,Wang JF, Wu JL, Driskell L, Wang WY, Zhang T, Gu X, Zheng XY. 2010. Spatial analysis of neural tube defects in a rural coal mining area. International Journal of Environmental Health Research 20(6): 439-450.

2011

3.         Hu Y, Wang JF, Li XH, Ren D, Zhu J. 2011. Geographical detector-based risk assessment of the under-five mortality in the 2008 Wenchuan earthquake, China. PLoS ONE 6(6): e21427.

4.         Zou B, Wilson JG, Zhan FB, Zeng YN, Wu KJ. 2011. Spatial-temporal variations in regional ambient sulfur dioxide concentration and source-contribution analysis: A dispersion modeling approach. Spatial-temporal variations in regional ambient sulfur dioxide concentration and source-contribution analysis: A dispersion modeling approach. Atmospheric Environment 45: 4977-4985.

2012

5.         Gajos M. 2012. Geoinformation technologies in biomedicine and health care: review of scientific journals. E. Piętka and J. Kawa (Eds.): ITIB 2012, LNCS 7339: 510–524.

6.         Li LF, Wang JF, Wu J. 2012. A spatial model to predict the incidence of neural tube defects. BMC Public Health 12: 951.

7.         Wang JF, Hu Y. 2012. Environmental health risk detection with GeogDetector. Environmental Modelling & Software 33: 114-115.

8.         刘彦随, 杨忍, 2012. 中国县域城镇化的空间特征与形成机理. 地理学报 67(8): 1011-1020.

Liu YS, Yang R.2012.Spatial characteristics and formation mechanism of the county urbanization in China. Acta Geographica Sinica 67(8): 1011-1020.

2013

9.         Cao F, Ge Y, Wang JF. 2013. Optimal discretization for geographical detectors-based risk assessment. GIScience & Remote Sensing 50(1): 78-92.

10.     Li XW, Xie YF, Wang JF, Christakos G, Si JL, Zhao HN, Ding YQ, Li J. 2013. Influence of planting patterns on Fluoroquinolone residues in the soil of an intensive vegetable cultivation area in north China. Science of the Total Environment 458-460: 63-69.

11.     Lee WC. 2013. Assessing causal mechanistic interactions: a peril ratio index of synergy based on multiplicativity. PLoS ONE 8(6): e67424. doi:10.1371/journal.pone.0067424.

12.     Raghavan RK, Brenner KM, Harrington Jr JA, Higgins JJ, Harkin KR. 2013. Spatial scale effects in environmental risk-factor modelling for diseases. Geospatial Health 7(2): 169-182.

13.     Wang JF, Wang Y, Zhang J, Christakos G, Sun JL, Liu X, Lu L, Fu XQ, Shi YQ, Li XM. 2013. Spatiotemporal transmission and determinants of typhoid and paratyphoid fever in Hongta District, China. PLoS Neglected Tropical Diseases 7(3): e2112.

14.     Wang JF, Xu CD, Tong SL, Chen HY, Yang WZ. 2013. Spatial dynamic patterns of hand-foot-mouth disease in the People’s Republic of China. Geospatial Health 7(2): 381-390.

2014

15.     Bai HX, Ge Y, Wang JF, Li DY, Liao YL, Zheng XY. 2014. A method for extracting rules from spatial data based on rough fuzzy sets. Knowledge-Based Systems 57: 28-40.

16.     Hu Y, Gao J, Chi M, Luo C, Lynn H, Sun LQ, Tao B, Wang DC, Zhang ZJ, Jiang QW. 2014. Spatio-temporal patterns of schistosomiasis Japonica in lake and marshland areas in China: the effect of snail habitats. American Journal of Tropical Medicine and Hygiene 91(3): 547–554.

17.     Hu Z, Tang GA, Lu GN. 2014. A new geographical language: a perspective of GIS. Journal of Geographical Sciences 24(3): 560-576.

18.     Huang JX, Wang JF, Bo YC, Xu CD, Hu MG. 2014. Identification of health risks of Hand, Foot and Mouth Disease in China using the Geographical Detector Technique. International Journal of Environmental Research and Public Health 11: 3407-3423.

19.     Luo W. 2014. Impact cratering as a major factor controlling valley dissection density on MARS - a geographical detector approach. 45th Lunar and Planetary Science Conference. 2580.pdf.

20.     Qian Q, Zhao J, Fang LQ, Zhou H, Zhang WJ, Wei L, Yang H, Yin WW, Cao WC, Li Q. 2014. Mapping risk of plague in Qinghai-Tibetan Plateau, China. BMC Infectious Diseases 14: 382.

21.     Ren Y, Deng LY, Zuo SD, et al. 2014. Geographical modeling of spatial interaction between human activity and forest connectivity in an urban landscape of southeast China. Landscape Ecology 29(10): 1741-1758.

22.     Wu JL, Zhang CS, Pei LJ, Chen G, Zheng XY. 2014. Association between risk of birth defects occurring level and arsenic concentrations in soils of Lvliang, Shanxi province of China. Environmental Pollution 191: 1-7.

23.     Xu EQ, Zhang HQ. 2014. Characterization and interaction of driving factors in karst rocky desertification: a case study from Changshun, China. Solid Earth 5: 1329-1340.

24.     蔡芳芳,濮励杰. 2014. 南通市城乡建设用地演变时空特征与形成机理. 资源科学 36(4): 0731-0740.

Cai FF, Pu LJ. 2014. Spatial-Temporal characteristics and formation mechanism of Urban-Rural construction land in Nantong City. Resources Science 36(4): 0731-0740.

25.         悦,蔡建明,任周鹏,杨振山. 2014. 基于地理探测器的国家级经济技术开发区经济增长率空间分异及影响因素. 地理科学进展 33(5): 657-666.

Ding Y, Cai JM, Ren ZP, Yang ZS. 2014. Spatial disparities of economic growth rate of China’s National-level ETDZs and their determinants based on geographical detector analysis. Progress in Geography 33(5): 657-666.

26.         丹,舒晓波,尧    波,曹安庆. 2014. 江西省县域人均粮食占有量的时空格局演变. 地域研究与开发 33(4): 157-162.

Hu D, Shu XB, Yao B, Cao QA. 2014. The evolvement of spatial-temporal pattern of per capita grain possession in counties of Jiangxi Province. Areal Research And Development 33(4): 157-162.

27.     李成悦,王    腾,周    . 2014. 湖北省区域经济格局时空演化及其影响因素分析. 发展研究 2014(1): 47-51.

Li CY, Wang T, Zhou Y. 2014. The evolvement of Spatial-Temporal and determinants of regional economic patterns in Hubei Province. Development Research 2014(1): 47-51.

28.     倪书华. 2014. 空间统计学及其在公共卫生领域中的应用. 汕头大学学报(自然科学版)29(4): 61-67.

Ren SH. 2014. Spatial statistics and its application to the field of public health. Journal of Shantou University(Natural Science) 29(4): 61-67.

29.     通拉嘎,徐新良,付    颖,魏凤华. 2014. 地理环境因子对螺情影响的探测分析. 地理科学进展 33(5): 625-635.

Tong LG, Xu XL, Fu Y, Wei FH. 2014. Impact of environmental factors on snail distribution using geographical detector model. Progress in Geography 33(5): 625-635.

30.     魏凤娟,李江风,刘艳中. 2014. 湖北县域土地整治新增耕地的时空特征及其影响因素分析. 农业工程学报 30(14): 267-275.

Wei FJ, Li JF, Liu YZ.2014. Spatial-temporal characteristics and impact factors of newly increased farmland by land consolidation in Hubei province at county level. Transactions of the Chinese Society of Agricultural Engineering 30(14): 267-276.

31.         ,  石培基. 2014. 甘肃省县域城镇化地域差异及形成机理. 干旱区地理 37(4): 838-845.

Yang B, Shi PJ. 2014. Geographical features and formation mechanism of county level urbanization in Gansu Province. Arid Land Geography 37(4): 838-845.

32.     俞佳根,叶世康. 2014. 空间视角下中国对外直接投资与产业结构升级水平研究. 商业经济研究 34: 127-128.

Yu JG, Ye SK.2014. Outward foreign direct investment and industrial structure upgrade level from the perspective of spatial in China. Journal of Commercial Economics 34: 127-128.

2015

33.     Chen YH, Ge Y, Heuvelink GBM, Hu JL, Jiang Y. 2015. Hybrid constraints of pure and mixed pixels for soft-then-hard super-resolution mapping with multiple shifted images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(5): 2040-2052.

34.     Hu Y, Bergquist R, Lynn H, Gao FH, Wang QZ, Zhang SQ, Li R, Sun LQ, Xia CC, Xiong CL, Zhang ZJ, Jiang QW. 2015. Sandwich mapping of schistosomiasis risk in Anhui Province, China. Geospatial Health 10: 324.

35.     Hu Y, Li R, Bergquist R, Lynn H, Gao FH, Wang QZ, Zhang AQ, Sun LQ, Zhang ZJ, Jiang QW. 2015. Spatio-temporal transmission and environmental determinants of schistosomiasis Japonica in Anhui Province, China. PLoS Neglected Tropical Diseases 9(2): e0003470. doi:10.1371/journal.pntd.0003470.

36.     Lee WC. 2015. Testing for sufficient-cause gene-environment interactions under the assumptions of independence and Hardy-Weinberg equilibrium. American Journal of Epidemiology 182(1): 9–16.

37.     Shen J, Zhang N, Gexi geduren, He B, Liu CY, Li Y, Zhang HY, Chen XY, Lin H. 2015. Construction of a GeogDetector-based model system to indicate the potential occurrence of grasshoppers in Inner Mongolia steppe habitats. Bulletin of Entomological Research 105: 335-346.

38.     Yang R, Liu YS, Long HL, Qiao LY. 2015. Spatio-temporal characteristics of rural settlements and land use in the Bohai Rim of China. Journal of Geographical Sciences 25(5): 559-572.

39.     Zhu H, Liu JM, Chen C, Lin J, Tao H. 2015. A spatial-temporal analysis of urban recreational business districts: A case study in Beijing, China. Journal of Geographical Sciences 25(12): 1521-1536.

40.     毕硕本,    , 陈昌春, 杨鸿儒,    . 2015. 地理探测器在史前聚落人地关系研究中的应用与分析. 地理科学进展 34(1): 118-127.

Bi SB, Ji H, Chen CC, Yang HR, Shen X.2015.Application of geographical detector in human-environment relationship study of prehistoric settlements. Progress in Geography 34(1): 118-127

41.     崔日明,  俞佳根. 2015. 基于空间视角的中国对外直接投资与产业结构升级水平研究. 福建论坛 (人文社会科学版) 2015(2): 26-33.

Cui RM, Yu JG.2015. Outward foreign direct investment and industrial structure upgrade level from the perspective of spatial in China. Fujian Tribune (The Humanities & Social Sciences Monthly) 2015(2): 26-33.

42.     李一凡,王卷乐,高孟绪. 2015. 自然疫源性疾病地理环境因子探测及风险预测研究综述. 地理科学进展 34(7): 926-935.

Li YF, Wang JL, Gao MX. 2015. A review of geographical and environmental factor detection and risk prediction of natural focus diseases. Progress in Geography 34(7): 926-935.

43.     徐秋蓉 郑新奇. 2015. 一种基于地理探测器的城镇扩展影响机理分析法. 测绘学报 44 S0: 96-101.

Xu QR, Zheng XQ.2015. Analysis of influencing mechanism of urban growth using geographical detector. Acta Geodaetica at Cartographica Sinica 44(S0): 96-101.

44.        , 刘彦随, 龙花楼, 陈呈奕. 2015. 基于格网的农村居民点用地时空特征及空间指向性的地理要素识别——以环渤海地区为例. 地理研究 34(6): 1077-1087.

Yang R, Liu YS, Long HL, Chen CY. 2015. Spatial-temporal characteristics of rural residential land use change and spatial directivity identification based on grid in the Bohai Rim in China. Geographical Research 34(6): 1077-1087.

45.         佳,刘吉平. 2015. 基于地理探测器的东北地区气温变化影响因素定量分析. 湖北农业科学 54(19): 4682-4687.

Yu J, Liu JP.2015. Quantitative Analysis with Geographical Detector on the influence factor of temperature variation in Northeast China. Hubei Agricultural Sciences 54(19): 4682-4687.

46.     湛东升, 张文忠, 余建辉,   , 党云晓. 2015. 基于地理探测器的北京市居民宜居满意度影响机理. 地理科学进展 34(8): 966-975.

Zhan DS, Zhang WZ, Yu JH, Meng B, Dang XY.2015. Analysis of influencing mechanism of residents’ livability satisfaction in Beijing using geographical detector. Progress in Geography 34(8): 966-975.

47.         , 任志远. 2015. 基于Whittaker滤波的陕西省植被物候特征. 中国沙漠 45(4): 901-906.

Zhang H, Ren ZY.2015. Remote sensing analysis of vegetation phenology characteristics in Shanxi Province based on Whittaker smoother method. Journal of Desert Research 35(4): 901-906.

48.         , 刘家明,    ,   ,   . 2015. 北京城市休闲商务区的时空分布特征与成因. 地理学报 70(8): 1215-1228.

Zhu H, Liu JM, Tao H, Li G, Wang R.2015.Temporal-spatial pattern and contributing factors of urban RBDs in Beijing. Arta Geographica Sinica 70(8): 1215-1228.

2016

49.     Chen K, Ni MJ, Cai MG, Wang J, Huang DR, Chen HR, Wang X, Liu MY. 2016. Optimization of a coastal environmental monitoring network based on the Kriging method: a case study of Quanzhou Bay, China. BioMed Research International. http://dx.doi.org/10.1155/2016/7137310.

50.     Du Z, Xu X, Zhang H, Wu Z, Liu Y. 2016. Geographical detector-based identification of the impact of major determinants on aeolian desertification risk. PLoS ONE 11(3): e0151331.

51.     Fan LX, Wu EQ, Liu J, Qu XC, Ning BA, Liu Y. 2016. Distribution Characteristics of Spermophilus dauricus in Manchuria City in China in 2015 through “3S” Technology. Biomedical Environmental Sciences 29(8): 603-608.

52.     Fei XF, Wu JP, Liu QM, Ren YJ, Lou ZH. 2015. Spatiotemporal analysis and risk assessment of typhoid cancer in Hangzhou, China. Stochastic Environmental Research and Risk Analysis 30: 2155–2168.

53.     Fei XF, Wu JP, Liu QM, Ren YJ, Lou ZH. 2015. Spatiotemporal analysis and risk assessment of thyroid cancer in Hangzhou, China. Stochastic Environmental Research and Risk Assessment 30: 2155–2168.

54.     Ju HR, Zhang ZX, Zuo LJ, Wang JF, Zhang SR, Wang X, Zhao XL. 2016. Driving forces and their interactions of built-up land expansion based on the geographical detector – a case study of Beijing, China. International Journal of Geographical Information Science 30(11): 2188–2207.

55.     Liang P, Yang XP. 2016. Landscape spatial patterns in the Maowusu (Mu Us) Sandy Land, northern China and their impact factors. Catena 145: 321-333.

56.     Liao YL, Zhang Y, He L, Wang JF, Liu X, Zhang NX, Xu B. 2016. Temporal and spatial analysis of neural tube defects and detection of geographical factors in Shanxi Province, China. PLoS ONE 11(4): e0150332. doi:10.1371/journal.pone.0150332.

57.     Lou CR, Liu HY, Li YF, Li YL. 2016. Socioeconomic drivers of PM2.5 in the accumulation phase of air pollution episodes in the Yangtze river delta of China. International Journal of Environmental Research and Public Health 13: 928.

58.     Luo W, Jasiewicz J, Stepinski T, Wang JF, Xu CD, Cang XZ. 2016. Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophysical Research Letters 43(2): 692-700.

59.     Ren J, Gao BB, Fan HM, Zhang ZH, Zhang Y, Wang JF. 2016. Assessment of pollutant mean concentrations in the Yangtze estuary based on MSN theory. Marine Pollution Bulletin 113: 216-223.

60.     Ren Y, Deng LY, Zuo SD. Song XD, Liao YL, Xu CD, Chen Q, Hua LZ, Li ZW. 2016. Quantifying the influences of various ecological factors on land surface temperature of urban forests. Environmental Pollution 216: 519-529.

61.     Tan JT, Zhang PY, Lo KV, Li J, Liu SW. 2016. The urban transition performance of resource-based cities in northeast China. Sustainability 8: 1022; doi:10.3390/su8101022.

62.     Todorova Y, Lincheva S, Yotinov I, Topalova Y. 2016. Contamination and ecological risk assessment of long-term polluted sediments with heavy metals in small hydropower cascade. Water Resources Management 30: 4171-4184.

63.     Wang JF, Zhang TL, Fu BJ. 2016. A measure of spatial stratified heterogeneity. Ecological Indicators 67: 250-256.

64.     Wang XG, Xi JC, Yang DY, Chen T. 2016. Spatial differentiation of rural touristization and its determinants in China: a geo-detector-based case study of Yesanpo scenic area. Journal of Resources and Ecology 7(6): 464-471.

65.     Wu RN, Zhang JQ, Bao YH, Zhang F. 2016. Geographical detector model for influencing factors of industrial sector carbon dioxide emissions in Inner Mongolia, China. Sustainability 8(2): 149.

66.     Yang R, Xu Q, Long HL. 2016. Spatial distribution characteristics and optimized reconstruction analysis of China ’s rural settlements during the process of rapid urbanization. Journal of Rural Studies 47: 413-424.

67.     Zhang N, Jiang YC, Liu CY, Shen J. 2016. A cellular automaton model for grasshopper population dynamics in Inner Mongolia steppe habitats. Ecological Modelling 329: 5-17.

68.     Zhang T, Yin F, Zhou T, Zhang XY & Li XX. 2016. Multivariate time series analysis on the dynamic relationship between Class B notifiable diseases and gross domestic product (GDP) in China. Scientific Reports 6: 29.

69.     Zhao XY, Cai J, Feng DL, Bai YQ, Xu B. 2016. Meteorological influence on the 2009 influenza a (H1N1) pandemic in mainland China. Environmental Earth Sciences 75: 878.

70.     陈昌玲,张全景,吕    晓,黄贤金. 2016. 江苏省耕地占补过程的时空特征及驱动机理. 经济地理 36(4): 155-163.

Chen CL, Zhang QJ, Lv X, Huang XJ. 2016. Analysis on spatial-temporal characteristics and driving mechanisms of cropland occupation and supplement in Jiangsu Province. Economic Geography 36(4): 155-163.

71.     陈业滨,李卫红,黄玉兴,李晓歌,华家敏. 2016. 广州市登革热时空传播特征及影响因素. 热带地理 36(5): 767-775.

Chen YB, Li WH, Huang YX, Hua JM. 2016. Spatio-temporal spreading features and the influence factors of Dengue Fever in downtown Guangzhou. Tropical Geography 36(5):767-775.

72.     李俊刚,闫庆武,熊集兵,黄园园. 2016. 贵州省煤矿区植被指数变化及其影响因子分析. 生态与农村环境学报 32(3): 374-378.

Li JG, Yan QW, Xiong JB, Huang YY. 2016. Variation of vegetation index in coal mining areas in Guizhou Province and its affecting factors. Journal of Ecology and Rural Environment 32(3): 374-378.

73.         涛,廖和平,褚远恒,孙 海,李 靖,杨 . 2016. 重庆市农地非农化空间非均衡及形成机理. 自然资源学报 31(11): 1844-1857.

Li T, Liao HP, Zhu YH, Sun H, Li J, Yang W.2016.Spatial disequilibrium and its formation mechanism of farmland conversion in Chongqing. Journal of Natural Resources 31(11): 1844-1857.

74.     李媛媛,徐成东,肖革新,罗广祥. 2016. 京津唐地区细菌性痢疾社会经济影响时空分析. 地球信息科学学报 18(12): 1615-1623.

Li YY, Xu CD, Xiao GX, Luo GX. 2016. Spatial-temporal analysis of social-economic factors of Bacillary dysentery in Beijing-Tianjin-Tangshan,China. Journal of Geo-information Science 18(12): 1615-1623.

75.         颖,王心源,周俊明. 2016. 基于地理探测器的大熊猫生境适宜度评价模型及验证. 地球信息科学学报 18(6): 767-778.

Liao Y, Wang XY, Zhou JM.2016.Suitability assessment and validation of giant panda habitat based on Geographical Detector. Journal of Geo-information Science 18(6): 767-778.

76.     陶海燕,潘中哲,潘茂林,卓  莉,徐  勇,鹿  . 2016. 广州大都市登革热时空传播混合模式. 地理学报 71(9): 1653-1662.

Tao HY, Pan ZZ, Pan ML, Zhuo L, Xu Y, Lu M.2016.Mixing spatial-temporal transmission patterns of metropolis dengue fever:a case study of Guangzhou , China. Acta Geographica Sinica 71(9): 1653-1662.

77.         方,牛振国,许盼盼. 2016. 基于景观格局的常熟市地表热环境季节变化特征. 生态学杂志 35(12): 3404-3412.

Wang F, Niu ZG, Xu PP.2016.Seasonal variation of the surface thermal environment in Changshu City based on landscape pattern. Chinese Journal of Ecology 35(12): 3404-3412.

78.     王录仓,武荣伟,刘海猛,周  鹏,康江江. 2016. 县域尺度下中国人口老龄化的空间格局与区域差异. 地理科学进展 35(8): 921-931.

Wang LC, Wu RW, Liu HM, Zhou P, Kang JJ. 2016. Spatial patterns and regional differences of population ageing in China based on the county scale. Progress in Geography 35(8): 921-931.

79.     王录仓,武荣伟. 2016. 中国人口老龄化时空变化及成因探析-基于县域尺度的考察. 中国人口科学 2016(4): 74-84.

Wang LC, Wu RW. 2016. A study on spatial-temporal pattern of population ageing and its factors in China: based on county-scale examination. Chinese Journal of Population Science 2016(4): 74-84.

80.     王曼曼,吴秀芹,吴   斌,张宇清,董贵华. 2016. 盐池北部风沙区乡村聚落空间格局演变分析. 农业工程学报 32(8): 260-271.

Wang MM, Wu XQ, Wu B, Zhang YQ, Dong GH. 2016. Evolution analysis of spatial pattern of rural settlements in sandy area of northern Yanchi. Transactions of the Chinese Society of Agricultural Engineering 32(8): 260-271.

81.     王少剑,王   洋,蔺雪芹,张虹鸥. 2016. 中国县域住宅价格的空间差异特征与影响机制. 地理学报 71(8): 1329-1342.

Wang SJ, Wang Y, Lin XQ, Zhang HO. 2016. Spatial differentiation patterns and influencing mechanism of housing prices in China: based on data of 2872 counties. Acta Geographica Sinica 71(8): 1329-1342.

82.        帅,刘士彬,段建波,戴   . 2016. OSDS注册用户空间分布特征及影响因素分析. 地球信息科学学报 18(10): 1332-1340.

Xie S, Liu SB, Duan JB, Dai Q. 2016. Spatial distribution characteristics of OSDS registered users and its influencing factors. Journal of Geo-information Science 18(10): 1332-1340.

83.        忍,刘彦随,龙花楼,王   洋,张怡筠. 2016. 中国村庄空间分布特征及空间优化重组解析. 地理科学 36(2): 170-179.

Yang R, Liu YS, Long HL, Wang Y, Zhang YJ. 2016. Spatial distribution characteristics and optimized reconstructing analysis of rural settlement in China. Scientia Geographica Sinica 36(2): 170-179.

84.        磊,武建军,贾瑞静,梁   念,张凤英,倪  永,刘  . 2016. 京津冀PM2.5时空分布特征及其污染风险因素. 环境科学研究 29(4): 483-493.

Zhou L, Wu JJ, Jia RJ, Liang N, Zhang FY, Ni Y, Liu M. 2016. Investigation of temporal-spatial characteristics and underlying risk factors of PM2.5 pollution in Beijing-Tianjin-Hebei area. Research of Environmental Sciences 29(4): 483-493.

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85.     Adegboye OA, Gayawan E, Hanna F. 2017. Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula. PLoS ONE 12(7): e0181215.

86.     Benedetti R, Espa G, Taufer E. 2017. Model-based variance estimation in non-measurable spatial designs. Journal of Statistical Planning and Inference 181: 52–61.

87.     Cao Z , Liu T, Li X, Wang J, Lin HL, Chen LL, Wu ZF, Ma WJ. 2017. Individual and interactive effects of socio-ecological factors on dengue fever at fine spatial scale: a geographical detector-based analysis. International Journal of Environmental Research and Public Health 14: 795.

88.     Caulley L, Sawada M, Hinther K, Ko Y-t, Crowther JA, Kontorinis G. 2017. Geographic distribution of vestibular schwannomas in West Scotland between 2000-2015. PLoS ONE 12(5): e0175489.

89.     Chen H, Leinonen I, Marshall B, Taylor AJ. 2017. Conceptual spatial crop models for potato production. Advances in Animal Biosciences: Precision Agriculture (ECPA) 2017. 8(2): 678–683.

90.     Cheng SF, Lu F. 2017. A two-step method for missing spatio-temporal data reconstruction. ISRS International Journal of Geo-Information 6: 187.

91.     Dai YH, Zhou WX. 2017. Temporal and spatial correlation patterns of air pollutants in Chinese cities. PLoS ONE 12(8): e0182724.

92.     Du ZQ, Zhang XY, Xu XM, Zhang H, Wu ZT, Pang J. 2017. Quantifying influences of physiographic factors on temperate dryland vegetation, Northwest China. Scientific Reports 7: 40092.

93.     Fang YB, Wang LM, Ren ZP, Yang Y, Mou CF, Qu QS. 2017. Spatial heterogeneity of energy-related CO2 emission growth rates around the world and their determinants during 1990–2014. Energies 10: 367.

94.     Fu ZL, Zhou KC, Sun YJ, Han YT. 2017. Irregularly shaped cluster detection using a CPSO distribution-free spatial scan statistic. IEEE Access 5: 24863-24872.

95.     Gao BB, Lu AX, Pan YC, Huo LL, Gao YB, Li XL, Li SH, Chen ZY. 2017. Additional sampling layout optimization method for environmental quality grade classifications of farmland soil. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI: 10.1109/JSTARS.2017.2753467.

96.     Gao H, Tang YW, Jiang LH, Li H, Ding HF. 2017. A novel unsupervised segmentation quality evaluation method for remote sensing images. Sensors 17: 2427.

97.     Ge EJ, Zhang RJ, Li DK, Wei XL, Wang XM, Lai PC. 2017. Estimating risks of inapparent avian exposure for human infection: avian influenza virus A (H7N9) in Zhejiang province, China. Scientific Reports 7: 40016.

98.     Goudzrzi S, Jozi SA, Monavari M, Karbasi A, Hasani H. 2017. Assessment of groundwater vulnerability to nitrate pollution caused by agriculture practices. Water Quality Research Journal 7: 20.

99.     Gu H, Fan WJ, Liu K, Qin SW, Li XY, Jiang JM, Chen EF, Zhou YB, Jiang QW. 2017. Spatio-temporal variations of typhoid and paratyphoid fevers in Zhejiang Province, China from 2005 to 2015. Scientific Reports 7: 5780.

100.  Hellwig E, Hijmans RJ. 2017. Spatio-temporal variation in childhood growth in Nigeria: a comparison of aggregation and interpolation, International Journal of Digital Earth. DOI: 10.1080/17538947.2017.1330905.

101.  Hu Y, Xia CC, Li SZ, Ward MP, Luo C, Gao FH, Wang QZ, Zhang SQ, Zhang ZJ. 2017. Assessing environmental factors associated with regional schistosomiasis prevalence in Anhui Province, Peoples’ Republic of China using a geographical detector method. Infectious Diseases of Poverty 6: 87.

102.  Li J, Zhu ZW, Dong WJ. A new mean-extreme vector for the trends of temperature and precipitation over China during 1960–2013. Meteorology and Atmospheric Physics 129: 273–282.

103.  Li FZ, Zhang F, Li X, Wang P, Liang JH, Mei YT, Cheng WW, Qian Y. 2017. Spatiotemporal patterns of the use of urban green spaces and external factors contributing to their use in central Beijing. International Journal of Environmental Research and Public Health 14: 237.

104.  Li J, Zhu ZW, Dong WJ. 2017. A new mean-extreme vector for the trends of temperature and precipitation over China during 1960–2013. Meteorology Atmospheric Physics 129: 273–282.

105.  Liao YL, Xu B, Wang JF, Liu XC. 2017. A new method for assessing the risk of infectious disease outbreak. Scientific Reports 7: 40084. DOI: 10.1038/srep40084.

106.  Liao YL, Wang JF, Du W, Gao BB, Liu X, Chen G, Song XM, Zheng XY. 2017. Using spatial analysis to understand the spatial heterogeneity of disability employment in China. Transactions in GIS 21(4): 647–660.

107.  Liu YS, Yuan XM, Guo L, Huang YH, Zhang XL. 2017. Driving force analysis of the temporal and spatial distribution of flash floods in Sichuan province. Sustainability 9: 1527; doi: 10.3390/su9091527.

108.  Onozuka D, Hagihara A. 2017. Extreme temperature and out-of-hospital cardiac arrest in Japan: A nationwide, retrospective, observational study. Science of the Total Environment 575(2017): 258-264.

109.  Qiao PW, Lei M, Guo GH, Yang J, Zhou XY, Chen TB. 2017. Quantitative analysis of the factors influencing soil heavy metal lateral migration in rainfalls based on geographical detector software: a case study in Huanjiang County, China. Sustainability 9: 1227.

110.  Qiu BW, Lu DF, Tang ZH, Song DJ, Zeng YH, Wang ZZ, Chen CC, Chen N, Huang HY, Xu WM. 2017. Mapping cropping intensity trends in China during 1982-2013. Applied Geography 79: 212-222.

111.  Penman BS, Gupta S, Shanks GD. 2017. Rapid mortality transition of Pacific Islands in the 19th century. Epidemiology and Infection 145: 1–11.

112.  Shrestha A, Luo W. 2017. An assessment of groundwater contamination in Central Valley aquifer, California using geodetector method. Annals of GIS 23: 149-166.

113.  Shrestha A, Luo W. 2017. Analysis of groundwater nitrate contamination in the central valley: comparison of the Geodetector Method, Principal Component Analysis and Geographically Weighted Regression. ISPRS International Journal of Geo-Information 6: 297.

114.  Song YZ, Wang XY, Tan Y, Wu P, Sutrisna M, Cheng JCP, Hampson K. 2017. Trends and opportunities of BIM-GIS integration in the architecture, engineering and construction industry: a review from a spatio-temporal statistical perspective. ISPRS International Journal of Geo-Information 6: 397.

115.  Strand G. 2017. A study of variance estimation methods for systematic spatial sampling. Spatial Statistics 21: 226-240.

116.  Tan JT, Lo K, Qiu FD, Liu WX, Li J, Zhang PY. 2017. Regional economic resilience: resistance and recoverability of resource-based cities during economic crises in northeast China. Sustainability 9: 2136.

117.  Tian L, Li YF, Yan YQ, Wang BY. 2017. Measuring urban sprawl and exploring the role planning plays: A Shanghai case study. Land Use Policy 67: 426-435.

118.  Wang JJ, Ma JJ, Liu JQ, Zeng D DJ, Song C, Cao ZD. 2017. Prevalence and risk factors of comorbidities among hypertensive patients in China. International Journal of Medical Sciences 14(3): 201-212.

119.  Wang Y, Wang SJ, Li GD, Zhang HG, Jin LX, Su YX, Wu KM. 2017. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Applied Geography 79: 26-36.

120.  Wang ZS, Yue Y, Li QQ, Nie K, Tu W, Liang S. 2017. Analyzing risk factors for fatality in urban traffic Crashes: a case study of Wuhan, China. Sustainability 9: 897; doi:10.3390/su9060897.

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122.  Wu C, Ye XY, Du QY, Luo P. 2017. Spatial effects of accessibility to parks on housing prices in Shenzhen, China. Habitat International 63: 45e54.

123.  Xiao QY, Liu HJ, Feldman MW. 2017. Tracking and predicting hand, foot, and mouth disease (HFMD) epidemics in China by Baidu queries. Epidemiology and Infection 145(8): 1699-1707.

124.  Xu CD. 2017. Spatio-temporal pattern and risk factor analysis of hand, foot and mouth disease associated with under-five morbidity in the Beijing–Tianjin–Hebei region of China. International Journal of Environmental Research and Public Health 14: 416.

125.  Xu CD, Li YY, Wang JF, Xiao GX. 2017. Spatial-temporal detection of risk factors for bacillary dysentery in Beijing, Tianjin and Hebei, China. BMC Public Health 17: 743.

126.  Xu Q, Dong YX, Yang R. 2017. Influence of different geographical factors on carbon sink functions in the Pearl River Delta. Scientific Reports 7: 110.

127.  Yang SF, Hu SG, Li WD, Zhang CR, Torres JA. Spatiotemporal effects of main impact factors on residential land price in major cities of China. Sustainability 9: 2050.

128.  Yang Y, Wang LM, Cao Z, Mou CF, Shen L, Zhao JN, Fang YB. 2017. CO2 emissions from cement industry in China: a bottom-up estimation from factory to regional and national levels. Journal of Geographical Sciences 27(6): 711-730.

129.  Ye H, Hu XY, Qun R, Lin T, Li XH, et al. 2017. Effect of urban micro-climatic regulation ability on public building energy usage carbon emission. Energy and Buildings 154: 553–559.

130.  Yuan XM, Liu YS, Huang YH, Tian FC. 2017. An approach to quality validation of large-scale data from the Chinese Flash Flood Survey and Evaluation (CFFSE). Natural Hazards 89(2): 1-12.

131.  Zhan DS, Kwan MP, Zhang WZ, Wang SJ, Yu JH. 2017. Spatiotemporal variations and driving factors of air pollution in China. International Journal of Environmental Research and Public Health 14: 1538.

132.  Zhang KS, Sun D, Shen SW, Zhu Y. 2017. Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data. Journal of Transport and Land Use 10(1): 675-694.

133.  Zhao YJ, Deng QY, Lin Q, Cai CT. 2017. Quantitative analysis of the impacts of terrestrial environmental factors on precipitation variation over the Beibu Gulf Economic Zone in Coastal Southwest China. Scientific Reports 7: 44412.

134.  Zou B, Jiang XL, Duan XL, Zhao XG, Zhang J, Tang JW, Sun GQ. 2017. An integrated H-G scheme identifying areas for soil remediation and primary heavy metal contributors: a risk perspective. Scientific Reports 7: 341.

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Bi SB, Ling DQ, Ji Q, Shen X, Wang J. 2017. Fuzzy comprehensive evaluation of the human settlement environment of the prehistoric settlement sites in the Zhengzhou-Luoyang Area. Scientia Geographica Sinica 37(6): 904-911.

136.      超,马春光. 2017. 中国大宗商品期货交割库空间布局及影响因素. 地理科学 37(1): 125-129.

Chen C, Ma CG. 2017. Study of spatial distribution and influence elements of bulk commodity delivery warehouses. Scientia Geographica Sinica 37(1): 125-129.

137.      . 2017. 湖北省公路交通可达性空间格局演化及影响因素. 城市建筑 2017(8): 318-321.

Chen Q.2017. Spatial pattern evolution and influencing factors of highway traffic accessibility in Hubei Province. Urbanism and Architecture 2017(8): 318-321.

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Chen XL, Cao YN, Liu WJ. 2017. A comparative study of spatial interpolation methods for meteorological elements in subtropical mountainous and Hilly regions of China. Geomatics & Spatial Information Technology 40(12): 51-56.

139.      跃,刘振捷. 2017. 中国西部地区城镇化发展格局及影响因素研究. 世界农业 11: 227-231.

Chen Y, Liu ZJ. 2017. Study on the development pattern and influencing factors of urbanization in Western China. World Agriculture 2017(11): 227-231.

140.  蔡 进,廖和平,李 靖. 2017. 重庆市转户进城农户城市融入水平及影响因素研究. 西南大学学报(自然科学版)39(4): 108-114.

Cai J, Liao HP, Li J. 2017. Study on urban integration level and its influencing factors of former rural household farmers in Chongqing City. Journal of Southwest University(Natural Science Edition) 39(4): 108-114.

141.      愫,陈报章. 2017. 城市医疗设施空间分布合理性评估. 地球信息科学学报 19(2): 185-196.

Ding S, Chen BZ. 2017. Rationality assessment of the spatial distributions of urban medical facility. Journal of Geo-information Science 19(2): 185-196.

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Dong YX, Xu Q, Yang R, Xu CD, Wang YY. 2017. Delineation of the northern border of the tropical zone of China’s mainland using Geodetector. Acta Geographica Sinica 72(1): 135-147.

143.  方叶兵,王礼茂,牟初夫,张   宏,屈秋实. 2017. 中国石油终端利用碳排放空间分异及影响因素. 资源科学 39(12): 2233-2246.

Fang YB, Wang LM, Mou CF, Zhang H, Qu QS. 2017. Determinants of spatial disparities of petroleum terminal utilization carbon emissions in China. Resources Science 39(12): 2233-2246.

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Gao YF, Liu DW, Liu HM, Wang LX. 2017. Research progress association between ambient air pollution and congenital heart disease. J Environ Occup Med 34(12): 1111-1122.

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Guo CY, Shi RH, Zhou YY, Zhang XY. 2017. Analysis risk factors of air pollution over the Yangtze river delta using remote sensing and geographical detector. Resources and Environment in the Yangtze Basin 26(11): 1805-1814.

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Jiang K, Tong YL. 2017. The detection and analysis of the green efficiency of water resources by the “four modernizations”. Territory & Natural Resources Study 2017(4): 43-44.

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Li HW, Wan Q. 2017. Study on rainfall index selection for hazard analysis of mountain torrents disaster of small watersheds. Journal of Geo-information Science 19(3): 425-435.

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