Welcome to visit GeoDetector Website !

Last updated on 17 March, 2018


GeoDetector Software




How to Use This Software

Output of GeoDetector

Download of GeoDetector Software and Example Datasets


Bibliography of GeoDetector

Developers and Contact Information




Spatial stratified heterogeneity (SSH), referring to the spatial patterns that within strata are more similar than between strata such as landuse types and climate zones, is ubiquitous in spatial data. As a set of information SSH is a window for humans to understand the nature since Aristotle time. In another aspect, SSH would cause confounding if global models were applied to the datasets with SSH, the problem dissolves if SSH is identified then individual models can be applied in each stratum.

Geographical detector is a novel tool to investigate SSH: (1) measure and find SSH of a variable Y; (2) test both linear and nonlinear association between two variables Y and X according to the consistency of their spatial patterns; and (3) investigate the interaction between two explanatory variables X1 and X2 to Y. All of the tasks are implementable by the geographical detector q-statistic (Fig. 1):



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.


Fig. 1. Schematics of Geographical Detector q-statistic

(Notation: Yi stands for the value of a variable Y at a sample unit i; h(Y) stands for a partition of Y; h(X) stands for a partition of an explanatory variable X. In GeoDetector, the terms “stratification”, “classification” and “partition” are equivalent.)


q  [0, 1], q = 0 indicates that Y is not SSH, or there is no association between Y and X; q = 1 indicates that Y is SSH perfectly, or Y is completely determined by X; the value of q-statistic indicates the degree of SSH of Y, or how much Y is interpreted by X. Please notice that the q-statistic measures the association between X and Y, both linearly and nonlinearly.

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.


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. 2 and Fig. 4 appear. Fig. 2 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. 2. Then go to Step 2.


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Fig. 2. 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. 3, please transform the GIS data into Excel data as Fig. 2.


Fig. 3. Data in GIS format


Step 2. Run GeoDetector software

Only one operation interface was designed (Fig. 4). 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.


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Fig. 4. User interface for GeoDetector


Output of GeoDetector

GeoDetector outputs results from the risk detector, factor detector, ecological detector, and interaction detector in four Excel spreadsheets (Fig. 5).


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Fig. 5. Interface for GeoDetector results


In the “Risk detector” sheet (Fig. 6), 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”.


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Fig. 6. Results of risk detector


The Fig. 7 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.


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Fig. 7. Results of factor detector


In the “Ecological detector” sheet (Fig. 8), 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.


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Fig. 8. Results of ecological detector


The format of the results for the interaction detector is shown in Fig. 9.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).


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Fig. 9. Results of interaction detector


Table 1 Redefined interaction relationships

Graphical representation




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)




q(X1Ç X2) > q(X1)+ q(X2)


Enhance, nonlinear


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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


The Geodetector software 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/


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. 2015. 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.

[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.

[5] Lecture ppt in 170624: Geodetector and its Applications in Environmental and Social Sciences(地理探测器及其在环境和社会科学中的应用)


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.         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.

3.         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 (2011) 4977e4985


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

5.         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.


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

7.         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.

8.         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.

9.         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), 2013, pp. 169-182

10.     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.

11.     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.


12.     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.

13.     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.

14.     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.

15.     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.

16.     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.

17.     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.

18.     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.

19.     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.

20.         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.

21.         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.

22.     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.

23.     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.

24.     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.

25.     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.

26.         ,  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.

27.     俞佳根,叶世康. 2014. 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.


28.     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.

29.     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.

30.     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.

31.     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.

32.     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.

33.     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.

34.     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.

35.     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.

36.     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.

37.     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.

38.     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.

39.        , 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.

40.        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.

41.     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.

42.        , 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.

43.        , 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.


44.     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.

45.     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.

46.     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.

47.     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.

48.     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.

49.     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.

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

51.     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.

52.     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.

53.     Luo W, Jasiewicz J, Stepinski T, Wang JF, Xu CD, Cang XZ. 2015. Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophysical Research Letters 43(2): 692-700.

54.     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: 216223.

55.     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.

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

57.     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.

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

59.     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.

60.     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.

61.     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.

62.     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(2016): 5-17.

63.     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.

64.     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.

65.     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.

66.     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.

67.     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.

68.        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.

69.     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.

70.        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.

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Developers and contact information

Email: xucd@Lreis.ac.cn (Chengdong Xu), wangjf@Lreis.ac.cn (Jinfeng Wang)

Address: Room 2305, A11 Datun Road, Beijing, China


Acknowledgement: NSFC, MOST


Copyright: 201 Spatial Analysis Group, IGSNRR, CAS.


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