Welcome to visit GeoDetector Website !

Last updated on 22 April, 2017


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 is a universal driver of biological diversity and evolution, environmental patterns and tyranny, and inter-regional conflicts and cooperation. Geographical detector tests the spatial stratified heterogeneity of a variable Y (the phenomena that Y is more similar within strata than between strata, such as climate zones, and many ecological variables); or tests the association between two variables Y and X according to the consistency of their spatial distributions (overlaying Y and X, please refer to Fig. 1).

The philosophy of geographical detector is that variable Y is associated with variable X if their spatial distributions tend to be identical. The association between Y and X is measured by:

q = 1 - SUMLh=1(Nhsh2)/Ns2

where s2 stands for the variance of Y; N stands for the size of study population of Y (the size of study area or the size of study human population, for example); the study population of Y is composed of L strata (h = 1, 2, …, L). The strata of Y may exist already, or are constructed by classification, or formed by laying Y over X which consists of strata (please refer Fig. 1). q Î [0, 1], q = 0 indicates that Y is not spatially stratified heterogeneous, or there is no association between Y and X; q = 1 indicates that Y is perfectly spatially stratified heterogeneous, or Y is completely determined by X; the value of q-statistic indicates the degree of spatial stratified heterogeneity of Y, or how much Y is interpreted by X.

Geographical detector consists of four functions:

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

(2)    The factor detector quantifies the influence of environmental risk factors X, by q-statistic;

(3)    The ecological detector identifies the impact differences of 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.

The software presented here was developed using Excel for implementing GeoDetector theory. The tool is free of charge, freely downloadable, and easy to use, and was designed without any GIS plug-in components and with “one click” execution.

Users can run the following demo, then simply replace your own data into the Excel file, click Run and you get results !


How to Use This Software

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.

说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 图5-a说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 说明: 图5-b

(a)                                (b)

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(c)                                (d)

Fig. 1. Maps showing (a) rate of NTD, i.e. Y; (b-d) suspect environmental strata, i.e. Xs


1.       Prepare the grid file (Users can neglect this step and go to step 2 if you can prepare your data in Excel as Figure 3)

In the software, grids are used to extract information of the disease and environmental risk factor variables. This can be implemented by GIS tools (e.g. the intersect analysis tool in the ArcMap). The density of the grid can be specified in advance based on the research objective. The more grid points there are, the higher is the resulting accuracy, but also the greater is the time consumed, and therefore, there needs to be a balance in practice. Once the grid layer has been determined, information about the disease and environmental risk factors can be extracted at the location of the grids. Fig.2 is the “grid” file, which has been used as input data of GeoDetector software.

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Fig. 2. Grid points for input data


2.       Import grid data into GeoDetector

(1)    Download the excel Geodetector software. In the following section “Software and Examples Data Download”, one click any Example and download it, unzip the downloaded file, you will find an excel file (this is Geodetector software with demo data) and double click the excel file, Fig3 and Fig.4 appear. Fig.3 gives the format of the input grids data for the GeoDetector, where each row denotes a grid and each column includes the disease prevalence (Y) and environmental risk factor variables (X).

(2)    Input your data into the excl Geodetector software in the format of Fig.3.


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

(Note: Y is numerical; X is 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.       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 environmental factor variables can be selected into their corresponding list boxes 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

The results of GeoDetector are divided into those from the risk detector, factor detector, ecological detector, and interaction detector, which are presented in four Excel spreadsheets (Fig. 5).

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Fig. 5. Interface for GeoDetector results


In the “Risk detector” sheet, 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, results of the statistically significant differences between two environmental risk factors are presented (Fig. 8). If RiskRi (risk factor names in row)is significantly bigger than RiskCj (risk factor names in column), the associated value is “Y”, while “N” expresses the opposite meaning.

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Fig. 8. Results of interaction 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 Table1).

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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





Download of Geodetector Software and Example Datasets

The software was developed using Excel 2007. It is completely free.

1: GeoDetector Software with an Example of a Disease Dataset

2: GeoDetector Software with an Example of a Toy Dataset

3: GeoDetector Software with an Example of a NDVI Dataset


The 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 170328: 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.         Wang JF, Hu Y. 2012Environmental health risk detection with GeogDetector. Environmental Modelling & Software 33: 114-115

4.         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.


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

6.         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.

7.         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.

8.         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.


9.         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.

10.     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 Ecol. DOI 10.1007/s10980-014-0094-z.

11.     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.

12.     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.

13.        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.

14.        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.

15.     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.

16.     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.

17.     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.

18.        , 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.

19.     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.


20.     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. doi:10.1007/s00477-015-1123-4.

21.     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.

22.     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.

23.     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.

24.     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.

25.     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.

26.     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.

27.        , 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.

28.        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.

29.     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.

30.        , 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.

31.        , 刘家明, 陶慧, 李玏,   . 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.


32.   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. doi:10.1371/journal.pone.0151331.

33.     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. http://dx.doi.org/10.1080/13658816.2016.1165228.

34.     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. doi:10.1007/s00703-016-0464-y.

35.     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.

36.     Liao YL, et al. 2016. Using spatial analysis to understand the spatial heterogeneity of disability employment in China. Transactions in GIS. doi: 10.111 1/tgis.12217

37.     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.

38.     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.

39.     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.

40.     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. http://dx.doi.org/10.1016/j.envpol.2016.0 6.0 04.

41.     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.

42.     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.

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

44.     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.

45.     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.

46.     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. http://dx.doi.org/10.1016/j.jrurstud.2016.05.013.

47.     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.

48.     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. DOI:10.1038/s41598-016-0020-5.

49.     陈昌玲,张全景,  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.

50.     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.

51.     李俊刚,闫庆武,熊集兵,黄园. 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.

52.        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.

53.     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.

54.        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. 地球信息科学学报 18(6): 767-778.

55.     陶海燕,潘中哲,潘茂林,卓莉,徐勇,鹿苗. 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.

56.        方,牛振国,许盼. 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.

57.     王录仓,武荣伟,刘海猛,周  鹏,康江. 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.

58.     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.

59.     王曼曼,吴秀芹,吴  ,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.

60.     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.

61.        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.

62.        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.

63.        ,武建军,贾瑞静,梁  念,张凤英,倪永,刘明. 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 2016, 29(4): 483-493.


64.     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.

65.     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 2017, 10: 367.

66.     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.

67.     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.

68.     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.

69.     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.

70.     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.

71.     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 (2017): 26e36.

72.     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.

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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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