Welcome to the GeoDetector Website


GeoDetector Software



Geographical detector theory consists of four components: the risk detector indicates potential risk areas, the factor detector quantifies the influence of various environmental risk factors, the ecological detector identifies the impact differences of two risk factors, and the interaction detector reveals whether the risk factors have an interactive influence on the disease. 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.


How to use this software

Geographic data on neural-tube birth defects (NTD) in villages are provided as experimental data, including data for the health effect layers “NTD incidence” and environmental factor layers, “elevation”, “soil”, and “watershed”. Their field names are defined as Y and X1, X2, X3 respectively.

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(a)                                (b)

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(c)                                (d)

Fig. 1. Maps showing (a) rate of NTDs, (b-d) suspect environmental strata


1.       Prepare the grid file

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

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 and environmental risk factor variables.


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


3.       Run GeoDetector Software

Only one operation interface was designed. 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.

Fig. 4. User interface for GeoDetector


Interface of Results

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).

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”.

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.


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.

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).

Fig. 9. Results of interaction detector


Table 1 Redefined interaction relationships in a coordinate axis

Graphical representation


Interaction relationship

q(xÇy) < Min(q(x), q(y))

Weaken, nonlinear

Min(q(x), q(y))< q(xÇy) < Max(q(x), q(y))

Weaken, uni-

q(xÇy) > Max(q(x), q(y))

Enhance, bi-

q(xÇy) = q(x)+ q(y)


q(xÇy) > q(x)+ q(y)

Enhance, nonlinear


Min(q(x), q(y))

q(x)+ q(y)

Max(q(x), q(y))




Software and Example Data Download

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

1: GeoDetector with Example of Disease Dataset

2: GeoDetector with Example of Toy Dataset

3: GeoDetector with Example of NDVI Dataset



The software can be cited as: Wang J-F, Li X-H, Christakos G, Liao Y-L, Zhang T, Gu X & Zheng X-Y. 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, 2010, 24(1): 107-127.


GeoDetector Bibliography

1.         Wang J-F, Li X-H, Christakos G, Liao Y-L, Zhang T, Gu X & Zheng X-Y. 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, 2010, 24(1): 107-127.

2.         Luo, W., J. Jasiewicz, T. Stepinski, J. Wang, C. Xu, and X. Cang (2016), Spatial association between dissection density and environmental factors over the entire conterminous United States, Geophysical Research Letters.

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

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

5.         Hu Y, Wang J-F, Li X-H, 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.

6.         Li X-W, Xie Y-F, Wang J-F, Christakos G, Si J-L, Zhao H-N, Ding Y-Q, 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.         Yin Ren, Luying Deng, Shudi Zuo et.al. Geographical modeling of spatial interaction  between human activity and forest connectivity in an urban landscape of southeast China.Landscape Ecol,2014,DOI 10.1007/s10980-014-0094-z.

8.         毕硕本,计晗,陈昌春,杨鸿儒,沈香. 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.2015,34(1):118-127.

9.         Shen J, Zhang N, Gexigeduren, 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  17: 1-12.

10.     朱鹤; 刘家明; 陶慧; 李玏; 王润, 北京城市休闲商务区的时空分布特征与成因. 地理学报 2015, (08), 1215-1228

11.     张晗; 任志远, 基于Whittaker滤波的陕西省植被物候特征. 中国沙漠 2015, (04), 901-906.

12.     东升; 张文忠; 余建辉; 孟斌; 党云晓, 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 2015, (08), 966-975.

13.     杨忍; 刘彦随; 龙花楼; 陈呈奕, 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 2015, (06), 1077-1087.

14.     崔日明; 俞佳根, 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, (02), 26-33

15.     杨勃; 石培基, 甘肃省县域城镇化地域差异及形成机理. 干旱区地理 2014, (04), 838-845

16.     倪书华, 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) 2014, (04), 61-67.

17.     He, Z., Jiaming, L., Chen, C., Jing, L., & Hui, T. (2016). A spatial-temporal analysis of urban recreational business districts: A case study in Beijing, China. Journal of Geographical Sciences, 25(12), 1521-1536.


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.

Last updated: 11/11/2015


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