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Software for measure and attribution of/by spatial stratified
heterogeneity (SSH), a universal characteristic of the nature at all scales |
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4. Download of the software, with example
datasets |
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1.
Introduction
Spatial Stratified Heterogeneity (SSH) refers to the phenomena that the within strata are more similar than the between strata. Examples are landuse types and climate zones in spatial data, seasons and years in time series, occupations, age groups, incomes strata. SSH occurs in all scales from universe to DNA, offers windows for human beings to understand the nature since Aristotle time. Geodetector, i.e. Geographical
Detector, is a statistical tool to measure SSH and to make attribution for/by SSH (Fig. 1):
(1) measure and find SSH among data; (2) test the coupling between two
variables Y and X, according to their SSHs, without
assumption of linearity of the association; and (3) investigate interaction between two
explanatory variables X1
and X2 to a response
variable Y, without any specific
form of interaction such as the assumed product in econometrics (Fig. 2).
Each of the tasks can be accomplished by the Geodetector q-statistic:
Fig. 1. Principle of Geodetector (The bottom map, the
color indicates the values of a population Y. The top map, the population Y is stratified into strata {h};
the terms “stratification” and “partition” are equivalent, can be either
classification or zonation. Between the two maps is the equation q(Y|{h}), in which the numerator is the
summation of the within strata variance and the denominator is the pooled
variance.) where N
and s2 stand for the number
of units and the variance of Y in a
study area, respectively; the population Y
is composed of L strata (h = 1, 2, …, L). The strata of Y
(red polygons in Fig.1) are a
partition of Y, either by itself h(Y)
or by an explanatory variable X
which is a categorical h(X). 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). The strata of Y (red
polygons in Fig.1) are a partition
of Y, either by Y itself or by an explanatory variable
X. X is a categorical variable or 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. The terms “stratified heterogeneity (SH)”, “stratification”,
“classification” and “partition” are equivalent. SH can
be either spatial (spatial stratified heterogeneity, SSH) or aspatial such as
time and any attributes. Interpretation of q value (Fig.1). The value of q is strictly within [0, 1]. (1) If Y is stratified by Y
itself, then q = 0 indicates that Y is absent of SH; q = 1 indicates that Y is SH perfectly; 100q% measures the degree of SH of Y. (2) If Y is stratified by an explanatory variable X, then q = 0 indicates
that there is no coupling between Y
and X; q = 1 indicates that Y
is completely determined by X; X explains 100q% of Y. Please notice
that the q-statistic measures the
association between X and Y, both linearly and nonlinearly. Geodetector q
statistic helps understand spatial confounding, sample bias and overfitting. (1)
Confounding arises if a global model was applied to a SH
population, leading to statistical insignificance. The problem can be simply
avoided if SH is identified (by Geodetector q statistic) then modelling in the strata, separately. (2)
A sample would be biased if a population is SH
and the sample do not cover all strata. The problem can be solved if SH is
identified (by Geodetector q
statistic) then apply bias remedy models such as Heckman regression and
Bshade method. (3)
Local models aim to overcome heterogeneity but
often suffer overfitting and too many parameters to interpret. The problems
can be avoided if modelling in strata or stratifying the outputs of a local
model then interpreting the stratified parameters. Functions of Geodetector: (1)
The risk detector maps response variable in strata: Y(X); (2)
The factor detector q-statistic
measures the degree of SH of a variable Y;
and the determinant power of an explanatory variable X of Y; (3)
The ecological detector identifies the difference of the impacts
between two explanatory variables X1
~ X2; (4)
The interaction detector reveals whether the risk factors X1 and X2 (and more X) have an
interactive influence on a response variable Y (Fig.2).
Fig.
2. Interaction between
explanatory variables X1 and X2 impacting on a response variable Y: q(Y|X1 |
2.
Tutorial
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 is the format of
the input data for the Geodetector: each row denotes a sample unit (e.g. a
village); the 1st column record the response variable 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.
Fig. 3. Input data in Excel and the execution interface (Note: Y is numerical; X MUST
be categorical, e.g. landuse types, seasons. 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.
Fig. 5. User interface for Geodetector back to the top ||
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3.
Output
Geodetector outputs results
from the risk detector, factor detector, ecological detector, and interaction
detector in four Excel spreadsheets (Fig.
6).
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”.
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.
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.
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).
Fig. 10. Results of interaction detector Tab.
1. Interaction between Explanatory Variables (Xs)
Legend
|
4.
Download of the software , with 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 5: Geodetector
Software in QGIS (please use google to access) |
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. 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.
丁 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. 胡 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 勇.
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. 杨 勃, 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, 计 晗, 陈昌春, 杨鸿儒, 沈 香. 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. 杨 忍, 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. 于 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, 孟 斌, 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. 张 晗, 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. 朱 鹤, 刘家明, 陶 慧, 李 玏, 王 润. 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. 李 涛，廖和平，褚远恒，孙 海，李 靖，杨 伟.
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. 廖 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 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. 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. 王 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. |