We are excited to be holding our own GIS Day event on November 19th, 2014! GIS Day is a global grassroots educational event that enables Geographic Information Systems (GIS) users and vendors to showcase real-world applications of GIS to schools, businesses, and the general public. Organizations that utilize GIS around the world participate by holding or sponsoring an event of their own. The first formal GIS Day took place in 1999. In 2005, more than 700 GIS Day events were held in 74 countries around the globe. Esri president and co-founder Jack Dangermond credits Ralph Nader with inspiring the creation of GIS Day. He saw GIS Day as providing an opportunity for the world to learn about the uses of GIS in mapping geography, and what that mapping technology could provide. He wanted GIS Day to be a grassroots effort and open to everyone to participate. Recognizing the power that GIS technology could provide for healthcare, eHealth Africa as an NGO organization stepped to the forefront of using GIS applications to track polio in Nigeria. Using GIS technology, eHealth is able to map out areas previously unreached during immunization campaigns. Once the area is mapped, much-needed polio vaccinations are able to be distributed and the polio epidemic is brought another step closer to being controlled and eliminated. The theme of GIS Day is “Discovering the world through GIS.” GIS Day provides an international forum for users of GIS technology to demonstrate real-world applications that are making a difference in our society and around the world. We are excited to take part in GIS Day 2014 on November 19th. We look forward to joining with our community partners in discussing GIS usage, and to take a close look at the exciting contributions GIS provides around our world.

1. Adamu Mustapha PhD

2. There are number of multivariate Geostatistical analyses used
in environmental studies to identify the spatial and temporal
variation of the datasets.
1. Hierarchical Agglomerative Cluster Analysis (HACA)
2. Principal Component Analysis (PCA)
3. Multiple Linear Regression (MLR)
4. Pearson’s Product Moment Correlation Analysis
5. Discriminant Analysis

3. 1. Hierarchical Agglomerative Cluster Analysis (HACA)
HACA is a multivariate Geostatistical technique whose primary
purpose is to assemble similar objects based on characteristic
they possess (Shrestha and Kazama, 2007)
The level of similarities at which observation are merged are
used to construct a dendrogram of clusters (Singh et al., 2004;
Chen et al., 2007; Juahir et al. 2011, Mustapha et al. 2012).
The resultant clusters exhibit high internal (within clusters)
homogeneity and high external (between groups) heterogeneity.

4. Jakara Dam
0 3 Km
Sources apportionment of Jakara
Basin (Upstream)
Domestic sources
Industrial sources
Agricultural sources
Nigeria
S1 S2
S3
S4
S5
S6
S7
S15
S11 S12
S9
S8
S10
S13 S14
S16
S17
S18
S19
S20
S21
S22
S23
S25
S26
S30
S29
S28
S27
S1, S2, . .. Sampling sites
S24
Sampling Points

6. 2. Principal component analysis and or factor analysis (PCA)
PCA is a multivariate Geostatistical statistical technique that
examine the underlying pattern or relationship of a large number
of variables. It is use to get information about inter-relationships
among a set of variables
PCA group the variables into smaller and more meaningful set
of factors

7. How do we determine the number of factors to be retained?
We use the Kaiser’s-one- Criterion also known as the eigen-value
rule of >1
We equally use the Catell’s scree plot
It produce plot of the eigenvalues, looking at the plot where it
becomes horizontal, then Cartell;s recommends retaining all the
factors above this points.
These factors with eigenvalues 1 and >1 contribute the most
variance in the data sets.

9. The Important parameters in the factor have factor high factor
loading. Liu et al. 2003 suggest the following loading on
parameters
0 – 0.4 Low loading
0.5 -0.7 Moderate loading
> 0.7 High loading

10. Parameters Unit PC1 PC2 PC3 PC4 PC5
Pb mg L-1 0.960 -0.111 0.105 0.060 -0.098
Cd mg L-1 0.953 -0.101 0.145 -0.049 -0.040
Cr mg L-1 0.940 -0.037 0.148 -0.049 -0.060
Hg mg L-1 0.854 -0.070 0.020 -0.086 -0.299
Fe mg L-1 0.706 0.172 0.327 -0.353 -0.078
EC μS/cm -0.659 -0.139 -0.181 0.094 -0.144
Ni mg L-1 0.620 -0.014 -0.331 0.602 -0.234
BOD5 mg L-1 0.537 0.594 -0.114 -0.399 0.399
DS mg L-1 -0.156 0.835 0.557 0.074 -0.167
TS mg L-1 0.042 0.670 0.514 0.133 -0.036
pH -0.418 0.260 0.633 0.217 -0.073
DO mg L-1 0.166 -0.583 -0.617 -0.017 0.240
COD mg L-1 0.332 0.527 0.565 0.187 0.090
Turbidity NTU 0.342 -0.009 0.126 0.788 0.191
Hardness mg L-1 0.178 -0.257 0.367 0.162 0.809
Eigen value 5.53 2.21 1.96 1.42 1.13
% Variance 36.91 14.79 13.06 9.49 7.57

11. 3. Multiple Linear Regression (MLR)
 MLR is used to fit a model to our data and use it to predict the
value of the Y (DVs) from one or more IV’s.
 Predicting out come from one or several predictors.
 Mathematical techniques LSM is used to establish the line that
best describes the data.
Friday, November 28, 2014 11

12. Regression analysis is to derived a prediction equation
Y=bo +b1x1+b2x2+b3x3+……bpxp
Where:
Y = dependent variable
Xs = independent variables
bo = Y-intercept
b1 = regression coefficient

13.  Before interpreting the result of MLR, there is need to check
for assumptions of regression analysis. i.e. Normality, linearity
and multicolinearity (Berry, 1993).
Friday, November 28, 2014 13

14. The normal p-p plot of regression standardized residuals revealed all
observed Values fall roughly along the straight line. This indicates
residuals are from normally Distributed population
Friday, November 28, 2014 14

15. Assumption sof linear regression model
Colinearity/Multicolinearity
Problem with correlation between Ivs that occurs when
Ivs are highly correlated which make it difficult to
determine the contribution of Ivs.
Tolerance value
Variance Inflation Factor (VIF)
Condition index

16. a. Tolerance
This is the amount of variability not explain by other Ivs,
small tolerance value indicates high Multicolinearity smaller
than 0.10
b. Variance Inflation factor (VIF)
This is the inverse of the tolerance. The cutoff threshold of
VIF must be >1.0

17. c. Condition index statistics
Condition Index (CI) is a measure of the relative amount of
variance associated with an eigen value. A large CI indicates a
high degree of collinearity
A value of CI greater than 15 indicates a possible problem and an
index greater than 30 suggests a serious problem with collinearity
(Kutner et al. 2004).

18. R = 0.986
R2 = 0.971
Model R
R
Square
Adjusted
R Square
SE of the
Estimate
R Square
Change
Change Statistics
F
Change
df1 df2
Sig. F
Change
Durbin-
Watson
1 0.986 0.971 0.840 2.331 0.971 7.382 15 5 0.018 2.651
Friday, November 28, 2014 18

19. Model
Estimates of coefficient for the model
Unstandadized
BETA Std. Error
Standardized
Coefficients
BETA t Sig. Tolerance VIF
1 (Constant) 102.748 39.602 2.594 0.018
Iron mg/l 0.438 0.127 0.778 3.449 0.000 0.250 15.897
Mercury
mg/l 2.442 1.906 3.500 1.281 0.000 0.333 1304.69
Chromium
mg/l -0.852 0.672 -3.188 -1.267 0.000 0.290 1105.85
Cadmium
mg/l -5.695 2.019 -11.900 -2.821 0.000 0.540 3110.806
Lead mg/l 3.719 1.317 12.358 2.823 0.001 0.889 3350.478
From the table the largest beta coefficient is 3.719 (lead), the
variable make a unique contribution in explaining DV.
Friday, November 28, 2014 19

20. 4. Pearson’s Product Moment Correlation Analysis
Identify the significant relationship between bivariate
  
n xy x y
( )( )

2 2 2 2
   
( ) ( )
r
n x n y y

     
Table 2 Guildford rule of thumb for interpreting correlation analysis (r)
r value Interpretation
0.0 to 0.29 Negligible or little correlation
0.3 to 0.49 Low correlation
0.5 to 0.69 Moderate or marked correlation
0.7 to 0.89 High correlation
0.9 to 1.00 Very high correlation

21. Headache Fever Backpain JointPain StickInjuries Scabies Rashes Catarh Cough Breathprob Diarrhoea EyeProblem StomachPain
Headache 1 0.505 0.547 .788** 0.575 .679* 0.308 0.191 -0.419 0.268 0.043 0.184 -0.049
Fever 1 0.624 0.525 0.571 .786** 0.183 0.498 0.085 .686* 0.204 .762* 0.619
Backpain 1 0.537 .862** .849** .701* 0.543 0.156 .823** 0.056 0.344 0.352
JointPain 1 0.551 .672* 0.246 0.181 -0.246 0.422 -0.443 0.344 -0.171
StickInjuries 1 .827** 0.452 0.369 0.301 .778** -0.083 0.207 0.064
Scabies 1 0.58 0.389 -0.087 .816** 0.023 0.57 0.367
Rashes 1 0.23 -0.058 0.528 -0.047 0.134 0.324
Catarh 1 0.308 0.441 0.18 .656* 0.437
Cough 1 0.314 -0.221 -0.012 0.054
Breathprob 1 -0.141 0.534 0.346
Diarrhoea 1 0.058 0.624
EyeProblem 1 0.602
StomachPain 1

22. Thank you