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Implementing the Analysis Information System
IN 2004
In the sub Saharan region of Africa
In the Northern Africa region
WHYThis difference of level?
Overall Aims:Find spatial-temporal trends and patterns between environmental, governance and human pressure variables that affect the level of WSS and thus, the human development of a country.
Abstract:In the framework of the Millennium Development Goals (MDG) one of the targets is to halve, by 2015, the proportion of the population without sustainable access to safe drinking water and basic sanitation.
There is a lack of understanding about how and where the governmental and international cooperation efforts should be put in order to improve the access level to Water Supply and Sanitation services (WSS).
The final objective of this PhD is to better understand the relationships and key variables involved in an improved WSS management.
Modeling the Spatial-temporal behavior of the
Water Resources Management VariablesCélineCéline DONDEYNAZ, DONDEYNAZ,
Civil Engineering Department
Prof D. Chen, Dr C Carmona-Moreno, Dr X Zhang
The Management of Water Resources is a complex and cross-cutting issue
Variables are clustered by pillars for better understanding the relationships between them.
STEP 2. Estimating Missing data
The objective of this step is to process data missing by geographical and cluster value interpolation methods. Estimated values should increase the coherency of the dataset and the variance.
QUALITATIVE APPROACH
Several methods have been used for interpolation depending on the observations behaviors:
STEP 1. Analysis of the variable dataset’s coherency
51 variables are being analyzed in this framework. Because of these variables are issued from different collection methodologies, the coherence of the variable dataset is checked through a multi-correlation approach for the different pillars. Iteration of PCA process is essential to take into account the data interpolation results and adjust the dataset.
Human pressure Pillar
In this figure we can see that there is a relationship between urban growth, the slum growth and rural population. Water Quality is linked to industrial activities, urban population and domestic withdrawal.
= Pillar coherence
Governance Pillar
The perception of corruption is correlated the several general measurement of the general governance but Official Aid seems not contributing to the governance indicators.
Environmental Pillar
Water bodies surface are correlated with precipitation and water resources estimates. Dams are not linked with other variables present in this pillar.
Robustness and coherency of the Variable dataset
A) Hot deck imputation method
Looking at highly correlated variables to the BOD, Kyrgyzstan and Uzbekistan are close to those of Sudan. We impute the median distance between these two values.
B) Linear Regression modeling method
The BOD variable is readily modelised through a linear regression model:
R² = 0.981
Only three values are out of the confidence intervals. The regression equation allows us to estimate missing values.
Precipitation mm/y
An example of the kriging method is illustrated in this figure. Precipitation is spatially interpolated because of its geographical coherence.
Pred(BOD-emissions- pond pop-98) / BOD-emissions- pond pop-98
-5
0
5
10
15
20
-5 0 5 10 15 20
Pred(BOD-emissions- pond pop-98)
BO
D-e
mis
sio
ns-
po
nd
po
p-9
8
C) Kriging interpolation method
Kriging belongs to the family of linear least squares estimation algorithms.
The aim of kriging is to estimate the value of an unknown real-valued function, f, at a point, x , given the values of the function at some other points, x1,…., xn.
Temporal Analysis
Moving more in depth, times series for each variables will be examined to find out trends over time .
The consistency issue requires a strict examination of data series and methodologies. The feasibility and this second phase will be start as soon as processing data methodologies will be well defined and tested for replication on time series.
Geo-spatial Analysis
The geographical model brings out the geo-spatial relationships between the different variables. Country profiles can be easily identified. Policy makers will have immediate details on where and what the development efforts should be applied.
The System Outputs
This work aims to make the results readily understood by the policy makers leading to development of several outputs. • Thematic profiles by countries and by pillars will be automatically provided by the system. • Geographical tools facilitate comparison with other variables and layouts with complementary information.
Spider graph
By displaying the several variables by pillars, this graph helps to identifying any weakness that need to be tackled.
Variables will need to be processed into a composite value for each pillar.
Geographical Interface
The geo-spatial distribution of the variables will allow the policy makers to analyze the regional context particularly in terms of access to the water sanitation and supply services that should be taken into account in the geo-politic strategies.
Environmental Pillar- State of the Water resources
- Quality
- Quantity
Accessibility to Water Supply and Sanitation Services for population
Human pressure Pillar
- Human Activity pressure
- Demographic-urbanization pressure
Human development Pillar
- Health
- Education
- Income
Governance Pillar
- Institutional stability and effectiveness
- Corruption control
- Political commitment
THE APPROACH
Human development Pillar
Human Development index is correlated with the Literacy and Life Expectancy. Correlations can be found between diseases, poverty and literacy.
= Pillar coherence
Thematic Analysis
The thematic model supports the identification of the variables influencing the access to the water sanitation and supply services. This will assess the impact of the different variables in the society welfare.
E-mail Contact: [email protected]
http://geodata.grid.unep.ch/
Where efforts and resources should be put to increase WSS services?
56 % Access to clean
water
37 % Access to basic
sanitation facilities
77 % Access to basic
sanitation facilities
91 % Access to clean
water
