Safe Water for Everyone

Water as a Resource

Water is the most important commodity on our planet and irreplaceable in various ways. The amount of water which goes into products we consume or use daily, is simply stunning much more than a drop in the bucket. To give you an example: According to Hoekstra and Chapagain (2006) one glass of beer (250 ml), takes nearly 75 liters of water in its making. This is the equivalent of 25 daily water rations of one fully grown human. At the same time children across the world die of thirst and famine. “Yep, I think I am going to pass on the beer today, guys”. The underlying assumption that water is the basis of life and is crucial for the survival combined with the fact that today about 1.1 billion people lack access to safe drinking water and 2.6 billion people live without access to basic sanitation, shows what a tidal wave water is in different regions of our globe (Chigonda, 2011).

Motivation

Reading up on these rather alarming statistics and studies made us reflect on how serious this topic is, therefore calling us to further investigate the world of water and how it is distributed in different forms on our Earth. Here, this application dives down into statistical anomalies and tries to explain certain ebbs and flows, indicators may show. In more detail, we want to assess how different socio-economic factors correlate to water quality. Do really only wealthy countries have access to clean water or does it have to do with large inequalities in societies which correlate with water quality?

Expected findings

We expect that access to clean drinking water and sanitation services have a clear link to how much money the government have in their disposal. For us it makes sense to assume that the only reason that the basis of life is not provided for the people is the lack of financial means. If this is not the case we want to assess if inequalities in this particular country are rather large. This would give a hint about how a big part of society lives and maybe could give clues on why the government is unable to finance a basic water infrastructure. On top of this, we want to pay attention to rapid currents of change in countries' water infrastructure and see if we can actually link to big national efforts to improve water quality and access to water services.

Now take a deep breath and plunge into the following section, taking a look at the relation of water related issues in an economic setting. In order to associate water quality to a country’s economy, the respective gross domestic product is used. The gross domestic product (GDP) represents the value of all final commodities and services produced within a year.

Accessibility to drinking water, accessibility to sanitation services, as well as open defecation are set in relation to the GDP. Hence connections between economic and water variables become clear as the water calms down after the first big wave.

Now let's tap into the subject of how our water factors have developed over time. This section not only illustrates that static phenomena play a key role in understanding the issue of making water accessible to everyone, but also change over time in regard to various improvements or maybe setbacks. Here, change is mapped in comparison to the year of 2000. The color blue indicates that improvements have been made, whereas red is a sign that there have been setbacks in this country. Choose your topic of interest and click on any country to see the exact numbers. Click on the arrows at the bottom of the page to go back and forth in time. If you want to look at the development, you can repeat that process until you have reached the year 2018, to which are the most recent changes are displayed. The value shown is always in respect to the year 2000.

Let’s switch shores for a change and examine with watery eyes the implications the lack of water quality tows in its wake. This dashboard expresses how serious water quality deficiencies are, indicating the responsibility of water-related deaths worldwide and for each country.

On our journey we have seen many changes and differences of water quality, the accessibility to it and considered possible reasons which may cause these concerns.

In the first paragraph, containing the applications showing the relationship matrices between GDP and drinking water, sanitation and defecation, the results match our expectations and predictions. Europe and North America show a sufficient accessibility together with a high GDP, whereas several African countries have trouble providing adequate drinking water and sanitation services for their population, while their GDP per capita ranks rather low. A few interesting exceptions do exist however, for example India, which has a low GDP per capita, but provides rather adequate drinking water services. This seems fairly strange because there are numorous reports on arsenic drinking water and related diseases in the Bengal region (Mazumder, 1998; Bhattacharya, 1997; Mazumder, 2000). However, these reports are already several years old and therefore perhaps outdated. The map animation showing water quality development in the second paragraph, indicates that the country has been continually improving its drinking water and sanitation services throughout the past years.

On the other hand, countries such as the Central African Republic seem to be showing signs of regression. It is likely that such tendencies are due to domestic politics, turmoil or even civil wars, leading back to the year 2004. It wasn’t until the year of 2014 and new elections, that the Central African Republic was able to development and improve their sanitation infrastructure. The crisis is far from over but here one would have to wait another decade to really get a grasp of how this issue could be resolved (Charity Water; Reliefweb; Unicef, 2014).

In the last paragraph the implications of a lack of water services is visualized. It shows a map of deaths in percent, which are due to deficient access to safe drinking water or sanitation services. Countries such as Afghanistan and various African countries alike the Democratic Republic Kongo, Angola or Nigeria, show very high percentages. Once again, wars and political unrests have been predominant during the past two decades in these areas. Hence, such issues are a possible explanation for the lack of safe water service infrastructure in countries facing similar conflicts.

The GINI-Index, as described above, can be used to verify specific differences of results and expected results. The African nations Namibia and Angola show a negative relationship between GDP and access to drinking water, as well as sanitation. This means these countries have a reasonably high GDP per capita, but simultaneously poor access to drinking water as well as sanitation. Interesting here, is that the GINI-Index map of these countries indicates a somewhat high value, which implies comparatively high-income inequalities. This suggests that governments need to support and improve lower income communities.

Generally, the hypothesis that a high GDP relates directly to a high number of people who have access to adequate water services, is proven. In the graphs shown in the implication paragraph it is can be seen that GDP and the discussed water services correlate for a vast majority of the countries.

The plots indicate a clear trend that the richer a country the higher the access to water services is for the population of these countries. In addition, the scenario vice versa, conventionally seems to be proven too. Deviations from the hypothesis can partially be clarified by the means of the GINI-Index, which is able to show the governments underwhelming performance of providing support towards social endeavors. Also, one has to acknowledge, that not all deviations from the hypothesis can be explained through merely looking at the GDP or the country’s GINI-Index. These suspicions are likely due to the government’s officials inaccurately measuring, data manipulations or mistakes from our or the WHO’s side. Furthermore, conflict, war and other political uncertainties are major contributors to a lack of water services and should consequently be considerably addressed more. Especially since the innocent civil population is greatly affected by such matters.

Data Sources

In this application WHO and World Bank datasets were used as basic data input. Both institutions have large and thorough amount of information on water and the different quality levels countries can provide for their citizens. Three main datasets were chosen which are looked in more detail:

Basic and safely managed sanitation services
Basic and safely managed drinking water services
Open defecation

With these datasets this application does different statistical analysis in combination with GDP and other socio-economic factors.

Further a dataset for GDP was used to show how wealthy a country is and which countries have financial issues which cause the problematic water infrastructure.

WHO

Shows different Water quality databases. One which shows water quality on a local scale and a second where water quality is shown per country, as Basic and safely managed sanitation services, Basic and safely managed drinking water services and Open defecationHave a look here:

UNO Water Quality Data
WHO Worldwide Access to WHS Services Data

World Bank

Provides GDP of each country per capita. Have a look here:

World Bank Worldwide GDP Data

Literature

Bhattacharya, P., Chatterjee, D., & Jacks, G. (1997). Occurrence of arsenic-contaminated groundwater in alluvial aquifers from delta plains, eastern India: Options for safe drinking water supply. International Journal of Water Resources Development, 13(1), 79-92.

Charity water, “Central African Republic”, https://www.charitywater.org/our-projects/central-african-republic, (Access: 22.05.20, 18:52)

Chigonda, T. (2011). Thirst in the midst of the twin lakes: a quest for understanding Norton’s ironical water woes. Journal of Sustainable Development in Africa, 13(1), 295-303.

Hoekstra, A. Y., & Chapagain, A. K. (2006). Water footprints of nations: water use by people as a function of their consumption pattern. In Integrated assessment of water resources and global change (pp. 35-48). Springer, Dordrecht.

Mazumder, D. N. G., Haque, R., Ghosh, N., De, B. K., Santra, A., Chakraborti, D., & Smith, A. H. (1998). Arsenic levels in drinking water and the prevalence of skin lesions in West Bengal, India. International journal of epidemiology, 27(5), 871-877.

Mazumder, D. N. G., Haque, R., Ghosh, N., De, B. K., Santra, A., Chakraborti, D., & Smith, A. H. (2000). Arsenic in drinking water and the prevalence of respiratory effects in West Bengal, India. International journal of epidemiology, 29(6), 1047-1052.

Reliefweb, “Water is life”, https://reliefweb.int/report/central-african-republic/water-life, (Access: 22.05.20, 18:52)

Unicef (2014), “Central African Republic: Drinking water restored to over 183,000 people ahead of the rainy season”, https://www.unicef.org/media/media_72778.html, (Access: 22.05.20, 18:52)

Media

Video stock footage by:  

Videezy.com

Image Sustainable Development Goals: 

cybercom.com

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