Posts Tagged ‘risk’

This was one of discussion questions of the Disasters and Ecosystems MOOC.

Actually the answer is simple. The formula for successful environmental degradation consists of 2 variables – overpopulation and capitalism.

When there are a lot of people – most of them a poor, uneducated and hungry. When you are hungry you will do everything to become less hungry today even if it can potentially lead to negative consequences tomorrow, which you may not even foresee if you are uneducated.

Humans are good in adaptation. When the adaptation is strong enough it leads to abuse (for example, if you are well adopted at the stock market you start abusing it to increase your profit even if it will cost dearly to the other stakeholders – people value their own well-being much more than the other’s and of course much more than the well-being of environment especially when they know that their own impact seems negligible compared to impact of the entire population).  When you live in condition of free market of capitalistic world – you are your only hope for not being hungry (or being more wealthy) now. And as you know from the economic theory – the capitalist economy needs a constant grows of consumption and production – so you need more and more resources to just sustain the economy. In conditions of capitalist market people value today’s profit much more than losses of tomorrow

You see – the capitalist economy needs people to consume more and more; more people – more consumption; more people – more poverty and lack of education; more hungry uneducated people people – more people willing to do anything to survive now and don’t even bother themselves about the future.

Overpopulation and a consumption society (created by capitalist economy) inter-stimulate each other and destroy the environment for the today’s profits or food and doesn’t care much of the consequences of tomorrow because most are either uneducated or doesn’t care at all plus you have to live through today to face consequences of your actions tomorrow (a day-by-day living).

Obviously there are 3 steps to improve the situation:

  • Decrease the population.
  • Educate people.
  • Create new sustainable economy model that would equally value tomorrow’s losses and today’s profits, and would not rely on constantly increasing consumption.

It is amusing coincidence that another MOOC that I took this week (Geospatial Intelligence & the Geospatial revolution) mentioned [natural] disasters. About the other course see my recent Disasters: Myth or the Reality post.

In Geospatial Intelligence they gave a weird assignment: one need to mark the location on the world map where the next international natural disaster will occur O_o. This is not and easy task by any means and the lecturer suggested to use one’s ‘gut feeling’ if one’s knowledge is insufficient (I suppose it is close to impossible to find someone who can make such a prediction taking into account all the types of the disasters). Though the link to the International Disasters Database was given, so I accepted the challenge (to make a data-driven prediction). To predict the exact location of the next disaster one would need a lot of data – far more that you can get out of that database so my goal was to make prediction at the country level. (BTW the graphs from my post about disasters seems to be based on the data from this database – I saw one of them at that site)

I passed a query to the database and saved the output to process it with R. The dataframe looks like this:

year | country | continent | occurrence | deaths | injured | homeless | total_affected | total_damage
Example of disasters dataset

So how to predict the country with the next disaster? I came up with the idea to calculate cumulative average occurrence of disasters per country per year and plot it on the graph to see the trends. If I would just calculate average occurrence of disasters per country for the whole time of the observations I would have significant issues choosing from countries that would have close numbers. Plus the total average disasters per year can be misleading by itself due to it can be high because of high amount of disasters in the beginning of XX century but relatively low number in XXI.

The formula for the calculation of the cumulative average for the given year that I used was:

Cumulative_Average = Total_Occurences / ( Given_Year – (Starting_Year – 1) ) ,

where: Total_Occurrences is the sum of occurrences of disasters for given country in time interval between the starting year and the given year (inclusive).

Here is the plot I got for the short-list countries (plotting the results for all the 180 countries from the dataset makes plot unreadable):

Cumulative average number of disasters

It is clear that China and Indonesia are the two most likely candidates for the next disaster to strike, with a China having a lead. I’m not ready to provide insight on the reasons of the increasing number of natural disasters in the countries at the plot now (especially for Turkey and Iran). Maybe it is just that the events become documented more often?… It should be investigated further.

The code

Here is the code to create the plot above. ‘sqldf’ package was really helpful for divide data for the short list countries from the rest of 180 countries.

# Load natural disasters data ---------------------------------------------

dis <- read.csv("~/R/Disasters/Natural_disasters.csv")

# Create data frame with average number of disasters per year -------------

average_events <- data.frame(country = character(),
year = numeric(),
disasters_per_year = numeric(),
stringsAsFactors = F)

countries <- unique(dis$country)

starting_year <- min(dis$year) - 1 # we subtract 1 year to have numbers greater than 0 further on

for (country in countries) {
data <- dis[dis$country == country,] # we need data for one country at a time
disasters_count <- 0
years <- unique(data$year)

for (year in years) {
total_years <- year - starting_year
y_data <- data[data$year == year,]
n_disasters <- sum(y_data$occurrence)
disasters_count <- disasters_count + n_disasters
average_disasters <- disasters_count / total_years
row <- data.frame(country = country, year = year, disasters_per_year = average_disasters)
average_events <- rbind(average_events, row)

# Plot data about average number of disasters per country per year --------
# Data for 180 countries is hard to plot, lets filter mots affected.
# Let's use SQL to query data: subset data for countries that had more than 0.6 disasters per year
# in any year after 2000
danger <- sqldf('SELECT * FROM average_events WHERE country IN
(SELECT DISTINCT country FROM average_events WHERE disasters_per_year >= 0.6 AND year > 2000)')

p <- ggplot(danger, aes (x = year, y = disasters_per_year)) +
geom_line(size = 1.2, aes(colour = country, linetype = country)) +
labs(title = 'Cumulative average number of disasters per year',
x = 'Year',
y = 'Average number of disasters cumulative') +
guides(guide_legend(keywidth = 3, keyheight = 1)) +
theme(axis.text.x = element_text(angle=0, hjust = NULL),
axis.title = element_text(face = 'bold', size = 14),
title = element_text(face = 'bold', size = 16),
legend.position = 'right',
legend.title = element_blank(),
legend.text = element_text(size = 12),
legend.key.width = unit(1.5, 'cm'),
legend.key.height = unit(1, 'cm'))


I enrolled a MOOC titled “Disasters and Ecosystems: Resilience in a Changing Climate” which is organised by the UNEP (and other organisations… which names I’m going to learn by heart cause they have like 2 minutes of credits after each lecture O_o ). Not that I know nothing about disasters, risks or climate change (I’m a geographer and ecologist after all), but I was curious about the product that was made by organisation of this class.

The third video (and first video that is not an introduction) they teach us about the disasters; differences between hazard and disaster; and risks. Well… the thing they told, the graphs they showed – that what inspired the title of this post.


Here see some definitions they use.

Disaster. When they say “disaster” they mean “natural disaster” that was enhanced by human [mismanagement].

Risk – a potential losses due to disasters.

Hazard – A dangerous phenomenon, substance, human activity or condition that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage.

Exposure – People, property, systems, or other elements present in hazard zones that are thereby subject to potential losses.

Vulnerability – the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard


The risk

They presented a “great” formula for (a disaster) risk evaluation that they use in the UN:
Risk = Hazard * Exposure * Vulnerability
where: Exposure = People * ExposureTime
Vulnarability – succeptability to hazard.
Well these characteristics do correspond to the risk, but the formula is stupid! I already wrote about that: Risk = Probability * Damage. And this formula actually corresponds to the definition they give (see Terminology section). We can’t get a monetary outcome from their formula. We can’t get numeric numeric output out of that formula at all: can you multiply flood by people? Can you???!!!

A Disaster with Disasters

The fail with the risk evaluation is a common mistake, but the fail with disaster – that is what really cool!
Take a look at this plot (which is from reading materials from the course):

What can you conclude from this plot? That the world is doing to hell and we all will fall to disaster? Let’s look closer. The exposure is growing faster for poorer countries (and it is the only conclusion they make in lecture)… but the total number of people exposed (and for each type of countries) seems to be the almost unchanged! Interesting… This means (see the definition for the exposure) that there are just a 150% increase of property value in the dangerous area of the poorer countries (and 25% for the richest) on a span of 30 years. Does this graph shows us only the economic grows? I think it does… (reminds me of my previous post).
Now to the most delicious part. Take a look at this two graphs from the lecture readings:
Deaths dynamics


Damage dynamics
This is interesting. Despite the population growth and all that questionable “climate change” staff people die less (in total numbers), see fig. 1, but the damage increases, see fig. 2. Did they take inflation into account for the damage graph? Do not know… I think they didn’t, otherwise they would use “discounted damage” term instead of just “damage” and would indicate the base year. So the second graph seems to demonstrate inflation and may be the economic grows.
Clearly disasters are not that disastrous. Despite the new on the TV on the subject the nature’s wrath even enhanced by human is less and less dangerous for human lives. The pockets are to suffer: the storm in port wrecking the humble fisherman’s boat or a trawler – that’s the difference.


From these graphs I can conclude one thing – it is safer to live now than in the past, a disaster should not be feared as a deadly havoc. To my mind the disaster nowadays is entirely economic issue. See, if we loose less people and (maybe) more money – we should just develop more advanced insurance techniques to cover economic damage and relax. The disasters should just be studied as phenomena to develop cheap early warning systems, let the property be destroyed (just cover the losses with insurance) and additional employment to be created (rebuilding).
This is my conclusion form the graphs I showed here: the disaster is an ancient myth! Just buy insurance! LOL

There was a scientific seminar dedicated to environmental risks assessment in the scientific-research centre where I work. A speaker was awfully ignorant in subject unfortunately. As a person who is experienced in environmental risk assessment (see my posts about risks and a particular methodology) I was afraid that I will be the one to ask the speaker (quite an old man) some inconvenient question about formulas he used, but luckily he was ashamed by someone else.

During the discussion the question of monetary aspect of the risk and damage to environment was raised: whether it is possible to use money as the measure of risks that only applicable to environment itself. In other words: is it rational to use money when assessing possible damage to solely ecosystem (there are no money in ecosystem by itself), and how to perform such assessment?

What do YOU think? I wasn’t able to find an appropriate answer at that moment, but now I believe I have a point. My answer is YES, we can use money to assess risks and damage dealt to ecosystem only.

Firstly the assessment is made by humans and for humans. And humans understand monetised value more easily. The approach that I want to propose is about assessment of money that have to be spent to recover ecosystem to exact the same state it was prior to caused or possible damage. Just imagine how much money one have to spent for recreating and reintroduction of just one extinguished species (a tasmanian wolf for example). Here you are a monetised damage to environment.

Another approach I have in mind is about evaluation of risks via relative live value of species (which can be easily monetised too). Lets use this formula for evaluation of life of individual of a given species: V=(1/N)*P, where V – relative value, N – population of the given species (or given areal of species), P – total population of the human beings. We will have a relative value as 1 for humans and 1*(P/N) for a given species. For example for a tiger we will have its relative individual value about 1 076 900! Literally, if we have a choice whether to save 1 million people or a single tiger, the tiger must be saved – not a million of people!!!

And we can monetise this value by multiplication on the average value of the single human life (you can play a bit with numbers given here).

So the damage to ecosystem may be assessed via loss of number of individuals of species that live in a given ecosystem and we are able to easily evaluate a relative value of the individuals of the each species, and it can be easyly monetised.

I’m looking for the approaches for the environmental risks estimation at the given area. I have my own ideas for the current task, but it is always interesting to know what other people do.

Today I acquainted myself with a master thesis on a subject of spatial analysis of epidemiology of some disease. I was curious because it was about risk mapping. But I was totally disappointed. The guy didn’t mention the definition for the “risk” term he used and it is quite obvious why. Instead of the risk assessment (which implies monetary estimation of the probability of undesired event) he estimated the probability of contracting disease… The thesis is good actually, but the guy did much lesser job than one should when he studies risks.

The same shit exists in Russian legislation. It defines “environmental risk” only as s probability of event that harms environment. So for example if I’m studying risks of the fire at illegal dumps then I can assess just a probability of such events at the given region and don’t bother myself with possible damage estimation…

Remember: [Risk] = [Probability] * [Damage]. If you do not estimate damage – you are calculating something else, so name it accordingly.