Why are natural environments often degraded in the name of economic progress even when this invites disaster, and how can this be overcome?

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.

How to Predict Where Will Next Disaster Strike?

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:

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.

library(ggplot2)
library(sqldf)
library(grid)
#library(gridExtra)


# 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'))
           
plot(p)

Disasters: Myth or the Reality?

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.

Terminology

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

Fails

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.

Conclusion

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: disasters are an ancient myth! Just buy insurance! LOL

Fire Dynamics In Leningrad Region in 2006

Today I decided to play with TimeManager plugin for QGIS that was introduced by underdark some time ago. After some search across my spatial storage I found only one dataset that contained timestamps: FIRMS fire data that I used for creation of a methodology for burn-out probability calculation (for illegal dumping environmental risk assessment) and for assessment of human influence on fire starting.

Seems that TimeManager is not quite stable after the certain number of features in layer. At least on my machine it crushed several times (during slider manipulation) on the whole dataset of about 7000 points but worked just fine with its one-year subset of about 2700 points. But anyway overall impression is very good.

The main issue was to create a video from the generated .png files. I used console ffmpeg utils. Simple command:
:~>  ffmpeg -r 1/1 -i frame%03d.PNG -vcodec yuv420p -video_size 1126x560 output.flv
produced a 3-minute video above. One second corresponds to 1 day of the most  flammable year for the Leninngrad region in a decade. Video covers period from April to November 2006. I was too lazy to create custom undercover and just loaded OSM via OpenLayers plugin)))

Relation Between Fires and Distanse to the Nearest Road (Recalculated)

As you may already know, I'm a proud owner of AMD FX-8150 8-core CPU. And I've purchased it not for gaming reasons, but for science. My previous CPU was painfully slow with such calculations as determination of the relation between fires and distance to the nearest highway. I even didn't try to perform that calculations on the whole dataset of the roads mapped in OSM in Leningrad region. But now I can do this!

With the new CPU I've recalculated previous distribution (with the same data) in dependence only on highways and performed new calculation on the whole roads dataset. Some numbers first: 

• 6,990 - number of fire points detected by FIRMS for the last 10 years in Leningrad region;
• 10,966 - number of the highway features used as highways for calculations;
• 87,422 -number of features from whole dataset of roads;
• 2,3 Gb RAM and a single core were consumed by R during calculations for the whole dataset.

Results:

Recalculated fire distribution for the highways

Recalculated values for the highways are different to the acquired at the last time despite the data was the same. But there were hardware update and most important - software updates for R and its packages (OS was updated too). But this graph looks far more reasonable than the previous one.

Lets see what we've got for the whole roads dataset (I will compare it to the graph above).

Distribution calculated for the whole dataset of roads

The maximum distance from road decreased almost in to times: from 41 to 26 kilometres. The distance for the highest values decreased accordingly: a rapid decreasing stops at 7 kilometres and for only highways it was 18 kilometres.

So the first 5 kilometres from the road are the most probable zone fore the fire event. This distance is easily covered on foot in two hours. Another evidence of the massive anthropogenic impact on fire starting.

If I will ever lay hands on the road data from the topographic maps (here OSM data used) I will perform the calculation again to get the most precise data.

Conclusion: FX-8150 worth buying )))

Living in USA is Bad for Your Health Too

I posted about increase of the health risks in Russia recently, but the situation in rest of the world isn't much better and USA is an example. The richest country of the world actually is the least place I would like to live. The cause is that if you'll look deeper - it is the same as Russia (and I think in future Russia will be very much alike US), but corruption is legalised (through the lobby institution) and religion de-facto took the upper hand in the temporal power. 

Legalised corruption is worse than a regular because it is harder to fight it. If the mighty one do not like the law he pays for the authorities to close their eyes, but when corruption is legalised it is more efficient to pay for changing of the law. Such law changes were paid by oil and gas companies (and Bush administration got some nice revenue) and it is possible for them to avoid laws for air and water protection... And here the story of the "Gasland" documentary starts (cool thematic map on that site by the way).

Have you ever seen the water on fire? Of course you remember about the explosion and the oil spill on the BP's platform and burning oil on the surface of the sea. But what about the tap water? Scary?

I suggest you watch the "Gasland" documentary and not jus because it is about environmental issues, but because the film itself is great! Especially I loved the moments with cool maps and space imagery. There are plenty of them and they help to understand the scale of the disaster

For now here is the trailer:

Fire at the "Krasny Bor" (a lanfill for hazardous waste)

There was a fire at the landfill for hazardous waste "Krasny Bor". First of all it is not a big secret that hazardous waste is being "incinerated" (of course it isn't proper incineration, it is just an activity to free some space for a new waste) there, so specialists were not surprised that a fire occurred.

It is interesting and revealing that

"specialists of Rosprirodnadzor became aware about the fire at the "Krasny Bor" landfiil via mass-media"

LOL... but actually it isn't fun at all...

musora.bolshe.net launched 500 actions to get rid of illegal dumps

Musora.bolshe.net [No.more.waste - my unofficial translation to English] is another environmental-oriented NGO in Russia. Fellows try to solve multiple waste problems like separate waste collection and illegal dumping (but not illegal landfilling - unfortunately, this fish is not for their teeth for now) in countries of the former USSR.

Guys were inspired by Estonian Let's do it project and its international offspring and decided to carry out 500 actions to get rid of illegal dumps on the 15 of May.  Hope that information about locations and composition of illegal dumps they're asking to provide is going to be reflected at the Global waste map. 

Musora.bolshe.net created a map for the scheduled actions. There are only 3 actions at the moment, hope this number will grow. You can pick up an action from this map and take part in it.

P.S. When I discovered Global waste map today there were only a couple of places with litter in St. Petersburg and whole Russia. Immediately I decided to reveal some of the "inconvenient truth" to the civilised world and upload data about large illegal landfill inside the borders of St.Petersburg  and a thematic "photo" (RGB true-color + panchrom composition from WorldVeiw-2 imagery). Seems, it is the largest illegal landfill in Europe at this map. Unfortunately I have to register (don't know if I will) before I will be able to edit composition of the illegal landfill.

40 000 m³ illegal landfill inside St. Petersburg

BP rebranding

It is hard to find some one who is not aware about one of th biggest crime against environment that was done by infamous British Petroleum. They loosing so much money with that oil spill every day that no one of us would be able to gain even in ten honest lives. The stain on the company name is irremovable. But... it's face is rebrandable! I bet in a three years they will re-brand the enterprise. But some good fellows already did this hard job for BP... for free! Rebranding competition organized by Greenpeace is almost over and I want to put here the works which are on the top of my list.

Here we go:

Best rebranded logo:

That's it!

Best illustration:

1-st place

2-nd place

Best wildlife:

It's leading anyway

Best slogan:

1-st place

2-nd place

3-rd place

Best "WTF?!":

Double hit

But is it satisfying enough? NO! Until the lust BP's toilet seat will be sold from auction to remediate (if it only would be possible!) destroyed environment, until every last motherfuker director and owner of the company will be drowned in the oil - there won't be any satisfaction >:(