Tag Archives: enigma

Climatic Change At A Glance

Mmm. Lost a planet, Master Obi-Wan has. How embarrassing (Yoda, Attack Of The Clones)

Some time ago I published this post in KDnuggets in which I analyze historical temperatures to show how we are gradually heading toward a warmer planet. Simple data science to obtain a simple (and increasingly accepted) conclusion: the global warming is real. Despite I was criticized I still believe what I said then: you don’t have to be a climatologist to empirically confirm global warming.

This experiment is another example of that. It is still simpler than that since it is only based on visual perception but I think is also quite conclusive. In this case, I represent U.S. temperature outliers from 1964 to 2013; a map per year. Dataset contains station ID, name, min/max temperature, as well as degree coordinates of the recorded weather. Original weather data collected from NOAA and anomalies analysis by Enigma. You can download data here.

Anomalies are divided into four categories: Strong Hot, Weak Hot, Weak Cold and Strong Cold. For each station, I represent difference between number of Cold and Hot anomalies (independently of the strength) so Blue bubbles represent stations where total number of Cold anomalies during the year is greater that total number of Hot ones and Red ones represent the opposite. Size of bubbles is also proportional to this indicator. As an example, following you can see the map of year 1975:

tonopah
It seems 1975 was hot in the right a cold on the left side. Concretely, in TONOPAH Station (Nevada) were registered 30 anomalies and most of them (26) where due to cold temperatures. This is why bubble is blue. This GIF shows the evolution of all these maps from 1964 to 2013:

anomalies

Maybe it is just my personal feeling but don’t you see how red bubbles are gradually winning to blue ones? Maybe I am a demagogue.

This code generates a dynamic map by year in html format:

library(data.table)
library(stringr)
library(leaflet)
library(RColorBrewer)
library(classInt)
library(dplyr)
library(htmlwidgets)
anomalies = fread("enigma-enigma.weather.anomalies.outliers-1964-2013-05ce047dbf3e67f83db9ae841545a333.csv")
anomalies %>%
  mutate(year=substr(date_str, 1, 4)) %>%
  group_by(year, longitude, latitude, id, station_name) %>%
  summarise(
    Strong_Hot=sum(str_count(type,"Strong Hot")),
    Weak_Hot=sum(str_count(type,"Weak Hot")),
    Weak_Cold=sum(str_count(type,"Weak Cold")),
    Strong_Cold=sum(str_count(type,"Strong Cold")),
    total=n()) %>%
  mutate(score=sign(-Strong_Hot-Weak_Hot+Weak_Cold+Strong_Cold)) %>%
  mutate(color=ifelse(score==1, "Blue",ifelse(score==0, "White", "Red"))) -> anomalies2
for (i in unique(anomalies2$year))
{
  anomalies2 %>%
    filter(year==i) %>%
    leaflet() %>%
    fitBounds(-124, 34, -62, 40) %>%
    addProviderTiles("Stamen.TonerLite") %>%
    addCircleMarkers(lng = ~longitude,
                     lat = ~latitude,
                     radius = ~ifelse(total < 20, 2, ifelse(total < 27, 4, 8)),
                     color= ~color,
                     stroke=FALSE,
                     fillOpacity = 0.5,
                     popup = ~paste(sep = "
", paste0("<b>", station_name, "</b>"),
                                    paste0("Strong Hot: ", Strong_Hot),
                                    paste0("Weak Hot: ", Weak_Hot),
                                    paste0("Weak Cold: ", Weak_Cold),
                                    paste0("Strong Cold: ", Strong_Cold))) -> m
    saveWidget(m, file=paste0("m", i, ".html"))
}
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Visualising The Evolution Of Migration Flows With rCharts

Heaven we hope is just up the road (Atlas, Coldplay)

Following with the analysis of migration flows, I have done next two visualizations. These charts are called bump charts and are very suitable to represent rankings. This is what I have done:

  • Obtaining top 20 countries of the world according to % of migrants respect its population
  • To do this, I divide total number of migrants between 1960 and 2009 by the mean population in the same period.
  • I do the same to obtain top 20 countries of the world according to % of immigrants.
  • In both cases, I only consider countries with population greater than 2 million.
  • For these countries, I calculate % of migrants in each decade (60’s, 70’s, 80’s, 90’s and 00’s), dividing total number of migrants by mean population each decade
  • I do the same in the case of immigrants.
  • Instead of representing directly % of migrants and immigrants, I represent the ranking of countries according these indicators by decade

This is the bump chart of migrants:

migrants2And this is the one of immigrants:

inmigrants2Some comments:

  • There is a permanent exodus in Puerto Rico: all decades (except 70’s) is located in the top 1 of countries with most migrants respect its population
  • Ireland is also living a diaspora although in the 00’s decade has lost some positions
  • Albania, Georgia and Bosnia and Herzegovina are gaining positions. Is East Europe gradually becoming uncomfortable?
  • Jamaica is also moving up in this sad competition.
  • On the other hand, Hong Kong and Israel are persistently leaders as receivers
  • Saudi Arabia has presented an impressive growth receiving immigrants since 70’s
  • United States does not appear in the immigrants ranking
  • Singapore is gaining positions: in the 00’s decade is the third receiver country
  • Also in the 00s, Switzerland is the first European country in the ranking, holding the fifth position

I like using rCharts as well as using Enigma data sets, as I have done previously. If you want to play with these charts, you can download them here. If you want to know where to find both datasets, read this. Or do it yourself with the next code:

library(data.table)
library(rCharts)
library(dplyr)
setwd("YOUR WORKING DIRECTORY HERE")
populflows = read.csv(file="enigma-org.worldbank.migration-remittances.migrants.migration-flow-c57405e33412118c8757b1052e8a1490.csv", stringsAsFactors=FALSE)
population = fread("enigma-org.worldbank.hnp.data-eaa31d1a34fadb52da9d809cc3bec954.csv")
population %>% 
  filter(indicator_name=="Population, total") %>% 
  as.data.frame %>% 
  mutate(decade=(year-year%%10)) %>% 
  group_by(country_name, country_code, decade) %>% 
  summarise(population=mean(value)) %>% 
  plyr::rename(., c("country_name"="country")) -> population2
populflows %>% filter(!is.na(total_migrants)) %>% 
  group_by(migration_year, destination_country) %>% 
  summarise(inmigrants = sum(total_migrants))  %>% 
  plyr::rename(., c("destination_country"="country", "migration_year"="decade"))   -> inmigrants
populflows %>% filter(!is.na(total_migrants)) %>% 
  group_by(migration_year, country_of_origin) %>% 
  summarise(migrants = sum(total_migrants)) %>%  
  plyr::rename(., c("country_of_origin"="country", "migration_year"="decade"))   -> migrants
# Join of data sets
migrants %>% 
  merge(inmigrants, by = c("country", "decade")) %>%
  merge(population2, by = c("country", "decade")) %>%
  mutate(p_migrants=migrants/population, p_inmigrants=inmigrants/population) -> populflows2
# Global Indicators
populflows2 %>% 
  group_by(country) %>% 
  summarise(migrants=sum(migrants), inmigrants=sum(inmigrants), population=mean(population)) %>% 
  mutate(p_migrants=migrants/population, p_inmigrants=inmigrants/population)  %>% 
  filter(population > 2000000)  %>%
  mutate(rank_migrants = dense_rank(desc(p_migrants)), rank_inmigrants = dense_rank(desc(p_inmigrants))) -> global
# Migrants dataset
global %>% 
  filter(rank_migrants<=20) %>% 
  select(country) %>% 
  merge(populflows2, by = "country") %>% 
  arrange(decade, p_migrants) %>%
  mutate(decade2=as.numeric(as.POSIXct(paste0(as.character(decade), "-01-01"), origin="1900-01-01"))) %>%
  plyr::ddply("decade", transform, rank = dense_rank(p_migrants)) -> migrants_rank
# Migrants dataset
global %>% 
  filter(rank_inmigrants<=20) %>% 
  select(country) %>% 
  merge(populflows2, by = "country") %>% 
  arrange(decade, p_inmigrants) %>%
  mutate(decade2=as.numeric(as.POSIXct(paste0(as.character(decade), "-01-01"), origin="1900-01-01"))) %>%
  plyr::ddply("decade", transform, rank = dense_rank(p_inmigrants)) -> inmigrants_rank
# Function for plotting
plotBumpChart <- function(df){
  bump_chart = Rickshaw$new()
  mycolors = ggthemes::tableau_color_pal("tableau20")(20)
  bump_chart$layer(rank ~ decade2, group = 'country_code', data = df, type = 'line', interpolation = 'none', colors = mycolors)
  bump_chart$set(slider = TRUE, highlight = TRUE, legend=TRUE)
  bump_chart$yAxis(tickFormat = "#!  function(y) { if (y == 0) { return '' } else { return String((21-y)) } } !#")
  bump_chart$hoverDetail(yFormatter = "#! function(y){return (21-y)} !#")
  return(bump_chart)
}
plotBumpChart(migrants_rank)
plotBumpChart(inmigrants_rank)

A Segmentation Of The World According To Migration Flows ft. Leaflet

Up in the sky you just feel fine, there is no running out of time and you never cross a line (Up In The Sky, 77 Bombay Street)

In this post I analyze two datasets from Enigma:

  • Migration flows: Every 10 years, since 1960, the World Bank estimates migrations worldwide (267.960 rows)
  • World population: Values and percentages of populations for each nation examined beginning in year 1960, by the World Bank’s Health, Nutrition and Population project (4.168.185 rows)

Since the second dataset is very large, I load it into R using fread function of data.table package, which is extremely fast. To filter datasets, I also use dplyr and pipes of magrittr package (my life changed since I discovered it).

To build a comparable indicator across countries, I divide migration flows (from and to each country) by the mean population in each decade. I do this because migration flows are aggregated for each decade since 1960. For example, during the first decade of 21st century, Argentina reveived 1.537.850 inmigrants, which represents a 3,99% of the mean population of the country in this decade. In the same period, inmigration to Burundi only represented a 0,67% of its mean population.

What happened in the whole world in that decade? There were around 166 million people who moved to other countries. It represents a 2.58% of the mean population of the world. I use this figure to divide countries into four groups:

  • Isolated: countries with both % of inmigrants and % of migrants under 2.58%
  • Emitter: countries with % of inmigrants under 2.58% and % of migrants over 2.58%
  • Receiver: countries with % of inmigrants over 2.58% and % of migrants under 2.58%
  • Transit: countries with both % of inmigrants and % of migrants over 2.58%

To create the map I use leaflet package as I did in my previous post. Shapefile of the world can be downloaded here. This is how the world looks like according to this segmentation:

Migration Flows

Some conclusions:

  • There are just sixteen receiver countries: United Arab Emirates, Argentina, Australia, Bhutan, Botswana, Costa Rica, Djibouti, Spain, Gabon, The Gambia, Libya, Qatar, Rwanda, Saudi Arabia, United States and Venezuela
  • China and India (the two most populous countries in the world) are isolated
  • Transit countries are concentrated in the north hemisphere and most of them are located in cold latitudes
  • There are six emitter countries with more than 30% of emigrants between 2000 and 2009: Guyana, Tonga, Tuvalu, Jamaica, Bosnia and Herzegovina and Albania

This is the code you need to reproduce the map:

library(data.table)
library(dplyr) 
library(leaflet)
library(rgdal)
library(RColorBrewer)
setwd("YOU WORKING DIRECTORY HERE")
populflows = read.csv(file="enigma-org.worldbank.migration-remittances.migrants.migration-flow-c57405e33412118c8757b1052e8a1490.csv", stringsAsFactors=FALSE)
population = fread("enigma-org.worldbank.hnp.data-eaa31d1a34fadb52da9d809cc3bec954.csv")
# Population
population %>% 
  filter(indicator_name=="Population, total") %>% 
  as.data.frame %>% 
  mutate(decade=(year-year%%10)) %>% 
  group_by(country_name, country_code, decade) %>% 
  summarise(avg_pop=mean(value)) -> population2
# Inmigrants by country
populflows %>% filter(!is.na(total_migrants)) %>% 
  group_by(migration_year, destination_country) %>% 
  summarise(inmigrants = sum(total_migrants))  %>% 
  merge(population2, by.x = c("destination_country", "migration_year"), by.y = c("country_name", "decade"))  %>% 
  mutate(p_inmigrants=inmigrants/avg_pop) -> inmigrants
# Migrants by country
populflows %>% filter(!is.na(total_migrants)) %>% 
  group_by(migration_year, country_of_origin) %>% 
  summarise(migrants = sum(total_migrants)) %>%  
  merge(population2, by.x = c("country_of_origin", "migration_year"), by.y = c("country_name", "decade"))  %>%
  mutate(p_migrants=migrants/avg_pop) -> migrants
# Join of data sets
migrants %>% 
  merge(inmigrants, by.x = c("country_code", "migration_year"), by.y = c("country_code", "migration_year")) %>%
  filter(migration_year==2000) %>% 
  select(country_of_origin, country_code, avg_pop.x, migrants, p_migrants, inmigrants, p_inmigrants) %>% 
  plyr::rename(., c("country_of_origin"="Country", 
                    "country_code"="Country.code", 
                    "avg_pop.x"="Population.mean",
                    "migrants"="Total.migrants",
                    "p_migrants"="p.of.migrants",
                    "inmigrants"="Total.inmigrants",
                    "p_inmigrants"="p.of.inmigrants")) -> populflows2000
# Threshold to create groups
populflows2000 %>% 
  summarise(x=sum(Total.migrants), y=sum(Total.inmigrants), z=sum(Population.mean)) %>% 
  mutate(m=y/z) %>% 
  select(m)  %>% 
  as.numeric -> avg
# Segmentation
populflows2000$Group="Receiver"
populflows2000[populflows2000$p.of.migrants>avg & populflows2000$p.of.inmigrants>avg, "Group"]="Transit"
populflows2000[populflows2000$p.of.migrants<avg & populflows2000$p.of.inmigrants<avg, "Group"]="Isolated" 
populflows2000[populflows2000$p.of.migrants>avg & populflows2000$p.of.inmigrants<avg, "Group"]="Emitter" 
#Loading shapefile from http://data.okfn.org/data/datasets/geo-boundaries-world-110m 
countries=readOGR("json/countries.geojson", "OGRGeoJSON") 
# Join shapefile and enigma information 
joined=merge(countries, populflows2000, by.x="wb_a3", by.y="Country.code", all=FALSE, sort = FALSE) 
joined$Group=as.factor(joined$Group) 
# To define one color by segment 
factpal=colorFactor(brewer.pal(4, "Dark2"), joined$Group) 
leaflet(joined) %>%
  addPolygons(stroke = TRUE, color="white", weight=1, smoothFactor = 0.2, fillOpacity = .8, fillColor = ~factpal(Group)) %>%
  addTiles() %>%
  addLegend(pal = factpal, values=c("Emitter", "Isolated", "Receiver", "Transit"))

A Simple Interactive Map Of US Prisons With Leaflet

The love of one’s country is a splendid thing. But why should love stop at the border? (Pablo Casals, Spanish cellist)

Some time ago, I discovered Enigma, an amazing open platform that unifies billions of records from thousands of government sources to make the world of public data universally accessible and useful. This is the first experiment I have done using data from Enigma. This is what I did:

  1. Create a free account, search and download data. Save the csv file in your working directory. File contains information about all prison facilities in the United States (private and state run) as recorded by the Department of Corrections in each state. Facility types, names, addresses (or lat/long coordinates) ownership names and detailed. In sum, there is information about 1.248 prison facilities.
  2. Since most of the prisons of the file do not contain geographical coordinates, I obtain latitude and longitude using geocode function from ggmap package. This step takes some time. I also remove closed facilities. Finally, I obtain a data set with complete information of 953 prison facilities.
  3. After cleaning and filling out data, generating the map is very easy using leaflet package for R. I create a column named popup_info pasting name and address to be shown in the popup. Instead using default OpenStreetMap basemap I use a CartoDB one.

In my opinion, resulting map is very appealing with a minimal effort. Since I cannot embed the map here, this is a screenshot of it:

jailsThis plot could be a good example of visual correlation, because it depends on this. Here you have the code. To see the map in your browser, press Show in new window option, a little arrow on the upper left side of the RStudio viewer window:

library(dplyr)
library(ggmap)
library(leaflet)
setwd("YOUR WORKING DIRECTORY HERE")
prisons = read.csv(file="enigma-enigma.prisons.all-facilities-bd6a927c4024c16d8ba9e21d52292b0f.csv", stringsAsFactors=FALSE)
prisons %>% 
  mutate(address=paste(facility_address1, city, state, zip, "EEUU", sep=", ")) %>%
  select(address) %>% 
  lapply(function(x){geocode(x, output="latlon")})  %>% 
  as.data.frame %>% 
  cbind(prisons) -> prisons
prisons %>%  
  mutate(popup_info=paste(sep = "<br/>", paste0("<b>", facility_name, "</b>"), facility_address1, city, state, zip)) %>% 
  mutate(lon=ifelse(is.na(longitude), address.lon, longitude),
         lat=ifelse(is.na(latitude),  address.lat, latitude)) %>%
  filter(!is.na(lon) & !grepl("CLOSED", facility_name)) -> prisons
leaflet(prisons) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(lng = ~lon, 
                   lat = ~lat, 
                   radius = 3, 
                   color = "red",
                   stroke=FALSE,
                   fillOpacity = 0.5,
                   popup = ~popup_info)