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Visualizing international migration flows using UN migrants stock data

• A shiny app for visualizing international migration flows to and from a region using UN migrants stock data.

• Colors to visualize direction of movement, and number of arcs to visualize size of movement.

• It's very easy to use, just select the region, theme, year to update the output. That's it!

Screenshot of options

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• The migration data was obtained from [United Nations, Department of Economic and Social Affairs, Population Division] (http://esa.un.org/unmigration/TIMSA2013/data/UN_MigrantStock_2013.xls)

• The world map shape file was obtained from [NaturalEarthData.com] (naturalearthdata.com). The location and population of cities were obtained from the cities database at [geonames.org] (geonames.org)

• Code for reading migration data, world map shape file and cities database:

data2013 <- read.xlsx2("data/UN_MigrantStockByOriginAndDestination_2013.xls",
                       sheetName = "Table 10",
                       startRow = 16, colIndex = c(2, 4 , 10:241),
                       colClasses = c("character", rep("numeric", 232)))
wmap <- readShapeSpatial("data/110m_cultural/ne_110m_admin_0_countries.shp")
places <- read.csv("data/cities1000.csv", header=FALSE, stringsAsFactors=FALSE)

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• With some processing, a data-frame with the required arc connections is created. Following is an example a section of the dataframe for Australia

##   source destination stock lat.d lon.d lat.s lon.s stocklog id
## 1     AD          AU    22   -27   133  42.5   1.5        3  1
## 2     AD          AU    22   -27   133  42.5   1.5        3  1
## 3     AD          AU    22   -27   133  42.5   1.5        3  1
## 4     AE          AU  5890   -27   133  24.0  54.0        9  2
## 5     AE          AU  5890   -27   133  24.0  54.0        9  2
## 6     AE          AU  5890   -27   133  24.0  54.0        9  2
## 7     AE          AU  5890   -27   133  24.0  54.0        9  2
## 8     AE          AU  5890   -27   133  24.0  54.0        9  2

• In the next step, the source and destination coordinates are replaced with locations of cities in the from the country or region.

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• The coordinates for each origin or destination in a region are sampled from 15 most populated cities in that region with probability based on the population.

• Since the sampling is done at random, the map generated each time is different.

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• Finally the great circle arcs obtained using gcIntermediate function are and plotted using ggplot2 over the world map.