## unemployment statistics in the UK

October 14, 2009 2 comments

Visualizing recent trends in benefit claimant counts in the UK.

Unemployment data from the Guardian Data Blog.

Constituency coordinates courtesy of the TheyWorkForYou API.

The three heatmaps show, respectively, from left to right:-

(1) the %age change in those claiming benefits (hotspot in the Thames Valley)

(2) the %age of the workforce out of work and claiming benefits (hotspots in the Midlands, Hull, London, Liverpool, Glasgow)

(3) the gender ratio of claimant percentages. Red=higher ratio of male to female claimants, blue=lower ratio of male to female claimants

Categories: Uncategorized

## homicide rates

homicide rates

Originally uploaded by stevefaeembra

A visualisation of the homicide rates across the world.

## Scatter plots with Basemap and Matplotlib

A while back I used the flickr api to map 24 hours worth of geotagged photos.

My previous attempts needed some manual Photoshop work to superimpose the plots on a map. The next logical step is to do the whole process – from start to finish – in code, and remove the manual steps.

To do this, I tried the awesome Basemap toolkit. This library allows all sorts of cartographic projections…

Installing Basemap

Basemap is an extention available with Matplotlib. You can download it here (under matplotlib-toolkits)

I installed the version for Python 2.5 on Windows; this missed out a dependency to httplib2 which I needed to install separately from here.

Getting started

Let’s assume you have 3 arrays – x, y and z. These contain the longitudes, latitudes, and data values at each point. In this case, I binned the geotagged photos into a grid of degree points (360×180), so that each degree square contained the number of photos tagged in that degree square.

Setting up

```from basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
import string
import matplotlib.cm as cm

x=[]
y=[]
z=[]
```

Now, you need to populate the x,y and z arrays with values. I’ll leave that an exercise to you ðŸ™‚ All three arrays need to be the same length.

Now, you need to decide which projection to use. Here, I’ve used the Orthographic projection.

```m = Basemap(projection='ortho',lon_0=-50,lat_0=60,resolution='l')
```

Here is the secret sauce I took a while to work out. That’ll teach me not to R the FM. This line transforms all the lat, lon coordinates into the appropriate projection.

```x1,y1=m(x,y)
```

The next bit, you can decide which bits you want to plot – land masses, country boundaries etc.

```m.drawmapboundary(fill_color='black') # fill to edge
m.drawcountries()
m.fillcontinents(color='white',lake_color='black',zorder=0)
```

Finally, the scatter plot.

```m.scatter(x1,y1,s=sizes,c=cols,marker="o",cmap=cm.cool,alpha=0.7)
plt.title("Flickr Geotagging Counts with Basemap")
plt.show()
```
Categories: flickr, Matplotlib, Python

## visualising the nationality of Nobel Peace Prize Winners

Visualizing the nationality of Nobel Peace Prize winners over time

## Image Steganography with PIL

Steganography is greek for ‘hidden writing‘; the act of hiding a message inside another message.

In this case, hiding an image inside another image, without it being obvious to the viewer. The example I’ll give here is only a ‘toy’ implementation, for two reasons:-

• easily cracked: it wouldn’t take the authorities long to spot the hidden message, not least because the algorithm is described on wikipedia ðŸ˜‰
• fragile: the hidden image-within-an-image can easily be broken, if the image has its colours changed afterwards.

But it does illustrate how to do bitwise-manipulation of images in PIL using the ImageMath module, which is the purpose of the post.

How it works

The watermark – the image we wish to hide – is a bitonal image, with black and white pixels only. It’s then resized to be the same size as the original image.

We ‘smuggle’ the watermark inside the original by replacing the LSB (least significant bit) of each colour channel (R,G and B) in the original with the corresponding pixel in the watermark – either 1 for white, or 0 for black.

This image shows the binary arithmetic…least significant bit on the right.

Hiding the watermark image inside our image

For this, we’ll need these imports…

```from PIL import Image, ImageMath
```

and open the two files. The watermark is scaled to match the size of the original image.

```watermark=Image.open(r"c:\watermark.png")
original=Image.open(r"c:\original.jpg")
watermark=watermark.resize(original.size)
```

ImageMath only works with single channel (greyscale) images, so we need to split the two images into their three channels (Red, Green and Blue) using the split() method.

```red, green, blue = original.split()
wred, wgreen, wblue = watermark.split()
```

Now, using ImageMath. ImageMath lets you write simple expressions using values from one or more images. Here, ‘a’ and ‘b’ are bound to the values in the original and watermarked images, respectively. The convert() call is needed to prevent problems later; we need to cast the results back to a greyscale image (mode ‘L’).

```red2 = ImageMath.eval("convert(a&0xFE|b&0x1,'L')", a=red, b=wred)
green2 = ImageMath.eval("convert(a&0xFE|b&0x1,'L')", a=green, b=wgreen)
blue2 = ImageMath.eval("convert(a&0xFE|b&0x1,'L')", a=blue, b=wblue)
```

Okay, so now we have three channels whose LSBs have been replaced with the LSB of the watermark.

But we need to combine the 3 channels back to get an RGB image ready for saving.

```out = Image.merge("RGB", (red2, green2, blue2))
out.save(r"c:\merged.png")
```

Open the original and the processed images; can you see any difference?

Extracting the hidden image

All this is for nought if you can’t extract the hidden image afterwards.

This is simpler, as we only need to produce a black/white image from the LSB of the image. Here, I’ve only bothered with the Red channel.

```stegged=Image.open(r"c:\merged.png")
red, green, blue = stegged.split()
watermark=ImageMath.eval("(a&0x1)*255",a=red) # convert to 0 or 255
watermark=watermark.convert("L")
watermark.save(r"c:\extracted-watermark.png")
```
Categories: Python

## map of the flags of the world

September 26, 2009 4 comments

A map of the flags of the world.

Categories: flickr, Python

## Using Flickr API to get the views, faves and comments of your most popular images

One of the first things I wanted to do with the Flickr API was to get some stats on my most popular images.

You can get this info through the web front end, but there’s no option to download the stats in delimited format (such as CSV) so it can be analysed in a spreadsheet.

I wanted to work out if there was a pattern emerging in the key stats for my 200 most popular images…

1. Number of Views
2. Number of Favourites
3. Number of Groups posted to
4. Number of sets an image is in

Using a Python script (v2.5) and Beej’s FlickR API, this is fairly straightforward. It doesn’t require authentication.

The script runs slowly as it ‘plays nice’, leaving a seconds pause between calls, courtesy of the time.sleep() function. I don’t want to thrash the server.

```# -*- coding: UTF8 -*-

import flickrapi
import datetime
import time
import string

# enter your api key below
api_key = 'PUT_YOUR_API_KEY_HERE'

# enter the user id below (you can use flickr.people.findByUsername to get this for any user)
# it'll look something like 99999999@N99
userid='USER_ID_TO_SEARCH'

# delimiter. Use comma if you want, I tend to use ~
DELIMITER="~"

# dump number of views in delimited format

if __name__ == '__main__':
#output format : "photoid,title,views,faves,groups,sets"
flickr = flickrapi.FlickrAPI(api_key)
photos = flickr.photos_Search(user_id= userid,extras='views', per_page='200', page='1', sort='interestingness-desc')
for photo in photos.find('photos'):

title = string.replace(photo.get('title'),",","") #in case you want to use comment as a delimiter ;0)

# number of views
id = photo.get('id')
views=photo.get('views')

# fave count (up to 50)
faves = flickr.photos_getFavorites(photo_id=id,per_page=50)
countfaves=faves.find('photo').get('total')
time.sleep(1)

# pools and sets posted to
contexts=flickr.photos_getAllContexts(photo_id=id)
posted_groups=len(contexts.findall('.//pool'))
posted_sets=len(contexts.findall('.//set'))
time.sleep(1)

# output as delimited text
converted=DELIMITER.join(tokens)

print converted
```

This script dumps to the console, rather than a file; but it’s easily modified to write to a file. It should work with comma as a separator (for CSV use) as the title tag is stripped of commas…

Here’s some sample output from my photostream…

The format is :
photo id~title~views~favourites~groups~sets

``` 2113237108~north-berwick-old-pier~259~25~21~6~3 3598511429~paris photo heatmap~736~6~12~5~3 3609118442~heart texture~861~10~26~0~2 2304836447~persistence de-motivator~4964~1~4~1~1 2347673075~bergen-ole-bull-plass-lensbaby~184~12~7~6~4 1621047086~banners-down-princes-street~325~11~8~4~2 3688253826~St Anthony's Chapel Edinburgh~124~15~15~9~1 2717978614~st marys~88~9~7~3~2 ```

Once you have the output saved to a text file, you can import it into a spreadsheet (like OpenOffice or Excel) and play around with the figures ðŸ™‚

Categories: flickr, Python