Scottish Referendum

A quick look at the density / spatial pattern from geotweets (i.e. those from smartphones mainly)… filtered on keywords as detailed in the image.

Mainly North of the border then….

 

SQLITE to CSV on Mac

Open Terminal

cd to directory with sqlite database in it

 sqlite3 dbname

sqlite> .headers on
sqlite> .mode csv
sqlite> .output test.csv
sqlite> select * from tbl1;
sqlite> .output stdout

source:  http://stackoverflow.com/questions/6076984/how-to-export-the-results-of-my-query-to-csv-file-sqlite

Big-O notation notes

Big-O notation is a relative representation of the complexity of an algorithm. These notes taken from these 3 sources:
http://stackoverflow.com/questions/487258/plain-english-explanation-of-big-o
http://bigocheatsheet.com/
http://stackoverflow.com/questions/487258/plain-english-explanation-of-big-o

O(n²):

• 1 item: 1 second
• 10 items: 100 seconds
• 100 items: 10000 seconds

Notice that the number of items increases by a factor of 10, but the time increases by a factor of 10². Basically, n=10 and so O(n²) gives us the scaling factor n² which is 10².
O(n):

• 1 item: 1 second
• 10 items: 10 seconds
• 100 items: 100 seconds

This time the number of items increases by a factor of 10, and so does the time. n=10 and so O(n)'s scaling factor is 10.
 
O(log n):

• 1 item: 1 second
• 10 items: 2 second
• 100 items: 3 seconds

O(1):

• 1 item: 1 second
• 10 items: 1 second
• 100 items: 1 second

If we add two 100 digit numbers together we have to do 100 additions. If we add two 10,000 digit numbers we have to do 10,000 additions.
See the pattern? The complexity (being the number of operations) is directly proportional to the number of digits n in the larger number. We call this O(n) or linear complexity.
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Now if you were instructing a computer to look up the phone number for "John Smith" in a telephone book that contains 1,000,000 names, what would you do? Ignoring the fact that you could guess how far in the S's started (let's assume you can't), what would you do?
A typical implementation might be to open up to the middle, take the 500,000^th and compare it to "Smith". If it happens to be "Smith, John", we just got real lucky. Far more likely is that "John Smith" will be before or after that name. If it's after we then divide the last half of the phone book in half and repeat. If it's before then we divide the first half of the phone book in half and repeat. And so on.
This is called a binary search and is used every day in programming whether you realize it or not.
So if you want to find a name in a phone book of a million names you can actually find any name by doing this at most 20 times. In comparing search algorithms we decide that this comparison is our 'n'.

• For a phone book of 3 names it takes 2 comparisons (at most).
• For 7 it takes at most 3.
• For 15 it takes 4.
• …
• For 1,000,000 it takes 20.

That is staggeringly good isn't it?
In Big-O terms this is O(log n) or logarithmic complexity. Now the logarithm in question could be ln (base e), log₁₀, log₂ or some other base. It doesn't matter it's still O(log n) just like O(2n²) and O(100n²) are still both O(n²).
It's worthwhile at this point to explain that Big O can be used to determine three cases with an algorithm:

• Best Case: In the telephone book search, the best case is that we find the name in one comparison. This is O(1) or constant complexity;
• Expected Case: As discussed above this is O(log n); and
• Worst Case: This is also O(log n).

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Polynomial Time

Another point I wanted to make quick mention of is that any algorithm that has a complexity of O(n^a) is said to have polynomial complexity or is solvable in polynomial time.
O(n), O(n²) etc are all polynomial time. Some problems cannot be solved in polynomial time. Certain things are used in the world because of this. Public Key Cryptography is a prime example. It is computationally hard to find two prime factors of a very large number. If it wasn't, we couldn't use the public key systems we use.

Flyover of Chambers Street in Edinburgh

Flyover of Chambers Street on a Digital Surface Model built from a LIDAR dataset.

Foot tracking - part 2

More foot tracking.. this time along a corridor... down stairs.. and along another longer corridor with a bend in it....

Otherwise.. much the same as part 1!

Geotagged FlickR locations

Looking at the FlickR dataset… really amazing how many images are captured these days…

I’ve loaded the dataset into PostgreSQL/PostGIS which is doing a great job of handling it all. Shapefiles max out at 2GB so I needed about 11 of them to handle it.. which was pain.

 

Also the new QGIS 2.4 Chugiak is so much faster at rendering… as it uses multithreading. Great stuff!

Europe

 

 

                  Channel Islands

Notes from SICSA – future cities workshop

 

Some useful links from meeting in Dundee Uni on Tue 22 July 2014

SICSA - http://www.sicsa.ac.uk/
http://www.sicsa.ac.uk/themes/future-cities

 

Scottish Seven Cities
http://scottishcities.wordpress.com/
http://www.sckc.org.uk/
Aberdeen.
Dundee.
Edinburgh.
Glasgow.
Inverness.
Perth.
Stirling.

 

London Datastore — http://data.london.gov.uk/

Manchester - http://futureeverything.org/

Javascript version of processing   http://processingjs.org/

 

Smart Citizen - http://www.smartcitizen.me/

FAB LAB http://www.fablabbcn.org/

http://www.ncl.ac.uk/gps/staff/profile/wen.lin#tab_research