QGIS Time manager

Time manager is a fantastic plugin allowing you to create a time lapse video of geospatial data provided it comes with a date-time attribute. The following video shows data on where energy advice visits took place using the created date time stamp from the survey plotted every 24 hours.


PG routing – driving distance

Building on the work of previous posts looking at building routable networks and finding the shortest path in a network, this post will look at creating buffers around nodes based on a cost attribute specifed by the user.

The driving distance function, which comes with pg routing 2.0, creates an alpha shape around a node, normally based on the distance someone could travel from that node, or how far someone could go in a time interval.

For this example, we are looking at where someone could travel in 1km along the network from a central node. The following query selects all the nodes that are within 1 km of node 2000.

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PG routing – shortest path algorithms

Once a routable network has been set up we can start to find the shortest path (using Dijkstra’s algorithm) between two points. From here on, I will be using the OSM data for the London road network.

The query use’s a ‘cost’ column – which could be anything thing you like – to calculate the shortest cost from one node to another. In this example I decided to use travel time (rather than distance).

First, I changed the distance of each vertex (the way table) from km to miles, and then calculated the time it would take to traverse each vertex whether walking, cycling or driving. To make this more accurate, I updated the max speed (forward and backward) along each vertex dependent on its class (as listed in the class table).

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PG Routing – Creating a routable network

As an open source alternative to ESRI’s Network Analyst, PG routing provides a very solid platform to perform routing queries. This post, in tandem with posts on shortest distance algorithms and driving distance calculations, will highlight certain basic functionalities within pg routing that I have been playing around with over the past few weeks. I should add that most of this would not have been possible without the help of Anita Graser’s blog, which contains many wonderful posts on the seemingly unlimited capabilities of QGIS!

Pg routing can be downloaded here. Copy the bin, lib and share folders over to your postgres installation folder. Test that it is been installed properly by creating a pg routing enabled database using

 CREATE extension pg_routing 

and then running pgr_version() which should result in details of the pg routing you installed.

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Processing OS Codepoint with polygons

Following on from an earlier post updating OS Codepoint data , this post will now look at how to process OS Codepoint with polygons. This dataset provides a polygon for each postcode, including vertical streets (represented by squares) which is where a building has more than one postcode.

1) From the data provided by OS all the shapefiles (for each postcode sector) need to be merged into one. This can be achieved using QGIS ‘Merge to one’ tool under the vector toolbar or by writing a simple python script in arcmap utilising the merge tool.

2) We are going to create two extra fields in the attribute table ‘PCOutcode’ and ‘PCSector’ – this will provide us with additional symbology options with GIS. For ‘PCOutcode’, use =RTrim(Left(Postcode,4)) to capture the first part of each postcode, and for ‘PCSector’ use =RTrim(Left(Postcode,5)) where Postcode is the postcode field and RTrim removes any trailing spaces.

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Land cover classification using eCognition

Using eCognition, I created a land cover classification (based on the Phase one habitat classes) for the Dyfi catchment in Mid Wales. A rule based approach was conducted using multi-temporal Landsat data and terrain data. This allowed vegetation changes between seasons to be used in delineating classes as well as variables such as aspect, slope and elevation. Rules were also created from values for each landsat band and region growing techniques. The result is displayed below:


A common task within GIS is geocoding; the process of plotting postcodes within a GIS using an address locator. Cleaning the data for this process requires several steps. The format requires postcodes to be a length of 7 characters. This is easily achieved using the following formula in Excel:

=IF(LEN(A2) = 6,REPLACE(A2,3,1,MID(A2,3,1)&” “),IF(LEN(A2)=7,A2,IF(LEN(A2)=8,SUBSTITUTE(A2,” “,””))))

This works for postcodes of lengths 6,7 and 8. Other lengths can be added but it is rare you would encounter these. Save this as a csv file, ensuring the column has a header.

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