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Geospatial Development, GIS & Cartography


My passions in life all revolve around the great outdoors. When out hiking, running or mountain biking in the mountains, I’ve always been intrigued when stumbling across incredibly remote buildings, imagining the human stories connected to them and how the buildings came to be part of the landscape.

I thought it would be interesting to combine this with my work interests as a GIS Developer to find out where the most remote buildings in Britain are, using Open Source tools & datasets.

(of a place) situated far from the main centres of population; distant.

For the…

Mackenzie River, North West Territories, Canada. Image credits: NASA

I recently put out a post on LinkedIn offering a CV/Resume review to recent Geoscience, GIS and Geography graduates who are currently job hunting as it’s a tough period in which to be looking. Although I wanted to be able to help everyone individually, the response was overwhelming so unfortunately I can’t get back to everyone who reached out with an individual response. Instead I thought I’d compile a few CV tips based around mistakes I have seen time and time again, having reviewed hundreds of CVs and applications across multiple recruitment rounds in different industries.

Job hunting can be…

A satellite image of the Grand Canyon taken by Landsat 8.
A satellite image of the Grand Canyon taken by Landsat 8.
The Grand Canyon taken by Landsat 8 (Image courtesy of USGS)


Orfeo Toolbox is a powerful open-source library for processing remote sensing imagery, produced by the National Centre for Space Studies (CNES) — the French National space agency. It is written in C++ with bindings for Python 3.5 and can also be used for processing within QGIS.

Anaconda is a Python Distribution used extensively for data science and geospatial applications.

Whilst the functionality of Orfeo Toolbox is excellent, the installation instructions aren’t overly comprehensive, so here are some more detailed instructions for setting it up for use with Conda on Windows:

Installation Instructions

(Assuming you are using the full Anaconda distribution and Pycharm…

Spatial joins are the bread and butter of Spatial Data Science and Analysis. They are very similar to standard table joins in any relational database system where the data of two tables can be merged based on an attribute. The only difference is that a spatial join allows data to be joined based upon it’s spatial relationship i.e. is point X within polygon Y etc.

Whilst PostGIS, the PostgreSQL database extension is used extensively in GIS/geospatial applications, GeoPandas — a geospatial Python library, is used extensively for data science applications. …

Data from

On first reflection, if someone asked you the question: “How long the coastline of Great Britain?” (or any other landmass for that matter) You’d probably give it your best guess and return an integer in miles or kilometres.

The true answer however, is that it depends! Determining the length of a countries coastline is nowhere near as simple as it would first appear.

In 1951, Lewis Fry Richardson, a mathematician and pacifist was studying armed conflict. …

Data courtesy of OpenStreetMap.

Spatial intersections are one of the more common operations performed in geospatial analysis however something that is often under appreciated is the impact that the order in which you construct your query has on it’s run time.

Take the following simple example where we have two PostGIS tables:

  • research_area.buildings — which contains 21,000 building footprint polygons
  • research_area.landuse — which contains a multi polygon classifying areas of a particular land usage.

If you wanted to find out which buildings are situated within these polygons of designated land usage, you could make use of the ST_Intersects query. It is common to see…

Simon Wrigley

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