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Python Geospatial Development - Second Edition
Python Geospatial Development - Second Edition

Python Geospatial Development - Second Edition: If you're experienced in Python here's an opportunity to get deep into Geospatial development, linking data to global locations. No prior knowledge required – this book takes you through it all, step by step. , Second Edition

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Python Geospatial Development - Second Edition

Chapter 1. Geospatial Development Using Python

This chapter provides an overview of the Python programming language and geospatial development. Please note that this is not a tutorial on how to use the Python language; Python is easy to learn, but the details are beyond the scope of this book.

In this chapter, we will cover:

  • What the Python programming language is, and how it differs from other languages

  • An introduction to the Python Standard Library and the Python Package Index

  • What the terms "geospatial data" and "geospatial development" refer to

  • An overview of the process of accessing, manipulating, and displaying geospatial data

  • Some of the major applications for geospatial development

  • Some of the recent trends in the field of geospatial development

Python


Python (http://python.org) is a modern, high level language suitable for a wide variety of programming tasks. It is often used as a scripting language, automating and simplifying tasks at the operating system level, but it is equally suitable for building large and complex programs. Python has been used to write web-based systems, desktop applications, games, scientific programming, and even utilities and other higher-level parts of various operating systems.

Python supports a wide range of programming idioms, from straightforward procedural programming to object-oriented programming and functional programming.

While Python is generally considered to be an "interpreted" language, and is occasionally criticized for being slow compared to "compiled" languages such as C, the use of byte-compilation and the fact that much of the heavy lifting is done by library code means that Python's performance is often surprisingly good.

Open-source versions of the Python interpreter are freely available for all major operating systems. Python is eminently suitable for all sorts of programming, from quick one-off scripts to building huge and complex systems. It can even be run in interactive (command-line) mode, allowing you to type in commands and immediately see the results. This is ideal for doing quick calculations or figuring out how a particular library works.

One of the first things a developer notices about Python compared with other languages such as Java or C++ is how expressive the language is: what may take 20 or 30 lines of code in Java can often be written in half a dozen lines of code in Python. For example, imagine that you wanted to print a sorted list of the words that occur in a given piece of text. In Python, this is trivial:

words = set(text.split())
for word in sorted(words):
  print word

Implementing this kind of task in other languages is often surprisingly difficult.

While the Python language itself makes programming quick and easy, allowing you to focus on the task at hand, the Python Standard Libraries make programming even more efficient. These libraries make it easy to do things such as converting date and time values, manipulating strings, downloading data from websites, performing complex maths, working with e-mail messages, encoding and decoding data, XML parsing, data encryption, file manipulation, compressing and decompressing files, working with databases—the list goes on. What you can do with the Python Standard Libraries is truly amazing.

As well as the built-in modules in the Python Standard Libraries, it is easy to download and install custom modules, which can be written in either Python or C.

The Python Package Index (http://pypi.python.org) provides thousands of additional modules which you can download and install. And if that isn't enough, many other systems provide python "bindings" to allow you to access them directly from within your programs. We will be making heavy use of Python bindings in this book.

Note

It should be pointed out that there are different versions of Python available. Python 2.x is the most common version in use today, while the Python developers have been working for the past several years on a completely new, non-backwards-compatible version called Python 3. Eventually, Python 3 will replace Python 2.x, but at this stage most of the third-party libraries (including all the GIS tools we will be using) only work with Python 2.x. For this reason, we won't be using Python 3 in this book.

Python is in many ways an ideal programming language. Once you are familiar with the language itself and have used it a few times, you'll find it incredibly easy to write programs to solve various tasks. Rather than getting buried in a morass of type-definitions and low-level string manipulation, you can simply concentrate on what you want to achieve. You end up almost thinking directly in Python code. Programming in Python is straightforward, efficient, and, dare I say it, fun.

Geospatial development


The term "geospatial" refers to information that is located on the earth's surface using coordinates. This can include, for example, the position of a cell phone tower, the shape of a road, or the outline of a country:

Geospatial data often associates some piece of information with a particular location. For example, the following is an interactive map from the http://www.bbc.co.uk/ website, showing the percentage of people in each country with access to the Internet in 2008:

Geospatial development is the process of writing computer programs that can access, manipulate, and display this type of information.

Internally, geospatial data is represented as a series of coordinates, often in the form of latitude and longitude values. Additional attributes such as temperature, soil type, height, or the name of a landmark are also often present. There can be many thousands (or even millions) of data points for a single set of geospatial data. For example, the following outline of New Zealand consists of almost 12,000 individual data points:

Because so much data is involved, it is common to store geospatial information within a database. A large part of this book will be concerned with how to store your geospatial information in a database, and how to access it efficiently.

Geospatial data comes in many different forms. Different Geographical Information System (GIS) vendors have produced their own file formats over the years, and various organizations have also defined their own standards. It is often necessary to use a Python library to read files in the correct format when importing geospatial data into your database.

Unfortunately, not all geospatial data points are compatible. Just like a distance value of 2.8 can have a very different meaning depending on whether you are using kilometers or miles, a given latitude and longitude value can represent any number of different points on the earth's surface, depending on which projection has been used.

A projection is a way of representing the curved surface of the earth in two dimensions. We will look at projections in more detail in Chapter 2, GIS, but for now just keep in mind that every piece of geospatial data has a projection associated with it. To compare or combine two sets of geospatial data, it is often necessary to convert the data from one projection to another.

Note

Latitude and longitude values are sometimes referred to as unprojected coordinates. We'll learn more about this in the next chapter.

In addition to the prosaic tasks of importing geospatial data from various external file formats and translating data from one projection to another, geospatial data can also be manipulated to solve various interesting problems. Obvious examples include the task of calculating the distance between two points, or calculating the length of a road, or finding all data points within a given radius of a selected point. We will be using Python libraries to solve all of these problems, and more.

Finally, geospatial data by itself is not very interesting. A long list of coordinates tells you almost nothing; it isn't until those numbers are used to draw a picture that you can make sense of it. Drawing maps, placing data points onto a map, and allowing users to interact with maps are all important aspects of geospatial development. We will be looking at all of these in later chapters.

Applications of geospatial development


Let's take a brief look at some of the more common geospatial development tasks you might encounter.

Analyzing geospatial data

Imagine that you have a database containing a range of geospatial data for San Francisco. This database might include geographical features, roads, the location of prominent buildings, and other man-made features such as bridges, airports, and so on.

Such a database can be a valuable resource for answering various questions. For example:

  • What's the longest road in Sausalito?

  • How many bridges are there in Oakland?

  • What is the total area of the Golden Gate Park?

  • How far is it from the Pier 39 to the Moscone Center?

Many of these types of problems can be solved using tools such as the PostGIS spatially-enabled database. For example, to calculate the total area of the Golden Gate Park, you might use the following SQL query:

select ST_Area(geometry) from features
  where name = "Golden Gate Park";

To calculate the distance between two places, you first have to geocode the locations to obtain their latitude and longitude. There are various ways to do this; one simple approach is to use a free geocoding web service, such as this:

http://nominatim.openstreetmap.org/search?q=Pier 39, San Francisco,CA

This returns a latitude value of 37.82 and a longitude value of -122.42.

Note

These latitude and longitude values are in decimal degrees. If you don't know what these are, don't worry; we'll talk about decimal degrees in Chapter 2, GIS.

Similarly, we can find the location of the Moscone Center using this query:

http://nominatim.openstreetmap.org/search?q=Moscone Center, San Francisco,CA

This returns a latitude value of 37.80 and a longitude value of -122.44.

Now that we have the coordinates for the two desired locations, we can calculate the distance between them using the Proj Python library:

import pyproj

lat1,long1 = (37.82,-122.42)
lat2,long2 = (37.80,-122.44)

geod = pyproj.Geod(ellps="WGS84")
angle1,angle2,distance = geod.inv(long1, lat1, long2, lat2)

print "Distance is %0.2f meters" % distance

This prints the distance between the two points:

Distance is 2833.64 meters

Note

Don't worry about the "WGS84" reference at this stage; we'll look at what this means in Chapter 2, GIS.

Of course, you wouldn't normally do this sort of analysis on a one-off basis like this—it's much more common to create a Python program that will answer these sorts of questions for any desired set of data. You might, for example, create a web application that displays a menu of available calculations. One of the options in this menu might be to calculate the distance between two points; when this option is selected, the web application would prompt the user to enter the two locations, attempt to geocode them by calling an appropriate web service (and display an error message if a location couldn't be geocoded), then calculate the distance between the two points using Proj, and finally display the results to the user.

Alternatively, if you have a database containing useful geospatial data, you could let the user select the two locations from the database rather than typing in arbitrary location names or street addresses.

However you choose to structure it, performing calculations like this will usually be a major part of your geospatial application.

Visualizing geospatial data

Imagine that you wanted to see which areas of a city are typically covered by a taxi during an average working day. You might place a GPS recorder into a taxi and leave it to record the taxi's position over several days. The results would be a series of timestamps, latitude and longitude values as follows:

2010-03-21 9:15:23  -38.16614499  176.2336626
2010-03-21 9:15:27  -38.16608632  176.2335635
2010-03-21 9:15:34  -38.16604198  176.2334771
2010-03-21 9:15:39  -38.16601507  176.2333958
...

By themselves, these raw numbers tell you almost nothing. But when you display this data visually, the numbers start to make sense:

You can immediately see that the taxi tends to go along the same streets again and again. And if you draw this data as an overlay on top of a street map, you can see exactly where the taxi has been:

(Street map courtesy of http://openstreetmap.org).

While this is a very simple example, visualization is a crucial aspect of working with geospatial data. How data is displayed visually, how different data sets are overlaid, and how the user can manipulate data directly in a visual format are all going to be major topics of this book.

Creating a geospatial mash-up

The concept of a "mash-up" has become popular in recent years. Mash-ups are applications that combine data and functionality from more than one source. For example, a typical mash-up may combine details of houses for rent in a given city, and plot the location of each rental on a map, as follows:

This example comes from http://housingmaps.com.

The Google Maps API has been immensely popular in creating these types of mash-ups. However, Google Maps has some serious licensing and other limitations—as does Google's main competitor, Bing. Fortunately, these are not the only options; tools such as Mapnik, Openlayers, and MapServer, to name a few, also allow you to create mash-ups that overlay your own data onto a map.

Most of these mash-ups run as web applications across the Internet, running on a server that can be accessed by anyone who has a web browser. Sometimes the mash-ups are private, requiring password access, but usually they are publicly available and can be used by anyone. Indeed, many businesses (such as the housing maps site shown in the previous image) are based on freely-available geospatial mash-ups.

Recent developments


A decade ago, geospatial development was vastly more limited than it is today. Professional (and hugely expensive) Geographical Information Systems were the norm for working with and visualizing geospatial data. Open source tools, where they were available, were obscure and hard to use. What is more, everything ran on the desktop—the concept of working with geospatial data across the Internet was no more than a distant dream.

In 2005, Google released two products that completely changed the face of geospatial development. Google Maps and Google Earth made it possible for anyone with a web browser or a desktop computer to view and work with geospatial data. Instead of requiring expert knowledge and years of practice, even a four-year old could instantly view and manipulate interactive maps of the world.

Google's products are not perfect: the map projections are deliberately simplified, leading to errors and problems with displaying overlays; these products are only free for non-commercial use; and they include almost no ability to perform geospatial analysis. Despite these limitations, they have had a huge effect on the field of geospatial development. People became aware of what was possible, and the use of maps and their underlying geospatial data has become so prevalent that even cell phones now commonly include built-in mapping tools.

The Global Positioning System (GPS) has also had a major influence on geospatial development. Geospatial data for streets and other man-made and natural features used to be an expensive and tightly controlled resource, often created by scanning aerial photographs and then manually drawing an outline of a street or coastline over the top to digitize the required features. With the advent of cheap and readily-available portable GPS units, anyone who wishes to can now capture their own geospatial data. Indeed, many people have made a hobby of recording, editing, and improving the accuracy of street and topological data, which are then freely shared across the Internet. All this means that you're not limited to recording your own data, or purchasing data from a commercial organization; volunteered information is now often as accurate and useful as commercially-available data, and may well be suitable for your geospatial application.

The open source software movement has also had a major influence on geospatial development. Instead of relying on commercial toolsets, it is now possible to build complex geospatial applications entirely out of freely-available tools and libraries. Because the source code for these tools is often available, developers can improve and extend these toolkits, fixing problems and adding new features for the benefit of everyone. Tools such as PROJ.4, PostGIS, OGR, and GDAL are all excellent geospatial toolkits which are benefactors of the open source movement. We will be making use of all these tools throughout this book.

As well as standalone tools and libraries, a number of geospatial Application Programming Interfaces (APIs) have become available. Google have provided a number of APIs, which can be used to include maps and perform limited geospatial analysis within a website. Other services, such as the OpenStreetMap geocoder we used earlier, allow you to perform various geospatial tasks that would be difficult to do if you were limited to using your own data and programming resources.

As more and more geospatial data becomes available, from an increasing number of sources, and as the number of tools and systems which can work with this data also increases, it has become increasingly important to define standards for geospatial data. The Open Geospatial Consortium, often abbreviated to OGC (http://www.opengeospatial.org) is an international standards organization which aims to do precisely this: to provide a set of standard formats and protocols for sharing and storing geospatial data. These standards, including GML, KML, GeoRSS, WMS, WFS, and WCS, provide a shared "language" in which geospatial data can be expressed. Tools such as commercial and open source GIS systems, Google Earth, web-based APIs, and specialized geospatial toolkits such as OGR are all able to work with these standards. Indeed, an important aspect of a geospatial toolkit is the ability to understand and translate data between these various formats.

As GPS units have become more ubiquitous, it has become possible to record your location data as you are performing another task. Geolocation, the act of recording your location as you are doing something, is becoming increasingly common. The Twitter social networking service, for example, now allows you to record and display your current location as you enter a status update. As you approach your office, sophisticated To-do list software can now automatically hide any tasks which can't be done at that location. Your phone can also tell you which of your friends are nearby, and search results can be filtered to only show nearby businesses.

All of this is simply the continuation of a trend that started when GIS systems were housed on mainframe computers and operated by specialists who spent years learning about them.

Geospatial data and applications have been "democratized" over the years, making them available in more places, to more people. What was possible only in a large organization can now be done by anyone using a handheld device. As technology continues to improve, and the tools become more powerful, this trend is sure to continue.

Summary


In this chapter, we briefly introduced the Python programming language and the main concepts behind geospatial development. We have seen:

  • That Python is a very high-level language eminently suited to the task of geospatial development.

  • That there are a number of libraries which can be downloaded to make it easier to perform geospatial development work in Python.

  • That the term "geospatial data" refers to information that is located on the earth's surface using coordinates.

  • That the term "geospatial development" refers to the process of writing computer programs that can access, manipulate, and display geospatial data.

  • That the process of accessing geospatial data is non-trivial, thanks to differing file formats and data standards.

  • What types of questions can be answered by analyzing geospatial data.

  • How geospatial data can be used for visualization.

  • How mash-ups can be used to combine data (often geospatial data) in useful and interesting ways.

  • How Google Maps, Google Earth, and the development of cheap and portable GPS units have "democratized" geospatial development.

  • The influence the open source software movement has had on the availability of high quality, freely-available tools for geospatial development.

  • How various standards organizations have defined formats and protocols for sharing and storing geospatial data.

  • The increasing use of geolocation to capture and work with geospatial data in surprising and useful ways.

In the next chapter, we will look in more detail at traditional GIS, including a number of important concepts which you need to understand in order to work with geospatial data. Different geospatial formats will be examined, and we will finish by using Python to perform various calculations using geospatial data.

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Key benefits

  • Build your own complete and sophisticated mapping applications in Python.
  • Walks you through the process of building your own online system for viewing and editing geospatial data
  • Practical, hands-on tutorial that teaches you all about geospatial development in Python

Description

Geospatial development links your data to places on the Earth's surface. Writing geospatial programs involves tasks such as grouping data by location, storing and analyzing large amounts of spatial information, performing complex geospatial calculations, and drawing colorful interactive maps. In order to do this well, you'll need appropriate tools and techniques, as well as a thorough understanding of geospatial concepts such as map projections, datums and coordinate systems. Python Geospatial Development - Second Edition teaches you everything you need to know about writing geospatial applications using Python. No prior knowledge of geospatial concepts, tools or techniques is required. The book guides you through the process of installing and using various toolkits, obtaining geospatial data for use in your programs, and building complete and sophisticated geospatial applications in Python. Python Geospatial Development teaches you everything you need to know about writing geospatial applications using Python. No prior knowledge of geospatial concepts, tools or techniques is required. The book guides you through the process of installing and using various toolkits, obtaining geospatial data for use in your programs, and building complete and sophisticated geospatial applications in Python. This book provides an overview of the major geospatial concepts, data sources and toolkits. It teaches you how to store and access spatial data using Python, how to perform a range of spatial calculations, and how to store spatial data in a database. Because maps are such an important aspect of geospatial programming, the book teaches you how to build your own “slippy map” interface within a web application, and finishes with the detailed construction of a geospatial data editor using Geodjango. Whether you want to write quick utilities to solve spatial problems, or develop sophisticated web applications based around maps and geospatial data, this book includes everything you need to know.

Who is this book for?

Experienced Python developers who want to learn about geospatial concepts, work with geospatial data, solve spatial problems, and build map-based applications. This book will be useful those who want to get up to speed with Open Source GIS in order to build GIS applications or integrate Geo-Spatial features into their existing applications.

What you will learn

  • Access, manipulate and display geospatial data from within your Python programs
  • Master the core geospatial concepts of location, distance, units, projections and datums
  • Read and write geospatial data in both vector and raster format
  • Perform complex, real-world geospatial calculations using Python
  • Store and access geospatial information in a database
  • Use points, lines and polygons within your Python programs
  • Use Python-based tools for converting geospatial data into good looking maps
  • Build complete web-based mapping applications using Python
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Table of Contents

11 Chapters
Geospatial Development Using Python Chevron down icon Chevron up icon
GIS Chevron down icon Chevron up icon
Python Libraries for Geospatial Development Chevron down icon Chevron up icon
Sources of Geospatial Data Chevron down icon Chevron up icon
Working with Geospatial Data in Python Chevron down icon Chevron up icon
GIS in the Database Chevron down icon Chevron up icon
Working with Spatial Data Chevron down icon Chevron up icon
Using Python and Mapnik to Generate Maps Chevron down icon Chevron up icon
Putting It All Together – a Complete Mapping System Chevron down icon Chevron up icon
ShapeEditor – Implementing List View, Import, and Export Chevron down icon Chevron up icon
ShapeEditor – Selecting and Editing Features Chevron down icon Chevron up icon

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sushmit sarmah Jan 05, 2014
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This book is an excellent starting point for anyone who wants to get started on Geospatial development. The author takes you from the very basics of geospatial development explaining all definitions that you would require to get proficient in this subject. It is then a deep dive into programming in python starting with all the different libraries that are needed and how to make use of them. Code examples are provided for each topic.The best way to follow this book is to read and then practice the examples alongside. Very soon you would get comfortable with using the different tools available in python for geospatial development like gdal/ogr, shapely, mapnik, etc. It teaches you where to get your data, how to convert them from different formats, then customize them to your liking, store it in a database and the pros and cons of different databases and their spatial capabilities.You learn how to create your own maps with mapnik going into great detail with all the options available in mapnik. Lastly you learn how to use all the skills learnt throughout the book to create a web application.I would recommend this book to anyone who wishes to get started with geospatial development. All you need is some familiarity with python. It gives you a solid background and a starting point from which to learn more about the subject.
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W_v_N Sep 16, 2013
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This book explained very clearly everything I needed to know about the "under the hood" workings of a GIS. I really like how it deals with every aspect, from the database to the graphics and everything in between. I highly recommend this book to anyone who wants a better understanding of how each component of a GIS function and work together to display an intelligent map.
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Dean Howell Aug 17, 2013
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Python Geospatial Development - Second Edition.The book is a comprehensive guide to all aspects of using Python in geospatial development. The book is a hands on tutorial taking you right from the libraries you and tools you will require, through every aspect of development to putting it all together in a complete mapping application.If you are new to the geospatial industry the book even has sections on the fundamentals of coordinate systems, projections, datums and common GIS data formats.The best part of the book, in my opinion, is all the code examples that guide you through every aspect of becoming a proficient geospatial developer. There are also many reference sites pointing you to further information about specific platforms, software packages and developer forums.I also liked the section on data formats and where to obtain data. This can be very useful for developers starting in the industry or even those who have been in the industry for a while. Data is the key to any good geospatial system.So if you are looking for a great resource to take your geospatial system to the next level, I highly recommend Python Geospatial Development - Second Edition.
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  17. Venezuela
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Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela