Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Applied Geospatial Data Science with Python
Applied Geospatial Data Science with Python

Applied Geospatial Data Science with Python: Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

eBook
$29.99 $43.99
Paperback
$54.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Colour book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Table of content icon View table of contents Preview book icon Preview Book

Applied Geospatial Data Science with Python

Introducing Geographic Information Systems and Geospatial Data Science

It is estimated that human society generates several quintillion bytes of data every day. The amount and speed with which our society generates data are also estimated to increase yearly as more and more devices become connected. Devices in the palms of our hands generate rich data assets ranging from detailed human movement data to data on purchasing behavior that connects online transactions with those made at physical storefronts. At the same time, remote sensing devices located outside our atmosphere are generating detailed images, known as satellite imagery, of the Earth at a 0.5-meter resolution, and this detail is improving at a breakneck pace.

Our ability to produce data is only rivaled by our ability to process that same data. Computer ecosystems are rapidly evolving and Moore’s Law, which states that computing power will double roughly every 2 years, is alive and well! Advances in CPUs, GPUs, and data storage components, combined with improved coding languages and analytical methods, allow us to process data and make data-informed decisions faster than ever.

With all of this data and improved technology, organizations and individuals are looking for better and more efficient ways to derive meaning from data that may have once been treated as a byproduct of a technical process. This desire to find meaning in data has led to data science being one of the most in-demand skill sets of the 21st century.

In the introductory chapter of this book, Geospatial Data Science with Python, we’ll begin by defining Geographic Information Systems (GIS), data science, and geospatial data science. These definitions will lay the groundwork and begin to develop a common vernacular that will enable you, the data scientists and traditional GIS professionals, to work in harmony to solve some of the most complex and, dare we say it, fun problems of modern times.

In this chapter, we will cover the following topics:

  • What is GIS?
  • What is data science?
  • What is geospatial data science?

What is GIS?

GIS stands for Geographic Information Systems. GIS are computerized systems used in the creation, collection, organization, analysis, and visualization of geospatial data. Geospatial data is a representation of the real world and it is rooted in geography. Geography is the study of the physical features of the Earth and its atmosphere, as well as how human activity impacts both. Human activity is looked at through many lenses, such as population distribution and land usage.

To represent the Earth in a GIS, you will leverage one of two data formats: vectors or rasters. Figure 1.1 shows a stylized version of how real-world data can be represented in vector and raster formats. We’ll define and discuss both of these terms in more detail in Chapter 2, What Is Geospatial Data and Where Can I Find It?

Figure 1.1 – Real-world data in vector and raster format

Figure 1.1 – Real-world data in vector and raster format

A typical GIS enables you to query and combine data assets in relation to the spatial relationship of each asset. This data is then visualized in the form of a static or interactive map or within a mapping application.

Geospatial data stored within a GIS comes in many different formats and from many different domains. A GIS used in local government may include information on the land parcels of local neighborhoods, the roads that run through that neighborhood, and the location of public service infrastructure, such as hospitals and fire stations. A GIS servicing a local weather station may include some of these assets, but will likely also include other types of data, such as real-time feeds of storm paths, rainfall totals, and wind speeds at various points around an area at various times. In Chapter 2, What Is Geospatial Data and Where Can I Find It?, we will focus more on various types of spatial data, their file structure, including shapefiles and GeoJSON, and some of the public sources in which spatial data can be found.

In your day-to-day life, you’ve likely used a GIS platform or an application more frequently than you may have realized. Take, for instance, Google Maps, which is arguably the most used GIS application in the world. Google Maps allows you to search for points of interest around you, such as a coffee shop or an auto mechanic, find directions to these points of interest, and also understand adverse conditions such as rush-hour traffic or roadworks that may impact your commute. There are many other forms of GIS applications out there, including applications that trace the route of an Amazon delivery vehicle as it approaches your home, applications that help you understand where public busses and transit hubs are located, and even applications that help monitor the spread of infectious diseases, as we mentioned in the preface to this book.

In addition to web and mobile GIS systems, there are also desktop-based, point-and-click GIS platforms that allow users to perform more complex spatial operations and analyses. These platforms are often used by specialized GIS practitioners who often have the title of geographer, GIS analyst, GIS engineer, or GIS specialist. These systems are used in a variety of different industries for different purposes. A GIS analyst in local government may use a desktop GIS platform to edit parcel boundaries within a town while a GIS analyst for a rail operator may use it to monitor the operation status and location of each railcar. The uses of GIS and the industries in which it is used are near limitless.

Typically, desktop GIS systems are provided by vendors, with the most dominant vendor in the space being Esri. As the dominant player in the GIS space, Esri’s proprietary software integrates into numerous other applications with other vendors, including Microsoft and AutoCAD. In more recent versions of its software, Esri has also extended its application to work with many open source data science languages, such as Python and R, and Integrated Development Environments (IDEs), such as Jupyter Notebook. This book will focus on open source Python packages that do not require licensing. In Chapter 4, Exploring Geospatial Data Science Packages, we will cover packages including GeoPandas, PySAL, and GeoViews, along with many others you’ll leverage in the case studies later in this book.

Now that you have an understanding of GIS, let’s now define what data science is. As we define data science, hopefully, you’ll begin to see how GIS and data science interact.

What is data science?

In the simplest terms, data science is the practice of deriving new insights from raw and disparate data assets and communicating those insights to stakeholders in a way that drives impact. The domain of data science combines facets of mathematics, statistics in particular, with computer science and industry- or domain-specific knowledge. Various sources and authors will define data science and the role of the data scientist differently, with some of these sources including soft skills such as communication and consulting as the fourth pillar of data science. These four pillars of data science are represented in Figure 1.2:

Figure 1.2 – Data science pillars

Figure 1.2 – Data science pillars

Each of these components alone can be tricky to master. It is important to recognize that most data scientists are not experts in all of these areas, but have foundational knowledge in these areas. This enables you to generate and communicate more robust and impactful insights with greater efficiency.

Before we dive deeper into the four components of data science, let’s briefly discuss the data science pipeline at a high level. The pipeline can be thought of as follows:

  1. Collecting
  2. Cleaning
  3. Exploring
  4. Processing
  5. Modeling
  6. Validating
  7. Storytelling

Often, the data science process is not performed in a linear fashion and instead can look something like the process displayed in Figure 1.3:

Figure 1.3 – Data science pipeline

Figure 1.3 – Data science pipeline

While these are the general steps to be completed within a data science pipeline, every pipeline looks a bit different based on the problem you’re trying to solve.

The skills needed at each of these steps also differ, which is why having knowledge across the four pillars of data science is so important. Now that we’ve talked about the data science pipeline, let’s break down the four pillars into a bit more detail.

Mathematics

To be successful as a data scientist, knowledge of mathematics is required, as it is the underpinning of data science. Data science often focuses on taking raw data from the past, identifying patterns in said data, and making a prediction about what will happen in the future based on those patterns. In order to do this, applications of calculus, linear algebra, and statistics are necessary. However, you don’t have to be an expert mathematician or statistician to understand how to identify and test the most suitable analytic method for the problem at hand.

Statistics is especially critical in the earlier stages of a data science process as you are sampling or developing a method for collecting data, as well as developing statistical hypotheses that will later be tested using the data you’ve collected. Statistics is also important as you begin to think through the types of algorithms that are appropriate for your analysis and then as you test each algorithm’s related assumptions. Chapter 6, Hypothesis Testing and Spatial Randomness, will introduce you to hypothesis testing and the concept of spatial randomness, which is a critical hypothesis to test within the context of a geospatial data science workflow. In later stages of the data science process, calculus and linear algebra become more important, as they are the foundation of most algorithms.

Having knowledge of these subjects will allow you to understand the model you’re developing, further refine its accuracy, and explain your model to end users in a comprehensible way. In Part 3, Geospatial Modeling Case Studies, of this book, we will focus on geospatial data science case studies that enact the full scope of the data science process and utilize a variety of algorithms in their solutions. Each case study will provide you with a greater perspective on how taking a geospatial data science approach to your analysis will provide you, and your stakeholders, with richer insights.

Computer science

Computer science is the next domain you’ll need to understand to become successful in data science, as most jobs in this field will require some knowledge of data storage as well as programming skills in Python, R, SAS, Structured Query Language (SQL), or another scripting language.

At the very beginning of the data science pipeline, you’ll need to know where your data is stored or will be stored once it is collected. Relying on traditional file-based storage systems that store data as individual files is no longer suitable for day-to-day work in large enterprise settings. Often, a data scientist will need to use SQL to query data from a relational database such as Teradata, Oracle, or Postgres SQL, to name a few. SQL allows data scientists to query data from different tables and join the related data together based on common identifiers. Data scientists are often required to understand how to connect to a database, query the individual tables that make up the database, and export and transfer the data to the platform that will be used for further analysis, modeling, and visualization.

For geospatial data scientists, SQL also enables you to begin working with geospatial data through the use of spatial SQL. Spatial SQL enables you to perform many spatial operations with ease including point-in polygon intersections, spatial unions or joins, buffers, and Euclidian, or crow-flies, distance calculations. Users of more traditional, desktop-based GIS applications are often amazed at the spatial operations that can be performed, and repeated, with a few simple lines of code.

In the later stages of the pipeline, you’ll need to know Python, R, or SAS in order to develop a machine learning or AI model on the data you’ve collected and explored in earlier phases. Toward the end of the data science pipeline, you’ll then want to use these languages to visualize and interpret your results. For the purpose of this book, we will focus on the Python scripting language, as it is one of the more robust and extensible languages for data science in general, and in particular for geospatial data science. In Chapter 4, Exploring Geospatial Data Science Packages, we will focus on setting up your Python-based geospatial data science environment and provide you with an overview of the packages needed to perform various types of analysis and modeling.

For geospatial data science, you will often run into problems that require the use of large datasets or computationally intensive solutions. In each of these cases, knowledge of computer science skills becomes more important, as these problems can be solved better by leveraging the advancements in distributed (or parallel) computing and big data storage arrays. These environments allow you to break the process and data down into smaller chunks that can be distributed to multiple worker nodes in the parallel compute ecosystem. Breaking down the problem in this way can take a process that would have taken days, weeks, or even months on a single desktop and reduce the time to minutes or seconds.

Industry and domain knowledge

Having the technical skills to pull and analyze data and then develop a model is meaningless without knowledge relevant to the specific industry or domain in which the data science problem is rooted. In data science, there is an adage that states garbage in, garbage out, which often refers to bad data being used to generate a bad model.

Someone who doesn’t have domain-related context will often pull data that isn’t relevant or useful to solve the problem at hand. This bad data, when used to develop a model, will often not yield the insights that the stakeholder was expecting. To prevent pulling bad data, you’ll often need to work hand in hand with stakeholders to understand the full context of the problem or issue you are trying to solve. Once this context is obtained, you’ll be able to pull relevant data, understand the data in relation to the problem, and develop a perspective on the algorithm best suited for the individual situation. Industry- and domain-based knowledge are also necessary as you are developing and validating the results of your model in the later stages of the pipeline.

Soft skills

Often, a data scientist will not be working with other technical individuals or even conducting technical processes for 100% of their day. While a data scientist is required to have a strong technical background and understand the intricate nuances of programming and mathematics, it is rare that the end users or those being supported by a data scientist will have this same knowledge base. As mentioned in the previous section, a data scientist will need to rely on their stakeholders to understand and frame the problem at hand. This requires strong communication and collaboration skills to develop the working relationship. This relationship then becomes even more critical when a data scientist has completed their process and is working to interpret meaningful results. Often, a data scientist will also need to have strong consulting and influencing skills, especially in the business world, as they will need to influence stakeholders to implement and rely on the results of their technical processes.

To be a data scientist, you’ll need to have a working knowledge across a wide array of topics as we’ve discussed in this section. However, data science is not a solo activity and you’ll often be able to rely on others on your team or in the data science community to support and help you develop in the areas in which you’re learning. Data science is a practice and we’re all learning and growing every day.

Having developed an understanding of GIS and data science, you should now start to have an inkling about how they combine to form geospatial data science. We’ll talk more about this powerful combination in the next section.

What is geospatial data science?

Geospatial data science lies at the intersection of data science and GIS as depicted in Figure 1.4:

Figure 1.4 – Geospatial data science Venn diagram

Figure 1.4 – Geospatial data science Venn diagram

Geospatial data science is a subclass of general data science that concentrates on geospatial data, its unique properties, and specialized techniques and computation methods necessary for deriving insights from this data. Instead of treating spatial data as another feature in a tabular dataset, spatial data science goes deeper into understanding why things are happening in a particular place and how they are related, or unrelated, to the things going on around it. Spatial data science focuses on identifying spatial relationships based on location, distance, and intersections between objects. We’ll talk more about spatial relationships in Part 2, Exploratory Spatial Data Analysis, of this book, as we conduct exploratory spatial data analysis.

While this book will focus primarily on geospatial data science, that is, data science focused on data pertaining to the Earth, it is worth noting that the concepts can be expanded and translated to general spatial data science. Spatial data science focuses on where the datum is located and how it is related across space and therefore can be applied to problems at a microscopic scale, such as the location of atoms in your body, or problems much larger, such as the distance between asteroids in the main belt between Mars and Jupiter.

By looking at data while taking into account its spatial relationships, you will often find substantial improvements to existing or developing models. As mentioned at the start of this chapter, the domain of spatial data science is rapidly growing and new avenues for exploration are uncovered every day.

Summary

In this chapter, we defined the differences and commonalities between GIS, data science, and geospatial data science. As we discussed data science, we took a deep dive into the four pillars of data science, which include mathematics, computer science, domain and industry knowledge, and soft skills.

We also briefly discussed the stages involved in the data science process. Parts 2 and 3 of this book will provide you with more hands-on experience in implementing the data science process through exploratory data analysis, hypothesis testing, and in-depth data science use cases, covering a variety of topics and algorithms.

We also discussed how the principles of geospatial data science can be applied more broadly within the domain of spatial data science to solve problems at a smaller, microscopic level, as well as larger, astronomical scales. The power of geospatial data science is only starting to be realized as industries, data storage, and computing methodologies evolve. We’re excited that you’ve decided to embark on this learning journey with us and are even more excited to see what you achieve in your journey to become a geospatial data scientist.

In the next chapter, we’ll dive deeper into the world of geospatial data, which we briefly described in this chapter as being a representation of the real world in vector or raster format. We’ll also spend time in the next chapter discussing the rich sources of open geospatial data.

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • Learn how to integrate spatial data and spatial thinking into traditional data science workflows
  • Develop a spatial perspective and learn to avoid common pitfalls along the way
  • Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded

Description

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.

Who is this book for?

This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You’ll need to have a foundational knowledge of Python for data analysis and/or data science.

What you will learn

  • Understand the fundamentals needed to work with geospatial data
  • Transition from tabular to geo-enabled data in your workflows
  • Develop an introductory portfolio of spatial data science work using Python
  • Gain hands-on skills with case studies relevant to different industries
  • Discover best practices focusing on geospatial data to bring a positive change in your environment
  • Explore solving use cases, such as traveling salesperson and vehicle routing problems
Estimated delivery fee Deliver to Russia

Economy delivery 10 - 13 business days

$6.95

Premium delivery 6 - 9 business days

$21.95
(Includes tracking information)

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 28, 2023
Length: 308 pages
Edition : 1st
Language : English
ISBN-13 : 9781803238128
Category :
Languages :
Concepts :

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Colour book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Estimated delivery fee Deliver to Russia

Economy delivery 10 - 13 business days

$6.95

Premium delivery 6 - 9 business days

$21.95
(Includes tracking information)

Product Details

Publication date : Feb 28, 2023
Length: 308 pages
Edition : 1st
Language : English
ISBN-13 : 9781803238128
Category :
Languages :
Concepts :

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total $ 149.97
Machine Learning with PyTorch and Scikit-Learn
$54.99
 Learning Geospatial Analysis with Python
$39.99
Applied Geospatial Data Science with Python
$54.99
Total $ 149.97 Stars icon

Table of Contents

16 Chapters
Part 1:The Essentials of Geospatial Data Science Chevron down icon Chevron up icon
Chapter 1: Introducing Geographic Information Systems and Geospatial Data Science Chevron down icon Chevron up icon
Chapter 2: What Is Geospatial Data and Where Can I Find It? Chevron down icon Chevron up icon
Chapter 3: Working with Geographic and Projected Coordinate Systems Chevron down icon Chevron up icon
Chapter 4: Exploring Geospatial Data Science Packages Chevron down icon Chevron up icon
Part 2: Exploratory Spatial Data Analysis Chevron down icon Chevron up icon
Chapter 5: Exploratory Data Visualization Chevron down icon Chevron up icon
Chapter 6: Hypothesis Testing and Spatial Randomness Chevron down icon Chevron up icon
Chapter 7: Spatial Feature Engineering Chevron down icon Chevron up icon
Part 3: Geospatial Modeling Case Studies Chevron down icon Chevron up icon
Chapter 8: Spatial Clustering and Regionalization Chevron down icon Chevron up icon
Chapter 9: Developing Spatial Regression Models Chevron down icon Chevron up icon
Chapter 10: Developing Solutions for Spatial Optimization Problems Chevron down icon Chevron up icon
Chapter 11: Advanced Topics in Spatial Data Science Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
(9 Ratings)
5 star 66.7%
4 star 22.2%
3 star 11.1%
2 star 0%
1 star 0%
Filter icon Filter
Top Reviews

Filter reviews by




Anonymous Mar 23, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I especially like the treatment of ethical issues in GIS as well as the easy to understand visuals. Great book.
Amazon Verified review Amazon
John D Jun 26, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Applied Geospatial Data Science with Python" is a wonderful book. It clearly describes the geospatial ecosystem to date and delves into some advanced topics on analysis. Whether you are an aspiring data scientist, a researcher, or a seasoned professional, this book will undoubtedly expand your skillset. My only critique would be that it doesn't focus as much on raster analysis.
Amazon Verified review Amazon
Yiqiao Yin Mar 28, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Confronted with an abundance of data, data scientists often find it challenging to convey geospatial analysis in an easily understandable manner for a broader audience. By employing Python for data visualization, those in non-technical positions can more effectively comprehend the problem and seek solutions.This book is designed to assist data scientists and GIS professionals in mastering geospatial data science workflows with Python. As you explore the book, you will encounter numerous geospatial Python libraries, facilitating the development of comprehensive spatial data science workflows. You will learn how to read, process, and manipulate spatial data effectively. Utilizing this data, you will proceed to create spatial data visualizations, enriching comprehension and narrating the data story through static and dynamic mapping applications. As you progress, you will build geospatial AI and ML models focused on clustering, regression, and optimization. These use cases can act as starting points for more advanced projects in a range of industries. Upon completing the book, you will be equipped to manage random data, pinpoint significant relationships, and construct geospatial data models.I highly recommend this book!
Amazon Verified review Amazon
tt0507 Apr 06, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As someone who has never dealt with Geospatial data, the book gave me a great introduction to several concepts such as GIS, utilizing geospatial data, and geopanda. The book explores deeply how to use the concepts and provides various examples. Lastly, the book also mentions geospatial data analysis applications such as vehicle route optimization and spatial clustering which were interesting to read.
Amazon Verified review Amazon
Gustavo May 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Nice title this Applied Geospatial Data Science with Python.The book is very good, and it explains the subject from the beginning, teaching what is GIS (geographic information systems), explaining the different types of files for Geospatial analysis, and then introducing us to how to work with that kind of data in Python.The author also covers the statistical side of the matter, explaining how to construct a hypothesis testing, featuring engineering, as well as how to develop geospatial models.This is a very complete book that I recommend to anyone who is interested in Geospatial analysis.
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is the delivery time and cost of print book? 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
What is custom duty/charge? Chevron down icon Chevron up icon

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