Although QGIS is. Also, different approaches of representing spatial reference system are enumerated. Apart from processing geospatial data, the book also covers plotting of geospatial data. If you are a GIS student or professional who needs an understanding of how to use ArcPy to reduce repetitive tasks and perform analysis faster, this book is for you. It is also a valuable book for Python programmers who want to understand how to automate geospatial analyses.
Python is a highly expressive language that makes it easy to write sophisticated programs. Combining high-quality geospatial data with Python geospatial libraries will give you a powerful toolkit for solving a range of geospatial programming tasks. The book begins with an introduction to geospatial analysis and programming and explains the ideas behind geospatial data.
You will explore Python libraries for building your own geospatial applications. You will learn to create a geospatial database for your application using PostGIS and the. Build your first interactive map and build location-aware applications using cutting-edge examples in Python About This Book Learn the full geo-processing workflow using Python with open source packages Create press-quality styled maps and data visualization with high-level and reusable code Process massive datasets efficiently using parallel processing Who This Book Is For Geospatial Development By Example with Python is intended for beginners or advanced developers in Python who want to work with geographic data.
The book is suitable for professional. An effective guide to geographic information systems and remote sensing analysis using Python 3 About This Book Construct applications for GIS development by exploiting Python This focuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution system—no compiling of C libraries necessary This practical, hands-on tutorial teaches you all about Geospatial analysis in Python Who This Book Is For If you are a Python developer, researcher, or analyst who wants to perform Geospatial, modeling, and GIS.
Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer codes.
It covers the latest research trends and developments in the areas of Electrical Engineering, Electronic and Communication Engineering, and Computer Science and Information. Author : T. Hitendra Sarma,V. This database might include geographical features, roads, the location of prominent buildings, and other man-made features such as bridges, airports, and so on.
Many of these types of problems can be solved using tools such as the PostGIS spatially-enabled database. 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:. This returns a latitude value of 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.
Now that we have the coordinates for the two desired locations, we can calculate the distance between them using the Proj Python library:. 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.
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:. 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:. 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.
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:. 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. 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 , 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. 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. 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.
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. 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. 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.
Erik Westra has been a professional software developer for over 25 years, and has worked almost exclusively in Python for the past decade.
In recent years, Erik has been involved in the design and implementation of systems matching seekers with providers of goods and services across a range of geographical areas, as well as real-time messaging, payment, and identity systems. This work has included the creation of real-time geocoders and map-based views of constantly changing data. Erik is based in New Zealand, and works for companies worldwide.
Publication date: May Publisher Packt. Pages ISBN Chapter 1. Geospatial Development Using Python.
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