تحميل كتاب تعلم تحليل بيانات الجغرافية المكانية باستخدام بايثون Learn Geospatial Analysis With python
استكمالا لسلسلة تعلم بايثون للمبتدئين learn python نقدم لكم في هذه المقالة تحميل كتاب تعلم تحليل بيانات الجغرافية المكانية باستخدام بايثون Download the book Learn Geospatial Analysis With python pdf for free، من تأليف جويل لوشيد Joel Lawhead.
An Introduction to the Contents of book Learn Geospatial Analysis With python
Geospatial Python Toolbox
The first three chapters of this book covered the history of geospatial analysis, the types of geospatial data that are used by analysts, and the major software and libraries found within the geospatial industry. We used some simple Python examples here and there to illustratecertain points, but we mainly focused on the field of geospatial analysis, independent of any specific technology. Starting here, we will be using Python to conquer geospatial analysis and we will continue with that approach for the rest of this book. This chapter explains the software you will need in your toolbox to do just about anything you want in the geospatial field.
We'll discover the Python libraries that are used to access the different types of data that were found in the vector data and raster data sections of Chapter 2, Learning Geospatial Data. Some of these libraries are pure Python, as well as some of the bindings to the different software packages that we looked at in Chapter 3, The Geospatial Technology Landscape.
Python and GIS
This chapter will focus on applying Python to functions that are typically performed by a geographic information system (GIS) such as QGIS or ArcGIS. These functions are the heart and soul of geospatial analysis. We will continue to use as few external dependencies as possible outside of Python itself so that you have tools that are as reusable as possible in different environments. In this book, we separate GIS analysis and remote sensing from a programming perspective, which means that, in this chapter, we'll mostly focus on vector data.
Python and Remote Sensing
In this chapter, we will discuss remote sensing. Remote sensing is about gathering a collection of information about the Earth without making physical contact with it. Typically, this means having to use satellite or aerial imagery, Light Detection and Ranging (LIDAR), which measures laser pulses from an aircraft to the Earth, or synthetic aperture radar. Remote sensing can also refer to processing data that's been collected, which is how we'll use the term in this chapter.Remote sensing grows in a more exciting way every day as more satellites are launched and the distribution of data becomes easier. The high availability of satellite and aerial images, as well as interesting new types of sensors launching each year, is changing the role that remote sensing plays in understanding our world. In remote sensing, we step through each pixel in an image and perform some form of query or mathematical process. An image can be thought of as a large numerical array.
In remote sensing, these arrays can be quite large, in the order of tens of megabytes to several gigabytes in size. While Python is fast, only C-based libraries can provide the speed that's needed to loop through arrays at a tolerable speed.
We'll use the Python Imaging Library (PIL) for image processing and NumPy, which provides multidimensional array mathematics. While written in C for speed, these libraries are designed for Python and provide a Pythonic API.
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