Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
 Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python Unleash the power of Python 3 with practical techniques for learning GIS and remote sensing

Arrow left icon
Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837639175
Length 432 pages
Edition 4th Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Joel Lawhead Joel Lawhead
Author Profile Icon Joel Lawhead
Joel Lawhead
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1:The History and the Present of the Industry
2. Chapter 1: Learning about Geospatial Analysis with Python FREE CHAPTER 3. Chapter 2: Learning about Geospatial Data 4. Chapter 3: The Geospatial Technology Landscape 5. Part 2:Geospatial Analysis Concepts
6. Chapter 4: Geospatial Python Toolbox 7. Chapter 5: Python and Geospatial Algorithms 8. Chapter 6: Creating and Editing GIS Data 9. Chapter 7: Python and Remote Sensing 10. Chapter 8: Python and Elevation Data 11. Part 3:Practical Geospatial Processing Techniques
12. Chapter 9: Advanced Geospatial Modeling 13. Chapter 10: Working with Real-Time Data 14. Chapter 11: Putting It All Together 15. Assessments 16. Index 17. Other Books You May Enjoy

History of geospatial analysis

Geospatial analysis can be traced back as far as 17,000 years ago, to the Lascaux cave in southwestern France. In this cave, Paleolithic artists painted commonly hunted animals and what many experts recently concluded are dots representing the animals’ lunar cycles to note seasonal behavior patterns of prey, such as mating or migration. Though crude, these paintings demonstrate an ancient example of humans creating abstract models of the world around them and correlating spatial-temporal features to find relationships. The following figure shows one of these paintings – a bull with four dots on its back, cross-referencing a lunar time reference:

Figure 1.2 – A cave painting of prey tagged with a lunar cycle reference to predict when it will appear in hunting grounds again

Figure 1.2 – A cave painting of prey tagged with a lunar cycle reference to predict when it will appear in hunting grounds again

Over the centuries, the art of cartography and the science of land surveying have developed, but it wasn’t until the 1800s that significant advances in geographic analysis emerged. Deadly cholera outbreaks in Europe between 1830 and 1860 led geographers in Paris and London to use geographic analysis for epidemiological studies.

In 1832, Charles Picquet used different halftone shades of gray to represent the deaths per thousand citizens in the 48 districts of Paris as part of a report on the cholera outbreak. In 1854, Dr. John Snow expanded on this method by tracking a cholera outbreak in London as it occurred. By placing a point on a map of the city each time a fatality was diagnosed, he was able to analyze the clustering of cholera cases. Snow traced the disease to a single water pump and prevented further cases. The following zoomed-in map section has three layers with streets, a labeled dot for each pump, and bars for each cholera death in a household:

Figure 1.3 – 1854 map of London tracking a cholera outbreak, with dots for the location of water pumps that were potential sources of the disease and bars showing the number of outbreaks per household

Figure 1.3 – 1854 map of London tracking a cholera outbreak, with dots for the location of water pumps that were potential sources of the disease and bars showing the number of outbreaks per household

Geospatial analysis wasn’t just used for the war on diseases. For centuries, generals and historians have used maps to understand human warfare. A retired French engineer named Charles Minard produced some of the most sophisticated infographics that were ever drawn between 1850 and 1870. The term infographics is too generic to describe these drawings because they have strong geographic components. The quality and detail of these maps make them fantastic examples of geographic information analysis, even by today’s standards. Minard released his masterpiece in 1869:

“La carte figurative des pertes successives en hommes de l’Armée Française dans la campagne de Russie 1812-1813,” which translates to “Figurative map of the successive losses of men of the French army in the Russian Campaign 1812-13.”

This depicts the decimation of Napoleon’s army in the Russian campaign of 1812. The map shows the size and location of the army over time, along with prevailing weather conditions. The following figure contains four different series of information on a single theme. It is a fantastic example of geographic analysis using pen and paper. The size of the army is represented by the widths of the brown and black swaths at a ratio of one millimeter for every 10,000 men. The numbers are also written along the swaths. The brown-colored path shows soldiers who entered Russia, while the black-colored path represents the ones who made it out. The map scale is shown to the right in the center as one French league (2.75 miles or 4.4 kilometers). The chart at the bottom runs from right to left and depicts the brutally freezing temperatures that were experienced by the soldiers on their march home from Russia:

Figure 1.4 – Charles Minard’s famous geographic story map showing the decimation of Napoleon’s army during the Russian Campaign of 1812. It combines geography, time, and statistics

Figure 1.4 – Charles Minard’s famous geographic story map showing the decimation of Napoleon’s army during the Russian Campaign of 1812. It combines geography, time, and statistics

While far more mundane than a war campaign, Minard released another compelling map cataloging the number of cattle sent to Paris from around France. Minard used pie charts of varying sizes in the regions of France to show each area’s variety and the volume of cattle that was shipped:

Figure 1.5 – Another map by Minard combining geography and statistics showing beef production in France using pie charts

Figure 1.5 – Another map by Minard combining geography and statistics showing beef production in France using pie charts

In the early 1900s, mass printing drove the development of the concept of map layers – a key feature of geospatial analysis. Cartographers drew different map elements (vegetation, roads, and elevation contours) on plates of glass that could then be stacked and photographed to be printed as a single image. If the cartographer made a mistake, only one plate of glass had to be changed instead of the entire map. Later, the development of plastic sheets made it even easier to create, edit, and store maps in this manner. However, the layering concept for maps as a benefit to analysis would not come into play until the modern computer age.

You have been reading a chapter from
Learning Geospatial Analysis with Python - Fourth Edition
Published in: Nov 2023
Publisher: Packt
ISBN-13: 9781837639175
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image