This chapter provides a foundation for geospatial analysis, which is needed to pursue any subject in the areas of remote sensing and GIS, including the material in the rest of the chapters of this book. The code for this book can be found in the following GitHub code repository: https://github.com/PacktPublishing/Learning-Geospatial-Analysis-with-Python-4th-Edition. We will be using Python 3.10.9 for the code examples, which will be provided through the Anaconda 3 platform. The code files for this chapter can be accessed on GitHub: https://github.com/PacktPublishing/Learning-Geospatial-Analysis-with-Python-Fourth-Edition/tree/main/B19730_01_Asset_Files.

In December 2019, doctors reported a cluster of cases of a mysterious pneumonia-like illness in Wuhan, China. At first, it was thought to be a minor outbreak, but as the number of cases continued to rise, it quickly became clear that this was something much more serious.

As the virus began to spread to other countries, the World Health Organization (WHO) declared a global health emergency on January 30, 2020. Despite this warning, many countries were slow to take action, and the virus continued to spread unchecked.

By March 2020, the virus had reached pandemic proportions, with cases reported in every corner of the globe. Governments scrambled to respond, implementing lockdowns and travel bans in an attempt to slow the spread of the virus.

As the number of cases and deaths continued to rise, the world watched in horror as hospitals became overwhelmed and healthcare systems struggled to keep up. For the first time in over a century, humanity found itself in a global pandemic, battling a new virus named COVID-19.

As with any new virus, there was no vaccine or even an effective treatment. Medical experts raced to develop a vaccine. The only solution in the short term was to buy time. To do that, the world needed a way to track the virus as it spread to focus resources in the areas it raged most intensely.

In the US, at Johns Hopkins University in Baltimore, Maryland, a PhD candidate named Ensheng Dong had watched as news of the virus spread from his home country – China. As a student, Dong studied both epidemiology and a technology called GIS, a computer system that displays and analyzes geographically referenced information. Dong became worried about his family’s safety, and when the first COVID case hit Washington, he wanted to take action.

The next day, he met with his faculty advisor, Dr. Lauren Gardner, who suggested he use his knowledge of epidemiology and GIS to create a dashboard that would track the virus for the world. Dong began scouring the internet for COVID data and posted it to an online map twice a day while barely sleeping in between. He posted red dots on a map with a dark background. In areas with a large number of cases, he would increase the size of the dot to show the severity of the spread. As word of the dashboard grew, Dong began receiving help to automate the data collection and posting process.

These dashboard map visualizations helped public health officials understand where the virus was spreading and identify hotspots that needed extra attention. It helped track the effectiveness of containment measures such as lockdowns and social distancing rules. It also allowed news organizations to notify the public.

The following figure shows the COVID-19 dashboard:

Figure 1.1 – The COVID-19 dashboard

Government organizations used other GIS maps as well in response to the pandemic to identify high-risk populations. By overlaying data on demographics, income levels, and pre-existing health conditions, GIS helped officials identify communities that were particularly vulnerable to the virus and target resources to those areas.

Officials also used GIS to help manage the logistics of the pandemic’s response. For example, they used it to plan the distribution of personal protective equipment, medical supplies, and vaccines. GIS also tracked the movements of healthcare workers and other essential personnel, ensuring they were deployed to where they were needed most.

In short, GIS has played a vital role in the response to the pandemic, providing critical information and tools to help organizations respond to the crisis more effectively.

Other uses of GIS

Geospatial analysis can be found in almost every industry, including real estate, oil and gas, agriculture, defense, politics, health, transportation, and oceanography, to name a few. For a good overview of how geospatial analysis is used in dozens of different industries, visit https://www.esri.com/en-us/what-is-gis/overview.

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

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

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

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

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.

Computer mapping evolved with the computer itself in the 1960s. However, the origin of the term GIS began with the Canadian Department of Forestry and Rural Development. Dr. Roger Tomlinson headed a team of 40 developers in an agreement with IBM to build the Canada Geographic Information System (CGIS). The CGIS tracked the natural resources of Canada and allowed those features to be profiled for further analysis. The CGIS stored each type of land cover as a different layer.

It also stored data in a Canadian-specific coordinate system, suitable for the entire country, which was devised for optimal area calculations. While the technology that was used was primitive by today’s standards, the system had phenomenal capability at that time. The CGIS included the following software features, all of which can still be found in modern GIS software over 60 years later:

• Map projection switching
• The rubber sheeting of scanned images
• Map scale change
• Line smoothing and generalization to reduce the number of points in a feature
• Automatic gap closing for polygons
• Area measurement
• The dissolving and merging of polygons
• Geometric buffering
• The creation of new polygons
• Scanning
• The digitizing of new features from the reference data

More about CGIS

The National Film Board of Canada produced a documentary in 1967 on the CGIS, which you can view at https://www.youtube.com/watch?v=3VLGvWEuZxI.

Tomlinson was often called the father of GIS. After launching the CGIS, he earned his doctorate from the University of London with his 1974 dissertation, titled The application of electronic computing methods and techniques to the storage, compilation, and assessment of mapped data, which describes GIS and geospatial analysis. Tomlinson ran his own global consulting firm, Tomlinson Associates Ltd., where he remained an active participant in the industry late in life. He was often found delivering keynote addresses at geospatial conferences.

CGIS is the starting point of geospatial analysis, as defined by this book. However, this book would not have been written if not for the work of Howard Fisher and the Harvard Laboratory for Computer Graphics and Spatial Analysis at the Harvard Graduate School of Design. His work on the SYMAP GIS software, which outputs maps to a line printer, started an era of development at the laboratory, which produced two other important packages and, as a whole, permanently defined the geospatial industry. SYMAP led to other packages, including GRID and the Odyssey project, which come from the same lab:

• GRID was a raster-based GIS system that used cells to represent geographic features instead of geometry. GRID was written by Carl Steinitz and David Sinton. The system later became IMGRID.
• Odyssey was a team effort led by Nick Chrisman and Denis White. It was a system of programs that included many advanced geospatial data management features that are typical of modern geodatabase systems. Harvard attempted to commercialize these packages with limited success. However, their impact is still seen today.

Virtually, every existing commercial and open source package owes something to these code bases.

Howard Fisher produced a film in 1967 using the output from SYMAP to show the urban expansion of Lansing, Michigan, from 1850 to 1965 by hand-coding decades of property information into the system. This analysis took months. However, in this day and age, it would take only a few minutes to recreate them because of modern tools and data.

More on SYMAP

You can watch the film at https://www.youtube.com/watch?v=xj8DQ7IQ8_o.

There are dozens of graphical user interface (GUI) geospatial desktop applications available today from companies, including Esri, ERDAS, Intergraph, ENVI, and so on. Esri is the oldest, continuously operating GIS software company, which started in the late 1960s. In the open source realm, packages including Quantum GIS (QGIS) and the Geographic Resources Analysis Support System (GRASS) are widely used. Beyond comprehensive desktop software packages, software libraries for building new software exist in the thousands.

GIS can provide detailed information about the Earth, but it is still just a model. Sometimes, we need a direct representation to gain knowledge about current or recent changes on our planet. At that point, we need remote sensing.