Applied Geospatial Data Science with Python by David S. Jordan

Author:David S. Jordan
Language: eng
Format: epub
Publisher: Packt
Published: 2023-11-15T00:00:00+00:00

Point pattern analysis

Up until now, this chapter has solely focused on spatial autocorrelation. Spatial autocorrelation is just one spatial structure that can be tested. Another spatial hypothesis test falls within the domain of point pattern analysis. Point pattern analysis centers around the patterns present within point data instead of the attributes associated with the point data.

Studying the patterns present in point data is very common in the study of infectious diseases. As we discussed at the start of this section with respect to first- and second-order spatial effects, diseases are often clustered together around infected individuals or other infectious origins. One of the earliest uses of maps to identify the origin of an infectious disease was Dr. John Snow’s famous cholera map. While Dr. Snow didn’t have the statistics or technology that we have today, he was able to use maps and spatial data to identify that the infection originated from contaminated drinking wells in London. If you’re unfamiliar with Dr. Snow’s work, you can visit this resource from the Royal College of Surgeons of England: https://www.rcseng.ac.uk/library-and-publications/library/blog/mapping-disease-john-snow-and-cholera/.

Today, we have more sophisticated measures and technology to understand the patterns that are present in our data. With these measures, you will often focus on the degree of aggregation in the point data, as measured in terms of dispersion and clustering. Figure 6.15 shows point data that is randomly distributed.

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