{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": [
"remove-cell"
]
},
"outputs": [],
"source": [
"from IPython.display import YouTubeVideo, IFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Concepts\n",
"\n",
"In this block, we focus on a particular type of geometry: points. As we will see, points can represent a very particular type of spatial entity. We explore how that is the case and what are its implications, and then wrap up with a particular machine learning technique that allows us to identify clusters of points in space."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Point patterns\n",
"\n",
"Collections of points referencing geographical locations are sometimes called _point patterns_. In this section, we talk about what's special about point patterns and how they differ from other collections of geographical features such as polygons.\n",
"\n",
"```{sidebar} Slides\n",
"\n",
"The slides used in the clip are available at:\n",
"\n",
"- `[HTML]` \n",
"- `[PDF]` \n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": [
"remove-input"
]
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IFrame(\"https://liverpool.instructuremedia.com/embed/a0140fa5-b720-4727-9edf-59248675908e\",\n",
" width=500,\n",
" height=300\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you have gone over the clip above, watch the one below, featuring Luc Anselin from the University of Chicago providing an overview of point patterns. This will provide a wider perspective on the particular nature of points, but also on their relevance for many disciplines, from ecology to economic geography.."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": [
"remove-input"
]
},
"outputs": [
{
"data": {
"image/jpeg": 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\n",
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"YouTubeVideo(\"BN94XXT6Io4\", width=700)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to delve deeper into point patterns, watch the video on the expandable below, which features Luc Anselin delivering a longer (and slightly more advanced) lecture on point patterns."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"tags": [
"remove-input",
"hide-output"
]
},
"outputs": [
{
"data": {
"image/jpeg": 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\n",
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"YouTubeVideo(\"Z2iT4JpqGZg\", width=700)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualisating Points\n",
"\n",
"Once we have a better sense of what makes points special, we turn to visualising point patterns. Here we cover three main strategies: one to one mapping, aggregation, and smoothing.\n",
"\n",
"```{sidebar} Slides\n",
"\n",
"The slides used in the clip are available at:\n",
"\n",
"- `[HTML]` \n",
"- `[PDF]` \n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": [
"remove-input"
]
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IFrame(\"https://liverpool.instructuremedia.com/embed/dc8884ba-b679-455b-990c-78b6475b547e\",\n",
" width=500,\n",
" height=300\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will put all of these ideas to visualising points into practice on the [Hands-on](lab_H) section."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(concepts_H:dbscan)=\n",
"## Clustering Points\n",
"\n",
"As we have seen in this course, \"cluster\" is a hard to define term. In [Block G](../bG/concepts_G) we used it as the outcome of an unsupervised learning algorithm. In this context, we will use the following definition:\n",
"\n",
"> *Concentrations/agglomerations of points over space, significantly more so than in the rest of the space considered*\n",
"\n",
"Spatial/Geographic clustering has a wide literature going back to spatial mathematics and statistics and, more recently, machine learning. For this section, we will cover one algorithm from the latter discipline which has become very popular in the geographic context in the last few years: Density-Based Spatial Clustering of Applications with Noise, or DBSCAN {cite}`ester1996density`.\n",
"\n",
"Wath the clip below to get the intuition of the algorithm first:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": [
"remove-input"
]
},
"outputs": [
{
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"\n",
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"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"YouTubeVideo(\"5E097ZLE9Sg\", width=700)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's complement and unpack the clip above in the context of this course. The video does a very good job at explaining how the algorithm works, and what general benefits that entails. Here are two _additional_ advantages that are not picked up in the clip:\n",
"\n",
"1. **It is not necessarily spatial**. In fact, the original design was for the area of \"data mining\" and \"knowledge discovery in databases\", which historically does not work with spatial data. Instead, think of purchase histories of consumers, or warehouse stocks: DBSCAN was designed to pick up patterns of similar behaviour in those contexts. Note also that this means you can use DBSCAN not only with two dimensions (e.g. longitude and latitude), but with many more (e.g. product variety) and its mechanics will work in the same way.\n",
"1. **Fast and scalable**. For similar reasons, DBSCAN is very fast and can be run in relatively large databases without problem. This contrasts with much of the traditional point pattern methods, that rely heavily on simulation and thus are trickier to scale feasibly. This is one of the reasons why DBSCAN has been widely adopted in Geographic Data Science: it is relatively straightforward to apply and will run fast, even on large datasets, meaning you can iterate over ideas quickly to learn more about your data.\n",
"\n",
"DBSCAN also has a few drawbacks when compared to some of the techniques we have seen earlier in this course. Here are two prominent ones:\n",
"\n",
"1. **It is not based on a probabilistic model**. Unlike the {ref}`LISAs `, for example, there is no underlying model that helps us characterise the pattern the algorithms returns. There is no \"null hypothesis\" to reject, no inferential model and thus no statistical significance. In some cases, this is an important drawback if we want to ensure what we are observing (and the algorithm is picking up) is not a random pattern.\n",
"1. **Agnostic about the underlying process**. Because there is no inferential model and the algorithm imposes very little prior structure to identify clusters, it is also hard to learn anything about the underlying process that gave rise to the pattern picked up by the algorithm. This is by no means a unique feature of DBSCAN, but one that is always good to keep in mind as we are moving from [exploratory analysis](../bF/concepts) to more confirmatory approaches.\n",
"\n"
]
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"## Further readings\n",
"\n",
"```{margin}\n",
"The chapter is available for free [here](https://geographicdata.science/book/notebooks/08_point_pattern_analysis.html)\n",
"```\n",
"\n",
"If this section was of your interest, there is plenty more you can read and explore. A good \"next step\" is the Points chapter on the GDS book (in progress) {cite}`reyABwolf`."
]
}
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