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Proximity analysis

Proximity analysis is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Proximity analysis rather than just read about it. In short: Proximity analysis is a class of spatial analysis tools and algorithms that employ geographic distance as a central principle. Distance is fundamental to geographic inquiry and spatial analysis, due to principles such as the friction of distance, Tobler's first law of geography, and Spatial autocorrelation, which are incorporated into analytical tools.

Proximity analysis — main illustration
Proximity analysis — illustration

Key takeaways

  • Proximity analysis belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Proximity analysis to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Proximity analysis from memory before moving on to harder problems.

Reference excerpt

Proximity analysis is a class of spatial analysis tools and algorithms that employ geographic distance as a central principle. Distance is fundamental to geographic inquiry and spatial analysis, due to principles such as the friction of distance, Tobler's first law of geography, and Spatial autocorrelation, which are incorporated into analytical tools. Proximity methods are thus used in a variety of applications, especially those that involve movement and interaction.

Distance measures

All proximity analysis tools are based on a measure of distance between two locations. This may seen as a simplistic geometric measurement, but the nature of geographic phenomena and geographic activity requires several candidate methods to measure and express distance and distance-related measures.

Euclidean distance, the straight-line geometric distance measured on a planar surface. In geographic information systems, this can be easily calculated from locations in a cartesian Projected coordinate system using the Pythagorean theorem. While it is the simplest method to measure distance, it rarely reflects actual geographic movement. Manhattan distance, the distance between two locations in a cartesian (planar) coordinate system along a path that only follows the x and y axes (thus appearing similar to a path through a grid street network such as that of Manhattan). Geodesic distance, the shortest distance between two locations that stays on the surface of the Earth, following a great circle. On a sphere, the formula uses the spherical law of cosines, but the method is significantly more difficult on an ellipsoid. Network distance, a measurement between two locations along a route within a constrained linear space, such as a road or utility network. Abstract distance, a measurement of distance in a space that is only indirectly related to geographic space, or only metaphorically spatial. Examples include social networks of interpersonal connections, information spaces of related concepts, and the hypertext network of the World Wide Web. Although these are not inherently geographic, projecting them into an abstract space allows geographic tools such as proximity analysis to be used to study them. Cost distance, a measurement along a route (in any of the above spaces) in which geometric distance is replaced by some other quantity that accumulates along the route (and is thus proportional to distance), called a cost because it generally serves as an undesirable quantity to be minimized. Travel time is the most common cost measurement, but other costs include carbon emissions, fuel consumption, environmental impacts, and construction costs.

Techniques There are a variety of tools, models, and algorithms that incorporate geographic distance, due to the variety of relevant problems and tasks.

Buffers, a tool for determining the region that is within a specified distance of a set of geographic features. Cost distance analysis, algorithms for finding optimal routes through continuous space that minimize distance and/or other location dependent costs. Voronoi diagram, also known as Thiessen polygons, an algorithm for partitioning continuous space into a set of regions based on a set of point locations, such that each region consists of locations that are closer to one of the points than any others. Distance decay, based on the Inverse square law, a mathematical model of how the influence of a phenomenon tends to be inversely proportional to the distance from it. A Gravity model is a similar model. Location analysis, a set of (usually heuristic) algorithms for finding the optimal locations of a limited set of points (e.g., store locations) that minimize the aggregate distance to another set of points (e.g., customer locations). A commonly used example is Lloyd's algorithm. Distance matrix, an array containing the distances (Euclidean or otherwise) between any two points in a set. This is frequently used as the independent variable in statistical tests of whether the strength of a relationship is correlated with distance, such as the volume of trade between cities.

Transport network analysis, a set of algorithms and tools for solving a number of distance routing problems when travel is constrained to a network of one-dimensional lines, such as roads and utility networks. For example, the common task of finding the shortest route from point A to point B, which is typically solved using Dijkstra's algorithm

References

External links Proximity tools in Esri ArcGIS OGC ST_DWithin function (PostGIS implementation)

Illustrations

Proximity analysis: Illustration of Dijkstra's Algorithm, a core element of Network Analysis
Illustration of Dijkstra's Algorithm, a core element of Network Analysis

Worked examples

Example 1 — a first encounter with Proximity analysis

Start with the simplest possible case. Write down what Proximity analysis claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Proximity analysis before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Proximity analysis ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Proximity analysis

In research
Proximity analysis appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Proximity analysis in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Proximity analysis is common in secondary-school and first-year university syllabi. It links to neighbouring topics Spatial analysis, so understanding it makes those chapters shorter.
In everyday life
Look for Proximity analysis outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Proximity analysis in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Proximity analysis means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Proximity analysis out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Proximity analysis in simple terms?

Proximity analysis is a class of spatial analysis tools and algorithms that employ geographic distance as a central principle. Distance is fundamental to geographic inquiry and spatial analysis, due to principles such as the friction of distance, Tobler's first law of geography, and Spatial autocor…

Why does Proximity analysis matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Proximity analysis?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Proximity analysis.

Tags

  • Spatial analysis

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