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Spatial Distribution Patterns & Uses

Devon Denomme, Christopher Muscato
  • Author
    Devon Denomme

    Devon has tutored for almost two years. They have a Bachelor's in Air Traffic Management from Embry-Riddle Aeronautical University and minored in Aviation Safety and Homeland Security. They also are AT-CTI certified.

  • Instructor
    Christopher Muscato

    Chris has a master's degree in history and teaches at the University of Northern Colorado.

Learn about spatial distribution. Identify types of spatial patterns, study examples of spatial distribution, and examine what spatial patterns are used for. Updated: 04/18/2022

What Is a Spatial Pattern?

The study of geography is used to describe the relationships between people of various social statuses and their local environment. Furthermore, geography can also be used to explore how unique groups coexist in one relative area or region. All humans (including those of differing professions, social status, financial statuses, and ethnicity) live within a physical space. The connection between real-life events and the locations in which they occur can be studied to gain perspective on the disbursement of ideas, social traits, and other notable human characteristics. The examination of where certain people or objects are in comparison to other people or objects can be achieved through the use of spatial patterns.

What is a spatial pattern, and how are spatial patterns used in geography? A spatial pattern, also known as a spatial distribution pattern or the study of spatial distribution, is an analysis tool used to study people or objects in terms of their physical location. During spatial distribution analysis, many people or objects are considered to create a graphic depiction of how a particular factor is disbursed over an area. For example, the spatial distribution pattern definition can be used to examine how green spaces (such as parks and environmentally-friendly sites) are distributed throughout a city to promote the production of inner-city health. Similarly, a spatial distribution analysis can help identify wealth distribution in a specified region.

Most often, spatial patterns take the form of a map and are color-coded to represent changes over a physical location. Factors and values are represented by unique colors or symbols to denote differences between physical spaces, such as mapping inner-city trees with a green dot, wealthy individuals with a yellow dollar sign symbol, and middle-class incomes with a blue triangle symbol. Spatial distribution is different from temporal distribution, which measures patterns of change according to time. An example of temporal distribution could be to map the locations where regional flooding occurred as the result of a hurricane, with different depictions representing the spread of flooding on a day-by-day or weekly basis. The affected regions would not be measured in terms of how far inland the floodwaters reached (which could be a consideration of spatial distribution) but would rather be coded solely to visualize how widespread floodwaters change over time.


Spatial patterns examine distances in physical location. The blue plots on this graph are clustered, meaning that their location is highly likely to be related by some variable, while the green plots are disbursed randomly and likely did not result from planning.

a group of plotted points, with random green distribution and clustered blue distribution


Spatial Distribution

Where are you right now? You are somewhere. The person next to you, they are somewhere as well. So is the person next to them. While this may seem like somewhat of an obvious observation, it's an important one. Human lives occur within physical space, and that means that we can study the relationship between events in our lives and the spaces in which they occur. Where do people live? How far does the average person commute to work? Which neighborhoods have younger families, which are wealthiest, and which have the highest annual rates of chicken pox? Our lives can be studied in terms of geography, and for that we turn to spatial distribution, or the study of things in terms of their physical locations. Basically, we're asking where things occur and how they relate to each other. It's a useful field of analysis. After all, everything happens somewhere.

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Uses of Spatial Distributions

Spatial distribution studies are most helpful in quantifying complex subjects, such as analyzing human behaviors or the interactions of ecosystems. Measuring certain factors in each of these scenarios or environments can help geographers, amongst professionals in many other fields, to gain a better understanding of how human factors, external behaviors, and environmental factors function and adapt to changes in their surroundings both individually and as part of a (relatively closely-located) group. Historians and geographers may likewise use spatial distribution to understand migration patterns and their effects on a region. Most spatial distribution patterns summarize raw data, but they can be interpreted into a more logical form of data analysis once they are verified and coded correctly to represent disbursement. Spatial patterns can also be combined with temporal patterns into spatio-temporal data to describe changes in a pattern across space and time for a more thorough analysis of variable factors.

Spatio-temporal analysis is used in non-geographical fields such as sociology, psychology, epidemiology, biology, business, and marketing. Sociologists and psychologists may utilize these forms of analysis to better understand human behaviors and interactions, as well as to explain how or why these interactions psychologically take place. Spatio-temporal analysis is highly important in epidemiological and biological studies because the spread of disease, employment of medicine, and changes to the human body can be tracked more closely, thus offering a cause of change and a potential solution to negative impacts caused by external factors such as disease. Business and marketing may also use spatial distribution analysis or spatio-temporal analysis to reach new clients and promote unique products to a more extensive consumer base.

Types of Spatial Patterns

The data obtained through spatial pattern analysis is highly influential to the appearance of the data distribution map that will be created later on. Three forms of imaging most commonly appear when spatial patterns are used to measure change or disbursement over a physical area. These three main types of spatial patterns include:

Patterns of Spatial Distribution

A spatial distribution study works by selecting a variable and plotting incidents of that variable on a map. For example, imagine that you wanted to know which neighborhoods in a town were the most expensive. Cost is your variable, so you assign colors to different values. Houses that cost $100,000- $200,000 are blue, for example. Then, fill in the map by giving each house its appropriate color. When it's done, look at the relationship between the colors. Are all the blue dots clumped together? Are they spread apart? What does this mean?

When studying spatial distribution, there are three basic patterns we expect to find. The first is uniform. A uniform pattern occurs when each data point is spaced within relatively equal distance. Imagine looking at a marching band in a parade. The musicians are spaced equally apart. This is a uniform pattern. Humans do not generally form uniform patterns without planning, so when we see this we can often assume it was a conscious decision. If we saw a uniform distribution of houses represented by blue dots on that housing cost map, we'd know that somebody had planned for the entire city to be consistently filled with houses of this cost.

The next major pattern we find is random. A random distribution indicates that there is no direct correlation between these data points. They did not form in relation to each other. If the houses represented by blue dots were randomly dispersed, we'd know that this community was not planned according to cost. People just built houses of various prices wherever they felt and did not try to form neighborhoods of similar houses.

The third of the basic patterns of spatial distribution is clustered, or clumped. Data points in a clustered distribution are clearly related to each other, but may not be exactly evenly spaced. What this shows us is that people are coming together based on some shared experience. If all of our blue-dotted houses are clustered together in one part of town, we know that people who can afford these houses want to try and stay near to each other. Maybe they don't want to associate with the poorer parts of town, or maybe they all have similar political or occupational backgrounds. We'd have to do more research to know for sure, but we can tell that something is encouraging these people to cluster together.

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Video Transcript

Spatial Distribution

Where are you right now? You are somewhere. The person next to you, they are somewhere as well. So is the person next to them. While this may seem like somewhat of an obvious observation, it's an important one. Human lives occur within physical space, and that means that we can study the relationship between events in our lives and the spaces in which they occur. Where do people live? How far does the average person commute to work? Which neighborhoods have younger families, which are wealthiest, and which have the highest annual rates of chicken pox? Our lives can be studied in terms of geography, and for that we turn to spatial distribution, or the study of things in terms of their physical locations. Basically, we're asking where things occur and how they relate to each other. It's a useful field of analysis. After all, everything happens somewhere.

Patterns of Spatial Distribution

A spatial distribution study works by selecting a variable and plotting incidents of that variable on a map. For example, imagine that you wanted to know which neighborhoods in a town were the most expensive. Cost is your variable, so you assign colors to different values. Houses that cost $100,000- $200,000 are blue, for example. Then, fill in the map by giving each house its appropriate color. When it's done, look at the relationship between the colors. Are all the blue dots clumped together? Are they spread apart? What does this mean?

When studying spatial distribution, there are three basic patterns we expect to find. The first is uniform. A uniform pattern occurs when each data point is spaced within relatively equal distance. Imagine looking at a marching band in a parade. The musicians are spaced equally apart. This is a uniform pattern. Humans do not generally form uniform patterns without planning, so when we see this we can often assume it was a conscious decision. If we saw a uniform distribution of houses represented by blue dots on that housing cost map, we'd know that somebody had planned for the entire city to be consistently filled with houses of this cost.

The next major pattern we find is random. A random distribution indicates that there is no direct correlation between these data points. They did not form in relation to each other. If the houses represented by blue dots were randomly dispersed, we'd know that this community was not planned according to cost. People just built houses of various prices wherever they felt and did not try to form neighborhoods of similar houses.

The third of the basic patterns of spatial distribution is clustered, or clumped. Data points in a clustered distribution are clearly related to each other, but may not be exactly evenly spaced. What this shows us is that people are coming together based on some shared experience. If all of our blue-dotted houses are clustered together in one part of town, we know that people who can afford these houses want to try and stay near to each other. Maybe they don't want to associate with the poorer parts of town, or maybe they all have similar political or occupational backgrounds. We'd have to do more research to know for sure, but we can tell that something is encouraging these people to cluster together.

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Frequently Asked Questions

What is a spatial pattern?

A spatial pattern is an analytical tool used to measure the distance between two or more physical locations or items. Spatial patterns are used in the study of spatial pattern analysis, which is more commonly known as spatial distribution. Spatial patterns usually appear in the form of a color coded map, with each color representing a specific and measurable variable to identify changes in relative placement.

What is an example of a spatial pattern?

Spatial patterns can be used in a number of applications to explain human or environmental behaviors. For example, a spatial pattern can explain how the Islamic faith has spread from the Arabian Peninsula to neighboring lands since its creation in 610 C.E. Although Islam appears as a clustered variable in the Middle East and northern Africa, a clustered spatial distribution represents a spread of culture to certain locations and a planned grouping of like-cultured individuals.

What do spatial patterns show in geography?

Spatial patterns can be used in geography, among many other fields of study, to analyze human behaviors or the interactions of ecosystems. More specifically, spatial patterns help geographers to understand changes in migration, the spread of culture, and the effects of those factors on other people. Spatial distribution is also used in the fields of sociology, psychology, epidemiology, biology, business, and marketing.

What are the 3 types of spatial distribution?

There are three main types of spatial patterns that can appear during the study of spatial distribution. Uniform patterns are denoted by an even distribution of plotted points, meaning that human planning was likely involved in the placement of a specific variable. Random distribution means that no correlation exists between variables and planning was not likely involved in placement. Spatial clustering occurs when most plotted points are located in a specific region, although they may not be separated evenly.

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