A fundamental tool in spatial analysis is called the intersection coefficient or g-index. The g-index quantifies how much of an area is covered by more than one cluster. It was first described in 1930, but it wasn’t until recently that it saw wide use.

The most well-known application of the index comes from urban geography. In this context, the g-index can be used to determine if there are high concentrations of people close to transit stations or office buildings. If you take the ratio of these two areas, then the lower the value of the quotient the less concentrated the population around the station or building.

However, the g-index isn’t limited to describing populations only. You can apply it to describe any type of clustering pattern, such as parks, schools or shopping centres. This article will go into greater detail about different applications of the index while also looking at some cool graphical representations.

Examples of spatial analysis

One important type of spatial analysis is to determine how different parts of your geography influence the outcome or behaviour of a variable.

A common example of this is determining whether there are differences in the average price of houses across the neighbourhood.

If there are higher-priced homes in more affluent areas, then that may contribute to increasing house prices in those areas. And since we already know that people who live in wealthier areas tend to pay higher monthly mortgage payments, studying how much difference income makes in-home pricing can help us understand why house prices are rising in certain areas.

Another way to do this kind of research is to see if any patterns are happening throughout your area. For instance, does every street have one restaurant? Or two? Are some areas mostly grocery stores while others include both restaurants and shopping?

These types of studies could indicate anything from something like having lots of close restaurants next to each other encourages business growth to be an undesirable neighbourhood for businesses because few people go out. – Daniel Mussen, Professor of Geography at Western University

There are many ways you can perform such analyses with Google Maps. Here are several examples using the same dataset. The first will look at which foods are expensive relative to their nutritional value, and the second will compare groceries near and far from a specific location.

Obliquely spatial analysis

Another way to do spatial analysis is called obliquely spatial analysis. This type of analysis was first described in 1921 by William Arthur Ogden, an English professor at UCLA. He referred to it as “spatial analysis” but most people now refer to it as an obliquely spatial analysis or simply spatial analysis.

The obliquely spatial analysis involves more than just creating maps! You also measure locations relative to each other. For example, if you wanted to know where all of the restaurants within one mile of the beach are located, that would not be enough to only make a map.

You would have to determine how many restaurants there are in total, what kind they are (fast food, vegetarian, etc.), and then calculate how far away each restaurant is from the ocean. All these numbers must be measured in miles so we can compare them properly.

The final number you get when performing this type of spatial analysis will be referred to as the location ratio or L-ratio. The average value for this is 0.84, which means that on average, there will be 84% more restaurants closer to the water than outside of this area.

Density-based spatial analysis

A density-based approach to cluster identification is one of the most fundamental clustering algorithms. This algorithm takes into account not only how close points are to each other, but also what fraction of the area they occupy together.

The way this works is by defining an internal region or space within which clusters likely exist and then determining if there is a higher concentration of points in the interior as opposed to outside the space.

Once this has been determined, the next step is to determine if these inside points form a continuous set of discrete clumps. If so, then the system can identify the identified cluster.

Buffer analysis

A buffer is typically defined as the area that surrounds an element or feature of a geometry space. In this case, we can use the built-in function “Buffer” to create a 1-kilometre (0.62 miles) wide border around each park.

To determine if there are too many parks within a given radius, you can calculate how much surface area the shape encloses by multiplying the length of the edge by the average perpendicular distance from the centre. This value then divided into the total amount of area in the polygon gives you the percentage of the area that is covered.

Using our example here, let’s see what happens when we increase the radii!

As you may have noticed, the lake district becomes more crowded as we add wider borders. More areas become covered due to the increased size of the parks, making it seem like there are not enough green spaces for the population.

However, looking at the graph below, we can see that the number of parks per person drops slightly after a certain threshold, where there are only so many places to go before you must look for new greener pastures.

This seems to contradict our initial assumption but remember – while longer paths are nice, there comes a point where people start looking for different types of walkways and green spaces. Having some limited open land can help inspire creativity and exploration.

Street address analysis

The next spatial analytical technique is called street address analysis or neighbourhood analysis. This can be done through online tools or software that have additional settings for this method, such as Google Maps or ArcGIS.

Street address analysis looks at where certain types of businesses are located near each other and determines if there are any patterns. For example, you could analyze all fast food restaurants within one mile of your starting location and see what kind they are.

There are several different ways to do this type of research. You could go door-to-door and take pictures or notes of every business, then use them to compare with lists or databases. Or, you could use online resources to gather information quickly.

This article will talk more about how to conduct basic street address analyses using either free or paid apps that help you organize information.

Quadrant analysis

In quadrant analyses, you determine which areas of the map are more important than others by looking at the coordinates in each area.

Two types of coordinate systems can be used for this technique- Cartesian (x, y) and polar (r, theta). Both work using the same principle but use different terminology.

In both cases, we want to know where is most important relative to other parts of the map. The terms “positive” and “negative” refer to whether an area is larger or smaller than another part of the map.

With cartesian coordinates, these are referred to as positive and negative x-values and with polar coordinates, they are called R-values greater than zero and less than one.

We can also describe the thetan values as being positive or negative depending on if the angle is increasing or decreasing. With g function shape parameters, we are determining how much it changes so a positive value means it becomes longer and a negative value makes it shorter.

So what do all of these things have in common? They are all ways to measure the importance of some other part of the map!

The more important an area is, the higher its coordinate will be and vice versa. By creating several charts, you can identify which regions matter the most and why.

Demographic analysis

The most basic form of demographic analysis is to compare one group to another by looking at their proportions or numbers in a given area, or within an entity such as a city, state, region, or country.

When doing this type of research, there are two main types of statistics that you will encounter. Population Statistics relate to overall population sizes (e.g., number of people) and numerical data about specific characteristics of the population (e.g., average age).

Sociological Statistics look at how individual behaviours affect larger groups. For example, we could determine what percentage of individuals in a community enjoy going out for walks every day. By determining how many people walk each day according to whether they belong to a sports team, we can assume the size of the community because teams typically have smaller and bigger members depending on the season. This theory can be extended to other activities like going to the movies or taking public transportation instead of using cars. If enough people do these things, then we can assume that the community enjoys them!

Concerning racial demographics, we could also calculate what per cent of the population belongs to a certain race, what proportion of people in a particular area is of a certain race, or what ratio of individuals of a race exist compared to those who are not. All three of these concepts apply when discussing diversity in communities.

There are several ways to perform demographic analyses.