The term spatial computing was coined in 2008 when computer scientist Doug Belshaw introduced it at a conference. He described it as using technology to work with, not just information about space, but spaces themselves. This is what he called “special” or “special” computing.

Belshaw saw applications for his research which focused on how computers could learn by interacting with and understanding real-world environments. For example, if you gave a robot learning its goal was to navigate through your house, it would have to understand where the doors are and how to get from one room to another.

This concept of spatial computing has spread beyond robotics and interactive teaching tools. It can be applied to any field that involves working with the knowledge of space, such as map making, game design, and architecture.

Spatial thinking can also be used to describe a pattern we see emerge more and more frequently in human cultures. As our lives become ever-connected via technology, there is an increasing need to think spatially. We must ask ourselves questions like, ‘Where will this thing I want go next?’ and ‘How do I get there?’

These types of questions require us to consider relationships between things, and how to move around them. They emphasize connections over discrete objects and look at how energy flows throughout a system.

We live in an increasingly spatial culture.

Why is spatial analytics important?

Recent developments in business intelligence (BI) have made it possible to perform powerful analytical functions using computer software. These so-called “spatial analytic” tools allow you to visualize, analyze, and interact with large datasets that are structured or patterned like maps.

By applying these advanced techniques to operational data, businesses can discover new patterns and insights about their operations that would otherwise go unnoticed.

At its most fundamental level, spatial analysis helps us understand how things relate to each other. By looking at clusters of activity, for example, analysts can determine where resources are overutilized and need adjustment, or where there’s excessive overlap between jobs and departments that lead to wasted effort.

It also allows us to see whether certain areas of an organization are underperforming and if necessary, what changes should be made to improve them. All this takes place within the context of organizational structure, which may influence the results.

Importance of spatial analytics

As technology moves more quickly than traditional ways of thinking, people lose sight of the long-term implications of its use. This is particularly true when analyzing vast amounts of data, where even small differences can mean significant shifts in conclusions.

Spatial analytics avoid this risk by focusing only on relevant information in the dataset while systematically eliminating unneeded variables. Because they don’t rely on individual facts as the basis for conclusions, they can produce much higher quality results than conventional approaches.

Spatial data sets

A spatial dataset contains information about locations. This could be your normal street address, or GPS coordinates, or even pictures of places.

Most software packages have the option to create a document, spreadsheet, or database file that contains lots of copies of the same piece of content (like an article). These files are called documents, spreadsheets, or databases because they look like pieces of paper, a sheet of notebook paper, or a book.

A document, spreadsheet, or database is sometimes referred to as a template. We use the term “template” here to refer to something that has already been designed with certain features and settings, so you can start using those features immediately after creating the template.

In the old days, people created documents by typing everything in manually. The templates we mentioned earlier were not very common back then, but these advanced tools exist now! Programs such as Microsoft Word, Google Docs, and Excel make it easy to produce large documents, spreadsheets, and databases.

These programs automatically include some of the feature Settings for you to work with. For example, if you wanted to insert a picture next to some text, you would choose ‘Image’ from the Settings area, find the photo you want to use, and click ‘Insert’.

Obtaining spatial data sets

The next step in developing your skills is to obtain some spatial datasets or x-values that you can use to test out concepts! Luckily, there are many free resources available online for you to explore.

There are several ways to acquire spatial datasets including creating your own, finding existing ones or using pre-made ones. As with any other tool, it’s important to know how to use it properly so that you get the most value out of it.

This article will go over different types of spatial datasets, how to use them and which one would be best for you to start practising with.

Analyzing spatial data sets

In this article, we will be looking at some basic concepts for analyzing spatial data. These include creating your datasets, defining regions, determining what kind of features you have, and how to organize and manipulate these features.

Spatial analysis is quite complex as there are many different ways to approach them. Several software applications can perform various types of spatial analyses, so it’s important to know which ones are appropriate for your research.

Some examples of spatial analytics include exploring patterns in where people live, understanding urban sprawl by studying land use and density, or assessing whether certain areas suffer from higher rates of crime. All of these things relate to geography, and thus spatial studies!

There are also apps designed specifically to do cartography. This involves mapping specific geographic information such as locations, routes, and facilities. Some versions even allow you to create and edit maps yourself, making it more than just presenting pretty pictures.

We will not go into too much depth here, but if you are interested in doing some advanced work, make sure to look up pre-existing literature and tutorials. You may also want to consider getting professional-level training since most spatial analysts have their bachelor’s degrees in geosciences or mathematics.

Tips for spatial analytics

The first step in performing spatial analyses is to determine what kind of analysis you want to do. There are three major types of spatial analytic techniques: classification, regression, and clustering.

Spatial data typically comes in two forms: discrete or quantitative (x- and y-coordinates) and qualitative (such as categorical variables). When doing spatial analysis, it is important to consider how these characteristics relate to each other. For example, if there are different neighbourhoods with differing average income levels, then we have done something meaningful when we compare averages because we have controlled for location.

When looking at differences in average incomes between areas, however, you must account for variability within those groups while calculating your average. This is called variance, and it should be considered when determining statistical significance.

Another way to look at this would be saying that higher average incomes in one area mean something when compared to another, even though they may not be significantly different. It just so happens that one has more money than the other!

There are several ways to perform spatial analyses. Two common ones are using shapefiles or GPS coordinates. Let’s take a closer look at both of them.

Additional resources

Systems like Minecraft, The Legend of Zelda: Breath of the Wild, and others use an algorithm called spatial x to create interactive experiences.

Spatial x is an intuitive system that allows players to explore content more easily. When you play these games using this method, you experience it directly!

There are no barriers when exploring the game. You can move in any direction without encountering dead ends or needing to backtrack. This effect was originally designed for computer gamers who wanted easy access to all areas of the map so they could find secrets and out-of-the-way items.

But digital artists have adapted this concept for other applications beyond gaming. By incorporating spatial x into your design process, you can achieve similar results!

Take some time to experiment with spatial x by looking through pictures and videos of the effect in action. Many websites and blogs discuss how to apply spatial x to your designs.

Spatial statistics

Another way to look at this is that spatial statistical analysis involves studying patterns, relationships, or fluctuations within space. For example, if you were interested in knowing more about how likely it is for cars to break down, you would check car breakdown data across cities to see what makes one city less likely to have vehicle failures than others.

You could also compare whether there are differences in vehicle failure rates between rural areas and urban ones – perhaps due to a lack of safety equipment like protective roofs in colder climates.

There are many ways to do spatial analyses, but all involve looking at two things closely together- where something is and what other things are. More often than not, these two components are linked together.

For instance, if you were doing spatial analyses on car accidents, then you would need to know both where accidents occur and what types of vehicles are involved. A lot of times, people will talk about ‘accidents’ being caused by drunk drivers, so you would want to make sure you include information about blood alcohol levels in your research.

Spatial analytics can be quite interesting! If you're ever feeling bored with non-spatial stats, try adding some extra dimensions to your studies to see what happens.