The role of Machine Learning in simplifying the operations and procedure of companies is well-established. 

The most popular examples of Machine Learning are Natural Language Processing (NLP) and Sentiment Analysis, the contributions of which have improved customer service and marketing practices.

One less discussed, but equally important (and groundbreaking) application of Machine Learning is Spatial Time-Series. 

Spatial Time-Series combines time series analysis with spatial series analysis.

Time series refers to a peculiar way of analysing a set of data points collected over a specific time interval. This differs from data collection because it aims at recording data at consistent time intervals rather than randomly collecting data 

Spatial series refers to a set of data points of a variable collected based on their position on two spatial coordinates. One of the most popular ways of collecting spatial data is by using GPS. 

Therefore, Spatial Time-Series refers to a Machine Learning algorithm that is fed with data points collected over a determined time interval and defined by their spatial position on two coordinates. It has numerous applications in the corporate world which can bring operational and financial benefits to the organisation. 


Spatial Time-Series is useful in predicting demand in a certain city or region depending on numerous factors. 

From logistics companies to retailers, understanding when certain items are going to be most demanded and requested can have numerous benefits. From ensuring that there are enough items in stock to increase production in preparation of the high volume of orders, the financial and brand reputation benefits are clear. 

For example, if you’re a kitchenware retailer and a new academic year is approaching in two months, you’ll benefit from conducting a Spatial-Time Series analysis. By providing the Machine Learning model with historic spatial and temporal data, you’ll be able to predict which university cities need which amount of kitchenware to satisfy the needs of incoming students. 


Managing a warehouse is the centre of a company’s supply chain and logistics operations, and reducing storage costs is one of the most popular challenges that companies need to overcome.

From optimising processes to ensure customer satisfaction, it provides profitability. At the end of the day, any accountant knows that inventory is cash thus reducing storage costs is essential to increase the profit margin. 

Usually, shortage costs are caused by errors in demand forecasting which can be avoided using Spatial Time-Series

However, there other advantages that Spatial Time-Series presents: 

  • Spatial Time-Series reduces holding costs which refer to the rent, bills and taxes associated with the space required to hold the inventory. By understand when and where stock is required, a company can be more effective in renting warehouse space and cut back on unnecessary storage 
  • Spatial Time-Series reduces handling costs which refer to the labour required in handling the stock. By predicting peak order periods in certain regions, companies can efficiently manage their staff and understand how much personnel is actually needed, cutting back on unwanted labour costs. 

Not bad, huh?


Every company understands that managing risk is essential to ensure the smooth operations of the firm. The ultimate goal is to understand and proactively manage them and optimise the success rate. 

Machine Learning and Spatial Time-Series enable companies to predict what is going to happen in the future, giving them an opportunity to either prevent them or put mitigating solutions in place. 

For example, city councils can use spatial time-series to understand what infrastructure policies they can implement to ensure that the cities are adaptable to future changes. This is particularly useful in case of predicting environmental threats such as flooding, heatwaves and snowfalls. 


With the temperature of the planet rising and weather becoming more extreme, companies can use the latest technologies to implement more environmentally friendly initiatives. 

Spatial Time-Series is a strategic tool that can bring numerous benefits. For example, city councils can use it to manage traffic, reduce carbon emissions, and identify strategic zones in which to place alternative methods of transport such as e-bikes. 


Spatial Time-Series is all about ensuring that things work in the right place at the right time. We know how frustrating it can be reaching the wrong destination or getting to an appointment ten minutes late. On a corporate level, wrong destinations and delays have financial and brand reputation repercussions and Spatial Time-Series is here to avoid that. 

Let’s speak about how Spatial Time-Series and TUBR can help you in overcoming any data challenges you’re facing! A quick email to is all you need.