What Are the Benefits of Geospatial Predictive Analysis

In today’s data-driven world, businesses and organizations are constantly seeking innovative ways to harness the power of data to make informed decisions. One such method that has gained prominence in recent years is geospatial predictive analysis.

By combining the capabilities of predictive analytics with geographic information systems, geospatial predictive analysis offers a unique and powerful approach. In this article, we will talk about predictive analytics, explore the benefits of geospatial predictive analysis, and its applications across various industries.

But let’s start with a short digression.

A Brief Overview of Data Analytics

Before delving into geospatial predictive analysis, it’s important to understand the broader concept of data analytics. While there are many definitions, we could define data analytics as the process of examining and interpreting data to extract valuable insights and support decision-making.

There are four basic types of data analytics:

• Descriptive Analytics: This type focuses on summarizing historical data to provide a description of past events and trends – it answers the question of ‘What happened?’
• Diagnostic Analytics: Diagnostic analytics aims to determine the reasons behind past events or trends – it aims to answer the question ‘Why did it happen?’
• Prescriptive Analytics: In this approach, data is used to recommend actions that will optimize future outcomes – it tries to answer the question ‘What should be done?’
• Predictive Analytics: Predictive analytics, our main focus, involves using data and statistical algorithms to find complex relationships and spatial patterns from large datasets in order to forecast future events or trends based on historical data – its purpose is to answer the question ‘What will happen?’

So What Is Predictive Analytics?

As we explained above, predictive analytics has the purpose of predicting future events or trends. More specifically, one key component of predictive analytics is the utilization of statistical algorithms, artificial intelligence, and machine learning techniques to identify the likelihood of future outcomes based on historical data patterns.

So, predictive analytics is based on machine learning. It is a valuable tool, for example, for businesses looking to anticipate customer behavior, optimize operations, and mitigate risks.

Predictive analytics is also commonly used in manufacturing for predicting maintenance needs – machine models can be trained to detect, with reasonable certainty, machine failures and proactively alarm of the need for maintenance.

There are many other use cases for predictive analytics across a range of industries, too many to discuss here. Wherever you can imagine a reasonably accurate future prediction is useful, predictive analytics has a home.

With the caveat that it can’t actually predict the future, naturally, it just analyzes existing data to come up with the most likely outcomes based on the way the specific machine learning model was trained. Now, let’s add spatial elements to everything we talked about.

What Is Geospatial Predictive Analysis?

Predictive geospatial analysis is a combination of geospatial data analytics and predictive analysis. It takes predictive analytics a step further by incorporating geographical data. It involves the utilization of geographic information systems (GIS) to enhance predictive models. The process typically includes four key steps:

1. Data Collection: Gathering relevant geographic data, which can include information about locations, boundaries, population spreads, demographics, and other types of location intelligence.
2. Data Cleaning and Curation: Ensuring the quality and consistency of the collected data through cleaning, validation, and integration with other datasets.
3. Data Annotation: Parallel with cleaning and curation, the data needs to be annotated and labeled so that it can be used for machine learning.
4. Analysis: Applying predictive analytics techniques to the geospatial data to generate forecasts and insights about geographical trends and events.

The Uses of Geospatial Predictive Analysis

Like predictive analysis in general, geospatial predictive analysis finds applications across various industries, bringing substantial benefits:

1. Urban Planning: City planners use geospatial predictive analysis to create urban models that forecast population growth, traffic patterns, and land usage, aiding in infrastructure development and resource allocation.
2. Retail: Retailers employ geospatial predictive models to optimize store locations, inventory management, and personalized marketing based on customer locations.
3. Agriculture: Farmers benefit from geospatial analysis to predict crop yields, optimize irrigation, and manage resources efficiently.
4. Healthcare: Healthcare providers use geospatial predictive analysis to identify disease outbreaks, allocate medical resources, and plan for public health interventions.
5. Security: Security forces leverage geospatial predictive modeling to anticipate and respond to threats, ensuring the safety of populations and critical infrastructure.
6. Environmental Conservation: Conservationists employ geospatial analysis to predict habitat changes, track wildlife populations, and plan conservation efforts.

In short, wherever you can imagine a reasonably accurate future prediction is useful and that prediction includes a geospatial component, geospatial predictive analytics has a home.

Utilizing Geospatial Predictive Analysis for Your Benefit

Geospatial predictive analysis is a powerful approach that enhances traditional predictive analytics by incorporating geographical data. If any of the hypothetical scenarios we mentioned are something you believe could benefit your organization, Aya Data can help.

We specialize in geospatial data science. Our in-house experts can help you collect and annotate data for ML models or we can build bespoke geospatial AI solutions for you. Schedule a free consultation if you have a geospatial project in mind and would like to discuss how Aya Data can help you realize it.