Spatial analytics merges public or private datasets with location information from geographic information systems.
Spatial analytics analyzes geographic data to identify and visualize patterns, trends, and relationships. Unlike traditional data analytics, which focuses on “what” and “when,” spatial analytics adds an extra layer by considering the “where” aspect. This element is crucial because the geographical context often provides valuable insights that can significantly influence strategic business decisions.
For example, traditional analytics may indicate that a retail store experienced increased sales during the last three months. While this information is valuable, it fails to offer insights into the geographical origins of these sales, the residential and workplace locations of the store’s customers, their shopping preferences, and movement behaviors.
On the other hand, spatial analytics can deliver deeper insights by revealing that some particular neighborhoods have attracted more customers. This knowledge enables more precise marketing strategies and could even lead to considering opening a new store in that area.
Historically, spatial analysis was mainly conducted within the Geographic Information Systems (GIS) framework, connecting location data with descriptive information and visualizing it on a map. However, nowadays, this process has expanded to incorporate the capabilities of data science and Machine Learning, opening up a world of possibilities for practical applications in different industries.
In this guide you will cover:
The relevance of spatial data nowadays
In a hyperconnected world, spatial data has increased significantly due to the widespread use of IoT devices, smartphones, and sensors. Spatial Analytics and Location Intelligence can provide a more comprehensive understanding of data that other analytics methods might overlook, whether it involves monitoring the global movement of goods or analyzing foot traffic in a retail store.
For example, PREDIK Data-Driven’s Spatial Analytics uses anonymized data from over 250 million devices, only in the US.
Benefits of spatial analytics for businesses
Spatial analytics offers several advantages that can have a significant impact on businesses. Here are some key benefits:
Increase in competitive advantage
Spatial insights play a crucial role in today’s competitive market. Understanding customer behavior on a geographical level can lead to more effective marketing strategies, improved customer engagement, and a more targeted approach to business.
It also helps businesses identify untapped high-potential markets and new expansion areas with low competition where target customers are located, thereby facilitating business expansion.
Better decision making
Integrating location information into your data analysis can offer valuable insights and improve the quality and accuracy of business decisions. For instance, you can evaluate potential investments by studying foot traffic patterns in a specific area and comprehending how consumer profiles differ in different markets or neighborhoods.
Cost efficient strategies
Spatial analytics can identify inefficiencies in different business operations, such as logistics, supply chain management, and resource allocation. By optimizing these areas, businesses can save money and improve productivity.
For example, brand stores can avoid cannibalization when expanding into an already-covered area, and retailers can allocate resources more effectively across various locations.
Less investment risks
Whether identifying declining areas early or discovering decreases in foot traffic before impacting revenue, spatial analytics can help businesses anticipate and mitigate risks effectively.
Better geographic visibility
Geospatial data allows better data visualizations on maps through user-friendly interfaces. Users can search for relevant geographic data using dashboards and elements such as city names, countries, and zip codes. This search feature simplifies identifying and analyzing points of interest in a region (like specific brand stores in a particular area).
Also, using various colors, shapes, and annotations, you can create a detailed representation of an area. For example, you could use different markers and annotations to distinguish sports stadiums, bars, and restaurants on a map, presenting geographic data more comprehensively.
How do companies use spatial analytics?
The main use of spatial analytics is in site selection. For instance, a food manufacturer might evaluate multiple potential sites in the southeastern US based on specific criteria such as price, location, proximity to residential areas, and workforce availability. This enables the manufacturer to concentrate its efforts on a few sites with the best chances of success.
By employing spatial analytics, the manufacturer saves time and reduces the risk of costly mistakes. Previously, evaluating several properties could take several hours. Now, with the efficiency of spatial analytics, this process is streamlined, allowing for quicker and more accurate decision-making. This efficiency not only saves time but also instills confidence in decision-making, relieving the stress of potentially costly conclusions.
Retail and Consumer Goods
Retailers use spatial analytics to pinpoint optimal store locations, conduct competitive analyses, manage inventory based on regional demands, and build targeted marketing campaigns.
For instance, a retailer might use spatial analytics to identify the best location for a new store based on factors like foot traffic, demographics, and proximity to competitors. By harnessing spatial data, businesses can gain deeper insight into customer demographics, resulting in more customized customer experiences.
Real Estate
The phrase “location, location, location” is highly relevant to the real estate industry. Investors, developers, and real estate agencies employ spatial analytics to identify property opportunities, conduct market research, and assess property value based on factors like resident patterns, area socio-demographics, and consumer behavior.
When making decisions about investing in commercial real estate, spatial data must be considered. This can include demographics of the area, local shopping behavior, foot traffic, public transportation routes, and more.
Transportation and Logistics
Understanding spatial dynamics is crucial in transportation for optimizing routes, managing traffic, and maintaining fleets. For instance, a logistics company could use spatial analytics to choose the most effective routes for their delivery trucks, considering factors such as traffic patterns and road conditions. Through analyzing and utilizing spatial data, logistics firms can cut costs significantly and enhance delivery times.
Spatial analytics can provide valuable insights across different industries, empowering decision-makers to improve operational efficiency and strategic planning. Whether you’re a retailer looking to expand or a logistics company aiming to optimize routes, spatial analytics provides the necessary insights to make well-informed decisions. This empowerment can help decision-makers feel more in control and proficient in their respective roles.
Understanding the four types of spatial analytics
Understanding Spatial Analytics involves recognizing the different types of analysis you can perform. Each type serves a unique purpose and can provide different insights into your data. According to Unacast, the four primary types are: descriptive, diagnostic, predictive, and prescriptive.
1. Spatial descriptive analytics
Spatial descriptive analytics is the most basic form of spatial analytics. It focuses on telling you what is happening or what has happened. This analysis uses maps, heat maps, and other visual tools to help businesses explore elements like visitation activity across different geographic locations.
This type of analysis helps organizations understand the current state of affairs straightforwardly.
2. Spatial diagnostic analytics
Spatial diagnostic analytics aims to explore why something has happened. It involves a deeper look into spatial data, investigating patterns, anomalies, and correlations.
For example, suppose a retailer observes that one store location outperforms others. In that case, diagnostic analytics can help determine which local factors (like population density, nearby attractions, etc.) may influence this outcome.
3. Spatial predictive analytics
Spatial predictive analytics forecast future events or trends based on existing data. It can involve complex models that consider several spatial factors. For example, a supermarket can use spatial analytics to anticipate future demand volumes in different neighborhoods, enabling better capacity planning.
4. Spatial prescriptive analytics
Prescriptive analytics is the most advanced form of spatial analytics. It offers actionable recommendations for handling a potential future scenario. These recommendations are based on simulations and models that consider many variables.
How does spatial analytics work?
Spatial analysis is a vital component of Geographic Information Systems (GIS). It allows users to model problems and find comprehensive solutions using geographical attributes.
1. Data collection
Data collection is essential for the spatial analysis process. It involves gathering data from diverse sources, including mobile devices.
Data acquired by these devices is used to create maps illustrating the geographical distribution of entities under consideration, such as a map displaying temperatures for different regions. The data can include satellite images or photographs (Depending on the analysis requirement).
2. Data analysis
In the second step, the collected data is analyzed using AI and ML solutions to generate results. Also, Machine Learning models are trained to identify objects or structures within an area when analyzing millions of images.
For example, “objects” can include parking lots, playgrounds, traffic zones, residential areas, among others. In this step, visualization tools are used to highlight different objects with various colors, shapes, or annotations, simplifying the process of object identification within large datasets.
3. Data presentation
Presenting post-analysis data can be time-consuming, as it involves emphasizing critical elements that reveal the findings. This task is made more accessible with data visualization tools to present relevant data and communicate with stakeholders.
Final thoughts
Spatial Analytics is a powerful tool that goes beyond traditional analytics because it adds a geographical dimension to data. This helps businesses make better decisions, improve efficiency, and gain a competitive advantage.
Its applications are diverse and impactful. With advancing technology, the future of Spatial Analytics looks promising, with more sophisticated tools and far-reaching applications. Understanding spatial data and using the right analytical tools can provide valuable insights, helping businesses make smarter decisions.
Whether you’re just starting to explore Spatial Analytics or already familiar with its potential, it can help you achieve a lot.
