The Power of Spatial Analytics in Business Intelligence

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The Power of Spatial Analytics in Business Intelligence

The future of BI driven with spatial analytics promises to be transformative. We present a wide variety of today’s use cases.

Since the pandemic, a newfound appreciation for spatial data has emerged. With digital transformation, enterprises collected more data. Research by the Environmental Systems Research Institute indicates that about 80% of this data contains a spatial component, for example, customer location details paired with information on transactions and purchasing habits. Spatial data can be further augmented with added layers of demographic details, including age, income, race, and education.

The integration of spatial components with transactional data allows enterprises to uncover patterns and correlations that were previously obscured. Conventional data representation methods that were mostly two-dimensional are swiftly giving way to the integration of spatial data and advanced visualization technologies. This shift facilitates more accurate insights, especially when paired with the power and complexities of multidimensional data. Leveraging this, a new branch of businesses intelligence has emerged, known as spatial analytics.

Spatial analytics involves understanding data in relation to its geographical context. Humans inherently think visually and spatially, which is why spatial analytics brings an immediate boost to comprehension. Maps excel in representing spatial phenomena or relationships and facilitate the understanding of statistical measures for a given area or region. Using maps with layers of location, business, and demographic data, enterprises visualize, analyze, and comprehend how various elements interconnect within a specified geographic area. This approach is crucial for making informed decisions, especially when “where” matters.

Spatial Data Interpretation Techniques  

Several data analytics techniques that lend themselves to adding value to spatial data have emerged. They enhance data interpretation and contribute significantly to the depth of insights derived from geographic information. These techniques amplify the accuracy of analysis and provide a more intuitive and interactive approach to understanding complex spatial relationships. Techniques include:

Geographic search enables on-the-fly location lookup without explicit geographic coordinates. This feature automatically populates and updates dashboards and digital maps with data relevant to a particular location or area.

Clustering visualizes geographic density patterns, allowing users to identify groupings of high or low values in data. Highlighted concentrations or anomalies assist nuanced planning and resource allocation and are useful for strategic decisions.

Formatting and annotating utilize colors, lines, and shapes to transform complex data sets into visually intuitive narratives. This improves data interpretation and is particularly effective for dissecting customer behaviors or market trends and for communicating insights.

Layers offer a multidimensional approach, going beyond data points on maps to enrich the narrative. Visual mapping with data sets overlaid on background maps provides a comprehensive appreciation of spatial relationships, transforming analytics into an immersive experience.

Target highlighting is another technique that allows selective analysis of data points on any layer for closer examination and focused insights. For example, a retail store manager analyzing sales data can use target highlighting to focus on customers who frequently buy certain products, are high-spenders, or live within a certain distance from the store. Highlighting these data points on a map can assist in identifying trends or patterns that might not be obvious when looking at the entire data set.

Temporal analysis examines spatial data over time to identify trends, changes, and time-based patterns. It offers a dynamic perspective, crucial for understanding how spatial relationships evolve, which is extremely important for insights into time-dependent phenomena.

For example, temporal analysis can be used in agriculture to analyze trends and correlations between historical crop yield data alongside weather data collected over several seasons. Farmers can use this to predict future crop yields based on forecasted weather conditions.

Another use of temporal analysis is by health officials to track the spread of a contagious disease. By analyzing data over time regarding disease cases, including the location and date of infection, officials can discern where the disease is most prevalent and how it is spreading.

Sentiment analysis in a spatial context probes into sentiments or opinions associated with specific geographic locations. Adding a qualitative layer to spatial data, this analysis provides insights into public perception and sentiment variations across different regions.

Network analysis and graph theory is applied to spatial data to examine relationships and connections between spatial entities. It allows us to build a web of connections between locations, like roads in a city. By analyzing these connections, we can understand how delays on one route can impact another or how to design efficient delivery routes. This empowers businesses and organizations to make data-driven decisions about everything from transportation planning to emergency response.

For example, consider a pizza delivery company using network analysis. They view city streets as a web, with pizzerias and customers as points. Analyzing this network helps them understand how traffic affects delivery routes, ensuring timely pizza arrivals.


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