Let’s Talk About Spatial Data Science

 In Our Thinking

If there is one trend that has been stealing the limelight for a while now, it has to be data science.
Today, companies, big and small, are dependent on data science for a wide array of services and features to achieve goals faster and deliver value to customers in a timely manner.
Now imagine if the location and geospatial data power conventional data science – the outcomes can provide a complete picture of things, in easy-to-recognize visual patterns and images.

It is no surprise that the global GIS and Spatial Analytics market is expected to touch $88.3 billion by 2020.

What is Spatial Data Science?

Spatial data science uses data from different kinds of geospatial technology — GPS, GIS, location sensors, remote sensing devices, social media, mobile devices, satellite imagery, etc. — to build data visualizations and unearth trends based on location.
This geo-referenced data, when applied to any event, can showcase historical changes and current patterns. 3D views of data through maps, graphs, and other statistics make data predictions far easier and more accurate while enhancing the utility, functionality as well as the relevance of data science.
Since spatial data science adds timing and location to traditional data, this additional context allows for a more complete picture of events. With spatial data science, you can instantly process huge amounts of geographic data, and interact with billions of mapped points. It allows you to explore geospatial data and understand how things change – in a given location – over time.

Why use Spatial Data Science?

Spatial data science can break data out of the endless rows and columns of traditional spreadsheets and organize it in the form of maps by time and space – paving the way for easy analysis. 

Since it recognizes patterns like distance and proximity hidden in massive datasets, it becomes easy to assess how things are changing over time and when and where change is most distinct. 

There are several reasons why you should leverage spatial data science: 

  • If you want to see data in the context of a visual map to understand how events are unfolding and how to react to those events.
  • If you want to visualize location-specific data in a multi-dimensional format and understand the impact of location on weather, customer behavior, public health, and more. 
  • If you want to understand why some events and activities are successful in some locations and why they aren’t in some. 
  • If you want to deduce new perspectives and perceive new trends and intuitive insights to identify business opportunities. 
  • If you want to see how geospatial conditions change in real-time to better prepare for change and determine future action.

Where to use Spatial Data Science?  

In a modern world where every business decision is based on the outcome of analytics, the applications of spatial data science are many. From detecting natural resources to weather modeling, population forecasting to sales trends, and more. 

Here are some popular use cases: 

  1. For retail companies, spatial data science can help understand the competitive landscape and devise strategies to set up a store, meet local demand, and grow – based on accurate demographic and customer information identified through latitude and longitudinal coordinates. Retailers can send customers offers in real-time as they visit a store, track real-time product performance, improve the overall retail experience. 
  2. For manufacturing companies, spatial data science can combine current data trends with historical patterns and drive informed decisions. Companies can see where market opportunities and threats develop, visualize bottlenecks in supply chains, optimize transport routes and outpace threats across global markets. Using spatial data science, manufacturers can also detect leaks and equipment failures, and alert staff to avert a disaster. 
  3. For weather companies, rapidly responding to extreme weather conditions is critical to minimize the impact caused by blizzards, tornadoes, forest fires, floods, and hurricanes. Spatial data science can provide timely visualizations to such companies, allowing them to plan for evacuation efforts. It can also help airline companies re-route their path and offer insurance companies a better way to assess property risk.
  4. For oil and gas exploration companies, spatial data science can help detect natural resources and improve efficiencies in exploration and field operations. Companies can set up plants in resource-heavy locations and make use of visualized data for evidence-based decision-making that reduces costs, minimizes risks, and improves output.
  5. For urban development companies, spatial data science can help planners visualize large datasets across energy, transportation and housing resources. Companies can compile and cross-filter data from various sources to see how crime, public health, education and housing/real estate outcomes vary by location and plan for setting up settlements. 
  6. For agricultural and farming companies, spatial data science can help understand location-based analysis and help farmers make better farming decisions. Farmers can assess weather conditions and soil quality and identify high yield areas for increased farm output. Using spatial data, they can also understand when and where to optimize the use of water and pesticides – and reduce costs and improve farming efficiency in the long run. 
  7. For network providers, spatial data science can help visualize call records and network logs to identify and fix issues – before customers experience service disruption. Since network signal strength tends to fluctuate in some locations over time, spatial data science can help understand where anomalies occur and then resolve them.
  8. For healthcare companies, spatial data science opens up a treasure trove of critical health-related information. Using location-based intelligence, companies can examine how the environment affects public health and assess the occurrence of certain diseases in a particular geographical location, set up geofences to contain their spread, and devise public health improvement plans. 

Are you ready for this spatial transformation? 

Leverage the technology today to unearth spatial trends and associations and enhance your business decisions.

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