Geospatial data is a type of information that describes objects or events with a specific location on or near the surface of the Earth. It combines location information and attribute information with temporal details.
Geospatial data is critical in transportation, urban planning, and logistics industries, as it provides context for decision-making that traditional data analysis can't.
Geospatial Data Definition
The location component is essential to geospatial data definition. It means that the data doesn't just exist in a vacuum. It naturally points to a real place (or set of places) somewhere on Earth. This makes geospatial data behave slightly differently than other types of data, but it also makes it easier to visualize and conceptualize.
Adding other attributes to geospatial data provides even more context and opens up more avenues for analysis. For example, adding a time component allows for monitoring dynamic objects and events, such as how close a delivery truck is to reaching its drop-off destination or if/when a severe storm over the ocean is likely to make landfall. Possibilities like these are also part of what makes geospatial data unique.
Examples of geospatial data include weather maps, real estate listings, contacts list, traffic and accident data, and other points of interest. This information has a geographic component that can tie it to an address or relative location.
Importance of Geospatial Data for Decision-Making
So why is geospatial data important? It adds spatial context to information. Connecting data to specific places and times in the physical world makes it easier to conceptualize. Patterns such as shopping habits, migrations, severe weather, and road traffic are much more apparent if they're mapped to a representation of what the world looks like, as opposed to just being numbers in a table.
The ability to recognize more of these patterns faster is what is giving organizations that use geospatial data a competitive advantage. Here are the Top 5 Uses of Geospatial Data:
- Mapping: Geospatial data is used to create maps that provide visual insights into the data.
- Site Selection: Geospatial data is used to identify the best locations for new businesses or facilities.
- Urban Planning: Geospatial data is used to plan and manage cities and towns.
- Network Planning: Geospatial data is used to plan and optimize transportation networks.
- Investment Research: Geospatial data is used to identify investment opportunities and trends in real estate, retail, and other industries.
Benefits of Geospatial Data Analytics
Geospatial big data analytics makes trends regarding space and time more visually apparent than they would be in a massive raw data set. This, in turn, offers many advantages over analyzing datasets without this type of context. To illustrate, here are four benefits of using geospatial data in analytics:
- Identifying spatial patterns and trends: Some relationships and connections cannot be understood without factoring in "where" (or "when") they are occurring.
- Segmentation: When location is added as an analysis component, you can begin to segment and filter based on geography, making your entire analysis more detailed.
- Accurate predictions lead to better decision-making: When you study a phenomenon over time in a particular location, you understand better why it happens and where and when it does. This helps you better predict what will happen and when and where it will happen. Then, you can plan out how you might react to (or even influence) these events.
- Real-time tracking and monitoring: Geospatial data can be updated in real-time, allowing more accurate tracking and monitoring of events and objects. For example, tracking the location and movement of a fleet of delivery trucks can help optimize routes and improve delivery times.
Geospatial data is an essential tool for decision-making in many industries. Its ability to provide spatial and temporal context to data makes it easier to identify patterns and make predictions.
CrateDB is an open-source, high-speed, multi-model database that is ideal for being used as a geospatial database.
With CrateDB's geospatial functions, it is easy to address different use cases. For instance, you can find the number of records within a particular area using the “within” function, locate the closest record with the distance function, and determine whether two areas overlap using the “within” and “intersects” functions. CrateDB supports versatile geospatial data types, including Array format and WKT format for geopoint data and GeoJSON format and WKT Formats for more complex geospatial shapes.