Learn what geospatial analysis is and how it works. Explore spatial analysis tools, software, methods and real-world applications across industries.
Geospatial analysis is a data-driven technique used to interpret geographic patterns, relationships and trends. Geospatial data includes information about the world around us: elevation, water and land cover, roadways, political boundaries, the buildings in place there, the names of geographic features and much more. With geospatial analysis, users work to understand how all of this relates to broader questions that may be impacted by geography.
That may all sound complex, but geospatial analysis is used all the time by both consumers and businesses. In the enterprise, geospatial analysis finds a home in efforts undertaken around urban planning, logistics, environmental monitoring, marketing and public health. You and I use geospatial analysis to check long-term weather trends, analyze housing prices across a city and figure out the best way to get from point A to point B with the help of an online map.
In this article, we’ll delve into the core principles of geospatial analysis and its components and explore how it can be put to use in a range of applications.
Geospatial data represents a vast and complex collection of information about the literal world around us. Anything that is related to a location on or near the surface of the earth comprises geospatial data. This data can relate to the type of foliage on the ground, the weather in the sky above, what’s buried deep beneath the surface, social or demographic information about the people living in the area or even photography that’s been captured of any of the above.
Geospatial analysis combines this geospatial data with other datasets to provide spatial, geographic context to it. In this way, data can develop additional layers of meaning that would never be available through traditional analysis. Data about the spread of a contagious disease can be plotted on a map to visualize its movement through a community. A century’s worth of hurricanes can be overlaid on a map of the Atlantic Ocean to draw conclusions about how these storms are changing as the earth warms. And at a finer-grained level, a retail chain can map where its most profitable stores are located — as well as where its best customers live in relation to those stores.
Without the incorporation of that additional layer of data in the form of geographic coordinates, maps and/or spatial dimensions, the results of these types of analysis would inherently be far less meaningful.
There are dozens of types of geospatial data in common use, each of which supports a different type of analysis. Some of the most frequently relied-upon types of geospatial data include:
Typically seen in the form of satellite imagery or other representative graphics where each pixel (or a collection of pixels) represents a piece of information. In a simplistic photo, green may represent forests and blue may represent water. Other types of raster images may use the color of a pixel to represent the high temperature of the day for that area, as seen in a weather map.
Vector geospatial data uses lines and polygons to describe features on the earth. Simple examples would be roadways or flight paths overlaid on a map. The boundaries of residential lots and the outlines of buildings seen on a GPS display are added examples of vector data.
Coordinates embedded into files (such as photos, indicating where they were taken) are commonly used for geoanalytical purposes, tying an image to a particular place.
Any device that collects its GPS location is by definition creating geospatial data. Additional types of related sensor data include localized environmental data collected by weather stations or traffic monitoring equipment.
Remote sensing data is collected by devices not bound to the earth — namely aircraft and spacecraft. This use of non-terrestrial collection methods allows for the creation of more detailed maps and a literal 30,000-foot view of earthbound problems which would be too difficult to assess from the ground.
All types of boundaries, from property lines to national borders, are essential forms of geospatial data. This type of data is especially crucial for retail analytics wherein an organization is trying to better understand traffic patterns and other information about where customers live and how they move in relation to the business.
Geospatial analysis has critical, strategic importance relevant to a wide range of decisions. Used properly, it can improve resource allocation, enhance situational awareness, support predictive modeling and make location-based intelligence a reality — across a swath of industries.
Geospatial analysis helps companies understand how geography impacts their business, guiding marketing investments and helping pinpoint the ideal location for new stores. It’s used to optimize the routes that delivery vehicles take — and to guide municipalities on where to build new roads and where to locate government services like new schools. In times of crisis, geospatial analysis plays a central role in tracking storms, pinpointing the source of infections and understanding the location and status of key infrastructure, such as power lines and water mains.
The central, shared idea behind all of these examples is that the addition of geospatial data to the mix of data adds important context to each of the challenges being addressed. While many of these problems may be solvable without the use of geospatial analysis, its inclusion makes for answers that are higher in quality, more reliable and more easily visualized.
The particulars of any given geospatial analysis task will vary based on the specific problem that is under review, but the general workflow of any geospatial analysis looks like this:
What problem is being solved and why does geospatial analysis matter to that solution?
Understanding what toppings are most popular on your restaurant’s hamburgers is not a geospatial question, but understanding where the top consumers of those burgers live in relation to the restaurant is.
Where is the data used for this analysis coming from, and how reliable is it?
Data you collect yourself (known customers and their addresses) is likely to be more reliable than a third-party database of suspected hamburger enthusiasts purchased for marketing purposes. The latter will likely require some preliminary cleaning to remove duplicates and inaccuracies. This data will then have to be converted into a format usable by geospatial analytics systems, such as longitude-latitude coordinates that can be plotted on a high-resolution map.
How do we use algorithms to understand what’s happening?
Data scientists use a range of models and algorithms to look for meaning in geospatial data, typically presenting that data visually in the form of overlays on a map. These algorithms look for correlation among data points, clusters of similar types of data and patterns in the way data points are distributed. In our hamburger devotee example, this analysis may turn up various hot spots of the biggest burger fans, which the business could then target with coupons or other marketing tactics. Data scientists may take this analysis further by overlaying additional information such as family size or estimated income level to further refine their understanding of the customers in the geographical region being analyzed. AI or machine learning tools may be used to predict how this population will change over time and how that may impact sales.
What is the best way to present this analysis to our audience?
By presenting this information visually through the use of maps, dashboards or interactive tools, these insights are designed to be both accessible and actionable.
Geospatial analysis is comprised of a bundle of technologies. Working together, they include:
These components acquire geospatial data from any of the sources outlined above: GPS sensors, satellite imagery, weather stations, historical databases and so on. Data is processed to ensure it is in the proper format (measurements in meters vs. feet, for example) and is free of errors and duplicates.
Unlike standard database systems, spatial databases, also known as geospatial databases, are specifically designed to store geospatial data, able to ingest both raster and vector data and process it accordingly. These systems include functions like spatial indexing and geometric calculators that are tuned for analyzing and visualizing geospatial data.
Geospatial analysis relies on algorithms such as 3D modeling, spatial correlation and so-called map algebra to manipulate and analyze geospatial information.
The vast majority of geospatial analytics tools output data in visual form, commonly overlaid on a map. Visualization tools do the heavy lifting of converting numeric information into more easily understood visuals.
By connecting to and integrating with GIS and BI platforms, geospatial analytics tools can combine the power of geographic data with more traditional business intelligence systems. This allows organizations to integrate data from multiple sources to better understand how geospatial trends may impact the business.
Geospatial analysis is widely used across a number of very different industries. Here’s a look at some of the most common use cases.
Geospatial analysis is crucial across virtually all urban planning use cases, ranging from road development to utility network planning to understanding the demographics of a municipality.
Geospatial analysis is used to understand the way land is being used (such as for logging or farming), to map pollution in urban areas and even to detect illegal activities like poaching.
Retail businesses use geospatial analysis to find the optimal place for new storefronts, analyze traffic patterns, determine the location of potential customers and find the proximity to competition. Once stores are built, geospatial analysis can be used to understand customer movements around the store and within it.
EMS services use geospatial analysis for everything from mapping physical hazards to planning evacuation routes in the event of an emergency. When disasters strike, geospatial analysis is key to tracking issues like hurricanes, tornadoes and wildfires, which are constantly in flux.
Telecom companies rely on geospatial analysis to decide where to place new cellular towers and where to run new cable. These calculations are based on a site’s proximity to users, the distance to other telecommunications equipment and even the location’s visibility to satellites.
Geospatial analysis provides organizations with a wealth of benefits. These include:
At its simplest, geospatial analysis improves our understanding of the world around us. Understanding that a tornado has touched down is meaningless without knowing precisely where it has hit.
Visualizing information in a map view allows managers to more quickly and intuitively understand a large amount of data, so they can make faster, better-informed decisions.
Armed with an understanding of how geography impacts operations, enterprises can make better decisions about where to deploy resources, increase staffing or adjust prices.
Many geospatial analyses are designed to track geographic conditions in real-time. (Again, think hurricane or wildfire trackers.) Decision-makers rely on these systems to direct emergency services and order evacuations.
The multidimensional nature of a map gives organizations inherently better predictive powers over large, complex datasets, including the ability to more intuitively forecast changes, spot risks and unearth business opportunities. The use of AI tools alongside geospatial analytics is enhancing this capability even further.
Why write a book when a map will tell the story better? If a picture is worth 1000 words, a map may be worth 10 times that amount. Humans intuitively “get” maps and can quickly understand how data overlaid upon them works, whether that is weather information, rent prices or demographics data.
Geospatial analysis isn’t a turnkey system and can require overcoming a few hurdles and limitations. With the right tools, training and governance, however, these challenges can be handily overcome:
All geospatial data sources are not made equal. Data may be out of date, missing key information for a region or time period or too broad to be of use. (The average daily temperature for the state of California will not help you locate the ideal spot for a new beach resort.)
Geospatial data involves particular formats that are not common in non-spatial systems, creating potential incompatibility problems. Some of these systems may struggle to ingest geospatial information, requiring advanced knowhow and custom programming to bridge the gap.
Geospatial analytics is a niche field with a small set of expert practitioners. Finding qualified professionals can be difficult — and costly.
Geospatial data sets can be huge and many are updated in real-time. Ingesting, storing and processing this data is often computationally intensive and, as a result, can be quite costly and complex.
Geospatial data can reveal personal information about the people residing within the area, which creates privacy issues. Even anonymized data can lead to misuse in the form of potentially illegal bias, discrimination and surveillance, often impacting residents en masse.
Where is geospatial analysis headed? As organizations embrace the global economy, the discipline is becoming more important than ever for enterprises that want to gain a stronger understanding of the world around them and their place in it.
AI is at the forefront of geospatial trends, enhancing predictive capabilities and making real-time geospatial intelligence faster, ultimately allowing for more responsive decision-making. The geospatial industry is also being driven by advances in IoT and edge computing, where sophisticated sensors are making it possible to process location data instantly at the source.
While geospatial tools are advancing rapidly in capabilities, they are simultaneously becoming more accessible to the masses. Today, non-technical users are increasingly able to access and analyze geospatial data without having to involve experts, by working with more intuitive and user-friendly platforms.
For any organization that relies on activities in the physical world, geospatial analysis is a critical part of today’s data strategy toolkit. Geospatial analysis tools empower organizations to uncover location-based insights, optimize operations and make smarter decisions — all of which use the real world for essential context.
GIS tools like ArcGIS Pro and Maptitude help organizations generate multi-dimensional, interactive, map-based visualizations from enterprise data. These tools can be used to design vehicle routes, explore geographic sales trends and otherwise visualize data in map form.
Geospatial analysis methodologies include a range of statistical techniques tuned for the intricacies of analyzing 2D and 3D space. These methods include such techniques as buffer analysis (finding zones of interest centered around various geographic points), network analysis (finding the optimal route on a road or waterway) and spatial autocorrelation (the degree of similarity between a geographic feature and neighboring ones).
Geospatial data varies widely in accuracy depending on its source, its age, how it was collected and more. For example, historical maps of a region dating back many decades may be off by several feet. Conversely, today’s USGS National Map Accuracy Standards (NMAS) require on large-scale maps that 90% of tested points must be accurate to within 1/30 inch.
Regardless of its source, all geospatial data must be checked and cleaned before it is relied upon for decision-making.
