The goal of this blog is to bridge the gap between the nerds of the geospatial industry and those who are often left wondering what the heck we’re talking about when we rave over the wide world of geospatial technology.

 

I love the business, technical, and operational aspects of the geospatial industry, they’re exciting! But the numerous rabbit holes and whacky jargon often leaves those outside the industry unsure what’s going on, or where to even start. Today, I will try to bridge this gap. For not only do I believe more people would fall in love with our exciting world of WHERE given a little primer, but one of the biggest hurdles to adoption outside of defense, government, and enterprise users is the mysticism swirling about what geospatial technology is in the first place, let alone its value proposition. 

 

In the simplest terms, geospatial technology describes the systems behind producing maps and deriving insights from them. 

 

In this blog, I will first briefly discuss why we care about where things are in the first place, providing a few examples of how geospatial tech is used. I will then dive into the various verticals responsible for making the magic of geospatial technology possible.

 

Why Do We Care About Where?

 

At the most basic level, maps are important navigational tools. They help folks get from point A to point B, and perhaps more importantly, safely back to point A.

But what if a railroad is built across the path from point A to point B, or a new housing development is erected there? You might want to go find a new route, or better yet, use your map to help city planners architect more efficient uses of the space to optimize the path from A to B with a pedestrian bridge. As businesses and homes spring up, you’ll want to know where they are, along with when they’re open, and perhaps who owns them. Businesses will want to know where to best position themselves for optimal foot traffic and reliable supply chains. Residents will want to know about school districts, commute times, parks, and grocery stores.

As the space between point A and point B becomes increasingly complex, geospatial technology can be used to track changes, optimize planning, and discover relationships between the different components of the physical environment to help people make better decisions. 

 

It’s hard to discuss why we care about where without citing Tobler’s First Law of Geography:

“[E]verything is related to everything else, but near things are more related than distant things.”

 

Now, there is controversy around this “law” but the underlying idea is hard to argue with: Things that are close to each other have some relationship, and often that relationship is significant for our decision making. 

 

Whether we are looking for the nearest grocery store, monitoring crop yields, or managing natural resources, geospatial technology helps us understand where things are and the relationships that exist between them based on their relative proximity to each other.

 

It’s one thing to use a map, we’ve likely all done that! But it’s another entirely to build one and use it to perform complex analysis. This is where most folks both inside and outside the industry can be overwhelmed with jargon! 

 

The Geospatial Ecosystem

 

To begin peeling back the layers of geospatial jargon, it helps to have an overarching context of the industry. The geospatial industry is traditionally broken up into the first four functional categories below. Today I’ll add special emphasis to the 5th. 

 

  1. Data Acquisition
  2. Data Processing
  3. Mapping Platforms
  4. Data Analytics
  5. Data Operationalization

 

These functions form an ecosystem that generates a positive feedback loop for both providers and end users of mapping technology. Now, for the sake of keeping this article somewhat consumable, I’m barely going to scratch the surface here, focusing on a few elements of each function, why they’re important, and how they contribute to downstream, and ultimately upstream, functions. 

Data Acquisition

To analyze the relationship of things based on their location, you first need to determine where they are! This requires going out into the physical world, or slapping a sensor on something before it enters the world, and gathering data.

These days data is collected using a variety of different sensors and collection platforms depending on the map being produced and the problem end users are trying to solve. 

 

Common data acquisition platforms include:

 

  • IOT and Mobile Devices: These devices often track the movement of people, animals, and assets on an individualized basis. They’re great for understanding traffic patterns and generating profiles on the objects being tracked.
  • Vehicles: You’ve likely seen the Google, Bing, TomTom, or Waymo vehicles driving around with big masts on top, or sensors all over. These folks are gathering data on streets, buildings, businesses, traffic patterns, and other built world assets from a street level perspective.
  • Airplanes, Drones, and Balloons: These platforms collect what’s referred to as “aerial” data. They’re great for collecting high definition data over small and medium sized areas of interest (AOIs) including buildings, neighborhoods, cities, or entire regions. General rule of thumb is the closer you can get, the higher the resolution you can yield, but the more limited you are in the area you can cover. 
  • Satellites: Collecting data from space is excellent for studying weather patterns and large areas. The resolution is limited by both technical hurdles from being so far away, as well as regulatory barriers. But it’s hard to beat satellite data as a scalable data set for regularly collecting data over large areas. 

 

In addition to these platforms, there are systems for underground and underwater mapping too, but I’ll leave them for a future article. 

 

The later three platform categories comprise the lionshare of what is known as the remote sensing ecosystem. They utilize sensors to gather data from a distance, including:

 

  • Cameras: Good old fashioned pictures are great for understanding what’s where, especially with modern object detection, which I’ll address in the next section. You’ll sometimes hear this data referred to as “RGB”, or red, green, blue, imagery data. These days cameras are able to capture extremely high resolution images, great for object detection. 
  • Multispectral/Hyperspectral: Sometimes red, green, and blue spectral bands aren’t sufficient to study the specific thing you’re trying to map. So multi and hyperspectral data uses special cameras to detect a wide range of spectral bands, each with unique properties that lend themselves to different use cases like detecting vegetation health or moisture levels.
  • LiDAR: Light Detection and Ranging is a powerful sensor type which uses lasers to measure the distance between the sensor and physical objects. They produce “point clouds” that represent these distances and form the basis for many 3D models. 
  • SAR: synthetic aperture radar is an up and coming sensor type gaining popularity with satellite data consumers for its ability to penetrate clouds. SAR data is great for determining change and the composition of objects because the radar waves react differently depending on the properties of the materials they encounter.

 

Which platform and sensor to use depends on the problem to be solved. There is no one size fits all solution, and oftentimes multiple platforms and sensors must be utilized to paint a full picture the the geospatial landscape you are trying to understand. 

Data Processing

Once data is acquired, it must be processed to orient it on a map and extract useful information. Gone are the days when it took hours for analysts to painstakingly scour geospatial imagery to extract information. Thanks to advancements in technology, particularly machine learning and cloud computing, this processing has evolved significantly.

 

Geospatial data processing involves taking the raw data collected from various sources and transforming it into a format that can be effectively used to create maps, perform analysis, and derive insights. 

 

Example of common processing techniques include:

 

  • Georeferencing: Georeferencing involves aligning imagery with precise geographic coordinates. This ensures data from different sources can be integrated into a single, coherent map. 
  • Object Detection: This technique is used for recognizing and delineating objects within geospatial imagery such as buildings, roads, vegetation, and even specific objects like cars, people, or business hours.
  • Image Classification: As we previously discussed, geospatial data includes various types of imagery, such as satellite or aerial photos. Image classification algorithms can automatically categorize these images into predefined classes, making it easier to analyze and interpret large datasets.
  • Change Detection: Detecting changes over time is crucial for monitoring dynamic environments. Machine learning models can compare different time-stamped geospatial images and pinpoint areas where significant changes have occurred. 
  • Semantic Segmentation: Semantic segmentation goes beyond object detection by assigning a specific class or label to each pixel in an image. This fine-grained analysis is essential for applications like crop monitoring, where different types of vegetation need to be identified and quantified.

 

These advanced techniques not only save time but also improve the accuracy and depth of information that can be extracted from geospatial datasets. Once the data is processed, it becomes the foundation for various applications, from monitoring supply chains and urban planning to asset management and environmental monitoring.

GIS Platforms

GIS Platforms are built to enable people to easily work with geospatial data, serving as the central hub where geospatial data comes to life. They typically come with a wide range of built in analytical tools and often allow developers to build custom tools on top of them to support specific use cases. 

 

Here are some key aspects of GIS platforms:

 

  • Data Integration: GIS platforms excel at integrating data from diverse sources. Whether it’s satellite imagery, GPS coordinates, or demographic statistics, GIS systems can bring it all together, ensuring information is both geographically accurate and coherent.
  • Mapping and Visualization: Creating maps is at the core of GIS platforms. Users can design maps tailored to their specific needs, adding layers of data, custom symbols, and thematic representations. 
  • Spatial Analysis: GIS platforms provide a plethora of spatial analysis tools. Users can perform tasks such as proximity analysis, spatial interpolation, network analysis, and more. These tools enable users and organizations to make informed decisions based on geographic patterns and relationships.
  • Customization: Many GIS platforms offer the flexibility to build custom applications and workflows. This empowers developers to create specialized tools for unique geospatial challenges. For example, a city planner might develop a custom GIS application to optimize public transportation routes, or a utility might develop a custom application to analyze the cost of maintaining their network in specific regions.
  • Data Sharing and Collaboration: GIS platforms often support collaborative work environments, allowing multiple users to access and edit geospatial data simultaneously. This feature is critical for teams and organizations that rely on producing real-time information.
  • Spatial Database Management: GIS platforms often come equipped with robust spatial database management systems. These databases store geospatial data efficiently, making it easy to query, retrieve, and update information.

 

GIS platforms play a pivotal role in making geospatial data available to those who wish to analyze and act on it, including urban planners and environmental conservationists to emergency responders and business analysts.

Data Analytics

After data has been acquired, processed, and pulled into a platform, it must be analyzed to derive insights. This phase is where the value of geospatial technology begins to shine, as it allows us to derive meaningful insights and make informed decisions based on location-based data.

 

Common analysis methods and objectives include:

 

  • Spatial Analysis: At the heart of geospatial analytics is spatial analysis, which explores the relationships between objects based on their location. This method involves tasks such as proximity analysis, hotspot detection, and spatial interpolation. For example, spatial analysis can help identify areas prone to natural disasters, optimize retail store locations, or assess the impact of different land use scenarios.
  • Geostatistics: Geostatistics is a specialized branch of statistics that focuses on spatial data. It’s instrumental in tasks like kriging, which estimates values at unmeasured locations based on surrounding data points. Geostatistics is used in fields like groundwater modeling, mineral exploration, population distribution, and climate prediction.
  • Network Analysis: Network analysis is essential for understanding the connectivity and accessibility of components in a network. It’s widely applied in utility resource planning and maintenance, transportation planning, emergency response, and supply chain optimization. For instance, it can help determine the shortest route for emergency vehicles or the most efficient distribution network for a logistics company.
  • Time-Series Analysis: Geospatial data often includes a temporal dimension (if it doesn’t its usefulness is significantly reduced), allowing analysts to track changes over time. Time-series analysis helps monitor trends, assess seasonal variations, and predict future developments.
  • Density Mapping: Density mapping involves visualizing the concentration or distribution of specific features in a given area. It’s used in demographic studies to map population density, by businesses to evaluate retail locations, and in environmental science to monitor wildlife habitats.
  • Spatial Modeling: Spatial modeling combines geospatial data with statistical models to make predictions or generate scenarios. For instance, it can be used to simulate the spread of wildfires, forecast land use changes, or predict the potential impact of urban development on local ecosystems.
  • Multicriteria Decision Analysis: This approach involves considering multiple criteria or factors when making decisions related to geography. For example, it can help select the best location for a new park by considering factors like accessibility, proximity to residential areas, and environmental impact.

 

These are just a few examples of the many analysis methods and objectives geospatial technology supports. The versatility of geospatial analytics makes it applicable across a wide range of industries and disciplines. Whether it’s for disaster response, environmental monitoring, urban planning, or business optimization, geospatial analytics empowers decision-makers with location-based insights to drive better outcomes.

 

Data Operationalization

While deriving insights from data analysis is undoubtedly a vital part of the geospatial ecosystem, it’s essential to recognize that it’s not the end goal. The ultimate purpose of geospatial technology extends beyond understanding what and where things are; it’s about taking action and leveraging this information to improve outcomes for individuals, communities, and organizations.

 

Examples of operationalizing geospatial data include:

 

  • Infrastructure Development: Geospatial data informs infrastructure development projects, ensuring that roads, bridges, and utilities are strategically placed and efficiently maintained. This not only enhances connectivity and accessibility but also promotes economic growth and sustainability.
  • Optimizing Supply Chains: Businesses use geospatial data to optimize their supply chains. By analyzing location-based information on suppliers, distribution centers, and customer demand, companies can streamline logistics, reduce delivery times, and minimize costs. This not only benefits the business but also enhances the customer experience.
  • Improving Healthcare: In the healthcare sector, geospatial technology plays a crucial role in improving service delivery. By mapping healthcare facilities, patient populations, and disease outbreaks, healthcare providers can identify areas with high healthcare needs. This information informs decisions about resource allocation, the placement of medical facilities, and emergency response planning.
  • Enhancing Public Services: Government agencies use geospatial data to enhance public services. Urban planners rely on location-based insights to optimize city layouts, transportation networks, and disaster response plans. Waste management services can use geospatial data to optimize collection routes, reducing costs and environmental impact.
  • Conservation and Resource Management: Environmental organizations leverage geospatial insights to protect natural ecosystems. From monitoring wildlife populations and tracking deforestation to improving recreational opportunities while minimizing their impact on local environments. By operationalizing this data, conservation efforts become more effective, leading to better resource management and ecological preservation.
  • Emergency Response: During disasters, geospatial technology is invaluable for emergency response teams. Real-time mapping and analysis of affected areas enable responders to allocate resources efficiently, plan evacuation routes, and assess damage. This can significantly reduce response times, save lives, and support recovery operations.

 

The key to operationalizing geospatial data is not just producing insights for organizational leaders, but reducing the time from insight to action by empowering those responsible for acting upon those insights in the data and context necessary to effectuate the desired change. By building technical solutions that bridge the gap between analyst, leaders, and operators, geospatial technology can dramatically improve outcomes for individuals and organizations. Even more importantly, as geospatial insights are acted upon, it’s vital to gather data on these actions and their outcomes to complete the feedback loop of the geospatial ecosystem.

 

Conclusion

Geospatial technology is a vital contributor to making informed decisions and driving positive change. It’s not just about maps; it’s about using location-based data to solve complex problems, optimize processes, and improve quality of life through informed action. 

 

It requires data to be collected from the real world, processed using advanced methodologies, made accessible through robust platforms, analyzed to gain critical insights, and operationalized to yield real change. 

 

As we continue to harness the potential of geospatial technology, I’m looking forward to the advent of even more innovative ways to operationalize geospatial data to create a better future.