What is Geospatial and Why is it Important?
Paul Young
Experience Senior Financial Planning, Analysis and Reporting SME seeking P/T or F/T job.
What is Geospatial Technology?
A collective term for tools that map and analyze the earth and most things on it, geospatial technology is making significant strides. These technologies help us to understand spaces, and monitor changes and patterns in landscapes, cities, and even societies.
What is Geospatial Data?
Geospatial data is any data that has a geographic component to it. It can describe features, objects, or even events that have a specific location. Geospatial data will combine information about the location and attributes of an object. Location information can be either dynamic or static.
A variety of methods achieve this type of data collection, like using remote sensing to collect data without being near the area. For example, Unmanned Aerial Vehicles (UAVs) gather information quickly and effectively, allowing for the collection of vast amounts of data.
Examples of Geospatial Data
Geospatial technologies collect data, but the representation of this information is different depending on the technology chosen. Here are some examples of geospatial data:
What are the Challenges with Geospatial Data?
ESG and Geospatial Data Management
When it comes to geospatial data visualization, there is no one-size-fits-all solution, as different tools have different advantages and disadvantages. Popular and widely used tools include QGIS, a powerful open-source software that offers flexibility and customization; ArcGIS , a comprehensive proprietary platform for data management, analysis, and visualization; Google Maps, a web-based service with interactive maps and satellite imagery; and Tableau, a business intelligence software with a powerful geospatial data visualization feature. Each of these tools are suitable for different levels of users and applications, from desktop to web to mobile. QGIS is great for advanced users who need more control, while ArcGIS is ideal for professionals who need a reliable tool. Google Maps is easy to use and accessible for anyone who wants to create simple visualizations. And Tableau is user-friendly and intuitive for creating visually appealing and informative visualizations.
*** This comes back to data architecture including how best to leverage structured, unstructured, and semi-structured data. Business Analytical tools can be leverage as solution, but that will come back to how the data is structured and linked as part of leveraging business analytical tools.
Top Geospatial Solutions:
At the top of our list, we have Microsoft Azure – a geospatial data pioneer that provides a comprehensive set of services for working with location intelligence data used by thousands of companies around the world. Azure has a vast portfolio of services and tools for integrating geospatial data into business operations and services. Its most notable geospatial service, however, is Azure Maps – a set of location APIs that provide developers with tools for building and integrating maps, geocoding, routing, and traffic data into their applications. Azure Maps provides a range of features that enable developers and enterprise organisations to build intelligence location-enabled and map-based experiences, creating new business opportunities using a comprehensive set of geospatial services, mapping APIs, and SDKs.
Our runner-up spot goes to Google, a widely recognised provider of geospatial data known for its extensive and accurate data that spans the globe, including satellite imagery, terrain, street maps, and 3D buildings. At the heart of Google's geospatial data capabilities is the Google Maps platform, which provides users with a wide range of mapping and location-based services. These include the ability to search for locations, get driving directions, view real-time traffic information, and explore 360-degree street-level imagery through Google Street View.
Next up we have IBM, whose geospatial offerings include IBM Watson Studio, IBM Cloud Pak for Data, and IBM Db2. These services provide users with tools for storing, processing, analyzing, and visualising geospatial data. One of the key IBM geospatial services is IBM Watson Studio, which is an AI-powered platform for building, training, and deploying machine learning models. Watson Studio offers several features for working with geospatial data, including support for popular geospatial libraries like GeoPandas, Shapely, and Fiona, as well as providing a range of tools for visualizing and analyzing geospatial data, such as interactive maps and data tables.
A leading provider of geographic information system (GIS) software, Esri allows organisations to manage, analyse, and visualise huge volumes of location-based data. Built on ArcGIS – a suite of software and services that provide a range of tools for data management, analysis, and visualization – Esri’s platform can be used to integrate a wide variety of data sources, including satellite imagery, LiDAR data, and IoT sensors. Esri's platform also provides a range of specialised solutions for specific industries and use cases. Its facilities solution, for instance, provides tools for managing and analyzing data related to infrastructure assets such as power lines and substations, while its solution for public safety provides tools for managing and analyzing data related to emergency response.
The first tech giant on our list, Amazon Web Services (AWS) provides a wide range of services that enable users to store, process, and analyse geospatial data at scale. AWS offers several services that allow users to work with geospatial data, including Amazon S3, Amazon EC2, Amazon RDS, Amazon Aurora, and Amazon DynamoDB. These services provide a robust and flexible platform for storing and managing geospatial data, as well as performing analysis and visualization tasks. One of the most important AWS services for geospatial data is Amazon S3. This service provides scalable and durable object storage that can handle large volumes of geospatial data. S3 allows users to store and retrieve data from anywhere in the world and integrates with other AWS services, such as Amazon EC2 and Amazon EMR, for processing and analysis.
Next up we have Safe Software – a leading provider of spatial data integration solutions with a powerful data integration platform to match. Safe Software's Feature Manipulation Engine (FME) allows organisations to seamlessly connect and transform geospatial data between various formats and systems, providing advanced capabilities for everything from bulk data movement (ETL) to quality assurance to data replication. Users can apply powerful filters, calculations, and spatial operations to their data – such as clipping, buffering, and merging – enabling them to tailor their geospatial data to their specific needs and create custom workflows for their projects.
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With its advanced analytics and visualisation tools, integration with other Cisco technologies, and specialised solutions for specific use cases, Cisco empowers organisations to make more informed decisions and optimize their operations. Its geospatial data solutions enable organisations to integrate, analyse, and visualise location-based data, helping them to make more informed decisions and improve their operations. Cisco's geospatial data solutions are built on their Data Intelligence Platform, a cloud-based platform that provides a range of tools and services for data management and analytics. The platform enables organisations to ingest and store large volumes of data from a variety of sources, including IoT sensors, GIS data, and social media.
Spatial AI focuses on understanding human behaviour and movement patterns through location data powered by AI and machine learning to help organisations make better decisions based on location-based decisions. Their platform enables users to ingest, process, and analyze large volumes of data from a variety of sources, including satellite imagery, GIS data, and IoT sensors. Spatial AI's platform leverages advanced machine learning algorithms to automatically identify patterns and relationships in the data, providing insights that might not be visible to the human eye. For example, their platform can automatically identify changes in land use over time, or detect anomalies in satellite imagery that might indicate potential security threats.
GaliGeo’s location intelligence platform allows organisations to make the most of the spatial element in every bit of data to enhance their analysis capabilities. The platform can integrate data from a wide range of sources, including satellite imagery, GIS data, and IoT sensors, and store it in a centralised database. Once the data is stored, users can then apply a range of advanced analytics and visualization tools to gain deeper insights into their data. For example, users can create 3D models of assets to visualize their locations and spatial relationships, or use machine learning algorithms to predict equipment failures and optimize maintenance schedules.
We kick off our list with Carto, which offers a powerful geospatial data integration platform that allows organisations to visualise, analyse, and share their geospatial data. Designed to help organizations unlock the full potential of their geospatial data, Carto's GIS and location intelligence platform provides a range of tools and features that enable users to easily work with and analyse their data. One of its key features is its powerful visualisation capabilities, allowing users to easily create dynamic maps and visualisations that display their geospatial data in a variety of formats, including heat maps, point clouds, and 3D maps. This enables organisations to quickly identify patterns and trends in their data, and make informed decisions based on the insights they uncover.
What are the benefits of geospatial technology?
Paul Young CPA is a former IBM Customer Success Manager that has deployed over 300 data and AI solutions across industries and geographies for the past 8 years. Paul is also an ESG SME expert of ESG policy and reporting as part of the operational, management, and regulatory reporting cycles.
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