Published: Jul 25, 2022

what is geospatial data, and how it is used for mobility insights


The advent of technological progress has led us to innovations we never expected to have before. In the past, we used whatever data we had to make sense of the world, yet it's not enough to solve most of its problems. But today, we can now harness the power of locational data to enrich lives and create more opportunities by taking advantage of geospatial data.

What is Geospatial?

Geospatial refers to any data tied, indicated, or related to a geographic location. It is data that has any geographic element tied to it. "Geo" is a Greek term that means Earth, and "spatial" means anything related to occupying space.

In its simplest term, geospatial is geography and mapping; it is "place-based", meaning it contains much information regarding a specific geographical area.

What is Geospatial Data?

Geospatial data, also known as geodata or spatial data, describe explicit or implicit data with locational information or geographic components. It represents data that shows features and objects present on Earth's surface. The location can be artificial or natural - as long as it has anything to do with a specific geographical space on the planet, it's geospatial.

Regarding cloud-based solutions, geospatial data is often thought of in terms of geocoding and mapping. The goal of Geocoding is to transform a description of a location into a reference that may be used to find it on a map.

But geospatial data isn't just about pretty pictures. It can provide critical insights into how we move about our world, including AI-powered predictions of traffic congestion, mapping the spread of disease, and understanding the impact of climate change.

When combined with other data sets, such as demographics, weather patterns, or economic indicators, geospatial data can help us answer complex questions and make better decisions.

DataSpark is a data analytics company specialising in making data insights more accessible to everyone. A large part of their work is understanding and extracting value from geospatial data.

The company uses geospatial data to power our heatmap visualisations. These heatmaps show the density of points within an area and are used to identify high and low-activity areas. The intensity of the colour on the map corresponds to the density of points, with darker colours indicating a higher density.

The data helps you decide better where to build bike lanes, bus rapid transit, and other infrastructure. With our company integrating with DataSpark, we can help you understand how people move around your city.

Where Can You Get It?

Geospatial data can help understand spatial relationships and create detailed maps. Such data can be sourced from Global Positioning System (GPS) data, satellite imagery, Internet of Things (IoT), geotagging, telematics devices, social media posts, and many more.

How Can You Retrieve It?

Retrieving data can be accomplished by using a geodata service. Geodata services can conduct remote geodatabase replication, execute queries within the geodatabase, and create copies using data extraction. It can provide geodatabase access through a local area network (LAN) or via the internet.

How is It Grouped?

Such a dataset, if left unorganised, can lead to confusion and discord among those who use them. So to keep it organised, experts group geospatial data into different themes. Themes are high-level data categorisation that is divisible into data subsets. These themes include transport networks, population distribution, ortho imagery, and many more.

Geospatial Analytics

Geospatial analytics can use the data quality from all kinds of technology (GPS, satellite imagery, location sensors, social media, etc.) to create data visualisation models to help better understand different phenomena concerned. It can also help with finding the relationships between people and places.

Experts can use this manipulation to create visual models such as maps, graphs, and statistics to showcase historical changes and current shifts. These visualisations can make the prediction of trends easier and more accurate.

To better understand consumer behaviour, businesses, especially those specialising in AI or Machine Learning, are increasingly relying on geospatial analytics. They may obtain newer and more accurate information with the aid of this technology.

These companies that offer geospatial analytics can process big data almost instantly. This allows users to interact with billions of mapped points while referencing real-time geospatial visualisations. Users with access to this level of geospatial analytics can examine big data across time and space and see how things have changed within days or years.

Types of Geospatial Data

The two primary forms of geospatial data, vector data and raster data, have different functions and advantages.

Vector Data

Vector data is ideal for creating general representations of objects or geographic features in the real world. It involves using points, lines, and polygons to represent spatial features.

There are three types of vector data:

  • Point Data - x, y coordinates used to represent discrete data points (Ex. schools, bridges, landmarks)

  • Line Data - used to describe a specific location with linear features. (Ex. rivers, railroads, streets, trails)

  • Polygon Data - used to define areas enclosed by lines that form polygons (Ex. plot boundaries, lakes, states, countries)

Vector data is ideal if a user wants a compact data structure and an accurate representation of the shapes. It's also a perfect option if the user wants an efficient encoding topology.

Raster Data

Raster data provides a visual representation of the world in evenly spaced grids of pixels. Instead of points, lines, and polygons, this type of geospatial data represents data using digital images. Each cell (often referred to as pixels) corresponds to a particular colour value.

When transferred to a GIS, these pixels can represent other data values such as temperature and elevation data. It's also called grid data because it represents grid cells distinguished according to rows and columns.

There are two types of raster data.

  • Discrete Rasters - each pixel represents a distinct theme or class. (Ex. population density, land-use, soil data)

  • Continuous Rasters - each pixel has gradually changing data (Ex. elevation data, weather data, and temporal information)

Raster data is suitable for complex analysis and efficient overlays.

How Does It Work?

Geospatial data is used to understand how phenomena such as human activity, population distribution, and transportation affect a specific geographic location. A geospatial information system will use software to collect and store this data. It can be analysed to create layered maps to understand events and trends better.

Leveraging geospatial data can drastically help predict geographic patterns. When appropriately used, it can be a game-changer in predicting geographic patterns, determining the suitability of a land arena, and solving community issues. It can also help different aspects of human behaviour, such as businesses, society, and their relationship with the environment.

There's no limit on how experts can use geospatial data to help humanity and its problems.

What is Geospatial Technology?

Geospatial technology has become an indispensable component of everyday life. We unknowingly use it every day, from how far you've run this morning to how you can navigate the city. Harnessing location information has improved people's daily lives and helped government and non-government organisations make important life-changing decisions.

Geospatial technology is an emerging field used to collect, analyse, and store geographic information. It is a collection of modern tools and systems like Artificial Intelligence (AI), Machine Learning (ML), and the digital twin that helps us understand and solve problems related to a specific location.

The technology is used to translate the information from geospatial data and reshape it into intelligence to help users make decisions and solve everyday problems as we've never been before.

Users can use this technology to map out the surface of the Earth, understand societies, aid in humanitarian relief, and many more. It can drastically help in any endeavour that could benefit from better visualisation and analysis of geographical data.

The most powerful value proposition of geospatial technologies is its ability to give users accurate and timely information that can help them adapt and respond quickly to emerging situations more effectively. With this technology, industries such as urban planning, engineering, data analytics, and utility companies can gain better insights into how they can formulate and execute plans related to their fields.

Applications

Geospatial data can reveal spatial patterns embedded in massive volumes of data that can't be accessed or mapped otherwise. Organisations can exploit geospatial technology to create an intelligent and interactive visualisation of maps and models. It can advocate social investigation and aid in policy-based research to help societies attain better living conditions.

Health professionals can use it to identify health trends and track infectious diseases. The military uses it for intelligence, cartography, and to protect a country from possible terrorist activities. Emergency responders use it to prepare disaster maps and display potential impact zones if a disaster occurs, such as a tsunami, forest fire, or an earthquake event.

Geospatial technology is predominant in today's society, and its potential gives users a massive advantage in their respective fields.

Examples of Geospatial Technologies

Three of the most common examples of geospatial technologies are as follows:

Geographic Information Systems (GIS) - A collection of geospatial software tools that combines maps with a database of other valuable and descriptive information. GIS has provided industries with valuable insights and drastically changed how the modern world works. Examples include disaster management, environmental impact analysis, and urban planning.

Remote Sensing - The process of acquiring information about an object or area from a distance. This technology has drastically changed how we understand and study the planet. In terms of AI and ML, remote sensing is used to create predictive models that can be applied in various fields such as agriculture, urban development, and disaster management.

Global Positioning Systems (GPS) - the most popular use of geospatial data. GPS is a network of U.S. satellites that can provide precise coordinate locations for civilians and the military. Examples include automated driving systems, automotive navigation systems, and time and frequency transfer.

What is Geospatial Intelligence?

Every event and human behaviour happens in a location - this information is geospatial data. The analysis of this geospatial data and the production of results are valuable information. However, analysing a geographic area for data is not intelligence, but deriving information from this data is.

Geointelligence, often coined as GEOINT, is a discipline that aims to exploit and analyse imagery and geodata to assess and visually depict the physical features of the world and the geographically-referenced activities that happen in it. It combines disciplines such as imagery analysis, intelligence, and mapping.

Because of its conception, people typically associate GEOINT with a military context, but we can't deny its impact on both the civilian and private sectors. Economic sectors such as telecommunications, transportation, artificial intelligence, health, and real estate actively use geo-intelligence to bolster their operations and improve people's quality of everyday life.

The basic principle of GEOINT is to exploit geospatial information and create related products for interested individuals such as planners, emergency responders, and policymakers. GEOINT's initial goal was to serve the U.S. government and provide defence, government analytics, and geospatial intelligence.

But it has now changed how enterprises approach logistics, marketing, and many other industry sectors. GEOINT uses GIS to process geospatial data and create valuable insights useful in creating products and services related to GEOINT.

What Can GEOINT Do?

There are different ways organisations can utilise geo-intelligence to their advantage. Here are some of them:

  • Processing Power - thanks to the ever-increasing magnitude of computing power, GEOINT is forecasted only to grow in the coming years. This technological progression will result in open data and large-scale imagery crowdsourcing. (Ex. Available data for crowdsourced mapping of relief efforts to aid victims of calamities and natural disasters).

  • Data Analytics - GEOINT will benefit from machine learning and predictive analytics. However, it can also contribute to it, resulting in the progression of fields such as health care, banking, and telecommunications.

  • Augmented and Virtual Reality - GEOINT can play a massive role in accurately modelling any real-world location into virtual and augmented environments. 

  • Disaster Response - analysed high-resolution images retrieved from space can help enterprises develop products, strategies, and prevention methods that can aid people with disaster management and response.

Who is a Geospatial Analyst?

Geospatial analysis has been growing quite well, and its impact in various sectors and industries is pretty evident. With this discipline comes geospatial analysts: experts who analyse geospatial data and design geodatabases for functions such as GIS applications.

The work of a geospatial analyst will significantly vary depending on which sector they are assigned to. Industries that usually require the help of geospatial analysts include the military, urban planning and government, retail, and real estate.

Geospatial analysts can efficiently perform data analysis, present data using both electronic and 3D methods, read satellite images and incorporate the data they present into geodatabases. Most of the time, they also act as a bridge that updates non-GIS users about how the project is going.

They must also work alongside geospatial engineers in charge of geospatial data collection to produce a desirable outcome.

Conclusion

Geospatial data represents spatial features and any geographic component present on Earth's surface. Organisations can gain valuable and practical insights by collecting, compiling, and analysing these geographic data. They can create life-altering decisions faster and potentially improve their operations and humanity's quality of life. 

Once collected and compiled, analysts can use geospatial data for geospatial technologies such as GIS, depending on what sector will use the data. 


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