You have used geospatial data if you have booked a road trip to determine the best route, searched online for the nearest pizzeria to find the closest restaurant based on your location, or even synced your position with social media updates.
Geospatial data, or spatial data, refer to information depicting features or objects on Earth’s surface. Geospatial pertains to everything related to a particular location on the globe, whether artificial or natural. This post will go into more detail on the applications of geospatial technology and how they can benefit our society.
What Geospatial Data Is
Geospatial data are details on things, occasions, or other features on or near the Earth’s surface. Geospatial data typically combines time information with location information that usually coordinates on the Earth and attribute information, the traits of the object, event, or phenomenon in question, and with temporal data, the time or life span at which the location and attributes exist.
The location may be static, such as the location of a particular piece of equipment, the site of an earthquake, or a group of poor children. It can also be dynamic, such as a moving vehicle or pedestrian and the spread of an infectious illness.
Large sets of spatial data are generally gathered from numerous sources in various forms to create geospatial data. These sources include census data, satellite imagery, meteorological data, mobile phone data, drawn images, and social media data. It is most helpful when geospatial data can be found, shared, analysed, and used with conventional business data.
Types of Geospatial Data
Information that is recorded along with a geographic indicator is known as geospatial data — it has two main types: vector data and raster data.
Vector data
Vector data is represented by points, lines, and polygons such as houses, cities, highways, mountains, and water bodies. A visual depiction employing vector data might show dwellings as points, roads as lines, and entire towns as polygons.
Raster data
Raster data is made up of pixelated or gridded cells that are categorised by row and column. Images produced with raster data are significantly more complex and include photos and satellite images.
Examples of Geospatial Data
- Vectors and Attributes – Descriptive data about a location, such as points, lines, and polygons
- Point Cloud – A group of co-located charted points that can be retextured to create 3D models
- Raster and Satellite Imagery – Detailed pictures of our planet captured from above
- Census Data – Released census information connected to specific geographic locations is used to assess the local trend
- Data from Mobile Devices – Calls are forwarded by satellite based on GPS coordinates
- Images That Have Been Drawn – CAD renderings of buildings or other structures that provide both architectural and geographic data
- Social Media Data – Posts on social media that data scientists can examine to spot new trends
Geospatial Data Collection
Many organisations seek to employ a service to receive curated geospatial data since the sheer volume of geospatial data that corporations typically require is unreasonably big. It doesn’t matter where you get your geospatial data from; data quality must always be preserved.
Models with insufficient data should be of more use. The adage “Bad data in, bad insights out” is painfully accurate. It should go without saying that businesses stand to gain a lot by using a system that curates and verifies data, ensuring that any “garbage” data is correctly accounted for.
Geospatial Data Management
With so much data available nowadays, managing it becomes critically important. As a result of their data overload, many organisations are turning to their internal data scientists for assistance in handling it. Up to 90% of data scientists’ work is reportedly spent on data-curation tasks like data organisation, cleaning and reformatting. Only 10% of their workday is still left for those data scientists to analyse data trends and use their findings to inform corporate policy.
Data management and collection can be done more effectively when a corporation outsources them to a product. The solution can handle many file types and is cloud-based and scalable. Data scientists can focus more on leveraging analytical insights and translating them into organisational advancement and business effect using a curated library of optimised information.
Geospatial Big Data Challenges
Large-scale geospatial data sets face a variety of difficulties. Because of this, many organisations need help to utilise geospatial data to its full potential.
The sheer amount of geographical data comes first. One estimate places the daily production of weather-related data at 100 TB. The majority of organisations face significant storage and access issues just from this. Additionally, geospatial data is dispersed across multiple files, making it challenging to locate the ones that hold the information required to address your particular issue.
Geospatial data is also archived in various forms and calibrated according to multiple standards. Data cleaning and reformatting are essential prerequisites for comparing, integrating, or mapping data.
The use of complex mathematics and specialised skills are also required when working with raw geospatial data to complete essential tasks like the spatial alignment of data layers. Analysts can only extract value from the data or advance their organisation’s business objectives if they are skilled and experienced.
Benefits of Using Geospatial Data
The following is a brief list of the advantages of geospatial data:
Cautions in advance
Geospatial data can alert organisations to impending developments that could impact their business through data anomalies.
Greater comprehension
Organisations can use geographic data to show why and how some analytics solutions are successful while others are not.
Increased effectiveness
Organisations can increase the general effectiveness of their processes by utilising the numerical accuracy offered by geographical data.
Final Thoughts
The advantage of spatial data over non-spatial data is its tremendous capabilities for automating processes and analysis due to its multi-dimensional nature. A specialised combination of skills and technology is needed for geospatial data to be managed effectively throughout its lifecycle.
