A Beginner's Guide to Geospatial Data

Have you ever searched online to find the nearest mall near you? Or customize your social media posts with your current trip location? How about finding your friend's address based on their city, neighborhood, or postal code? These examples show that you might have encountered what we call geospatial data. Let’s get to know more about geospatial data and how its power can solve problems in our daily lives.

INTRO

"Geo" comes from the Greek language, meaning Earth, and ‘Spatial’ means everything related to occupying space. Geospatial data is defined as objects or features represented on the Earth's surface with coordinate information such as latitude and longitude. Sometimes, people refer to it as spatial data or geodata. It includes all types of objects and features that have a specific location as their characteristic. Geospatial data comes in many forms, typically utilizing Geographic Information Systems (GIS) to understand the spatial relationships between different geospatial data. At the same time, GIS can adapt, analyze, and manage geospatial data. Some geospatial data are listed below.

TYPES OF GEOSPATIAL DATA

• Vector & Attribute Data: This is presented in the form of points, polygons, and various other descriptive location data.
• Point Cloud Data: Point Clouds are generated from LiDAR or photogrammetry systems. It is often applied in 3D modeling.
• Raster Data & Satellite Imagery: This type of geospatial data is created from remote sensing methods using high-resolution satellite imagery to get an overall view of what the Earth looks like from space.

We will delve deeper into two models of geospatial data: raster and vector. What makes them different from each other? Check the comparison in the table below.

Discrete data is data that is continuous but separated from each other, while dynamic data is data that changes whenever the recording process happens. Unlike the discrete boundary feature data, which shows the coordinate data loaded on each point, line, or area with tabular information in the form of attribute tables.

GEOSPATIAL FILE FORMATS

Here are some common file formats you might encounter in geospatial data processing:

• Shapefile (.shp)
• Keyhole Markup Language (.kml or .kmz)
• GeoTIFF (.tiff)
• Comma-Separated Values (.csv)
• GeoJSON (.geojson)

So, how did geospatial data become as widespread as it is today? Google played one of the biggest moments in geospatial history with the launch of Google Maps in 2005. This made mapping technology available to the general public. Everyone uses geospatial data now. It’s realized in various forms, widely applied by tech giants according to their needs, such as Twitter, Google, Facebook, and Pinterest. By understanding data at specific locations and times, we enable fast information transfer to help systems and users in many industries for various purposes.

HOW GEOSPATIAL DATA WORKS

Any information that contains certain geographical elements, whether natural or man-made, can be collected and stored using mapping software. After that, we can analyze it based on existing map layers to better understand trends and events. Not only with the current conditions, but also by predicting geographical patterns to solve some societal problems caused by land suitability levels. It also helps gain insights into human behavior in business, society, and the environment.

With the help of geospatial technology, we can enjoy copies of information from geospatial data. Then, we reshape it into insights to help users make decisions based on cases that were previously unknown or unrecognized. We cannot ignore the potential of geospatial data because it can provide the greatest benefits in relevant fields.

So, what do you think about utilizing geospatial data for your work and daily life? Read more geospatial articles on this website, and don’t forget to share them.