What is Geospatial Data & Why is it the Future?

Geospatial data has morphed into one of the most in-demand forms of data. Before the different commercial industries started tapping their potential, Geospatial Data was mainly used by the military, maritime, aeronautical organizations, and intelligence agencies.

For you to relate with the field I am about to unpack for you, let me take you to Delhi, India where cows are traffic guides. A cow, which is said to be India’s most holy animal, guides motorists on the smartest routes to use in an otherwise congested city.

How is that possible?

For a long time in India, cows that move in herds were a big problem for Delhi’s transportation system causing traffic snarl-ups in a busy city. But as the saying goes, in every problem lies an opportunity, and one organization seized it.

The cows carry GPS transmitter collars around their necks manufactured from recycled phones turning the “roadblocks” (cows) into traffic guides.

The takeaway from this experience is that a transportation problem was turned into a solution.

This now brings us to the wider Geospatial Data topic. What is it? How is it applied? Who uses it? What role is it playing in the current age and how has it evolved since it was first used?

Geospatial Data

Geospatial data is data about objects, phenomena, or events located on the surface of the earth. The location of the object or event may be static in the short term, for instance, the location of a road or a volcano eruption — or it might be dynamic, for instance, a moving car, a person riding a bicycle, or prevalence of an infectious disease.

One major feature of Geospatial Data is that it combines location information (coordinates of an object or event), attribute information (characteristics of the object or event), and on multiple occasions, temporal information (the time or longevity that the location and attributes exist).

A screengrab of geospatial data imagery. What is geospatial data and why is it important in everyday life? [Image Source: Point of Beginning]

Who uses Geospatial Data?

Geospatial data has morphed into one of the most in-demand forms of data. Before the different commercial industries started tapping their potential, Geospatial Data was mainly used by the military, maritime, aeronautical organizations, and intelligence agencies.

With time, different industries have learned that geospatial data provides unprecedented levels of insight and information. Consequently, Geospatial data is now incorporated into how many businesses around the world choose to conduct their business.

US soldier breaking down GIS data for his colleagues. [Image Source: Dan Lafontaine, PEO C3T, U.S. Army]

It is not just the suits who use it. Private citizens use Geospatial Data too. For instance, when they are searching for a specific road, geographical location or building.

Geospatial Data Formats

Geospatial data can be broken down into two broad categories.

  • Spatially referenced data
  • Attribute tables

Spatially Referenced Data

This type of data is represented by raster and vector forms including imagery.

Vector Data

Vector data structures represent specific objects, events, or phenomena on the earth’s surface and assign attributes to them.

Vectors comprise discrete geometrical locations (x, y values) known as vertices that define the shape of the spatial object.

Vector data can be categorized into three.

  • Point data
  • Line data
  • Polygon data
Point Data

Every point is denoted by a single x, y coordinate. There can be multiple points in a vector point file. Examples of point data include the location of specific survey plots or the location of a tree.

Point Data {Image Source: Data Carpentry}
Line Data

Lines consist of multiple (at least two) lines that are connected. A physical feature like a stream may be represented by a line while an artificial feature like a road can also be represented by a line. The line comprises a number of segments that form the backbone of the line data. Every curve in the stream denotes a vertex that defines the x, y location.

Line Data {Image Source: Data Carpentry}
Polygon Data

Polygon data consists of 3 or more vertices that are connected and closed. Bodies or masses such as oceans, countries, states, lakes, and plot boundaries are often represented by polygons.

Polygon Data {Image Source: Data Carpentry}

Raster Data

Raster data is griddled data or pixelated data synonymous with a specific geographical region. The value of the pixel can be defined as categorical (example: land use) or continuous (example: elevation).

Raster Data. Image Source: {National Ecological Observatory Network}

There are two types of raster data;

  • Continuous
  • Discrete
Continuous Data

Continuous data also referred to as surface data or non-discrete data represents scenarios when each location on the surface is a measure of the concentration level or its connection from a defined point in space or from the source that is emitting.

Continuous Elevation Map: Neon Harvard Forest Field Site. Image Source: {Data Capentry}
Discrete Data

Discrete data represents objects in both raster data and feature storage systems. Discrete data is also referred to as categorical or discontinuous data.

A discrete object’s start and end or boundary can be defined with ease, certainty, and precision.

One good example of a discrete object is a lake together with its surroundings. Other notable examples of discrete data include roads, buildings, and land parcels.

Attribute Data

Attribute data is information captured in tabular format to spatial features. Spatial data defines the where while attribute data defines the when, why what and where. Attribute data details characteristics about spatial data.

This type of data is represented in different field types:

  • Table or character
  • Floating
  • Date
  • BLOB

Character Data

Character data is for text-based values such as the name of a street or its descriptive values such as landmarks found on that street. Character attribute data is stored in alphanumeric symbols. Conversely, the character field could also encompass the rank. For instance, a street could be ranked “2” to represent the second most congested street or the second cleanest street in a town.

Date Data

Date attributes can spell out the date format including the day- month- year and sequence information. The same data can be used in calculations for instance comparing dates in a calculation for battery sales since a particular day.

Numeric Data

Numeric data is captured in numbers as opposed to natural language. Numeric data is sometimes referred to as quantitative data.

BLOB Data

BLOB is an abbreviation for Binary Large Object. This attribute type is used for storing information such as images, multimedia, or chunks of code in a field. This field stores object linking and embedding (OLE) which are objects created in other applications such as multimedia and images linked from the BLOB field.

Attribute data for a road in GIS. {Image Source: GIS Lounge}

GIS Use Cases

Public Transport

Strategic analysis of Geographic Information System (GIS) can help transform public transport in congested cities leading to improved quality of lifestyle for residents.

The perfect instance where GIS has been deployed to help a public transit problem is when Videocon Group, an Indian multinational conglomerate launched an initiative to help beat traffic jams on Delhi streets.

As mentioned in the first paragraph of this piece, Videocon in collaboration with local cow owners, attached special GPS transmitter collars made from recycled phones around the necks of multiple cows, turning the “roadblocks” (cows) into traffic guides.

The app helped users see the location of the cows so that they could avoid them and adopting smarter routes.

Retail Mapping

GIS (Geographic Information System) is playing an increasing role in various fields. In the retail sector, however, the impact of GIS is immense. Retail giants, distributors, and chain stores looking to set up new stores are now turning to GIS to choose physical locations for their stores.

Location intelligence is now being used as a tool to combine sales data and geographical spread which in turn provides data evidence on the right spaces to set up the new establishments.

Conversely, retailers have gone a step further and innovated with the aim of ascertaining the regions that each of their target markets would prefer, which enables the retailers to draw up winning strategies. These strategies include identifying peak hours and using that information to adequately deploy staff or manage parking spaces.

GIS is also used to analyze the stores that are profitable and the ones that are not.

Supply & Distribution Route Optimization by Leveraging Spatial Analytics

Supply and distribution companies have joined the bandwagon leveraging satellite imagery to optimize routes, cut down on logistics costs and eliminate deadhead (instances where drivers truck long distances without actually carrying goods).

Effectiveness can also be achieved if a warehouse is strategically located for logistics planning

For e-Commerce giants, the stakes are even higher. Customers expect to have their orders delivered as fast as possible and in peak condition.

According to data shared by Statista, Amazon’s shipping costs amounted to 61.1 billion U.S. dollars in 2020, up from 37.9 billion U.S. dollars in the previous year.

Amazon vans shipping goods in the United States. {Image Source: Wharton University of Pennsylvania}

To achieve this, companies like Amazon need to deploy modern route optimization techniques that rely on spatial data to work out the best warehouse-delivery destinations.

For Amazon and other E-Commerce companies, keenness to such detail might be the difference between building on their profits or reduction in revenues.

Agriculture – Predicting Crop Yields

The agriculture sector is also leveraging GIS. A bumper harvest or an underwhelming one can hurt a company’s bottom line or set it in the right direction.

Technological innovations and geospatial technology have helped create a robust eco-friendly agricultural sector capable of yielding nutritious food to the people. It is important to note that while nature cannot be controlled, GIS helps users better understand and manage aspects of nature such as weather. If leveraged properly, GIS can help in effective crop yield estimates, erosion identification and remediation, and soil amendment. The end result is more accurate and reliable crop estimates hence eliminating uncertainty.

Crop Prediction Technique Workflow: Image Source: {Sustainability and Artificial Intelligence Lab, Stanford University}

In August 2020, the UK government through the UK space agency announced £3.4 million worth of new funding for 10 leading-edge projects that back UK academics using space to tackle development challenges across the world.

Part of the war chest was earmarked to use satellite, airborne, and ground-based sensing technology to predict crop yields in Uganda. This the agency said would help plan harvesting, transport, and processing of future yields. This the agency said would be a more reliable predicting metric and would also enhance long-term food security as well as help farmers benchmark and improve productivity.

“The monitoring and forecasting system will be based on the integration of physical crop models, meteorological data, and earth observation satellite imagery.  Information will be provided to farmers, supply chain actors, governments, and international organisations.”

UK SPACE AGENCY

The Past, The Present, and Most Importantly… The Future

Geospatial data has evolved. There was a time when it was only used by security agencies. Fast forward to now, e-commerce giants are turning to geospatial data to craft the best strategies to deliver orders to customers.

How times change.

Now compare that with the cows in India who are now facilitating transport in Delhi instead of being a hindrance or a farmer in Texas using GIS to predict their harvest. Geospatial data is simply playing an important role across various fields in the current age.

It is not just now.

Geospatial data is the past, the present, and the future.