Geospatial Analysis

Introduction to Geospatial Data:

Geospatial data refers to any data that pertains to objects or features on the Earth's surface. It can encompass a wide range of information, from the location of a new store to the potential impact of a hurricane. Understanding geospatial data is crucial in a variety of industries, including retail, weather forecasting, transportation, environmental science, and many more. Common business problems that can be solved by Geospatial data include:

• Where should a brand locate its next store?
• How does the whether impact the regional sales?
• What is the best route to take in a car?
• Which area will be hit hardest by a hurricane?
• How does ice cap melting relate to carbon emission?
• Which area will be at the highest risk of fires?

Let’s start by learning to speak the language of geospatial data. In this blog post, we'll explore the basics of geospatial data, including the difference between vector and raster data, the use of geographic reference systems, and the distinction between georeferencing and geocoding.

1. Vector vs. Raster: 

The world of geospatial data is made up of two main types of data: vector data and raster data. 

Vector data represents geometries on the Earth's surface, such as roads, buildings, and restaurants. They are mathematical objects like points, lines, polygons etc that can be infinitely scaled without losing resolution. We can use vectors to present features and properties on Earth’s surface. You will most often see vector stored in shape files (.shp). Specific attributes that define properties will generally accompany vectors. For example properties of a building (like name address, price, build date etc)

On the other hand, raster data is a grid of pixels, each of which has a value that can represent colour, height, temperature, or other measurements. Raster data is commonly used to represent satellite images, elevation maps, and other forms of data that can be represented as pixels.

These are not your usual images. They contain RGB data that our eyes can see, and multispectral or even hyperspectral information from outside the visible electromagnetic spectrum. Instead of being limited to only 3 channels/colours (RGB), we can get images with many channels.

Things that are invisible to the naked eye, absorbing only a small part of the electromagnetic spectrum, can be revealed in other electromagnetic frequencies.

The default view in Google maps contains vectors, the “Satellite View” contains raster. Each pixel in the satellite image has value/colour associate with it.

Difference Between Vector and Raster

Vector	Raster
Points, Lines, Polygons	Pixels
Geometric objects, Infinitely Scalable	Fixed Grid, Fixed Resolution
.svg , .shp	.jpg, .png, .tif

2. Coordinate Reference System:

To identify specific locations on the Earth's surface, we use a geographic coordinate system known as the Coordinate Reference System (CRS). The CRS uses a combination of longitude and latitude to represent a location on the Earth's surface. However, converting the Earth's 3-dimensional surface into a 2-dimensional coordinate system can introduce some distortions, so it's important to choose the right CRS for the desired operation.

For example, try searching for 37.971441, 23.725665 on Google Maps. Those two numbers point to an exact place - the Parthenon in Athens, Greece. The two numbers are coordinates defined by the CRS.

    

It's important to note that no coordinate reference system is perfect, and each choice involves a tradeoff that distorts shape, scale/distance, or area. As a result, it's essential to choose the right CRS for the desired operation to avoid errors in geospatial analysis. Common pitfalls include mixing coordinate systems, using the wrong CRS for calculating areas or distances, and using an incorrect CRS for a desired operation.

Common CRS pitfalls:

• Mixing coordinate systems: When combining datasets, the spatial objects MUST have the same reference system. Be sure to convert everything to the same CRS.
• Calculating areas: Use an equal-area CRS before measuring a shape's area.
• Calculating distances: Use an equidistant CRS when calculating distances between objects.

3. Georeferencing vs Geocoding:

Georeferencing is the process of assigning coordinates to vectors or rasters so that they can be projected on a model of the Earth's surface. This allows us to create different map layers, such as satellite imagery and road networks. Georeferencing makes it possible to switch seamlessly between different map views, such as satellite and road network, in applications like Google Maps. 

On the other hand, geocoding is the process of converting human-readable addresses into geographic coordinates. 

In conclusion, geospatial data is a powerful tool for understanding and exploring the world around us. From identifying the best location for a new store to predicting the impact of a hurricane, geospatial data has a wide range of applications. Understanding the basics of vector data, raster data, CRS, georeferencing, and geocoding is the key to unlocking the full potential of geospatial data.