What were the main mapping techniques used in the era of big data?

In the era of big data, several mapping techniques were used to handle and analyze large amounts of data. Some of the main mapping techniques used during this time include:

1. Geographic Information Systems (GIS): GIS is a computer-based tool used to capture, store, manipulate, analyze, and present spatial or geographic data. It allows for the integration of various data sources, such as satellite imagery, aerial photographs, and survey data, to create detailed and interactive maps.

2. Remote Sensing: Remote sensing involves the collection of data about the Earth's surface from a distance, typically using satellites or aircraft. This technique allows for the acquisition of large-scale and high-resolution imagery, which can be used to create accurate and up-to-date maps.

3. Global Positioning System (GPS): GPS technology uses a network of satellites to determine the precise location of objects or individuals on the Earth's surface. It has become an essential tool for mapping, as it provides accurate coordinates that can be used to create detailed and georeferenced maps.

4. Data Visualization: With the advent of big data, the need for effective data visualization techniques has increased. Various software and tools are used to visually represent complex data sets on maps, allowing for easier interpretation and analysis.

5. Spatial Analysis: Spatial analysis involves examining patterns, relationships, and trends within geographic data. It helps in understanding the spatial distribution of data and identifying spatial patterns or clusters. Techniques such as spatial interpolation, hotspot analysis, and network analysis are used to analyze big data and derive meaningful insights.

6. Crowd-sourced Mapping: With the rise of social media and mobile technologies, crowd-sourced mapping has gained popularity. It involves the collection of data from the public, who contribute information about specific locations or events. This technique allows for the rapid collection of data on a large scale, which can be used to update and enhance existing maps.

Overall, these mapping techniques have revolutionized the way big data is handled and analyzed, enabling the creation of more accurate, detailed, and dynamic maps.