Important Tips to Build Remote Sensing Career

Spatial analytics is considered as a core infrastructure of modern IT world. This post will introduce spatial analytics, its applications, benefits, opportunities, challenges and future directions of spatial analytics in modern IT world.

What is spatial analytics?

Spatial analytics is a subset of big data analytics that deals with the analysis of data in geographical contexts. Spatial analytics is a field of analytics that uses spatial data to analyze and understand patterns and trends. It can be used for a variety of purposes, understanding how people move around, understanding where businesses are located, identify areas of high-value activity, assess the impact of strategic planning decisions, and monitor the effectiveness of public health interventions. Spatial analytics can also be used to improve the quality of urban planning and management, and to understand the dynamics environment and social networks. 

Spatial analytics is the process of extracting meaning from spatial data. It can be used to answer questions such as where customers shop, where crime hotspots are, and where resources are most needed. 

Geographically referenced data is called spatial data. Geographic reference or the location component of the data is represented using any number of coordinate reference systems, for example latitude and longitude.

Spatial data are usually categorized into following types: 

1. Point-referenced data 
2. Areal data 
3. Point patterns 
4. Network data 

There are a number of different techniques that can be used to analyze spatial data. Some of the most popular techniques include: 

• Geo-referencing: This technique uses coordinates to identify the location of objects in space. 
• GIS: This is a software program that is used to create maps. 
• Map algebra: This technique uses mathematical formulas to analyze spatial data. 
• Statistical analysis: This technique uses statistical methods to analyze data. 

The most important part of using spatial analytics is to interpret the results. Once the data has been analyzed, the next step is to use that information to make decisions.

Applications of spatial analytics

Business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies such as Audi, BMW, and Mercedes confirm that spatial (map) is considered as a core infrastructure of modern IT world. 

In modern age the applications of spatial analytics and data science is very important and the importance is increasing day by day almost every sectors in our daily life. There are many applications of spatial analytics that can be used in business and government. Some of these applications include: 

1. Location intelligence: This is the ability to understand where people are and what they are doing, and to use this information to improve business operations or policy decisions. 
2. Geo-targeting: This is the ability to deliver marketing and advertising messages to specific groups of people based on their location. 
3. Mapping: This is the creation of digital maps that can be used for navigation, research, and planning. Geo-tagging: This is the addition of location data to digital content, such as photos and videos. 
4. GIS: This is the use of geographic information systems to manage and analyze data. 
5. Crowdsourcing: This is the use of a large group of people to complete a task or project. 
6. Remote sensing: This is the use of sensors to collect data about our environment.

How is spatial analytics used in business?

Spatial analytics has a number of applications in business, including marketing, sales, and customer service. It can help businesses to understand customer behavior and preferences, identify opportunities, and target marketing campaigns. Spatial analytics can also help businesses to manage assets, optimize operations, and forecast future needs. 

There are 5 key capabilities of spatial analytics technologies and these are as below- 

1. Geographic Search 
2. Clustering 
3. Formatting and Annotating 
4. Layers 
5. Target Highlighting 

There are a number of ways spatial analytics is used in business. One way is to use it to understand how customers interact with a business. For example, a business might use spatial analytics to understand how customers move around the business, what products they buy, and how long it takes them to purchase a product. 

Another way spatial analytics is used in business is to understand how a business can be better organized. For example, a business might use spatial analytics to see where the most traffic is coming from and where the most business is being done. This information can help the business make decisions about where to put stores and how to market to their customers. 

Spatial analytics can also be used to understand how a business can be improved. For example, a business might use spatial analytics to see where employees are working the longest hours and where they can be replaced with less hours of work. This information can help the business figure out where to reduce costs or where to add employees. 

Spatial analytics is a powerful tool that can be used in business to improve decision-making, operations, and marketing. By understanding where customers are located and how they move around, businesses can better target their marketing efforts and sales efforts. Additionally, by understanding how employees interact with customers, businesses can optimize their customer service strategies. 

Spatial analytics can provide businesses with insights and data that can help them make better decisions about where to invest in business operations, where to allocate resources, and where to focus marketing efforts. By understanding how customers interact with businesses, companies can better target their marketing campaigns and allocate resources more effectively. Additionally, spatial analytics can help businesses understand customer preferences and patterns, helping them to better understand customer needs and preferences. In the long run, this can lead to increase customer loyalty and increase profits.

What are some of the benefits of spatial analytics?

Spatial analytics is a field of study that uses digital tools to analyze data in three-dimensional space. Benefits of spatial analytics include: 

• Better understanding of customer behavior 
• Improved decision making 
• More efficient planning and execution 

One of the key benefits of spatial analytics is that it can help businesses to better understand their customers and their locations. By understanding where customers are and what they are doing, businesses can better target their marketing and advertising efforts. Additionally, spatial analytics can help businesses understand the flow of traffic and help them plan better for future expansions.

What are some of the challenges of spatial analytics?

There are a number of challenges in spatial analytics, which can affect the accuracy, effectiveness, and reproducibility of results. One challenge is the lack of standardization in data definitions and the way data is collected. Another challenge is the difficulty of extracting meaningful insights from large data sets. Finally, spatial analysis can be time-consuming and complex, which can make it difficult to use analytics to make informed decisions.

 

What are some of the future directions for spatial analytics?

The spatial analytics industry is still in its infancy and there is much that can be done to improve its growth. Here are some future directions for spatial analytics: 

1. Further development of big data analytics techniques to improve spatial understanding. 
2. Expansion of geospatial services to new markets and industries. 
3. Better integration of spatial analytics with other data-driven tools and platforms. 
4. Continued focus on research and development of new spatial algorithms and tools. 
5. Examination of ways to make spatial analytics more accessible and user-friendly. 
6. Greater emphasis on education and training of spatial analysts. 
7. Development of industry standards for spatial analytics.

 

Conclusion

Spatial analytics is a branch of analytics that uses spatial techniques for data analysis and modeling. A spatial analyst uses spatial techniques to understand the structure of data, to identify patterns and relationships, and to make predictions about future behavior. 

Spatial analytics can be used to analyze data from a variety of sources, including customer surveys, market research, and geospatial mapping. It can also be used to improve the accuracy of predictions made by other analytics techniques, such as machine learning. 

Data science is the fastest growing profession, with the opportunities growing exponentially, year-over-year. Corporate leaders across the all sectors and Government leaders have come to recognize how impactful spatial analysis can be in the business and societal decision-making process, providing an additional level of insight. The widespread use of spatial analytics will likely continue to grow in the coming years. This is because almost every business in the world has a spatial component and spatial analytics can be used to improve the accuracy of predictions made by other analytics techniques, as well as to understanding the structure of data and to making predictions about future behavior.

 

Real Case Study-1: Facebook/Meta’s Recruitment for GIS & Maps Quality Analyst

Recently, I found a recruitment notice of Facebook’s parent company Meta. Meta is hiring GIS & Maps Quality Analyst for the Spatial Computing team within XR Maps Team. There are many positions in spatial analytics industries. The job responsibilities, education qualification and experiences are different for different positions and roles. Here you can find responsibilities, qualification and experiences for the GIS & Map Quality Analyst position in Meta. 

Check the responsibilities, minimum qualification and preferred qualification for the position GIS & Map Quality Analyst for the Spatial Computing team within XR Maps Team below: 

Responsibilities 

• Query the world OpenStreetMap (OSM) database to check quality of data 
• Inspect, process and edit geospatial data for data analytics and prototyping 
• Author scripts to analyze OSM data quality and improve OSM editing tools such as iD and Tasking Manager 
• Communicate detailed analysis to support decision making for strategy and product development Support creation of and review technical specifications 
• Develop stable processes for human validation for both OSM data production and map QA 
• Provide coaching, mentoring, and support to new map editors 
• Conduct consistency training to support changing needs of the team for editing and uploading data 
• Train other Quality Analysts and manage priorities for the QA team 
• Measure and track metrics for the editing teams 

Minimum Qualification 

• Degree in GIS or analytical field or planning (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research, Management Science) 
• Scripting experience with languages such as Python or Bash 
• Experience with GIS software such as ArcGIS, Quantum GIS, PostgreSQL/PostGIS, iD or JOSM
• Experience with remote sensing and analyzing aerial imagery

Preferred Qualification 

• 5+ years industry experience in GIS field 
• Experience with OpenStreetMap 
• Project management experience 
• Experience with map editing or map editing teams 

After seeing the requirement for the GIS & Map Quality Analyst of Meta, I hope you have got a glimpse about what you need to have to enter spatial analytics industries. I will add several case studies here in this post regularly so that you can grow your skills, working quality and experiences to build a successful career in spatial analytics industries.

FURTHER READING:

Important Tips to Build Remote Sensing Career

Author

Mithun Kumar

Research Engineer | Solutions Architect | Remote Sensing & GIS Developer

Scientific Officer & Head, Aeronautics & Space Applications division, SERC Bangladesh

and

Founder & President, Space and Environment Research Center (SERC), Bangladesh.

Founder & Director, Project Origin