Spatial Data Science: 4 Comprehensive Aspects
The world of Data Science has continued to evolve over the past decade or so, especially as new technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are now in the mix. For the uninitiated, Data Science simply refers to a mixture of Algorithms, Tools, and Models that are used to extract meaningful information from large sets of data.
Table of Contents
By 2010, it was clear that companies were increasingly reliant on using Big Data for Modeling and Forecasting. The biggest challenge at the time was the rising storage needs for such businesses. Thanks to frameworks like Hadoop, that is no longer the issue. Now, the compiled data can be turned into something meaningful using Data Science.
Many consider it to be the future of Artificial Intelligence, and it has led to the rise of several other subsets, such as Spatial Data Science. Spatial Data Science can help businesses derive important information about their customers and is rapidly becoming an integral part of the modern business world.
This article provides an overall picture of Spatial Data Science and focuses on the importance of Spatial Data Science. It also lays out the importance of using Spatial Data Science for Analysis and its applications in the real world.
Table of Contents
- What is Data Science?
- What is Spatial Data?
- What is Spatial Data Science?
- What is the Importance of Spatial Data Science?
- What are the Subcategories of Spatial Data Science?
- Spatial Data Science for Analysis
- What are the Applications of Spatial Data Science?
- Spatial Analysis vs Spatial Data Science
What is Data Science?
To extract value from data, Data Science combines multiple fields such as Statistics, Scientific Methods, Artificial Intelligence (AI), and Data Analysis. Data scientists are individuals who use a variety of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources to derive actionable insights.
Data Science refers to the process of cleansing, aggregating, and manipulating data to perform advanced data analysis. The results can then be reviewed by analytic applications and data scientists to uncover patterns and enable business leaders to make informed decisions.
The Data Science lifecycle is divided into five stages, each with its own set of responsibilities:
- Capture: Data collection, data entry, signal reception, and data extraction are all steps in the data collection process. This stage entails gathering unstructured and structured data in its raw form.
- Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, and Data Architecture are all terms that can be used to describe the processes that are used to prepare data. This stage entails converting raw data into a usable form.
- Process: Data Mining, Clustering/Classification, Data Modeling, and Data Summarization are the steps involved in the process. Data scientists examine the prepared data for patterns, ranges, and biases to see if it can be used in predictive analysis.
- Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, and Qualitative Analysis are some of the methods that can be used to analyze data. This is where the lifecycle gets going. This stage entails conducting various data analyses.
- Communicate: Data Reporting, Data Visualization, Business Intelligence, and Decision-Making are all concepts that should be explained. Analysts prepare the analyses in easily readable forms such as charts, graphs, and reports in the final step.
What is Spatial Data?
Any type of data that refers to a specific geographic area or location, whether directly or indirectly, is considered spatial data. Spatial data, also known as geospatial data or geographic information, can represent a physical object numerically in a geographic coordinate system. Spatial data, on the other hand, is much more than a map’s spatial element.
Because spatial data can contain more than just location-specific information, users can save it in a variety of formats. Analyzing this data allows researchers to learn more about how each variable affects individuals, communities, and populations.
There are several types of spatial data, but the two most common are geometric data and geographic data.
Geometric data is a two-dimensional flat surface-mapped spatial data type. Geometric data in floor plans is an example. Google Maps is a navigation app that uses geospatial data to provide precise directions. In fact, it is one of the most basic applications of spatial data.
Geographic data is information that is plotted on a globe. The sphere is almost always the planet Earth. Geographic data emphasizes the relationship between latitude and longitude and a specific object or location. A global positioning system is an example of geographic data.
What is Spatial Data Science?
Spatial Data Science is defined as a substratum of Data Science that primarily looks at Spatial Data. The aim is to focus on why events occur at specific locations, instead of simply looking at where they occur. There are 3 main aspects that Spatial Data Science looks at when Analyzing and Visualizing Data. These include:
- Any Spatial Interactions
Spatial Data is Analyzed using Modeling Software to determine how events occur, why they occur in specific regions, and then create forecasts and models. Many of the world’s leading companies, such as Apple, Microsoft, Audi, BMW, Uber, IBM, and Intel now use Spatial Data to evaluate business transactions and identify opportunities.
Spatial Data Science uses location to identify and resolve complex issues. Analysts, or Data Scientists, can extricate meaningful insights using specially designed algorithms and comprehensive methods for Analyzing Data. This primarily involves using Deep Learning Techniques and a range of Machine Learning Principles to aid in creating Forecasting Models.
By distilling insights from large sets of complex data, primarily through relative positions of observations, Analysts can create a map of sorts that shows each observation and its location, relative to others.
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What is the Importance of Spatial Data Science?
Spatial Data is used for a variety of reasons. It includes information about where events take place on different locations on Earth and provides detailed descriptions for things like Road Networks, Political Boundaries, and Borders, Development and Distribution of Urban Systems, Demographics of Specific Locations, Logistics Data, and other things.
Spatial Data Science can be used to track virtually any specific quantity. In most cases, the dimensions are usually calculated in Longitude and Latitude, though Altitude is now becoming more and more common. Analysts primarily use Geographical Information Systems and software programs to create models that show Spatial Data. They can then manipulate the data for conducting a Deeper Analysis.
The reason why Spatial Data Science is so important is that most of the Analytical Techniques used are not true to scale. As a result, Data Scientists can easily apply the same algorithms used on the map of a planet to the map of the entire universe. More importantly, they can also use these techniques to determine, for instance, the use of specific words in different languages and which regions they are most commonly spoken in.
What are the Subcategories of Spatial Data Science?
Spatial Data Science is further divided into 2 subcategories:
As you can imagine, both are used to define Space, but they both have slightly different feature sets, and the operational techniques are also different.
1) Vector Data
A Vector is simply defined as a quantity with a direction and a magnitude. It is denoted with a Line Segment made in a particular direction, with the length of the line determining the magnitude. Vector Data models are generally more conceptual. Each feature, for instance, is a Discrete Unit within the Dataset. It can be denoted using a polygon, a line, or even a point.
Mathematical representation is used to denote values for each point. Most Vector Data models also come with essential Metadata that provides a comprehensive description of the feature, such as the demographics in a region, or the distribution of urban systems. This is defined as an “attribute,” and is generally laid out in an attribute table. In a majority of the cases, Spatial Data Scientists tend to combine Spatial Metrics with Non-Spatial Dimensions while conducting a thorough analysis.
2) Raster Data
In the world of computer graphics and modeling, Raster Data is a Dot Matrix structure that represents either a rectangular or square grid of pixels. Each cell, or pixel in the grid, has an assigned value. Thus, each image can then be described using a Numerical Value.
The image above gives a pictorial understanding of how data can be shown using both Raster and Vector Modeling Principles. Each number assigned to a Raster Pixel can be used to denote Geographical features, as is the case above. It can also be used for describing other elements, such as Population Density, Rainfall Levels, or virtually anything else.
Because almost every color on the spectrum can be created using a combination of Red, Green, and Blue, Raster Data can also be used to show varying intensities of different Datasets. A popular use case is Satellite Imagery. Almost all thematic maps are created using Raster Data.
Spatial Data Science for Analysis
Canada was the first country that began using Spatial Data in Analytics back in 1960. The country used the world’s first Geographical Information Systems to create a catalog of, and record, natural resources. Today, Spatial Data Scientists use Geographical Information Systems to capture and analyze data using Data Science Tools to create Predictive Weather Models, forecast Population Growth in specific locations, or simply Analyze Sales Data in specific regions.
The Analytics Tools used by Spatial Data Scientists can provide break down large Data Structures and Spreadsheets, allowing them to visualize the information through the lens of time or space. By visualizing the data differently, Data Scientists can view the data, thus allowing them to identify patterns including affiliation, proximity, or contiguity.
Essentially, this makes it easy for Spatial Data Scientists to figure out how certain elements related to society have evolved. The 3 main benefits of Spatial Data Science include:
1) Interesting and Meaningful Insights
As the data is presented visually instead of just numbers on a Spreadsheet, Data Analysts can derive meaningful insights. It allows for significantly more accurate Predictive Modeling because Data Analysts can determine how certain events take place, and the best possible way to react to those events.
2) Tailored Solutions for Audiences
Spatial Data Science can make it easy for Data Analysts to understand how some countries or locations can achieve success when compared with others. For instance, why is Finland able to provide a better quality of preliminary education than the United States? Or why is the United States a more conducive environment for business than others?
3) Comprehensive Foresight
Because Spatial Data Science allows Data Analysts to see how conditions are changing in real-time, they can come up with much better solutions for organizations or governments to prepare for what lies ahead. This is an important argument for the prevalent use of Predictive Modeling and allows entities to be better prepared for events that are likely to transpire in the future.
What are the Applications of Spatial Data Science?
Spatial Data Science is now used in a variety of different fields. Here are some common Applications of Spatial Data Science:
Spatial Data Science is already employed for use in Global Meteorological Departments. By visualizing the impact of Climatic Events like Blizzards, Floods, or Wildfires, governments can better prepare for relief and evacuation efforts. Spatial Data Science is also used in avionics to chart routes and inform pilots about weather conditions. Insurance companies use Spatial Data Science to determine the risk depending upon volatile weather routes flown by different airlines.
Situational awareness is a key requirement when planning Logistics for Military Divisions. Spatial Data Science helps ensure that optimum resources are used when moving military units or resources from one location to another. This also helps in predicting maintenance schedules for military equipment in different Geographical Zones.
3) Urban Planning
Spatial Data Modeling and Analytics are perhaps most commonly employed in Urban Planning and Development. This helps city planners and governments better understand the impact of a growing population on the existing infrastructure. Ultimately, they can plan better for contingencies and introduce new projects to allow for better Urban Planning and Development. Multiple scenarios can be modeled and compared to ensure that the people in charge are available for all contingencies.
The applications for Spatial Data Science continue to increase by the day, especially as Advanced Technologies are made available with more enhanced Geospatial capabilities.
Spatial Analysis vs Spatial Data Science
How is spatial data science different from spatial analysis, before we get into the finer points? Let’s look at what spatial data science is because there isn’t always a clear line between the two.
Remember that the goal of data science is to extract useful information from data generated by computation and scientific research. Here are some of the most commonly used terms in spatial analysis and spatial data science, as well as which category they belong to.
|Spatial Analysis||Spatial Data Science|
|Identifying trends, clusters, and hotspots||Using data wrangling and integration techniques|
|Using site selection to optimize locations||Pattern recognition and classification are examples of machine learning techniques.|
|Investigating how features interact and why they occur.||As a data-driven science, data mining is used to investigate anomalies and associations.|
|Finding relationships between variables using exploratory analysis||Using big data, which is generated by sensors and other IoT data|
|Simulation and prediction are used to model location-based features.||Data engineering is used to clean data and apply ETL workflows.|
|Using geo-visualization and mapping||Programming workflows can be automated and operationalized.|
Both spatial analysis and spatial data science begin with raw location data, which they then analyze and turn into insights. However, the main concept is that spatial data science employs new and specialized techniques as well as automation.
This article provided an overall picture of Spatial Data Science. It stressed the importance of Spatial Data Science in the field of Data Analysis and how different organizations can make use of it to boost their business. The article also laid out some real-world applications of Spatial Data Science.
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