In the world of data science, Spatial data science is at the heart of contributing to solving planet-threatening problems such as deciding the location of solar park installation, building urban resilience, predicting crop yield, population density analysis for immunization or disease mapping, and more. Spatial data science combines two terms, Spatial, which is Geospatial, and data science; combining both can solve many business problems.
Spatial data science is a fast-expanding field in various corporate and public sectors. A global poll of 150 thought leaders across verticals found that 68% of corporations will likely expand their investment in spatial data science in the next two years. Industries, where the application of spatial data science will expand have a wide range of verticals, including real estate, cities and governments, management consulting, utilities, retails, telecommunication, green energy, and many more. It is the right time to skill yourself in spatial data science cases, applications, and various elements. This article gives overview of spatial data science’s elements, use cases, types of spatial data analysis, and it’s application.
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What is Spatial Data Science? (with Example)
Spatial analysis, commonly referred to as geospatial data science is a geographical solution that combines data science with geographic solutions like geographic information systems (GIS).In simple terms, Geospatial data science is one of the emerging elements of data science that consists of satellite imagery, location analytics, remote sensing, and raster and vector data in a GIS and protection system. We apply Geospatial analytics techniques to gather insights from spatial datasets. For example, Suppose the government wants to know which crops may grow in a specific location to help farmers create extra income and more efficient harvesting procedures. In that case, they can find these answers by leveraging spatial data science methodologies and geospatial technology. A few geospatial technology applications that utilize the spatial data science to help billions of customers are Google maps, Weather maps, real estate listings, transmutability, and surveillance.
What Makes Spatial Data Science Important for Data scientists?
Although spatial data science is a world that needs to be fully explored, data scientists understand that the rewards will be worth it. There is much to learn and return to general data science, especially as the world is ever-changing. Modern data science has already developed refined techniques that have benefited industries worldwide. This knowledge and tools enable organizations to refine the processes needed to extract and analyze data such as lead generation, consumer market behavior, and other market trends. Simply put, spatial science data can lead to highly efficient, transferable, and practical solutions such as resource management, software development, geodata dissemination, and much more.
Spatial data is known to be more intuitive, allowing you to be more innovative. With an easy-to-use spatial data platform at your side, using such an innovation could very well be the key to your ultimate success.
What is Spatial Analysis?
Spatial analysis includes any formal technique that studies entities using topological, geometric, or geographic properties. This specialized form of research allows you to understand coordinate and projection systems, geometric functions, and more. The spatial analysis will enable you to solve complex location problems and better understand what is happening in your world. Spatial analysis lends a new perspective to your decision-making.
Spatial analysis can be used for:
- Finding patterns, clusters, and hotspots
- Location optimization, such as site selection
- The study of the interaction between functions and why they occur
- Using exploratory analysis to find relationships between variables
- Position-based feature modeling involves simulation and prediction
- Mapping and visualization applications
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How Does Spatial Data Science work?
Only one in three data scientists claim to be specialist in geographical analysis, indicating that there are still very few spatial data scientists. Generally, five key steps comprise the standard workflow for spatial data scientists, which takes them from data collection to offering business insights after the process. The five Steps are:
- Data ingestion & Management
- Data Enrichment
- Data Visualization and Solution
Elements of Spatial Data Science
Here we are exploring elements of spatial data science:
1. Data Engineering
Data generation, storage, maintenance, usage, and distribution are all managed by the computer science field known as data engineering. Big data processing is aided by using computer languages like Python, SQL, and R.
It is conceivably the part of data science that requires the most time. However, data engineering is also a crucial component of analytics because the quality of the data we use to feed it depends on us.
For analysis, data are prepared by data scientists. They add fields, fill missing values, geo-enrich, and clean values, for instance. Data engineering and the required ETL workflow usually come first in a pipeline for data science.
2. Data exploration and visualization
Data exploration and visualization are one of the most important aspects of data science. It systematically examines the raw data to have a better understanding and thus make better-informed decisions. It is an essential part of the process of understanding data which helps data scientists quickly identify patterns and relationships, which can help us extract information from big data. You can also use visualization techniques to verify and make sure it makes sense.
The data visualization process is a continuous and iterative process that continues from start to finish. Before moving towards a solution, it improves data understanding and pattern recognition and guides data scientists towards better model development by helping them understand limitations or better opportunities. It enables you to define a blueprint for how to proceed towards a solution. Finally, you can tell a story about your data and share it with your audience.
3. Spatial analysis
GIS is all about spatial analysis. The spatial analysis explains where objects are, how they relate to one another, and how they are related. It can be used for site selection, space-time analysis, or predictive modeling. Spatial analysis is a tool for analyzing the distribution of individuals or any other feature in a geographic location. By measuring, quantifying, and comprehending our surroundings, you may find solutions to location-based challenges.
It involves more than just where the points are put. However, they also count rasters, lines, polygons, and non-spatial data as characteristics. Whether you want to demonstrate how people travel or look for trends like hot areas, this is a tool for spatial analysis.
4. Machine learning and AI
Machine learning and artificial intelligence are only two more tools for geographical analysis.
Machine learning speeds up any process by evaluating massive volumes of data without requiring human participation. For instance, using data, you may produce an accurate land cover by simply training a machine learning model.
Machine learning is a means to automate deriving insights from your data, from big data analysis to clustering. Machine learning is increasingly becoming a necessary component of every workflow due to the growing quantity of data businesses gather, store, and evaluate.
5. Big Data Analytics
Big data analytics has revolutionized many industries like no other. It is widely used in industries or work such as transportation, marketing, and retail. The tech world has been using big data analytics for years. But as the world becomes increasingly digitized, it becomes more critical. Big data analysis refers to the study of large volumes of data.
Although big data falls under the category of spatial analysis, the main idea is that you are analyzing it at a large volume. Spatial data, whether it be in the form of points, lines, polygons, or rasters, big data will be a beneficial tool in data science.
6. Modeling and scripting
Automation has been used for a very long time to eliminate manual labor. It saves us time and establishes a predictable workflow, enabling us to concentrate on more crucial duties with less effort.
A typical data science process automatically integrates everything from analytics to data engineering. You may then replicate the process and create an autonomous system.
ETL, or extract, load, and transform, plays a significant role after the analytics services are operational. It need not be a daily operation; it might be a weekly, monthly, or annual business activity.
What Skills and Technologies are Used in Spatial Data Science
The most widely used programming languages in this area are Python and R. Some in the community claim that R still has a more comprehensive selection of specific geospatial libraries.
Python's main data science libraries are more centralized and organized. Geopandas, QGIS, PostGIS, CARTOframes, and Esri are additional essential tools.
What Types of Data Do Spatial Data Scientists Use
Spatial data used by data scientists can be classified into the following types:
1. Point-referenced data
A spatial index of data that varies continuously across space. For instance, information from high-resolution satellites, fixed devices, and GPS tracking. These data are frequently helpful for model inference and prediction at unsampled locations.
2. Areal data
Information of a predetermined group of locations with clearly delineated boundaries. The boundaries can be arbitrary, as in the case of administrative units (like districts, regions, or counties), or they can be defined by a regular grid, as in the case of raster data. Model inference, prediction at unsampled locations, and spatial smoothing are typical applications.
3. Point patterns
Information about incidents where the locations of the events are random. This information helps assess any clustering or inhibition between the observations in this situation.
4. Network data
Information related to a group of straight lines connecting an ordered set of points. Examples include information from the internet, mobile phone networks, and mobility networks. The analysis of spatial networks and route optimization are examples of common applications.
Applications of Spatial Data Science
The adaptability of spatial data science is what makes it so attractive. Any industry vertical of different sizes and forms can use it. The following are just a few of the many widespread spatial data science example in today's world:
1. Spatial Planning
Geospatial data examine patterns of urban growth and expansion. When used correctly, it can reveal new areas for further development while considering several elements necessary for successful construction.
Geospatial technology is currently used to analyze soil data to determine which crops can grow in specific locations. It also helps farmers create more efficient harvesting practices. Food production has soared, and environmental standards have improved with the help of geospatial data.
Geospatial data is used in web navigation maps to provide helpful information to the public. Web maps are regularly updated based on geospatial data and are widely used in everyday life.
Telecommunications companies use geographical data to build and improve networks and track consumer requests and maintenance schedules. 5G mobile internet connectivity is being expanded using geospatial data.
GIS can help with several transportation problems, such as identifying dangerous intersections, improving road optimization, and choosing the optimal location for a new road or rail network.
6. Planning and Community Development
Geospatial data helps us understand and respond to global issues. As GIS technology advances, the number of new uses in the planning sector is increasing. GIS tools can change thinking and behavior by integrating geographic intelligence into strategic planning.
7. Natural resource management
Forests can be adequately maintained and managed using geospatial data. It is vital for the allocation and regional distribution of water, which is an essential component of the environment.
8. Estimate flood damage
Governments use geospatial data to identify flood-prone areas and plan relief.
Top 5 Spatial Data Analytics Software
Spatial data analysis software or GIS software transforms spatial data into information that is slowly becoming the cornerstone of business strategies and organizational outlooks.
Here are the top 5 spatial data analysis software that can help you get the most out of your spatial data sets.
ArcGIS is a prevalent GIS software part of the Esri Geospatial Cloud. Innovative mapping styles make it easy to create maps on the fly. A built-in analytics tool lets you extract insights from spatial data. GIS developers can use APIs and SDKs to connect data.
QGIS enables automatic map creation and spatial data processing and boasts a highly interactive community. Community members design several QGIS plugins for community members. An unrivaled addition is a support in the form of QGIS Stack Exchange.
FalconView specializes in displaying a wide range of sensory data. This includes satellite imagery, LiDAR, and digital elevation models (DEM). This software is often used for combat flight training programs and is very popular for defense and military applications.
GeoDa is an ideal tool for those new to spatial analysis. The device is primarily used for performing geostatistical analysis. Users can efficiently perform statistical functions such as regression analysis on spatial data to create exciting business and economic use cases.
Surfer is a LiDAR analysis software. Surfer allows you to switch between 2D and 3D mapping modes. Surfer provides a simple UI/UX to help you navigate through layers smoothly.
Surfer is actively used in industries like Agriculture, Archaeology, Construction, Education, Energy, Environmental, Exploration, Geophysical, Geotechnical, Geothermal, GIS, Government, Maritime, Military, Mining, Mudlogging, Oil & Gas, Renewable Energy, UAV / UAS, Utilities, and Water Resources.
Spatial Data Science Use Cases
Spatial data science has a wide variety of use cases, and some of them are:
- The spatial data analysis method is used to understand how cities grow by tracking the growth of buildings and infrastructure.
- Experts can assign risk scores to areas for planning and recovery in environmental disasters.
- Spatial scientists can track where nature declines by looking at forest area or lake area imagery over time.
- Spatial data analysis can be combined with health for disease outbreak monitoring and control.
- Leveraging GIS data science for location intelligence, surge pricing models, marketing, and recommendation systems.
- Merging spatial data and Landsat data with a Machine Learning model to predict crop yield
Who is a Spatial Data Scientist?
When applying non-spatial methods and tools from the standard data science toolkit, spatial data scientists treat the location attributes of data as any other attribute. They are skilled at scaling and running the computational components.
What Does Spatial Data Scientist Do?
A geospatial data scientist manages and analyzes large geospatial data sets such as satellite images, google maps, elevation data, demographic data, socio-economic data, and topography to gain useful insights about events happening on our planet.
For example, a geospatial data scientist uses satellite imagery to find illegal activities in the ocean by detecting pirate ships and oil spills. In marketing, geospatial data is used to segment customers using demographic data. The goal is to use these segments to help improve promotion and retention and acquire new customers. Communications companies use geospatial data analysis to predict where to place cell phone towers. In addition, the government uses geospatial data for search and rescue operations during disasters or natural hazards.
A geospatial data scientist must know how to handle large datasets of images and locations, visualize and query them, and use them to make predictions. To develop these skills, one can learn about geospatial libraries such as GDAL for reading/writing data, GIS software such as QGIS for visualization and querying, and basic machine learning algorithms for making predictions.
How to Become a Spatial Data Scientist
Skills that require to become a spatial data scientist:
- Python: learn the python programming fundamentals with a specific focus on Geospatial applications
- Geographic Data Science: Learn data science foundations and machine learning applications with Geospatial data.
- Automating GIS Processes: learn how to perform some everyday GIS tasks in Python.
- Version Control: Git and Git Versioning is a common skill among data scientists and is required in many job opportunities.
- Cloud: Cloud is an essential skill for next-generation geospatial data scientists to handle big data.
- AI and ML: AI and ML are disrupting geospatial technology by providing world-class solutions, so spatial data scientists require this skill to be part of it.
- SQL: Learn SQL to improve analytical knowledge and automate geospatial workflows.
In almost every industry, including healthcare, defense, marketing, social networking, urban planning, and resource monitoring, spatial data science and technology is an exciting subfield of data science. Therefore, geospatial data scientists are needed in both the public and private sectors, including government agencies, non-governmental organizations, and businesses like Facebook, Google, Uber, Digital Globe, and Orbital Insight, which rely on the analysis of such big data to make better operational and strategic decisions. Spatial data science will grow in the coming years so is spatial data science jobs opportunity so skilling yourself in this field can give you new dimensions to your skill set and job opportunities.
Spatial technologies and spatial data analysis will be crucial to solving future challenges and possibly providing planet-saving solutions. This is the right time for data scientists to learn about spatial data science and its application. The Data Science market is growing so does job opportunities. Get yourself job ready with the excellent Data Science certification program offered by Knowledgehut. Please check out the Data Science using R syllabus, which is part of the data science certification course.