Data Science

What is Spatial Data Science? Definition, Applications, Careers & More

Written by Karin Kelley
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Updated on April 4, 2024

Data science has been a game-changing technology for industries ranging from marketing to healthcare. Among the various categories of data science, spatial data science has been steadily gaining global importance.

In this article, we will explore spatial data science, its definition, workings, importance, and tools while also briefly discussing the career prospects of a spatial data scientist. Further, we will discuss the importance of selecting high-quality data science training to help you progress in this field.

What Does Spatial Data Science Mean?

Spatial data science refers to the subset of data science that deals with data related to distance, location, and spatial interactions. It goes beyond ‘how’ things happen and delves into ‘where’ and ‘why’ they occur. It provides information about the effect of the location on a particular parameter.

For example, data science will use the data of a crop produced over time to predict crop production for the next year. Spatial data science will add the crop’s locational data and the location’s weather to make a much more accurate prediction.

Also Read: Why Use Python for Data Science?

How is Spatial Data Science Useful?

Businesses attempting to expand their global reach need information about the target areas and demographics. They must customize their brand and products to appeal to the target audience, and spatial data science is tremendously useful in this process.

For instance, data science takes the sales figures and analyses the pattern to predict future sales. Spatial data science takes the sales figures, assesses them based on the location and demographics, and predicts the variation for each location, providing a more accurate prediction.

Further, spatial data science is also useful when businesses deal with products that analyze locational information, such as soil conditions, weather and rainfall, groundwater levels, etc. In short, spatial data analysis can analyze anything that changes with location.

Spatial Analysis vs. Spatial Data Science

Usually, analysis and data science go hand in hand. However, there are key distinctions between spatial analysis and spatial data science. Let’s examine some of them.

Spatial analysis assesses data for patterns, trends, clusters, and hotspots. Spatial data science uses analysis to predict multiple parameters.
The spatial analysis uses site selection and location allocation to optimize the locations used in the study. Spatial data science extrapolates this data to find a pattern to identify other similar locations.
Spatial analysis utilizes exploratory techniques to find the interactions between multiple variables. Spatial data science uses machine learning techniques to feed the results and extract more information about the interactions apart from the known variables.

Also Read: A Beginner’s Guide to the Data Science Process

How Does Spatial Data Science Work?

While the use of spatial data science may vary according to industry requirements, the same steps can be applied in every case. Here are the major steps involved in its operation.

Step 1: Collect the data

Gathering reliable data from reliable sources is essential for sound analysis and prediction. The data are typically obtained from surveys, GPS devices, government databases, remote-sensing equipment, crowd-sourcing, and historical and current documents.

Step 2: Clean and process the data

The data may be available in multiple formats and contain errors, duplications, or omissions. Hence, it must be preprocessed and cleaned to convert it into a usable format. Geographic coordinates are assigned to the data points using georeferencing and geocoding.

Step 3: Store the data

Appropriate storage of the data is critical to prevent loss or tampering. Storage, such as geographic information systems (GIS), must be compatible with the data format and have superb capabilities to manage, organize, and retrieve the data.

Step 4: Analyze the data

Determine the parameters that will give you the information you need from the data. Treat the data to spatial analysis. Choose the appropriate technique for the analysis. The typical methods include interpolation, queries, machine learning, regression, and network analysis. The choice of the method is crucial as it impacts the accuracy and reliability of the results.

Step 5: Use visualization tools

Visualization is the core of spatial data science. Use efficient tools to display your results in graphs, heat maps, distribution maps, and charts. The color scheme and format must be comprehensible. Add interactive elements to include more than one type of information on the same map.

Step 6: Use the results

Ascertain the credibility of the results and use them to make critical decisions. The parameters must set a standard for the project at hand so that any future data can be analyzed and compared to these parameters. The results also give tangible form to the massive amount of data and make it simple for the decision-makers and other stakeholders to understand the scenario.

Step 7: Devise models for predictions

Assess the data for typical trends and patterns and devise models for them. The models should extrapolate the information and give estimations for the future. They must be able to predict the trends and provide approximate parameters.

Step 8: Evaluate and redo

Certain data may be available, or circumstances may change that contradict the models. Hence, it is always a good idea to be flexible and reassess the data for modification. New technologies can be used to reprocess the data and arrive at a new, more accurate model.

Step 9: Report your findings

Visualization allows you to view the data lucidly. However, communicating the main points is equally crucial, as not everybody can understand the maps or charts. Hence, report the key findings and inferences of your analysis in detail. Concentrate on describing the significance of the model so that the rest of the parties can immediately grasp its applicability. Point out the need, inferences, and actionable items.

Applications and Use Cases of Spatial Data

Most decision-makers in today’s competitive business landscape adopt a data-driven approach to finalize projects. Numerical evidence adds to the feasibility and credibility of a project. Hence, spatial data science has found application in multiple instances, such as:

Environmental conservation and natural resource management
Monitoring of deforestation, ecosystems, and wildlife habitats
Planning transportation systems and city infrastructure
Planning roads, routes, highways, and railways
Manage supply chain logistics vis-a-vis route, destination, and time
Analysis of climate changes, weather forecasts, soil conditions, and water
Manage health resources, medical accessibility, analysis of disease occurrences, and predictions of medical requirements
Analyze historical natural disasters, their impact, and their level of preparedness to predict and plan for the future
Track agricultural variation and productivity and manage irrigation, fertilizer usage, crop planning, and reaping times

Let us go through some use cases that better explain the applications.

Planning for a Metro Train Route

The metro train has become an essential feature in most metropolitan cities. However, as most cities have grown without a plan, selecting the route and managing the permissions and construction require detailed information about the areas.

Spatial data science collects data about the population and location of streetlights, traffic signals, flyovers, shops, and houses. It analyses the data and provides the most convenient route for the metro based on the maximum requirement, easy construction with minimal traffic disruption, accessibility, and sufficient space for the infrastructure.

Site Selection for Government Health Centers

Government health centers typically target the underprivileged population who do not have affordable access to healthcare. Spatial data science helps in noting such areas, mapping the most common diseases and outbreaks, the demographics, and the presence of essential utilities. It then provides the best areas that can cater to multiple areas nearby and have the necessary testing and treatment facilities. The size and capabilities of the health centers can also be decided based on the health concerns of particular areas, such as a diabetes testing laboratory for areas with more obesity and glucose issues.

A well-structured data science program equips you with a solid knowledge of spatial data science and opens your mind to innovative applications.

Also Read: What Is Data Mining? A Beginner’s Guide

Essential Elements of Spatial Data Science

Experts in spatial data science must be well-versed in the essential elements that define the scope of this field. Here’s a glance at these elements.

Data engineering uses computer science to create pipelines that help generate, store, process, maintain, use, and distribute data, utilizing languages such as Python, R, or SQL.
Data exploration and visualization involve processing raw data and determining the trends, patterns, and relationships between various parameters to extract and visualize information.
In machine learning and artificial intelligence, you input raw data and create models that use machine learning techniques to predict trends.
Spatial analysis uses GIS to evaluate the data for relationships between various parameters based on location.
Modeling and scripting, where you create scripts that help automate the data input and processing regularly to avoid repetitive tasks.
Big data analytics, where you analyze a massive amount of data and further relate it to the geographical locations to gain deeper insights into the patterns for an optimized output.

Tools & Technologies Used in Spatial Data Science

A good spatial data scientist is on their toes to explore, use, and even devise new technologies to provide better analysis and modeling capabilities. Here are a few tools and technologies that are most commonly used.

Geographic information systems software includes suites such as ArcGIS, QGIS, GeoDa, Google Earth Pro, and GRASS GIS, which collect, store, analyze, and manage the information related to the latitudinal and longitudinal locations.
Open-source libraries, such as Fiona, Geospatial Data Abstraction Library (GDAL), Leaflet, PostGIS, GiST, and GeoServer, are collections of scripts and models used to analyze, model, and visualize the information.
Remote sensing tools, such as satellite imagery tools like Sentinel, unmanned aerial vehicles (UAV), hyperspectral imaging, and ERDAS IMAGINE, directly collect data from locations requiring regular monitoring or inaccessible.
The programming languages R and Python provide libraries with readymade templates and scripts contributed by the user community for building maps and designing custom applications.
Tools such as TorchGeo and GeoAI harness the power of machine learning and AI to finetune models and automate repetitive data capturing.
GPS and location data tools such as MAP, Google Maps, HERE, GISgraphy, and HERE gather data about the locations of areas, infrastructure, houses, farming lands, etc.

What Does a Spatial Data Scientist Do?

A spatial data scientist is usually a graduate or post-graduate in computer science, statistics, geography, geoinformatics, hydrology, earth science, civil engineering, or water and agricultural engineering.

As a spatial data scientist, you will be responsible for:

Create and manage geospatial data pipelines
Maintaining accurate and efficient processing of large amounts of geodata
Developing new models and applying machine learning techniques for finetuning and automation
Devise innovative methods to utilize the spatial analysis tools to their maximum potential
Work on novel solutions for organizations looking to integrate geospatial data in their data sets
Explore and utilize data visualization tools to derive maximum actionable information

Other than these, you must have excellent communication and report-writing skills and an ability to present the correct information in an understandable form. You should also be a team player and be able to collaborate with other departments and organizations as needed. People skills are crucial to accessing various data sources conveniently.

The spatial data scientist typically works with multiple types of data:

Vector, which deals with data in the form of points or lines such as coordinates and address locations.
Raster is stored and presented in multilayered maps in the form of pixels in rows and columns.
Attribute is the descriptive data describing multiple features for one specific area in a map.
Temporal, where the spatial data is collected with respect to time and plotted against both time and location.
Image, where the images of objects are related to their coordinates and presented in maps.

To treat such a vast variety of spatial data, you must possess certain skills such as:

Proficient in GIS technologies such as ArcGIS, QGIS, and SQL
Expertise in Python and R for data manipulation and analysis
Familiar with geospatial databases
Knowledgeable about geospatial data formats and coordinate systems
Conversant with database management and data preprocessing techniques

You will learn all these skills and more in a well-designed data science bootcamp.

Also Read: A Data Scientist Job Description: The Roles and Responsibilities in 2024

Why Build a Career in Spatial Data Science?

There is a massive demand for spatial data scientists as industries work towards integrating their existing and new data with locational details. You can specialize as an analyst, modeler, engineer, scientist, associate researcher, specialist, or consultant.

You can work in almost all sectors, such as urban planning, telecommunication, environmental conservation, marketing, research, defense, etc. This is a booming field, with a spatial data scientist earning an average annual salary of $136,301.

Master Data Science Skills to Get Started

The importance of spatial data science is steadily rising among enterprises looking to grow beyond borders. Data professionals with skills in it increasingly play vital roles in their expansion plans. With their ability to collate and analyze data based on the location of their customers, products, and facilities, spatial data scientists are valuable assets for global enterprises. As such, this is an opportune time to enter this dynamic field.

A comprehensive data science course that covers concepts such as SQL databases, Python language, data model building and evaluation, and machine learning is an excellent start. Such a course builds skills such as statistics, large language models, and generative AI.

Frequently Asked Questions

What is spatial data in data science?

Spatial data in data science provides information about the location and geographical areas of a particular set of data.

What is an example of spatial data?

An example of spatial data is the world map, which presents the languages spoken in various countries. Additional data may include the number of speakers per language, intra-country distribution, and changes in the number of speakers per year.

What is spatial data used for?

Spatial data is used to track and manage environmental changes, plan urban infrastructure and development, track the spread of diseases, and manage disasters.

What are the types of spatial data?

There are many spatial data types, such as vector, raster, temporal, image, and attribute. The kind of data decides the techniques and tools used for analysis and modeling.