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A growing industry with many unique career opportunities, geospatial data science is a multidisciplinary field combining geographic information systems (GIS) and data science. This guide explains how to start a career as a geospatial data scientist. We’ll cover an overview of the geospatial data scientist role, the steps to build the skills necessary for the field, how to get your first geospatial data position, and a job outlook for the industry.
What Is a Geospatial Data Scientist?
Simply put, a geospatial data scientist is a data scientist who specializes in the collection and analysis of spatial data. Like any data scientist, their work focuses on combing through big data for unique insights. But owing to their competency in geography, GIS, and geoscience, they are capable of working with the unique spatial data critical to various industries, from urban planning and logistics to agriculture and beyond.
For example, you might see geospatial data scientists analyze traffic patterns to identify common congestion areas and more optimal routes for delivery businesses. Or they may collect air quality data from various sources—sensors, satellite images, etc.—to model trends and help manage or reduce pollution levels.
Geospatial data scientists use specialized data analysis methods and software to reach actionable insights. Their work often requires visualizing data in a way that can be communicated clearly to decision makers. It also tends to involve programming languages to perform statistical analysis and data operations. While the details can vary based on industry and role, the typical geospatial data science workflow includes:
- collecting data
- cleaning and processing data
- storing and analyzing data
- applying the results
- creating data visualizations
- devising prediction models
- reporting data findings
What Do Geospatial Data Scientists Do?
Geospatial data scientists uncover patterns. Satellites, drones, and other technological advances have led to massive amounts of location-based data. Geospatial data scientists collect this data, analyze it, and interpret their findings with an eye on actionable next steps.
What does this look like? It can involve creating and managing geospatial data pipelines, processing geodata, and developing machine learning models that predict the future. Geospatial data scientists commonly work in teams with geography professionals, data engineers, and even other data scientists, and together their insights help organizations overcome their biggest challenges.
Important Skills and Experience
Geospatial data scientists require a mix of technical, analytical, and communication skills. To start, an aspiring geospatial data scientist must master GIS software programs. Many organizations use these tools to visualize and work with spatial data. However, not all geospatial data science work occurs in GIS software.
Geospatial data scientists rely on a variety of programming languages; the most important are Python, Structured Query Language, and R, which are necessary for advanced data mining, database management, and statistical analysis, and allow geospatial data scientists to draw valuable insights from large datasets.
Additionally, geospatial data scientists need strong communication and storytelling abilities. Ensuring your visualizations and analysis effectively communicate your findings is key to collaboration. A geospatial data scientist must simplify complex ideas so everyone can understand them. Effective data storytelling is especially crucial for communicating with less technical team members.
After these foundational competencies, the best means for furthering your career is to get direct, hands-on experience working with spatial data. According to Carto, 72% of data science teams lack team members with spatial data analysis skills. Many organizations rely on team members learning how to work with geospatial data on the job. Acquiring hands-on spatial data analysis experience at the outset of your career will help you stand out among other data professionals.
Become a Geospatial Data Scientist in Six Steps
Below, we explore the process of becoming a geospatial data scientist, covering educational recommendations, necessary skills, professional development, portfolio building, and connecting with geospatial professionals—all culminating in landing your first geospatial data science job.
Step 1: Earn a Degree in a Relevant Field
An undergraduate degree is a sound first step to a career in geospatial data science. This emerging field, which intersects GIS and data science, offers spatial data scientists flexibility in degree choice. A Carto study shows that only 25% of responding geospatial data professionals studied spatial data science in school. Of the remaining 75%, half learned their spatial data science skills on the job. Studying spatial data science can put you a step ahead of others by providing a solid theoretical foundation which you can then expand with hands-on geospatial experience.
Step 2: Build Data Science and GIS Skills
Next, you’ll want to learn the ins and outs of GIS software, specifically ArcGIS and QGIS. Additional competency in programming languages and machine learning techniques will enable more thorough geospatial data science work. Python is the ideal starting programming language. After Python, SQL and R are common for working with geospatial databases and statistical computing.
According to Carto, 61% of data science teams use Python as their primary programming language. Libraries such as GeoPandas, Rasterio, and Shapely offer useful spatial data analysis and visualization functionality. You’ll also want to practice with Pandas, NumPy, and Matplotlib to learn basic data analysis. Additionally, you should get familiar with the open-source SQL database Postgres and the PostGIS extension. Both are common in spatial data science and compatible with ArcGIS and QGIS.
Step 3: Build a Portfolio of Geospatial Data Projects
Building a portfolio of personal projects will enhance your geospatial data science skills and demonstrate your experience working with spatial data. You can work on projects independently using publicly available data from sources Kaggle and Free GIS Data. Choose data and projects relevant to your career goals, which will showcase your analysis and visualization abilities in a context that’s relevant to potential employers.
Step 4: Pursue Specialized Training and Certifications
Professional certifications and training programs validate your geospatial data skills. They also provide a structured path for continued development as a geospatial data professional. Two of the most impactful certifications for geospatial data scientists are Esri and GISP.
Esri Technical Certifications
The GIS software provider Esri offers a range of GIS certifications, including foundational, associate, and professional options. Certifications such as the ArcGIS Pro Associate, ArcGIS Pro Professional, GIS Fundamentals Foundation, and ArcGIS Developer Foundation validate your technical skills and ability to use ArcGIS for different GIS functions.
GISP Certification
The Geographic Information Systems Professional (GISP) certification program from the GIS Certification Institute is respected within the GIS industry. To become certified, first complete the GISP program and pass the certification exam. Candidates must hold a geospatial-related degree, at least four years of work experience, and a professional portfolio.
Step 5: Network!
Connections in the geospatial data industry help foster career growth. Networking with other GIS and data science professionals can expose you to new organizations and high-quality job opportunities. It also ensures you are up to date with the emerging trends in geospatial data science. You can make industry connections by attending conferences and joining professional organizations.
The Spatial Data Science Conference, Carto Spatial Data Science Bootcamp, and the Esri User Conference provide great opportunities to meet other geospatial data professionals. They also give you a unique opportunity to learn from experienced industry leaders. Professional organizations such as the American Association of Geographers (AAG) and the Open Geospatial Consortium (OGC) provide similar networking benefits. You can check out our resources section for a detailed list of associations.
Step 6: Apply for Your First Geospatial Data Science Position
Begin your geospatial data science career by applying for junior positions involving geospatial data and GIS technologies. You’ll also want to look for positions involving cross-functional work with data scientists and engineers. These positions will offer relevant experience for future growth in the geospatial data science field. Common roles that meet this criteria include GIS analyst, geospatial data analyst, GIS technician, spatial data scientist, and geospatial analyst.
Start your search on common job boards such as LinkedIn, Indeed, and Glassdoor. Geospatial data science positions range across industries so this will provide good coverage. If you are interested in urban planning or environmental science, search the USAJobs board for relevant geospatial positions. Additionally, you can search specifically for geospatial data roles at GISjobs and GeoSearch.
Job Outlook for Geospatial Data Scientists
The demand for geospatial data scientists is growing as more businesses realize the value of location-based data. Carto’s research reveals that 68% of organizations will increase their investment in spatial data science in the years to come. Many organizations are new to spatial data science, often relying on the standard data science team to perform spatial-related work. With geolocation data analysis becoming increasingly important, you can expect more companies to hire professionals with geospatial data engineering skills instead of offloading the work to the data science team.
Salary: How Much Do Geospatial Data Scientists Make?
The geospatial data scientist role is a skilled, technical position that pays well for the value of their contributions. As a niche position, with varying functions, precise pay will depend on the specifics of your role. According to the Bureau of Labor Statistics (BLS), the median salary for data scientists is $108,020 while the median annual wage for geographers is $90,880.
Like most positions, pay can vary based on experience and background. Companies tend to pay more to professionals with longer work histories and professional certifications. Additionally, pay can vary vastly based on employer type. For example, in the data science field the scientific research and development industry has a mean salary of $131,060 compared to $119,870 for the management of companies and enterprises industry.
Additionally, for geographers, government positions pay higher on average. According to the BLS, those working for the federal government earn $102,690 in median annual wages. Those working in professional, scientific, and technical services or educational services (state, local, and private) earn $71,510 and $64,220 on average, respectively.