Geospatial analysis applies statistical and other data analysis techniques to the study of geography and spatial relationships. Spatial Data Science (SDS) blends data science techniques and theory with the capabilities of geographic information systems (GIS) to provide new ways of looking at spatial data and using it to make predictions and analyze the effects of man and nature on the real world.
Geospatial analysis shouldn’t be confused for GIS or geomatics, even though the fields and resources are closely related. GIS is a foundational science for geospatial analysis, but a GIS master’s degree provides few of the data science tools necessary for the in-depth processing that geospatial analysts engage in. Geospatial analysis takes the data from GIS systems and uses advanced analytics techniques to come up with unique simulations, interpretations, and revelations that can only come from in-depth looks into big data sets.
- SMU - Master of Science in Data Science - Bachelor's Degree Required.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
Although most data scientists live in a rarified world of bits and digits, processing information relating to largely virtual concepts, geospatial analysts find themselves anchored with firm ties to the real world. Because of this, they find themselves working with a wide array of fields that also have roots in the physical and human terrain of the world, including:
- Military and intelligence gathering
- Epidemiology and health care
- Public utilities, local government, and natural resources
Geospatial Analysis is a Field of The Future That is Vital For Earth’s Future
The earliest uses of geospatial analysis were in mapping, both of surface and subsurface features, and mapping visualization remains an important part of the work performed with GIS. Gathering the millions of data points plotted by sensors like LIDAR, radar, and sonar and translating them into useful tools for humans remains one of the key functions of geospatial analysts.
The field goes much deeper than just mapmaking today, however. By applying advanced statistical analysis techniques to GIS data, geospatial analysts can come up with fresh looks at old information and extrapolate outcomes for a variety of planning scenarios. For example, a 2016 paper describes an advanced simulation of long-term zoning outcomes in Masshad, the second largest city in Iran. Applying neural network and Markov Chain analysis to the data takes it light years beyond the traditional city-planning exercises of staring at map boundaries and guessing what types of uses might beneficially coexist.
Geospatial analysts are increasingly working with ever more advanced sensor platforms that deliver more and more data. Satellite data has been profuse since the 1980s, and aerial mapping for even longer. But today, LIDAR scans from unmanned aerial vehicles can offer up-to-date information that earlier generations of GIS could only dream about. Data coming in from distributed networks, such as Google’s Waze traffic network also offers unprecedented depth and access. Esri’s ArcGIS, long a favorite of geospatial analysts working in city and government planning, now integrates directly with Waze data, providing planners with vast amounts of traffic and road report information.
Geospatial analysts have a number of different career paths they may pursue, but almost all of them are in fields that are increasingly hunger for data science experts.
Global Climate Change Can Only Be Mitigated With Solid Spatial Analysis
Geospatial analysis is a field that has been heating up with the earth’s atmosphere. Global warming may still be a political football, but in scientific and commercial sectors, executives are taking the threat seriously and beginning to take concrete steps to prepare for the climate changes that will come with it. Geospatial analysts are filling a critical role in this preparation by working with scientists and governments to:
- Map out areas likely affected by sea-level rise
- Predict temperature changes and their effects on vegetation and groundwater
- Forecast glaciation subsidence and recision
- Simulate impacts of climate change on human populations
These activities don’t simply help adjust to climate change impacts, but are also an important part of attempting to stave off the worst predictions of those changes. In 2008, for example, geospatial analysis was used to produce a global analysis of landforms that would be best suited for use as carbon sinks for countries looking for offsets to meet their obligations under the Kyoto Protocol. Without the ability to safely engage in such carbon sequestration, many nations would be unable to meet those obligations, driving global temperatures even higher.
Public Health in a Crowded World Relies on Spatial Analysis
Spatial models are also becoming big news in public health circles. Modeling the spread of infectious disease, or attempting to locate a source of environmental poisoning could save hundreds of thousands of lives in the future. Analysts are developing models to predict the spread of diseases by backtesting models against historical epidemics, such as the 2009-2010 H1N1 influenza outbreak in Vellore, India.
Although visualizations may be produced as part of such studies and simulations, creating the mathematical models and algorithms is the more useful part of the exercise, and the aspect that most directly involves geospatial analysis. This can lead to a better understanding of the dynamics of the disease process itself; comparison of virulence to environmental factors collected by other sensors, such as heat, humidity, or traffic patterns, can give virologists valuable information for combatting not just infections, but disrupting the disease process itself.
The ability to combine such disparate types of information is the edge that data science brings to geospatial analysis, and it is where having an advanced degree in the subject makes the biggest difference.
Choosing a Master’s Degree Program in Geospatial Analysis
A master’s degree in geospatial analysis offers a multidisciplinary education covering both data science principles and the processes of generating GIS data.
A good geospatial analysis program should provide in-depth education on both the fundamentals of data science and statistical analysis and on more specific topics such as:
- Remote sensing and digital imagery
- Geo-design and geo-visualization
- Spatial relationships and statistical analysis
These are the focus of studies in core Master’s of Geospatial Analysis (MSGA) programs such as at the University of Mary Washington in Virginia. That program is so tightly coordinated that there are no elective options, only core courses.
At some universities, such as at the Ohio State University, geospatial analysis is covered as a specialization or focus within a traditional GIS program. Elsewhere, spatial analysis can be a concentration within a specialty that specifically makes use of those techniques, such as the Master of Applied Science in Spatial Analysis for Public Health through Johns Hopkins.
In these programs, you can expect to find more overlap with the specialization area than in a pure geospatial analysis program. At OSU, the crossover with GIS means more electives and core courses dealing with traditional GIS concerns like city planning and urban design and tie-ins with concerns like political organization and disease rates in addition to spatial statistics and optimization. And at Johns Hopkins, the focus is clearly on environmental issues, disease risk factors, and access to care and nutrition.