Examining the Elements of Spatial Data Science
Spatial data is so ever-present that we tend to forget about its significance – from the simplest of maps to weather forecasts to GPS – all of this is based on spatial data. Spatial data science simply takes this to the next level – utilizing data science techniques and methods to use spatial data in new and unique ways.
In this article, we will discuss the elements of spatial data science, explain the typical spatial project workflow, and take a look at how the quality of spatial data is determined. By the end, you should have an understanding of what a geospatial project that uses machine learning and AI solutions is based on. But let’s start from the beginning.
What Is Spatial Data Science?
Spatial data science is a subfield of data science where the focal point is geographical information. In the simplest of terms, data science is an interdisciplinary field that looks for patterns and extrapolates insights and usable information from large volumes of data.
The logical extension of that principle is that (geo)spatial data science is an interdisciplinary field where the main purpose is to extrapolate insights and usable information in relation to geographical data.
More specifically, geospatial data science includes raster and vector data analysis, location analytics, projection system analysis, and satellite imagery. Now, if you are familiar with geographical solutions like Geographic Information Systems (GIS), it may seem like spatial data science and GIS are essentially the same.
However, there is a key difference – GIS tells where something is happening, while geospatial data science goes a step further and tries to answer how and why it is happening. To provide an example to further clear up this distinction – think of a fast-growing city.
GIS could tell you where the city is expanding. Spatial data science would incorporate this information but also analyze other data with the help of AI and machine learning algorithms to determine how it is expanding and look for patterns, ultimately creating a model that could project how it will expand in the future, which could help with urban planning.
And that’s just one example of how it can be utilized. Spatial data science can find a use for any project that relies on any type of geographical data – from government reforestation projects to resource management for logistics operations in the private sector. And that’s just scratching the surface, as this is a quickly growing field within data science.
But now, let’s discuss the elements of spatial data science.
The 6 Elements of Spatial Data Science
The typical data science workflow has six elements, starting with acquiring the data that is needed until practical insights can be provided. We should note that while these elements are also the steps of a spatial data science project, they are not run sequentially, but are tools and techniques that are run concurrently.
These six elements of the spatial data science workflow are:
Data engineering is typically the first step in the spatial data science pipeline. In the simplest of terms, it is the preparation of data for analysis. Data engineering can refer to anything that relates to data preparation, from data acquisition to annotation to developing systems that take in raw data and output data usable for analysis.
The data engineering part of a project is usually very labor intensive, particularly if large volumes of data need to be prepared for analysis. However, it is also a crucial part, as every following step is dependent on the quality of the spatial data set provided by the scientists.
Data Exploration and Visualization
Data exploration refers to the systemic exploration of raw data to find patterns and relationships. Data visualization refers to presenting the data in a visual format. It is a continuous process that lasts from the beginning to the end of a spatial data science project.
In the beginning, data presented in a visual format can help with the exploration process, enabling data scientists to more easily find patterns and causal relationships. Later, it can help create blueprints for problems and solutions. In the end, a visual representation of data can be used for presentations for clients, stakeholders, or any other type of audience.
Spatial analysis is the key facet of a spatial data science project. It utilizes all the prepared data to explain where objects are located in space and their relationships. Spatial analysis can analyse all features of a geographic space, from stationary objects like buildings to the distribution of people.
In the context of a spatial data science project, machine learning can be considered a tool for spatial analysis. It is the use of ML and AI to automatize the process of data analysis without direct human input. Most projects that utilize large volumes of data rely on machine learning to optimize their processes.
Machine learning spatial data models can also find patterns and gather insights that human scientists may miss. For many modern GIS or spatial projects, utilizing AI and ML solutions for geospatial analysis is quickly becoming a necessity, as opposed to an innovative approach.
Big Data Analytics
Big data simply refers to large volumes of data that may be needed for a project. The data is analyzed at scale to develop more accurate projections and models. Big data typically enables deeper insights than smaller datasets. Analyzing big data almost always necessitates the use of machine learning and AI techniques.
The ultimate goal of a spatial data science project is to create a self-functioning, automated system that can perform analyses and create projections based on input data. To create an automated spatial data science model, all the previous five elements need to be combined and the system needs to be modeled by data scientists.
Spatial Data Quality Elements
By now, it’s clear that any spatial data project is dependent on the quality of data that is utilized. Data science requires the use of accurate, relevant, and organized data, as bad input data will always lead to bad insights. Specifically for spatial data, the quality is determined by:
Lineage – describes the sources of spatial data, i.e., from where the data was derived and the methods that were used. It also includes all the transformations that were performed to get to the final information that will be used.
Positional accuracy – positional accuracy refers to the closeness of the coordinates of geographic elements to the values that are accepted as being true.
Attribute accuracy – attribute accuracy refers to the accuracy of qualitative and quantitative non-spatial data that is attributed to each geographic location (e.g., how accurate the population of a city region is).
Logical consistency – refers specifically to digital datasets and the relationship/structure of the data. It deals with the structural integrity of a spatial dataset based on a framework for modeling spatial data.
Completeness – completeness is a measure of the totality of the included features, i.e., the degree to which geographical attributes, features, and their relationships are included in geospatial data sets.
Utilizing Spatial Data Science for Unique Projects
Geospatial data science has a wide range of applications across many industries – from urban planning to asset management. Any geospatial project requires specific expertise and experience. Aya Data offers geospatial data science solutions.
We have a full-time team of data scientists with the experience required to see your project to completion. From optimizing your existing processes to filling expertise gaps with team augmentation to creating bespoke AI geospatial solutions, we can add value across the entire geospatial AI value chain.
If you have a specific plan in mind or just have an idea of what you would like your project to accomplish, schedule a free consultation with one of our senior members so that we can discuss how Aya Data can help with your project.